69 results on '"Hannes L. Röst"'
Search Results
2. Intensive lactation among women with recent gestational diabetes significantly alters the early postpartum circulating lipid profile: the SWIFT study
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Mi Lai, Stacey E. Alexeeff, Hannes L. Röst, Ziyi Zhang, Michael B. Wheeler, Feihan F. Dai, Amina Allalou, Erica P. Gunderson, and Anthony L. Piro
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Blood Glucose ,endocrine system diseases ,Breastfeeding ,Physiology ,030209 endocrinology & metabolism ,Type 2 diabetes ,Gestational diabetes mellitus ,03 medical and health sciences ,0302 clinical medicine ,Pregnancy ,Diabetes mellitus ,Lactation ,medicine ,Humans ,Prospective Studies ,030304 developmental biology ,0303 health sciences ,medicine.diagnostic_test ,business.industry ,Postpartum Period ,nutritional and metabolic diseases ,Type 2 diabetes risk ,Lipid metabolism ,General Medicine ,medicine.disease ,Lipids ,Gestational diabetes ,Diabetes, Gestational ,medicine.anatomical_structure ,Breast Feeding ,Diabetes Mellitus, Type 2 ,Cohort ,Medicine ,Female ,Lipid profile ,business ,Research Article - Abstract
BackgroundWomen with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D). It is estimated that 20-50% of women with GDM history will progress to T2D within 10 years after delivery. Intensive lactation could be negatively associated with this risk, but the mechanisms behind a protective effect remain unknown.MethodsIn this study, we utilized a prospective GDM cohort of 1010 women without T2D at 6-9 weeks postpartum (study baseline) and tested for T2D onset up to 8 years post-baseline (n=980). Targeted metabolic profiling was performed on fasting plasma samples collected at both baseline and follow-up (1-2 years post-baseline) during research exams in a subset of 350 women (216 intensive breastfeeding, IBF vs. 134 intensive formula feeding or mixed feeding, IFF/Mixed). The relationship between lactation intensity and circulating metabolites at both baseline and follow-up were evaluated to discover underlying metabolic responses of lactation and to explore the link between these metabolites and T2D risk.ResultsWe observed that lactation intensity was strongly associated with decreased glycerolipids (TAGs/DAGs) and increased phospholipids/sphingolipids at baseline. This lipid profile suggested decreased lipogenesis caused by a shift away from the glycerolipid metabolism pathway towards the phospholipid/sphingolipid metabolism pathway as a component of the mechanism underlying the benefits of lactation. Longitudinal analysis demonstrated that this favorable lipid profile was transient and diminished at 1-2 years postpartum, coinciding with the cessation of lactation. Importantly, when stratifying these 350 women by future T2D status during the follow-up (171 future T2D vs. 179 no T2D), we discovered that lactation induced robust lipid changes only in women who did not develop incident T2D. Subsequently, we identified a cluster of metabolites that strongly associated with future T2D risk from which we developed a predictive metabolic signature with a discriminating power (AUC) of 0.78, superior to common clinical variables (i.e., fasting glucose, AUC 0.56 or 2-h glucose, AUC 0.62).ConclusionsIn this study, we show that intensive lactation significantly alters the circulating lipid profile at early postpartum and that women who do not respond metabolically to lactation are more likely to develop T2D. We also discovered a 10-analyte metabolic signature capable of predicting future onset of T2D in IBF women. Our findings provide novel insight into how lactation affects maternal metabolism and its link to future diabetes onset.Trial registrationClinicalTrials.govNCT01967030.
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- 2021
3. Analyzing Assay Specificity in Metabolomics Using Unique Ion Signature Simulations
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Adamo Young, Premy Shanthamoorthy, and Hannes L. Röst
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Ions ,Proteomics ,0303 health sciences ,Chemistry ,High selectivity ,010401 analytical chemistry ,Selected reaction monitoring ,Mass spectrometry ,01 natural sciences ,Mass Spectrometry ,Signature (logic) ,Workflow ,0104 chemical sciences ,Analytical Chemistry ,Ion ,Matrix (chemical analysis) ,Identification (information) ,03 medical and health sciences ,Metabolomics ,Fragment (logic) ,Biological system ,030304 developmental biology - Abstract
Targeted, untargeted, and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have been popularized in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex coisolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite detection in complex backgrounds using precursor and fragment ion mass-to-charge (m/z) separation, comparing three benchmarked mass spectrometry (MS) methods [MS1, MRM (multiple reaction monitoring), and DIA]. Our simulations show that DIA outperformed MS1-only and MRM-based methods with regards to specificity by factors of ∼2.8-fold and ∼1.8-fold, respectively. Additionally, we show that our results are not dependent on the number of transitions used or the complexity of the background matrix. Finally, we show that collision energy is an important factor in unambiguous detection and that a single collision energy setting per compound cannot achieve optimal pairwise differentiation of compounds. Our analysis demonstrates the power of using both high-resolution precursor and high-resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both data-dependent acquisition (DDA) and MRM with regards to unique compound identification potential.
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- 2021
4. PRMT5 inhibition disrupts splicing and stemness in glioblastoma
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Nhat Tran, Heather Whetstone, Ian Restall, Patty Sachamitr, Julian Spears, Jasmin Coulombe-Huntington, Joseph Veyhl, Gary D. Bader, Mark Bernstein, Ahmed Aman, Mike Tyers, Hannes L. Röst, Sunit Das, Kirsten Hart, Bang-Chi Duong, Zahid Quyoom Bonday, Mathieu Lupien, Peter B. Dirks, Cheryl H. Arrowsmith, Maria M. Mangos, Laura M. Richards, Owen Whitley, Paul Guilhamon, María Sánchez-Osuna, H. Artee Luchman, Trevor J. Pugh, Jolene Caifeng Ho, Michael D. Cusimano, Samuel Weiss, Philippe Thibault, Amy Caudy, Olga Zaslaver, Panagiotis Prinos, Fiona J. Coutinho, Florence M.G. Cavalli, Wenjun Chen, Victoria Vu, Dalia Barsyte-Lovejoy, Xiaoyang Lan, Xinghui Che, Mathieu Durand, Michelle Kushida, Naghmeh Rastegar, Katlin B. Massirer, Benjamin Haibe-Kains, Lilian Lee, Evgeny Kanshin, Wail Ba-alawi, and Felipe Ciamponi
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Epigenomics ,0301 basic medicine ,Protein-Arginine N-Methyltransferases ,RNA Splicing ,Science ,General Physics and Astronomy ,Antineoplastic Agents ,Apoptosis ,Biology ,Article ,General Biochemistry, Genetics and Molecular Biology ,Transcriptome ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Cancer stem cell ,Cell Line, Tumor ,medicine ,Animals ,Humans ,Cell Proliferation ,Multidisciplinary ,Brain Neoplasms ,Cancer stem cells ,Drug discovery ,Protein arginine methyltransferase 5 ,Cell Cycle ,Cancer ,General Chemistry ,Cell cycle ,medicine.disease ,Xenograft Model Antitumor Assays ,Gene Expression Regulation, Neoplastic ,CNS cancer ,030104 developmental biology ,Cell culture ,030220 oncology & carcinogenesis ,RNA splicing ,Neoplastic Stem Cells ,Cancer research ,Female ,Epigenetics ,Stem cell ,Glioblastoma - Abstract
Glioblastoma (GBM) is a deadly cancer in which cancer stem cells (CSCs) sustain tumor growth and contribute to therapeutic resistance. Protein arginine methyltransferase 5 (PRMT5) has recently emerged as a promising target in GBM. Using two orthogonal-acting inhibitors of PRMT5 (GSK591 or LLY-283), we show that pharmacological inhibition of PRMT5 suppresses the growth of a cohort of 46 patient-derived GBM stem cell cultures, with the proneural subtype showing greater sensitivity. We show that PRMT5 inhibition causes widespread disruption of splicing across the transcriptome, particularly affecting cell cycle gene products. We identify a GBM splicing signature that correlates with the degree of response to PRMT5 inhibition. Importantly, we demonstrate that LLY-283 is brain-penetrant and significantly prolongs the survival of mice with orthotopic patient-derived xenografts. Collectively, our findings provide a rationale for the clinical development of brain penetrant PRMT5 inhibitors as treatment for GBM., The arginine methyltransferase PRMT5 is over-expressed in cancer and has a role in the maintenance of stem cells. Here, the authors show that PRMT5 inhibitors can block the growth of patient derived glioblastoma stem cell cultures in vitro and in vivo, suggesting that PRMT5 inhibition may be a useful therapeutic strategy
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- 2021
5. mspack: efficient lossless and lossy mass spectrometry data compression
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Felix Hanau, Idoia Ochoa, and Hannes L. Röst
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Statistics and Probability ,Lossless compression ,Computer science ,Data_CODINGANDINFORMATIONTHEORY ,Lossy compression ,Biochemistry ,Computer Science Applications ,Computational science ,Computational Mathematics ,Redundancy (information theory) ,Computational Theory and Mathematics ,Compression (functional analysis) ,Compression ratio ,Preprocessor ,Mass spectrometry data format ,Molecular Biology ,Data compression - Abstract
Motivation Mass spectrometry (MS) data, used for proteomics and metabolomics analyses, have seen considerable growth in the last years. Aiming at reducing the associated storage costs, dedicated compression algorithms for MS data have been proposed, such as MassComp and MSNumpress. However, these algorithms focus on either lossless or lossy compression, respectively, and do not exploit the additional redundancy existing across scans contained in a single file. We introduce mspack, a compression algorithm for MS data that exploits this additional redundancy and that supports both lossless and lossy compression, as well as the mzML and the legacy mzXML formats. mspack applies several preprocessing lossless transforms and optional lossy transforms with a configurable error, followed by the general purpose compressors gzip or bsc to achieve a higher compression ratio. Results We tested mspack on several datasets generated by commonly used MS instruments. When used with the bsc compression backend, mspack achieves on average 76% smaller file sizes for lossless compression and 94% smaller file sizes for lossy compression, as compared with the original files. Lossless mspack achieves 10–60% lower file sizes than MassComp, and lossy mspack compresses 36–60% better than the lossy MSNumpress, for the same error, while exhibiting comparable accuracy and running time. Availability and implementation mspack is implemented in C++ and freely available at https://github.com/fhanau/mspack under the Apache license. Supplementary information Supplementary data are available at Bioinformatics online.
