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Objective: The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature. Data Sources: We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020. Study Selection: DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented. Results: A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status). Conclusions: The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence). Supplemental data for this article is available online at http://dx.doi.org/.

A number of studies have demonstrated that patients with autoimmune disease have lower levels of vitamin D prompting speculation that vitamin D might suppress inflammation and immune responses in children with juvenile idiopathic arthritis (JIA). The objective of this study was to compare vitamin D levels in children with JIA at disease onset with healthy children. We hypothesized that children and adolescents with JIA have lower vitamin D levels than healthy children and adolescents. Data from a Canadian cohort of children with new-onset JIA (n= 164, data collection 2007-2012) were compared to Canadian Health Measures Survey (CHMS) data (n=4027, data collection 2007-2011). We compared 25-hydroxy vitamin D (25(OH)D) concentrations with measures of inflammation, vitamin D supplement use, milk intake, and season of birth. Mean 25(OH)D level was significantly higher in patients with JIA (79 ± 3.1 nmol/L) than in healthy controls (68 ± 1.8 nmol/L P.05). Patients with JIA more often used vitamin D containing supplements (50% vs. 7%; P.05). The prevalence of 25(OH)D deficiency (30 nmol/L) was 6% for both groups. Children with JIA with 25(OH)D deficiency or insufficiency (50 nmol/L) had higher C-reactive protein levels. Children with JIA were more often born in the fall and winter compared to healthy children. In contrast to earlier studies, we found vitamin D levels in Canadian children with JIA were higher compared to healthy children and associated with more frequent use of vitamin D supplements. Among children with JIA, low vitamin D levels were associated with indicators of greater inflammation.

Like humans, canine lymphomas are treated by chemotherapy cocktails and frequently develop multiple drug resistance (MDR). Their shortened clinical timelines and tumor accessibility make canines excellent models to study MDR mechanisms. Insulin-sensitizers have been shown to reduce the incidence of cancer in humans prescribed them, and we previously demonstrated that they also reverse and delay MDR development in vitro. Here, we treated canines with MDR lymphoma with metformin to assess clinical and tumoral responses, including changes in MDR biomarkers, and used mRNA microarrays to determine differential gene expression. Metformin reduced MDR protein markers in all canines in the study. Microarrays performed on mRNAs gathered through longitudinal tumor sampling identified a 290 gene set that was enriched in Anaphase Promoting Complex (APC) substrates and additional mRNAs associated with slowed mitotic progression in MDR samples compared to skin controls. mRNAs from a canine that went into remission showed that APC substrate mRNAs were decreased, indicating that the APC was activated during remission. In vitro validation using canine lymphoma cells selected for resistance to chemotherapeutic drugs confirmed that APC activation restored MDR chemosensitivity, and that APC activity was reduced in MDR cells. This supports the idea that rapidly pushing MDR cells that harbor high loads of chromosome instability through mitosis, by activating the APC, contributes to improved survival and disease-free duration.

Inter-individual variance in host immune responses following vaccination can result in failure to develop protective immunity leaving individuals at risk for infection in addition to compromising herd immunity. While developing more efficacious vaccines is one strategy to mitigate this problem, predicting vaccine responsiveness prior to vaccination could inform which individuals require adjunct disease management strategies. To identify biomarkers of vaccine responsiveness, a cohort of pigs (n = 120) were vaccinated and pigs representing the high (n = 6; 90th percentile) and low (n = 6; 10th percentile) responders based on vaccine-specific antibody responses following vaccination were further analyzed. Kinase-mediated phosphorylation events within peripheral blood mononuclear cells collected prior to vaccination identified 53 differentially phosphorylated peptides when comparing low responders with high responders. Functional enrichment analysis revealed pro-inflammatory cytokine signaling pathways as dysregulated, and this was further substantiated by detection of higher (p

Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.

The advent of high-resolution chromosome conformation capture assays (such as 5C, Hi-C and Pore-C) has allowed for unprecedented sequence-level investigations into the structure–function relationship of the genome. In order to comprehensively understand this relationship, computational tools are required that utilize data generated from these assays to predict 3D genome organization (the 3D genome reconstruction problem). Many computational tools have been developed that answer this need, but a comprehensive comparison of their underlying algorithmic approaches has not been conducted. This manuscript provides a comprehensive review of the existing computational tools (from November 2006 to September 2019, inclusive) that can be used to predict 3D genome organizations from high-resolution chromosome conformation capture data. Overall, existing tools were found to use a relatively small set of algorithms from one or more of the following categories: dimensionality reduction, graph/network theory, maximum likelihood estimation (MLE) and statistical modeling. Solutions in each category are far from maturity, and the breadth and depth of various algorithmic categories have not been fully explored. While the tools for predicting 3D structure for a genomic region or single chromosome are diverse, there is a general lack of algorithmic diversity among computational tools for predicting the complete 3D genome organization from high-resolution chromosome conformation capture data.

Plasmodiophora brassicae (Wor.) is an obligate intracellular plant pathogen affecting Brassicas worldwide. Identification of effector proteins is key to understanding the interaction between P. brassicae and its susceptible host plants. To date, there is very little information available on putative effector proteins secreted by P. brassicae during a secondary infection of susceptible host plants, resulting in root gall production. A bioinformatics pipeline approach to RNA‐Seq data from Arabidopsis thaliana (L.) Heynh. root tissues at 17, 20, and 24 d postinoculation (dpi) identified 32 small secreted P. brassicae proteins (SSPbPs) that were highly expressed over this secondary infection time frame. Functional signal peptides were confirmed for 31 of the SSPbPs, supporting the