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- 2021
6. SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing
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Markus J. Herrgård, Bo Burla, Lars Schrübbers, Douglas McCloskey, Pasquale Colaianni, Lars K. Nielsen, Oliver Kohlbacher, Timo Sachsenberg, Federico Torta, Mette Kristensen, Hannes L. Röst, Svetlana Kutuzova, and Oliver Alka
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Time Factors ,Computer science ,Peptides and proteins ,Computational biology ,010402 general chemistry ,Mass spectrometry ,computer.software_genre ,01 natural sciences ,Analytical Chemistry ,Automation ,Metabolomics ,Capillary electrophoresis ,Tandem Mass Spectrometry ,Lipidomics ,Fluxomics ,Electronic Data Processing ,Chromatography ,Reproducibility ,Data processing ,Chemistry ,010401 analytical chemistry ,Electrophoresis, Capillary ,Metabolomics data ,0104 chemical sciences ,Metabolism ,Calibration ,Data mining ,Gas chromatography ,computer ,Algorithms ,Chromatography, Liquid - Abstract
SmartPeak is an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of CE-, GC- and LC-MS(/MS) data, and HPLC data for targeted and semi-targeted metabolomics, lipidomics, and fluxomics experiments.HighlightsNovel algorithms for retention time alignment, calibration curve fitting, and peak integrationEnables reproducibility by reducing operator bias and ensuring high QC/QAAutomated pipeline for high throughput targeted and/or quantitative metabolomics, lipidomics, and fluxomics data processing from multiple analytical instrumentsManually curated data set of LC-MS/MS, GC-MS, and HPLC integrated peaks for further algorithm development and benchmarking
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- 2020
7. diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition
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Ruedi Aebersold, Ben C. Collins, Annie Ha, Stephanie Kaspar-Schoenefeld, Oliver Raether, Eugenia Voytik, Andreas-David Brunner, Max Frank, Matthias Mann, Florian Meier, Isabell Bludau, Nicolai Bache, Hannes L. Röst, and Markus Lubeck
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Proteomics ,Proteome ,Ion beam ,Analytical chemistry ,Mass spectrometry ,Biochemistry ,Ion Channels ,Ion ,03 medical and health sciences ,Fragmentation (mass spectrometry) ,Tandem Mass Spectrometry ,Cell Line, Tumor ,Humans ,PEPTIDE ,Data-independent acquisition ,STRATEGY ,RATES ,Molecular Biology ,030304 developmental biology ,Ions ,0303 health sciences ,Reproducibility ,Ion Transport ,ION MOBILITY SPECTROMETRY ,Chemistry ,Data Science ,Reproducibility of Results ,PLATFORM ,Ion current ,MASS-SPECTROMETRY ,Cell Biology ,QUANTIFICATION ,High-Throughput Screening Assays ,PROTEOMICS ,SENSITIVITY ,HeLa Cells ,Biotechnology - Abstract
diaPASEF makes use of the correlation between the ion mobility and the m/z of peptides to trap and release precursor ions in a TIMS-TOF mass spectrometer for an almost complete sampling of the precursor ion beam with data-independent acquisition.Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.
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- 2020
8. Comparing Machine Learning Architectures for the Prediction of Peptide Collisional Cross Section
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Emily Franklin and Hannes L. Röst
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1AbstractMass spectrometry is the method of choice in large-scale proteomics studies. One common method is data-independent acquisition (DIA), which allows for high-throughput analysis of biological samples, but also produces complex data. Methods of peptide separation, in addition to retention time, improve data analysis and there has been increasing interest in separating peptides based on collisional cross section (CCS), which is a measure of the size of a peptide. However, existing libraries that are used during data analysis lack CCS measurements, and this data is expensive and time-consuming to acquire. This has led to the desire to predict library values for mass spectrometry analysis. Here we compare three deep learning architectures, LSTM, CNN, and transformer, for the tasks of retention time and collisional cross section prediction. We show that the LSTM and CNN models perform similarly and that the transformer has a lower performance than expected.
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- 2022
9. DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics
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Oliver, Alka, Premy, Shanthamoorthy, Michael, Witting, Karin, Kleigrewe, Oliver, Kohlbacher, and Hannes L, Röst
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Metabolomics ,Biomarkers ,Mass Spectrometry ,Workflow - Abstract
The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
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- 2022
10. Cell Sex and Sex Hormones Modulate Kidney Glucose and Glutamine Metabolism in Health and Diabetes
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María José Soler, Max Kotlyar, Katherine Coulombe, James W. Scholey, Sergi Clotet-Freixas, Jon Mcgavock, Solene Pradeloux, Jérôme Lamontagne-Proulx, Marta Riera, Alex Boshart, Minna Woo, Olga Zaslaver, Chiara Pastrello, Tom Blydt-Hansen, Amandine Isenbrandt, Igor Jurisica, Allison Dart, Ana Konvalinka, Caitriona M. McEvoy, Sofia Farkona, Denis Soulet, Brandy Wicklow, Hannes L. Röst, Michael Chan, and Aninda Saha
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medicine.medical_specialty ,Kidney ,Kidney metabolism ,Type 2 diabetes ,Biology ,medicine.disease_cause ,medicine.disease ,Glutamine ,medicine.anatomical_structure ,Endocrinology ,Internal medicine ,Diabetes mellitus ,Dihydrotestosterone ,medicine ,Oxidative stress ,Hormone ,medicine.drug - Abstract
Male sex is a risk factor for progression of diabetic kidney disease, but the reasons for this predilection are unclear. Here, we demonstrate that cell sex and sex hormones alter the metabolic phenotype of human proximal tubular epithelial cells (PTECs). Male PTECs displayed increased glycolysis, mitochondrial respiration, oxidative stress, apoptosis, and high glucose-induced injury, compared to female PTECs. This phenotype was enhanced by dihydrotestosterone (DHT) and linked to increased mitochondrial utilization of glucose and glutamine. Studies in vivo pointed towards increased glutamine anaplerosis in diabetic male kidneys. Male sex was linked to increased levels of glutamate, TCA cycle, and glutathione cycle metabolites, in PTECs and in the blood metabolome of healthy youth and youth with type 2 diabetes. Conversely, female PTECs displayed increased levels of pyruvate, glutamyl-cysteine, cysteinylglycine, and a higher GSH/GSSG ratio than male PTECs, indicative of enhanced redox homeostasis. Finally, we identified transcriptional mechanisms that control kidney metabolism in a sex-specific fashion. While X-linked demethylase KDM6A facilitated metabolic homeostasis in female PTECs, transcription factor HNF4A mediated the deleterious effects of DHT in male PTECs. This work uncovers the role of sex in glucose/glutamine metabolism, that may explain the roots of sex dimorphism in the healthy and diabetic kidney.
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- 2021
11. Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women
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Bjarke Feenstra, Marie-Louise Hee Rasmussen, John K. Snyder, Xiaotao Shen, Mads Melbye, Brian D. Piening, Michael Snyder, Kévin Contrepois, Songjie Chen, Norman Lee, Line Skotte, Robert Tibshirani, Hannes L. Röst, Hanyah Zackriah, and Liang Liang
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Adult ,Physiology ,Gestational Age ,Biology ,metabolic clock ,Article ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Fetus ,0302 clinical medicine ,Metabolomics ,Pregnancy ,human pregnancy ,Metabolome ,Humans ,metabolic pathways ,Medicine ,gestational age ,030304 developmental biology ,0303 health sciences ,business.industry ,delivery prediction ,Obstetrics and Gynecology ,Gestational age ,General Medicine ,medicine.disease ,metabolomics ,machine learning ,Female ,Pregnant Women ,longitudinal profiling ,business ,METABOLIC FEATURES ,Biomarkers ,Metabolic Networks and Pathways ,030217 neurology & neurosurgery - Abstract
Summary Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities., Graphical Abstract, Highlights • Weekly metabolome of maternal blood changes dynamically through healthy pregnancy • A metabolic clock of five blood metabolites accurately predicts gestational age • Two to three metabolites identify labor onset within two, four, and eight weeks • Women with metabolic clocks that outpaced ultrasound evaluation tend to deliver earlier, Identification of blood metabolites in pregnant women that can accurately predict gestational age and provide insights into pregnancy variations undetected by ultrasound.
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- 2020
12. Democratizing Data-Independent Acquisition Proteomics Analysis on Public Cloud Infrastructures Via The Galaxy Framework
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M. C. Foell, Oliver Schilling, M. Fahrner, Hannes L. Röst, Matthias Bernt, and Bjorn Gruening
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Data processing ,Software ,Workflow ,business.industry ,Computer science ,Suite ,Cloud computing ,Usability ,business ,Data science ,Pipeline (software) ,Graphical user interface - Abstract
Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging due to the high complexity and large data and sample size, which require specialized software and large computing infrastructures. Most available open-source DIA software necessitate basic programming skills and cover only a fraction of the analysis steps, often yielding a complex of multiple software tools, severely limiting usability and reproducibility. To overcome this hurdle, we have integrated a suite of DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their usage on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate usability of the all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided, to further increase access for the proteomics community.
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- 2021
13. DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics
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Leon Bichmann, George Rosenberger, Phil Ewels, Hannes L. Röst, Timo Sachsenberg, Oliver Kohlbacher, Leon Kuchenbecker, Shubham Gupta, Julianus Pfeuffer, and Oliver Alka
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0301 basic medicine ,Data Analysis ,Proteomics ,Computer science ,Cloud computing ,computer.software_genre ,Biochemistry ,Mass Spectrometry ,03 medical and health sciences ,Software ,data-independent acquisition ,Data-independent acquisition ,automation ,Data processing ,030102 biochemistry & molecular biology ,business.industry ,cloud computing ,peptidomics ,Reproducibility of Results ,General Chemistry ,500 Naturwissenschaften und Mathematik::570 Biowissenschaften ,Biologie::570 Biowissenschaften ,Biologie ,Pipeline (software) ,Automation ,030104 developmental biology ,spectral library generation ,Pairwise comparison ,Data mining ,business ,computer ,Workflow management system ,data processing - Abstract
Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.
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- 2021
14. DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics
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Shubham Gupta, Wenyu Zhou, Hannes L. Röst, and Sara Ahadi
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Proteomics ,Analyte ,Time Factors ,Source code ,Streptococcus pyogenes ,Computer science ,media_common.quotation_subject ,Biochemistry ,Column (database) ,Analytical Chemistry ,03 medical and health sciences ,Data acquisition ,Robustness (computer science) ,Humans ,Data-independent acquisition ,Databases, Protein ,Molecular Biology ,Throughput (business) ,030304 developmental biology ,media_common ,Reproducibility ,0303 health sciences ,Elution ,business.industry ,030302 biochemistry & molecular biology ,Technological Innovation and Resources ,Reproducibility of Results ,Pattern recognition ,Dynamic programming ,Targeted proteomics ,Artificial intelligence ,Noise (video) ,Peptides ,business ,Retention time ,Sequence Alignment ,Algorithms ,Software - Abstract
SWATH-MS has been widely used for proteomics analysis given its high-throughput and reproducibility but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human-plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data-acquisition, acquisition by different operators or instruments, intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and successively performance of SWATH-MS data analysis. Here, we present a novel algorithm for retention time alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order swapped peaks. Furthermore, allowing RT-mapping in a certain window around coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperforms the current state-of-the-art approaches. In addition, on a real-world clinical data, our approach outperforms global alignment methods by mapping 98% of peaks compared to 67% cumulatively and DIAlignR can reduce alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy has also been demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.Abbreviations:AUCArea Under the CurveDIAData-independent acquisitionLCLiquid chromatographyLOESSLocal weighted regressionRSEResidual Standard ErrorRTRetention timeXICExtracted ion chromatogramsData Availability:Raw chromatograms and features extracted by OpenSWATH are available on PeptideAtlas.Servername: ftp.peptideatlas.orgUsername: PASS01280Password: KQ2592b
- Published
- 2019
15. Proteomic Characterization of a Candidate Polygenic Driver of Metabolism in Non-small Cell Lung Cancer
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Heba, Badr, Ron, Blutrich, Kaitlin, Chan, Jiefei, Tong, Paul, Taylor, Wen, Zhang, Ran, Kafri, Hannes L, Röst, Ming-Sound, Tsao, and Michael F, Moran
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Proteomics ,Lung Neoplasms ,Proteome ,Structural Biology ,Carcinoma, Non-Small-Cell Lung ,Humans ,DNA ,Molecular Biology - Abstract
Proteome analysis revealed signatures of co-expressed upregulated metabolism proteins highly conserved between primary and non-small cell lung cancer (NSCLC) patient-derived xenograft tumors (Li et al. 2014, Nat. Communications 5:5469). The C10 signature is encoded by seven genes (ADSS, ATP2A2, CTPS1, IMPDH2, PKM2, PTGES3, SGPL1) and DNA alterations in C10-encoding genes are associated with longer survival in a subset of NSCLC. To explore the C10 signature as an oncogenic driver and address potential mechanisms of action, C10 protein expression and protein-protein interactions were determined. In independent NSCLC cohorts, the coordinated expression of C10 proteins was significant and mutations in C10 genes were associated with better outcome. Affinity purification-mass spectrometry and in vivo proximity-based biotin identification defined a C10 interactome involving 667 proteins including candidate drug targets and clusters associated with glycolysis, calcium homeostasis, and nucleotide and sphingolipid metabolism. DNA alterations in genes encoding C10 interactome components were also found to be associated with better survival. These data support the notion that the coordinated upregulation of the C10 signature impinges metabolic processes that collectively function as an oncogenic driver in NSCLC.
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- 2022
16. Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry Based Workflow
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Joshua Charkow and Hannes L. Röst
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Analyte ,Materials science ,Fragmentation (mass spectrometry) ,Ion-mobility spectrometry ,Analytical chemistry ,Data-independent acquisition ,Context (language use) ,Mass spectrometry ,Spectral line ,Ion - Abstract
In bottom-up mass spectrometry based proteomics, deep proteome coverage is limited by high cofragmentation rates. This occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry which separates peptides by their collisional cross section. Here we investigate the capability of the Trapped Ion Mobility Spectrometry (TIMS) device to effectively separate peptide ions and quantify the separation power of the TIMS device in the context of a Parallel Accumulation-Serial Fragmentation (PASEF) workflow. We found that TIMS IM separation increases the number of interference-free MS1 features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5 fold. In a Data Dependent Acquisition (DDA) PASEF workflow, IM separation increased the number of spectra without cofragmentation by a factor of 4.1 and the number of high quality spectra 17-fold. This observed decrease in spectral complexity results in a substantial increase in peptide identification rates when using our data-driven model. In the context of a Data Independent Acquisition (DIA), the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in isolation window width (from 25Da to 6.5Da). Our study shows that TIMS IM separation dramatically reduces cofragmentation rates leading to an increase in peptide identification rates.
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- 2021
17. Automated Workflow for Peptide-Level Quantitation from DIA/SWATH-MS Data
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Hannes L. Röst and Shubham Gupta
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0303 health sciences ,Swath ms ,Computer science ,business.industry ,030302 biochemistry & molecular biology ,Sample (graphics) ,Computational science ,03 medical and health sciences ,Workflow ,Software ,Data-independent acquisition ,business ,Spectral data ,Prior information ,030304 developmental biology - Abstract
Data-independent acquisition (DIA) is a powerful method to acquire spectra from all ionized precursors of a sample. Considering the complexity of the highly multiplexed spectral data, sophisticated workflows have been developed to obtain peptides quantification. Here we describe an open-source and easy-to-use workflow to obtain a quantitative matrix from multiple DIA runs. This workflow requires as prior information an "assay library," which contains the MS coordinates of peptides. It consists of OpenSWATH, pyProphet, and DIAlignR software. For the ease of installation and to isolate operating system-related dependency, docker-based containerization is utilized in this workflow.
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- 2021
18. DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics
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George Rosenberger, Oliver Kohlbacher, Shubham Gupta, Leon Bichmann, Hannes L. Röst, Leon Kuchenbecker, Timo Sachsenberg, Oliver Alka, and Julianus Pfeuffer
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Computer science ,Process (computing) ,Functional data analysis ,Data-independent acquisition ,Sensitivity (control systems) ,Data mining ,Mass spectrometry ,Proteomics ,computer.software_genre ,Pipeline (software) ,computer - Abstract
Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.
- Published
- 2020
19. Underlying dyslipidemia postpartum in women with a recent GDM pregnancy who develop type 2 diabetes
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Dana Al Rijjal, Mi Lai, Michael B. Wheeler, Hannes L. Röst, Erica P. Gunderson, and Feihan F. Dai
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0301 basic medicine ,endocrine system diseases ,Physiology ,Type 2 diabetes ,Gestational diabetes mellitus ,Cohort Studies ,0302 clinical medicine ,Pregnancy ,Risk Factors ,Prospective Studies ,Biology (General) ,Prospective cohort study ,pathophysiology ,music.instrument ,General Neuroscience ,Postpartum Period ,General Medicine ,Lipids ,Type 2 Diabetes ,3. Good health ,Gestational diabetes ,Medicine ,Female ,lipids (amino acids, peptides, and proteins) ,Metabolic Networks and Pathways ,Research Article ,Human ,prospective study ,Adult ,Diabetes risk ,QH301-705.5 ,Science ,030209 endocrinology & metabolism ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Lactosylceramide ,Predictive Value of Tests ,Diabetes mellitus ,medicine ,Humans ,Human Biology and Medicine ,music ,Dyslipidemias ,General Immunology and Microbiology ,business.industry ,Lipogenesis ,nutritional and metabolic diseases ,medicine.disease ,Diabetes, Gestational ,030104 developmental biology ,Diabetes Mellitus, Type 2 ,lipidomics ,business ,Dyslipidemia ,Follow-Up Studies - Abstract
Approximately, 35% of women with Gestational Diabetes (GDM) progress to Type 2 Diabetes (T2D) within 10 years. However, links between GDM and T2D are not well understood. We used a well-characterised GDM prospective cohort of 1035 women following up to 8 years postpartum. Lipidomics profiling covering >1000 lipids was performed on fasting plasma samples from participants 6–9 week postpartum (171 incident T2D vs. 179 controls). We discovered 311 lipids positively and 70 lipids negatively associated with T2D risk. The upregulation of glycerolipid metabolism involving triacylglycerol and diacylglycerol biosynthesis suggested activated lipid storage before diabetes onset. In contrast, decreased sphingomyelines, hexosylceramide and lactosylceramide indicated impaired sphingolipid metabolism. Additionally, a lipid signature was identified to effectively predict future diabetes risk. These findings demonstrate an underlying dyslipidemia during the early postpartum in those GDM women who progress to T2D and suggest endogenous lipogenesis may be a driving force for future diabetes onset.
- Published
- 2020
20. Author response: Underlying dyslipidemia postpartum in women with a recent GDM pregnancy who develop type 2 diabetes
- Author
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Dana Al Rijjal, Mi Lai, Michael B. Wheeler, Hannes L. Röst, Feihan F. Dai, and Erica P. Gunderson
- Subjects
medicine.medical_specialty ,Pregnancy ,Obstetrics ,business.industry ,medicine ,Type 2 diabetes ,medicine.disease ,business ,Dyslipidemia - Published
- 2020
21. Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: A metabolic profiling study
- Author
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Brian J. Cox, Feihan F. Dai, Ying Liu, Mi Lai, Gabriele V. Ronnett, Michael B. Wheeler, Anne Wu, Hannes L. Röst, and Erica P. Gunderson
- Subjects
endocrine system diseases ,Physiology ,Disease ,Type 2 diabetes ,030204 cardiovascular system & hematology ,Biochemistry ,Endocrinology ,0302 clinical medicine ,Glucose Metabolism ,Pregnancy ,Risk Factors ,Blood plasma ,Medicine and Health Sciences ,Metabolites ,Medicine ,030212 general & internal medicine ,Amino Acids ,Young adult ,Organic Compounds ,Postpartum Period ,Monosaccharides ,General Medicine ,Middle Aged ,Type 2 Diabetes ,Body Fluids ,3. Good health ,Gestational diabetes ,Chemistry ,Blood ,Physical Sciences ,Cohort ,Disease Progression ,Carbohydrate Metabolism ,Female ,Metabolic Pathways ,Anatomy ,Research Article ,Adult ,medicine.medical_specialty ,Endocrine Disorders ,Carbohydrates ,Blood Plasma ,Young Adult ,03 medical and health sciences ,Diabetes mellitus ,Internal medicine ,Diabetes Mellitus ,Humans ,Hexoses ,business.industry ,Organic Chemistry ,Chemical Compounds ,Biology and Life Sciences ,nutritional and metabolic diseases ,Lipid Metabolism ,medicine.disease ,Diabetes, Gestational ,Metabolism ,Diabetes Mellitus, Type 2 ,Metabolic Disorders ,business - Abstract
Background Women with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D) during midlife and an elevated risk of developing hypertension and cardiovascular disease. Glucose tolerance reclassification after delivery is recommended, but fewer than 40% of women with GDM are tested. Thus, improved risk stratification methods are needed, as is a deeper understanding of the pathology underlying the transition from GDM to T2D. We hypothesize that metabolites during the early postpartum period accurately distinguish risk of progression from GDM to T2D and that metabolite changes signify underlying pathophysiology for future disease development. Methods and findings The study utilized fasting plasma samples collected from a well-characterized prospective research study of 1,035 women diagnosed with GDM. The cohort included racially/ethnically diverse pregnant women (aged 20–45 years—33% primiparous, 37% biparous, 30% multiparous) who delivered at Kaiser Permanente Northern California hospitals from 2008 to 2011. Participants attended in-person research visits including 2-hour 75-g oral glucose tolerance tests (OGTTs) at study baseline (6–9 weeks postpartum) and annually thereafter for 2 years, and we retrieved diabetes diagnoses from electronic medical records for 8 years. In a nested case–control study design, we collected fasting plasma samples among women without diabetes at baseline (n = 1,010) to measure metabolites among those who later progressed to incident T2D or did not develop T2D (non-T2D). We studied 173 incident T2D cases and 485 controls (pair-matched on BMI, age, and race/ethnicity) to discover metabolites associated with new onset of T2D. Up to 2 years post-baseline, we analyzed samples from 98 T2D cases with 239 controls to reveal T2D-associated metabolic changes. The longitudinal analysis tracked metabolic changes within individuals from baseline to 2 years of follow-up as the trajectory of T2D progression. By building prediction models, we discovered a distinct metabolic signature in the early postpartum period that predicted future T2D with a median discriminating power area under the receiver operating characteristic curve of 0.883 (95% CI 0.820–0.945, p < 0.001). At baseline, the most striking finding was an overall increase in amino acids (AAs) as well as diacyl-glycerophospholipids and a decrease in sphingolipids and acyl-alkyl-glycerophospholipids among women with incident T2D. Pathway analysis revealed up-regulated AA metabolism, arginine/proline metabolism, and branched-chain AA (BCAA) metabolism at baseline. At follow-up after the onset of T2D, up-regulation of AAs and down-regulation of sphingolipids and acyl-alkyl-glycerophospholipids were sustained or strengthened. Notably, longitudinal analyses revealed only 10 metabolites associated with progression to T2D, implicating AA and phospholipid metabolism. A study limitation is that all of the analyses were performed with the same cohort. It would be ideal to validate our findings in an independent longitudinal cohort of women with GDM who had glucose tolerance tested during the early postpartum period. Conclusions In this study, we discovered a metabolic signature predicting the transition from GDM to T2D in the early postpartum period that was superior to clinical parameters (fasting plasma glucose, 2-hour plasma glucose). The findings suggest that metabolic dysregulation, particularly AA dysmetabolism, is present years prior to diabetes onset, and is revealed during the early postpartum period, preceding progression to T2D, among women with GDM. Trial registration ClinicalTrials.gov Identifier: NCT01967030., Mi Lai and co-workers describe a metabolic signature associated with progression from gestational to type 2 diabetes., Author summary Why was this study done? Women with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D) later in life, and an estimated 35%–50% of GDM cases will progress to T2D within 10 years postpartum. Biological pathways and metabolites influencing progression from GDM to T2D have not been elucidated in humans. The main goal of the present work is to gain critical insight into the pathology of the transition from GDM to T2D, particularly metabolic changes involved in this process. A second goal is to devise more accurate means of identifying who will transition to T2D among women with GDM. What did the researchers do and find? We carried out a nested case–control study using a GDM prospective cohort of 1,010 women without T2D 6–9 weeks postpartum (study baseline). We performed metabolic profiling on fasting blood samples from women at 6–9 weeks postpartum (baseline) and up to 2 years post-baseline (follow-up). We found that significant dysmetabolism in lipids and amino acids in the early postpartum period was associated with future diabetes in women. Longitudinal analysis tracking changes in metabolic profiles over the course of diabetes progression revealed 10 specific metabolites that are associated with progression to T2D. We identified a small group of metabolites that could predict future T2D with great discriminative power, surpassing current clinical methods. What do these findings mean? A subclinical diabetes-like condition appears to already be present in the early postpartum period in women with previous GDM who later progress to T2D, and dysregulated amino acid metabolism is tightly associated with disease progression. Metabolites other than glucose may provide a simple and accurate alternative approach to assess risk of future T2D in women with a history of GDM.
- Published
- 2020
22. Publisher Correction: OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data
- Author
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George Rosenberger, Ruedi Aebersold, Ben C. Collins, Pedro Navarro, Olga T. Schubert, Lars Malmström, Witold Wolski, Johan Malmström, Saša M. Miladinović, Hannes L. Röst, and Ludovic C Gillet
- Subjects
Information retrieval ,Computer science ,Published Erratum ,Biomedical Engineering ,Data analysis ,MEDLINE ,Molecular Medicine ,Bioengineering ,Applied Microbiology and Biotechnology ,Biotechnology - Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
- Published
- 2020
23. Automated Workflow For Peptide-level Quantitation From DIA/ SWATH-MS Data
- Author
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Hannes L. Röst and Shubham Gupta
- Subjects
Swath ms ,Dependency (UML) ,Workflow ,Software ,Computer science ,business.industry ,Data-independent acquisition ,Data mining ,business ,computer.software_genre ,Sample (graphics) ,computer - Abstract
Data Independent Acquisition (DIA) is a powerful method to acquire spectra from all ionized precursors of a sample. Considering the complexity of the highly multiplexed spectral data, sophisticated workflows have been developed to obtain peptides quantification. Here we describe an open-source and easy-to-use workflow to obtain a quantitative matrix from multiple DIA runs. This workflow requires as prior information an “assay library”, which contains the MS coordinates of peptides. It consists of OpenSWATH, pyProphet and DIAlignR software. For the ease of installation and to isolate operating system related dependency, docker-based containerization is utilized in this workflow.
- Published
- 2020
24. DrawAlignR: An Interactive Tool for Across Run Chromatogram Alignment Visualization
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Justin Sing, Hannes L. Röst, Shubham Gupta, and Arshia Mahmoodi
- Subjects
Proteomics ,Analyte ,Computer science ,0206 medical engineering ,Peptide ,02 engineering and technology ,Biochemistry ,Plot (graphics) ,03 medical and health sciences ,Software ,Web application ,Data-independent acquisition ,Molecular Biology ,030304 developmental biology ,chemistry.chemical_classification ,0303 health sciences ,Chromatography ,business.industry ,030302 biochemistry & molecular biology ,Visualization ,chemistry ,business ,Peptides ,020602 bioinformatics ,Algorithms - Abstract
Multi-run alignment is widely used in proteomics to establish analyte correspondence across runs. Generally alignment algorithms return a cumulative score, which may not be easily interpretable for each peptide. Here we present a novel tool, DrawAlignR, to visualize each chromatographic alignment for DIA/SWATH data. Furthermore, we have developed a novel C++ based implementation of raw chromatogram alignment which is 35 times faster than the previously published algorithm. This not only enables users to plot alignment interactively by DrawAlignR, but also allows other software platforms to use the algorithm. DrawAlignR is an open-source web application using R Shiny that can be hosted using the source-code available at https://github.com/Roestlab/DrawAlignR.
- Published
- 2020
25. CHAPTER 16. Python in Proteomics
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Hannes L. Röst
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Workflow ,Scripting language ,Software prototyping ,Data mining ,Python (programming language) ,computer.software_genre ,Proteomics ,Mass spectrometry ,computer ,Smoothing ,computer.programming_language ,Visualization - Abstract
Python is a versatile scripting language that is widely used in bioinformatics and has generated increasing interest in the proteomics and mass spectrometry community. Computing and data analysis in mass spectrometry is very diverse and in many cases must be tailored to a specific experiment. This makes Python an excellent programming language for the task due to its flexibility, visualization capabilities and large number of powerful libraries. Python can be used to quickly prototype software, combine existing libraries into powerful analysis workflows while avoiding the trap of re-inventing the wheel for a new project. Here, we will describe data analysis and software prototyping of mass spectrometric data with Python using the pyOpenMS package. pyOpenMS is an open-source Python library for mass spectrometry, specifically built for the analysis of proteomics and metabolomics data in Python. pyOpenMS facilitates the execution of common tasks in protoemics (and other mass spectrometric fields) such as file handling, chemistry (mass calculation, peptide fragmentation, isotopic abundances), signal processing (smoothing, filtering, de-isotoping, retention time correction and peak-picking), identification analysis (including peptide search, PTM analysis, cross-linked analytes, FDR control, RNA oligonucleotide search and small molecule search tools), quantitative analysis (including label-free, metabolomics, SILAC, iTRAQ and SWATH/DIA analysis tools), chromatogram analysis (chromatographic peak picking, smoothing, elution profiles and peak scoring for SRM/MRM/PRM/SWATH/DIA data) as well as providing an interface for interacting with common tools in proteomics and metabolomics.
- Published
- 2020
26. CHAPTER 6. OpenMS and KNIME for Mass Spectrometry Data Processing
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Oliver Kohlbacher, Oliver Alka, Samuel Wein, Timo Sachsenberg, Eugen Netz, Julianus Pfeuffer, Hendrik Weisser, Leon Bichmann, Hannes L. Röst, and Marc Rurik
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business.industry ,Computer science ,Data management ,Python (programming language) ,Workflow engine ,Visualization ,Mascot ,Workflow ,Software ,business ,Software engineering ,Mass spectrometry data format ,computer ,computer.programming_language - Abstract
Computational mass spectrometry is plagued by a multitude of issues, including a heterogeneous software environment, complex workflows and proprietary tools. OpenMS addresses these challenges by providing robust open-source software for users and an open, well-designed software environment for developers. OpenMS is an open-source software C++ library for LC-MS data management and analyses using modern C++11. It offers an infrastructure for rapid development of mass spectrometry related software. OpenMS is free software available under the three clause BSD license. It comes with a variety of pre-built and ready-to-use tools for high-throughput Proteomics and Metabolomics data analysis (TOPPTools), covering most MS and LC-MS data processing and mining tasks, as well as visualization (TOPPView). OpenMS offers automated analyses for various quantitation protocols, including label-free quantitation, SILAC, iTRAQ, TMT, SRM, SWATH. It provides built-in algorithms for de novo identification and database search, as well as adapters to other tools like X!Tandem, Mascot, OMSSA, SIRIUS. It supports easy integration of OpenMS built tools into workflow engines like KNIME, Galaxy, WS-Pgrade, and TOPPAS. OpenMS supports the Proteomics Standard Initiative (PSI) formats for MS data including mzML, mzXML, mzIdentXML, pepXML. With pyOpenMS, OpenMS offers Python bindings to a large part of the OpenMS API to enable rapid algorithm development.
- Published
- 2020
27. Cell size homeostasis is maintained by CDK4-dependent activation of p38 MAPK
- Author
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Douglas S. Auld, Rachel L. Webster, Ceryl Tan, Ran Kafri, W. Brent Derry, Shixuan Liu, Nish Patel, John Concannon, Miriam Bracha Ginzberg, Yuan Wang, Hannes L. Röst, Jeremy L. Jenkins, Andreas Hilfinger, Seshu Iyengar, David Papadopoli, and Ivan Topisirovic
- Subjects
Cell type ,MAP Kinase Signaling System ,p38 mitogen-activated protein kinases ,Apoptosis ,Biology ,p38 Mitogen-Activated Protein Kinases ,Article ,General Biochemistry, Genetics and Molecular Biology ,Cell size ,03 medical and health sciences ,0302 clinical medicine ,Cyclin D1 ,Homeostasis ,Humans ,Molecular Biology ,Cell Size ,030304 developmental biology ,0303 health sciences ,integumentary system ,Cell growth ,Cell Cycle ,Cyclin-Dependent Kinase 4 ,Cell Cycle Checkpoints ,Cell Biology ,Cell cycle ,Cell biology ,030217 neurology & neurosurgery ,Function (biology) ,Developmental Biology - Abstract
While molecules that promote the growth of animal cells have been identified, it remains unclear how such signals are orchestrated to determine a characteristic target size for different cell types. It is increasingly clear that cell size is determined by size checkpoints-mechanisms that restrict the cell cycle progression of cells that are smaller than their target size. Previously, we described a p38 MAPK-dependent cell size checkpoint mechanism whereby p38 is selectively activated and prevents cell cycle progression in cells that are smaller than a given target size. In this study, we show that the specific target size required for inactivation of p38 and transition through the cell cycle is determined by CDK4 activity. Our data suggest a model whereby p38 and CDK4 cooperate analogously to the function of a thermostat: while p38 senses irregularities in size, CDK4 corresponds to the thermostat dial that sets the target size.
- Published
- 2021
28. Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS
- Author
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Ruedi Aebersold, Ben C. Collins, George Rosenberger, Tim D. Spector, Alfonso Buil, Yansheng Liu, Lars Malmström, Christina Ludwig, Ariel Bensimon, Martin Soste, Emmanouil T. Dermitzakis, and Hannes L. Röst
- Subjects
0301 basic medicine ,Phosphopeptides ,Proteomics ,Swath ms ,Proteomics methods ,Computer science ,Reference data (financial markets) ,Biomedical Engineering ,Twins ,Inference ,Bioengineering ,Bioinformatics ,Applied Microbiology and Biotechnology ,Article ,Mass Spectrometry ,03 medical and health sciences ,Humans ,ddc:576.5 ,030102 biochemistry & molecular biology ,Apolipoprotein A-I ,business.industry ,Pattern recognition ,Large sample ,030104 developmental biology ,Post translational ,Fully automated ,Protein processing ,Molecular Medicine ,Artificial intelligence ,business ,Peptides ,Protein Processing, Post-Translational ,Algorithms ,Biotechnology - Abstract
The consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is an essential prerequisite for the functional analysis of biological processes. Data-independent acquisition (DIA), a bottom-up mass spectrometry based proteomic strategy, exemplified by SWATH-MS, provides complete precursor and fragment ion information of a sample and thus, in principle, the information to identify peptidoforms, the modified variants of a peptide. However, due to the convoluted structure of DIA data sets the confident and systematic identification and quantification of peptidoforms has remained challenging. Here we present IPF (Inference of PeptidoForms), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. The data indicate that IPF reduced false site-localization by more than 7-fold in comparison to previous approaches, while recovering 85.4% of the true signals. IPF was applied to detect and quantify peptidoforms carrying ten different types of PTMs in DIA data acquired from more than 200 samples of undepleted blood plasma of a human twin cohort. The data approportioned, for the first time, the contribution of heritable, environmental and longitudinal effects on the observed quantitative variability of specific modifications in blood plasma of a human population.
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- 2017
29. Parallel accumulation – serial fragmentation combined with data-independent acquisition (diaPASEF): Bottom-up proteomics with near optimal ion usage
- Author
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Hannes L. Röst, Annie Ha, Max Frank, Ruedi Aebersold, Markus Lubeck, Oliver Raether, Matthias Mann, Eugenia Voytik, Ben C. Collins, Isabell Bludau, Stephanie Kaspar-Schoenefeld, Florian Meier, and Andreas-David Brunner
- Subjects
Reproducibility ,Data extraction ,Chemistry ,Proteome ,Data-independent acquisition ,Ion current ,Bottom-up proteomics ,Biological system ,Proteomics ,Ion - Abstract
Bottom-up proteomics produces complex peptide populations that are identified and quantified at the precursor or fragment ion level. Data dependent acquisition methods sequentially isolate and fragment particular precursors, whereas data independent acquisition (DIA) modes isolate and concurrently fragment populations of different precursors by cycling deterministically through segments of a predefined precursor m/z range. Although the selection windows of DIA collectively cover the entire mass range of interest, only a few percent of the ion current are sampled due to the consecutive selection of acquisition windows. Making use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro), we here devise a novel scan mode that samples up to 100% of the peptide precursor ion current. We analyze the acquired data by extending established targeted data extraction workflow for the analysis of DIA data by the additional ion mobility dimension, providing additional specificity in the precursor identification. Data acquired from simple protein mixtures verify the expected data completeness and data in single runs of a whole proteome digest demonstrate deep proteome coverage and a very high degree of reproducibility and quantitative accuracy, even from 10 ng sample amounts.
- Published
- 2019
30. OpenMS for open source analysis of mass spectrometric data
- Author
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Julianus Pfeuffer, Samuel Wein, Leon Bichmann, Oliver Kohlbacher, Marc Rurik, Timo Sachsenberg, Eugen Netz, Hendrik Weisser, Hannes L. Röst, and Oliver Alka
- Subjects
0303 health sciences ,03 medical and health sciences ,Open source ,Metabolomics ,Chromatography ,Chemistry ,030302 biochemistry & molecular biology ,Mass spectrometry ,Proteomics ,Mass spectrometric ,030304 developmental biology - Published
- 2019
31. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics
- Author
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Yansheng Liu, Ruedi Aebersold, Lars Malmström, Ben C. Collins, Matteo Zanella, Giuseppe D'Agostino, Ludovic C Gillet, Giuseppe Testa, Hannes L. Röst, George Rosenberger, and Pedro Navarro
- Subjects
Pluripotent Stem Cells ,Proteomics ,0301 basic medicine ,Analyte ,Streptococcus pyogenes ,Software tool ,Quantitative proteomics ,Proteomic analysis ,Computational biology ,Biology ,Proteome informatics ,Bioinformatics ,Biochemistry ,Article ,Mass Spectrometry ,03 medical and health sciences ,Sequence Analysis, Protein ,Protein methods ,Humans ,Protein Precursors ,Human Induced Pluripotent Stem Cells ,Molecular Biology ,Electronic Data Processing ,Reproducibility of Results ,Cell Biology ,Mass spectrometric ,Targeted proteomics ,030104 developmental biology ,Proteolysis ,sense organs ,Peptides ,Sequence Alignment ,Algorithms ,Software ,Biotechnology - Abstract
Nature Methods, 13 (9), ISSN:1548-7105, ISSN:1548-7091
- Published
- 2016
- Full Text
- View/download PDF
32. A multicenter study benchmarks software tools for label-free proteome quantification
- Author
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George Rosenberger, Ludovic C Gillet, Jörg Kuharev, Stephen Tate, Yasset Perez-Riverol, Chih-Chiang Tsou, Pedro Navarro, Brendan MacLean, Stefan Tenzer, Lukas Reiter, Alexey I. Nesvizhskii, Ruedi Aebersold, Oliver M. Bernhardt, Hannes L. Röst, and Ute Distler
- Subjects
0301 basic medicine ,Internationality ,Proteome ,Computer science ,media_common.quotation_subject ,Software tool ,Quantitative proteomics ,Biomedical Engineering ,Bioengineering ,computer.software_genre ,Bioinformatics ,Sensitivity and Specificity ,Applied Microbiology and Biotechnology ,Article ,Mass Spectrometry ,03 medical and health sciences ,Software ,Quality (business) ,media_common ,Label free ,Staining and Labeling ,030102 biochemistry & molecular biology ,business.industry ,Reproducibility of Results ,Benchmarking ,ComputingMethodologies_PATTERNRECOGNITION ,030104 developmental biology ,Multicenter study ,Benchmark (computing) ,Molecular Medicine ,Data mining ,business ,computer ,Algorithms ,Biotechnology - Abstract
The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.
- Published
- 2016
- Full Text
- View/download PDF
33. Machine Learning in Mass Spectrometric Analysis of DIA Data
- Author
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Adamo Young, Hannes L. Röst, Audrina Zhou, and Leon L. Xu
- Subjects
Proteomics ,Proteome ,Heuristic (computer science) ,Computer science ,Machine learning ,computer.software_genre ,Biochemistry ,Quantitative accuracy ,Machine Learning ,03 medical and health sciences ,Tandem Mass Spectrometry ,Data-independent acquisition ,Molecular Biology ,030304 developmental biology ,Complex data type ,0303 health sciences ,business.industry ,Deep learning ,030302 biochemistry & molecular biology ,Reproducibility of Results ,Mass spectrometric ,Scalability ,Artificial intelligence ,business ,computer ,Chromatography, Liquid - Abstract
Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) based methods are currently the top choice for high-throughput, quantitative measurements of the proteome. While traditional proteomics LC-MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data-independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert-knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data-driven DL pipelines.
- Published
- 2020
34. Expanding the Use of Spectral Libraries in Proteomics
- Author
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Lukas Käll, Reza M. Salek, Nico Jehmlich, Nuno Bandeira, Steffen Neumann, Bernhard Kuster, Johannes Griss, Hannes L. Röst, Stephen Tate, Johannes P. C. Vissers, Juan Antonio Vizcaíno, Henry H N Lam, Pierre-Alain Binz, Timo Sachsenberg, Mathias Walzer, Emma L. Schymanski, Mathias Wilhelm, Viktoria Dorfer, Ana Y. Wang, Dennis W. Wolan, Paul Wilmes, Bernard Delanghe, Eric W. Deutsch, Andrew W. Dowsey, Pieter-Jan Volders, Sebastian Böcker, Luis Mendoza, Robert J. Chalkley, Jim Shofstahl, and Yasset Perez-Riverol
- Subjects
0301 basic medicine ,Proteomics ,Biochemistry & Molecular Biology ,formats ,Computer science ,media_common.quotation_subject ,ENCODE ,Biochemistry ,Field (computer science) ,Article ,Workflow ,Proteomics Standards Initiative ,Databases ,03 medical and health sciences ,Peptide Library ,Tandem Mass Spectrometry ,Dagstuhl Seminar ,spectral libraries ,Animals ,Humans ,Quality (business) ,Data-independent acquisition ,Databases, Protein ,mass spectrometry ,meeting report ,media_common ,Protein ,General Chemistry ,Biological Sciences ,Data science ,Metadata ,030104 developmental biology ,ComputingMethodologies_PATTERNRECOGNITION ,Chemical Sciences ,standards - Abstract
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.
- Published
- 2018
35. Recommendations for the packaging and containerizing of bioinformatics software
- Author
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Fábio Madeira, Dan Søndergaard, Victoria Dominguez Del Angel, Daniel Blankenberg, Susheel Varma, Hannes L. Röst, Yasset Perez-Riverol, BioContainers Community, Olivier Sallou, Hervé Ménager, Bjorn Gruening, Michael R. Crusoe, Rafael C. Jimenez, Brian O'Connor, Timo Sachsenberg, Felipe da Veiga Leprevost, Pablo Moreno, Department of Computer Science [Freiburg], University of Freiburg [Freiburg], Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Department of Pathology [Ann Arbor, USA], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Bioinformatics Research Centre, Aarhus University [Aarhus], Donnelly Centre [Toronto, ON, Canada], University of Toronto, Wilhelm-Schickard-Institut für Informatik [Tübingen], Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Santa Cruz Genomics Institute, University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC), Université Paris-Saclay, Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Microbiology and Molecular Genetics, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Genomic Medicine Institute [Cleveland], Cleveland Clinic, ELIXIR Hub [Cambridge], This work was partially supported by ELIXIR-EXCELERATE. ELIXIR-EXCELERATE is funded by the European Commission within the Research Infrastructures programme of Horizon 2020, grant agreement numbers 676559. The BioContainers workshop (Paris 2017) and the BioContainers community that developed these recommendations are supported by the ELIXIR Tools platform. FVL is supported by NIH grants R01GM94231 and U24CA210967., BioContainers Community, European Project: 676559,H2020,H2020-INFRADEV-1-2015-1,ELIXIR-EXCELERATE(2015), Ménager, Hervé, ELIXIR-EXCELERATE: Fast-track ELIXIR implementation and drive early user exploitation across the life-sciences. - ELIXIR-EXCELERATE - - H20202015-09-01 - 2019-08-31 - 676559 - VALID, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), University of California [Santa Cruz] (UCSC), University of California-University of California, and Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)
- Subjects
0301 basic medicine ,best practices bioinformatics ,Computer science ,media_common.quotation_subject ,General Biochemistry, Genetics and Molecular Biology ,Scientific software ,Workflow ,Set (abstract data type) ,03 medical and health sciences ,Software ,0302 clinical medicine ,Bioinformatics software ,11. Sustainability ,Humans ,Quality (business) ,General Pharmacology, Toxicology and Pharmaceutics ,reproducibility ,media_common ,[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM] ,General Immunology and Microbiology ,containers and packages ,business.industry ,Computational Biology ,Articles ,General Medicine ,Opinion Article ,Research Personnel ,030104 developmental biology ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,Software engineering ,business ,030217 neurology & neurosurgery ,Research software - Abstract
International audience; Software Containers are changing the way scientists and researchers develop, deploy and exchange scientific software. They allow labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. However, containers and software packages should be produced under certain rules and standards in order to be reusable, compatible and easy to integrate into pipelines and analysis workflows. Here, we presented a set of recommendations developed by the BioContainers Community to produce standardized bioinformatics packages and containers. These recommendations provide practical guidelines to make bioinformatics software more discoverable, reusable and transparent. They are aimed to guide developers, organisations, journals and funders to increase the quality and sustainability of research software.
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- 2018
36. Longitudinal multi-omics of host-microbe dynamics in prediabetes
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Tejaswini Mishra, Orit Dagan-Rosenfeld, Blake M. Hanson, Colleen M. Craig, Daniel Hornburg, David Tse, Songjie Chen, Jethro S. Johnson, Tracey McLaughlin, Varsha Rao, Yanjiao Zhou, Sara Ahadi, Candice A. Allister, Liang Liang, Hoan Nguyen, Martin J. Zhang, Patricia Limcaoco, Erica Sodergren, Elizabeth Colbert, Brian D. Piening, Lei Chen, Bo-Young Hong, Melanie Ashland, Hannes L. Röst, Kévin Contrepois, Amir Bahmani, Brandon Albright, Daniel Spakowicz, Thi Dong Binh Tran, Eddy J. Bautista, Dalia Perelman, Eric X Wei, Lauren M. Petersen, George M. Weinstock, Kimberly R. Kukurba, Denis Salins, Brittany Lee-McMullen, Shannon Rego, Ahmed A. Metwally, Hassan Chaib, Xin Zhou, M. Reza Sailani, Sophia Miryam Schüssler-Fiorenza Rose, Wenyu Zhou, Benjamin Leopold, Jessilyn Dunn, Shana R. Leopold, Monika Avina, and Michael Snyder
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0301 basic medicine ,Male ,Proteome ,Datasets as Topic ,Disease ,Transcriptome ,Cohort Studies ,Prognostic markers ,0302 clinical medicine ,2.1 Biological and endogenous factors ,Insulin ,Prediabetes ,Longitudinal Studies ,Aetiology ,Respiratory Tract Infections ,Multidisciplinary ,Microbiota ,Gastrointestinal Microbiome ,Diabetes ,Vaccination ,Middle Aged ,Healthy Volunteers ,3. Good health ,Anti-Bacterial Agents ,Infectious Diseases ,Influenza Vaccines ,Female ,Pre-diabetes ,Infection ,Type 2 ,Adult ,General Science & Technology ,Physiological ,Genomics ,Biology ,Stress ,Article ,Vaccine Related ,Prediabetic State ,03 medical and health sciences ,Immune system ,Stress, Physiological ,medicine ,Diabetes Mellitus ,Humans ,Microbiome ,Obesity ,Metabolic and endocrine ,Aged ,Inflammation ,Host Microbial Interactions ,Prevention ,Type 2 Diabetes Mellitus ,Computational Biology ,medicine.disease ,030104 developmental biology ,Good Health and Well Being ,Glucose ,Diabetes Mellitus, Type 2 ,Immunology ,Insulin Resistance ,030217 neurology & neurosurgery ,Biomarkers - Abstract
Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states., Deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, alongside changes in the microbiome, in samples from individuals with and without prediabetes reveal insights into inter-individual variability and associations between changes in the microbiome and other factors.
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- 2018
37. From hype to reality: data science enabling personalized medicine
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Rainer Spang, Michael Rebhan, Teresa M. Przytycka, Jimeng Sun, Rudi Balling, Oliver Kohlbacher, Holger Fröhlich, Santosh Kumar, Daniel Ziemek, Andreas Schuppert, Niko Beerenwinkel, Daniel J. Stekhoven, Marloes H. Maathuis, Thomas Lengauer, Blaz Zupan, Susan A. Murphy, Yves Moreau, Andreas Weber, Matthias Schwab, and Hannes L. Röst
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0301 basic medicine ,Artificial intelligence ,PREDICTION ,Debate ,Big data ,610 Medizin ,lcsh:Medicine ,Personalized medicine ,Precision medicine ,Stratified medicine ,P4 medicine ,Machine learning ,Biomarkers ,Medicine ,Mainstream ,Prospective Studies ,media_common ,education.field_of_study ,Enthusiasm ,ddc:610 ,SIGNATURE ,General Medicine ,3. Good health ,HYBRID MODELS ,HEALTH ,Life Sciences & Biomedicine ,BIG DATA ,media_common.quotation_subject ,Population ,PATIENT ,03 medical and health sciences ,Medicine, General & Internal ,General & Internal Medicine ,CAUSAL ,BREAST-CANCER ,Humans ,education ,Science & Technology ,business.industry ,lcsh:R ,Data science ,EVOLUTION ,Clinical trial ,030104 developmental biology ,DISCOVERY ,business - Abstract
BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice. ispartof: BMC MEDICINE vol:16 issue:1 ispartof: location:England status: published
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- 2018
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38. Deep learning adds an extra dimension to peptide fragmentation
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Hannes L. Röst
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Physics ,0303 health sciences ,business.industry ,Deep learning ,Cell Biology ,Proteomics ,Tandem mass spectrometry ,Quantitative Biology::Genomics ,Biochemistry ,Peptide fragmentation ,03 medical and health sciences ,Fragmentation (mass spectrometry) ,Proteome ,Peptide sequencing ,Deep neural networks ,Artificial intelligence ,Biological system ,business ,Molecular Biology ,030304 developmental biology ,Biotechnology - Abstract
The interpretation of fragmentation patterns in tandem mass spectrometry is crucial for peptide sequencing, but the relative intensities of these patterns are difficult to predict computationally. Two groups have applied deep neural networks to address this long-standing problem in the proteomics field, extending theoretical spectra with an additional dimension of high-accuracy fragment ion intensities.
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- 2019
39. Initial Guidelines for Manuscripts Employing Data-independent Acquisition Mass Spectrometry for Proteomic Analysis
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Hannes L. Röst, Robert J. Chalkley, Michael J. MacCoss, and Jacob D. Jaffe
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Proteomics ,Manuscripts as Topic ,Computer science ,business.industry ,Guidelines as Topic ,Computational biology ,Mass spectrometry ,Biochemistry ,Mass Spectrometry ,Analytical Chemistry ,Editorial ,Text mining ,Humans ,Data-independent acquisition ,Peptides ,business ,Molecular Biology ,Software - Published
- 2019
40. xTract: software for characterizing conformational changes of protein complexes by quantitative cross-linking mass spectrometry
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Lukasz A. Joachimiak, George Rosenberger, Ruedi Aebersold, Judith Frydman, Thomas Walzthoeni, Alexander Leitner, Lars Malmström, and Hannes L. Röst
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Models, Molecular ,Protein Conformation ,Bioinformatics ,Mass spectrometry ,Biochemistry ,Article ,Mass Spectrometry ,03 medical and health sciences ,0302 clinical medicine ,Protein structure ,Luciferases, Firefly ,Chaperonin Containing TCP-1 ,Animals ,Luciferase ,Databases, Protein ,Molecular Biology ,030304 developmental biology ,chemistry.chemical_classification ,0303 health sciences ,Chemistry ,Cell Biology ,Structural heterogeneity ,Amino acid ,Cross-Linking Reagents ,Data Interpretation, Statistical ,Multiprotein Complexes ,Biological system ,Algorithms ,Software ,030217 neurology & neurosurgery ,Biotechnology - Abstract
Chemical cross-linking in combination with mass spectrometry generates distance restraints of amino acid pairs in close proximity on the surface of native proteins and protein complexes. In this study we used quantitative mass spectrometry and chemical cross-linking to quantify differences in cross-linked peptides obtained from complexes in spatially discrete states. We describe a generic computational pipeline for quantitative cross-linking mass spectrometry consisting of modules for quantitative data extraction and statistical assessment of the obtained results. We used the method to detect conformational changes in two model systems: firefly luciferase and the bovine TRiC complex. Our method discovers and explains the structural heterogeneity of protein complexes using only sparse structural information.
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- 2015
41. Absolute Proteome Composition and Dynamics during Dormancy and Resuscitation of Mycobacterium tuberculosis
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George Rosenberger, Maria Kogadeeva, Olga T. Schubert, Martin Gengenbacher, Ruedi Aebersold, Christina Ludwig, Ben C. Collins, Uwe Sauer, Stefan H. E. Kaufmann, Hannes L. Röst, Ludovic C Gillet, and Michael B. Zimmermann
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Proteomics ,Cancer Research ,Proteome ,Drug resistance ,Computational biology ,Bioinformatics ,Bacterial Physiological Phenomena ,Microbiology ,Mass Spectrometry ,Mycobacterium tuberculosis ,03 medical and health sciences ,Exponential growth ,Bacterial Proteins ,Ribosomal protein ,Virology ,Immunology and Microbiology(all) ,Molecular Biology ,030304 developmental biology ,chemistry.chemical_classification ,0303 health sciences ,biology ,030306 microbiology ,biology.organism_classification ,3. Good health ,Enzyme ,Regulon ,chemistry ,Dormancy ,Parasitology - Abstract
SummaryMycobacterium tuberculosis remains a health concern due to its ability to enter a non-replicative dormant state linked to drug resistance. Understanding transitions into and out of dormancy will inform therapeutic strategies. We implemented a universally applicable, label-free approach to estimate absolute cellular protein concentrations on a proteome-wide scale based on SWATH mass spectrometry. We applied this approach to examine proteomic reorganization of M. tuberculosis during exponential growth, hypoxia-induced dormancy, and resuscitation. The resulting data set covering >2,000 proteins reveals how protein biomass is distributed among cellular functions during these states. The stress-induced DosR regulon contributes 20% to cellular protein content during dormancy, whereas ribosomal proteins remain largely unchanged at 5%–7%. Absolute protein concentrations furthermore allow protein alterations to be translated into changes in maximal enzymatic reaction velocities, enhancing understanding of metabolic adaptations. Thus, global absolute protein measurements provide a quantitative description of microbial states, which can support the development of therapeutic interventions.
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- 2015
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42. aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data
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George Rosenberger, Lars Malmström, Christina Ludwig, Ruedi Aebersold, Hannes L. Röst, University of Zurich, and Malmström, Lars
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Proteomics ,Statistics and Probability ,Normalization (statistics) ,1303 Biochemistry ,Computer science ,Gene Expression ,Tandem mass spectrometry ,computer.software_genre ,Biochemistry ,142-005 142-005 ,Tandem Mass Spectrometry ,Lc ms ms ,1312 Molecular Biology ,1706 Computer Science Applications ,2613 Statistics and Probability ,Molecular Biology ,Proteins ,Chromatography liquid ,Applications Notes ,3. Good health ,Computer Science Applications ,Computational Mathematics ,R package ,Computational Theory and Mathematics ,Data mining ,PeptideAtlas ,Raw data ,computer ,2605 Computational Mathematics ,Algorithms ,Software ,Chromatography, Liquid ,1703 Computational Theory and Mathematics - Abstract
Motivation: The determination of absolute quantities of proteins in biological samples is necessary for multiple types of scientific inquiry. While relative quantification has been commonly used in proteomics, few proteomic datasets measuring absolute protein quantities have been reported to date. Various technologies have been applied using different types of input data, e.g. ion intensities or spectral counts, as well as different absolute normalization strategies. To date, a user-friendly and transparent software supporting large-scale absolute protein quantification has been lacking. Results: We present a bioinformatics tool, termed aLFQ, which supports the commonly used absolute label-free protein abundance estimation methods (TopN, iBAQ, APEX, NSAF and SCAMPI) for LC-MS/MS proteomics data, together with validation algorithms enabling automated data analysis and error estimation. Availability and implementation: aLFQ is written in R and freely available under the GPLv3 from CRAN (http://www.cran.r-project.org). Instructions and example data are provided in the R-package. The raw data can be obtained from the PeptideAtlas raw data repository (PASS00321). Contact: lars.malmstroem@imsb.biol.ethz.ch Supplementary information: Supplementary Data are available at Bioinformatics online.
- Published
- 2017
43. DIANA—algorithmic improvements for analysis of data-independent acquisition MS data
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Johan Teleman, Hannes L. Röst, Johan Malmström, George Rosenberger, Lars Malmström, Fredrik Levander, Uwe Schmitt, University of Zurich, and Malmström, Johan
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Proteomics ,Statistics and Probability ,Infectious Medicine ,1303 Biochemistry ,Streptococcus pyogenes ,Computer science ,Quantitative proteomics ,computer.software_genre ,Mass spectrometry ,142-005 142-005 ,Biochemistry ,Tandem mass spectrum ,Software ,Bacterial Proteins ,Tandem Mass Spectrometry ,1312 Molecular Biology ,1706 Computer Science Applications ,Data Mining ,Humans ,Data-independent acquisition ,2613 Statistics and Probability ,Databases, Protein ,Molecular Biology ,business.industry ,Peptide quantification ,Markov Chains ,Peptide Fragments ,Computer Science Applications ,Computational Mathematics ,ComputingMethodologies_PATTERNRECOGNITION ,Computational Theory and Mathematics ,Data analysis ,Data mining ,business ,2605 Computational Mathematics ,computer ,Algorithms ,1703 Computational Theory and Mathematics - Abstract
Motivation: Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification. Results: We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples. Availability and implementation: DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet). Supplementary information: Supplementary data are available at Bioinformatics online.
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- 2017
44. High Frequency Actionable Pathogenic Exome Mutations in an Average-Risk Cohort
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Denis Salins, Patricia Limcaoco, Monika Avina, Hannes L. Röst, Lars M. Steinmetz, Orit Dagan-Rosenfeld, Jonathan A. Bernstein, Colleen M. Craig, Michael Snyder, Shannon Rego, Elizabeth Colbert, Jessica Wheeler, Tracey McLaughlin, M. Reza Sailani, Jessilyn Dunn, and Wenyu Zhou
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Genetics ,0303 health sciences ,education.field_of_study ,medicine.medical_specialty ,Population ,Biology ,Bioinformatics ,3. Good health ,03 medical and health sciences ,0302 clinical medicine ,030220 oncology & carcinogenesis ,Cohort ,medicine ,OMIM : Online Mendelian Inheritance in Man ,Medical genetics ,Allele ,education ,Exome ,Exome sequencing ,Pharmacogenetics ,030304 developmental biology - Abstract
Whole exome sequencing (WES) is increasingly utilized in both clinical and non-clinical settings, but little is known about the utility of WES in healthy individuals. In order to determine the frequency of both medically actionable and non-actionable but medically relevant exome findings in the general population we assessed the exomes of 70 participants who have been extensively characterized over the past several years as part of a longitudinal integrated multi-omics profiling study at Stanford University. We assessed exomes for rare likely pathogenic and pathogenic variants in genes associated with Mendelian disease in the Online Mendelian Inheritance in Man (OMIM) database. We used American College of Medical Genetics (ACMG) guidelines were used for the classification of rare sequence variants, and additionally we assessed pharmacogenetic variants. Twelve out of 70 (17%) participants had medically actionable findings in Mendelian disease genes, including 6 (9%) with mutations in genes not currently included in the ACMG’s list of 59 actionable genes. This number is higher than that reported in previous studies and suggests added benefit from utilizing expanded gene lists and manual curation to assess actionable findings. A total of 60 participants (89%) had non-actionable findings identified including 57 who were found to be mutation carriers for recessive diseases and 21 who have increased Alzheimer’s disease risk due to heterozyg ous or homozygousAPOEe4 alleles (18 participants had both). These results suggest that exome sequencing may have considerably more utility for health management in the general population than previously thought.
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- 2017
45. Integrative Personal Omics Profiles during Periods of Weight Gain and Loss
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George M. Weinstock, Denis Salins, Sharon J. Pitteri, Cheng Zhang, Michael Snyder, Jessica Wheeler, Gucci Jijuan Gu Urban, Daniel Spakowicz, Shannon Rego, Dalia Perelman, Erica Sodergren, Shana R. Leopold, Tejaswini Mishra, Hannes L. Röst, Eddy J. Bautista, Imon Banerjee, Cynthia Chen, Tracey McLaughlin, Wenyu Zhou, M. Reza Sailani, Daniel L. Rubin, Brian D. Piening, Blake M. Hanson, Colleen M. Craig, Ulf Smith, Elizabeth Colbert, Kimberly R. Kukurba, Mark Gerstein, Liang Liang, Adil Mardinoglu, Charles Abbott, Kévin Contrepois, Sunjae Lee, and Christine Y. Yeh
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0301 basic medicine ,Adult ,Male ,Proteomics ,Histology ,Systems biology ,Genomics ,Computational biology ,Biology ,Weight Gain ,Article ,Pathology and Forensic Medicine ,03 medical and health sciences ,Metabolomics ,Weight loss ,Weight Loss ,medicine ,Humans ,Microbiome ,Obesity ,Precision Medicine ,Cell Biology ,Omics ,3. Good health ,030104 developmental biology ,medicine.symptom ,Insulin Resistance ,Weight gain ,Biomarkers - Abstract
Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.
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- 2017
46. BioContainers: An open-source and community-driven framework for software standardization
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Harald Barsnes, Laurent Gatto, Saulo Alves Aflitos, Marc Vaudel, Mingze Bai, Julianus Pfeuffer, Jonas Weber, Alexey I. Nesvizhskii, Roberto Vera Alvarez, Julian Uszkoreit, Hannes L. Röst, Björn Grüning, Johannes Griss, Felipe da Veiga Leprevost, Yasset Perez-Riverol, Pablo Moreno, Timo Sachsenberg, and Rafael C. Jimenez
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Proteomics ,0301 basic medicine ,Statistics and Probability ,Resource-oriented architecture ,Computer science ,Genomics ,Biochemistry ,03 medical and health sciences ,Software ,Metabolomics ,Software verification and validation ,Molecular Biology ,Software measurement ,business.industry ,Software development ,Computational Biology ,computer.file_format ,Applications Notes ,Data science ,Software quality ,Computer Science Applications ,Computational Mathematics ,030104 developmental biology ,Computational Theory and Mathematics ,Software deployment ,Software construction ,Executable ,business ,Software engineering ,Sequence Analysis ,computer - Abstract
Motivation BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). Availability and Implementation The software is freely available at github.com/BioContainers/.
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- 2017
47. Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms
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Hannes L, Röst, Ruedi, Aebersold, and Olga T, Schubert
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Proteomics ,Quality Control ,User-Computer Interface ,Statistics as Topic ,Reproducibility of Results ,Scientific Experimental Error ,Web Browser ,Peptides ,Mass Spectrometry ,Software ,Workflow - Abstract
Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.
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- 2017
48. Automated swath data analysis using targeted extraction of ion chromatograms
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Hannes L. Röst, Ruedi Aebersold, Olga T. Schubert, Comai, Lucio, Katz, Jonathan E., and Mallick, Parag
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0301 basic medicine ,Protocol (science) ,Swath ms ,Analyte ,Reproducibility ,Computer science ,business.industry ,Selected reaction monitoring ,Pattern recognition ,3. Good health ,03 medical and health sciences ,030104 developmental biology ,Targeted mass spectrometry ,Data-independent acquisition ,Artificial intelligence ,business ,Targeted proteomics ,SWATH ,SWATH MS ,SWATH acquisition ,DIA ,OpenSWATH ,pyProphet ,TRIC aligner ,Skyline - Abstract
Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.
- Published
- 2017
49. Quantitative proteomics: challenges and opportunities in basic and applied research
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Ruedi Aebersold, Ben C. Collins, Olga T. Schubert, Hannes L. Röst, and George Rosenberger
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0301 basic medicine ,Proteomics ,Proteomics methods ,business.industry ,Clinical Laboratory Techniques ,Systems Biology ,010401 analytical chemistry ,Quantitative proteomics ,Perspective (graphical) ,MEDLINE ,Molecular systems ,01 natural sciences ,Data science ,General Biochemistry, Genetics and Molecular Biology ,Mass Spectrometry ,3. Good health ,0104 chemical sciences ,03 medical and health sciences ,030104 developmental biology ,Humans ,Applied research ,Personalized medicine ,business - Abstract
In this Perspective, we discuss developments in mass-spectrometry-based proteomic technology over the past decade from the viewpoint of our laboratory. We also reflect on existing challenges and limitations, and explore the current and future roles of quantitative proteomics in molecular systems biology, clinical research and personalized medicine. ISSN:1750-2799 ISSN:1754-2189
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- 2017
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50. pyOpenMS: A Python-based interface to the OpenMS mass-spectrometry algorithm library
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Ruedi Aebersold, Hannes L. Röst, Lars Malmström, and Uwe Schmitt
- Subjects
Proteomics ,Protein Conformation ,Computer science ,computer.software_genre ,Biochemistry ,Mass Spectrometry ,Software ,Data Mining ,Databases, Protein ,Molecular Biology ,computer.programming_language ,Complex data type ,Signal processing ,business.industry ,Proteins ,Python (programming language) ,Data structure ,Workflow ,Operating system ,Mass spectrometry data format ,business ,computer ,Algorithm ,Algorithms ,Smoothing - Abstract
pyOpenMS is an open-source, Python-based interface to the C++ OpenMS library, providing facile access to a feature-rich, open-source algorithm library for MS-based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping, and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open-source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3-clause BSD licence).
- Published
- 2014
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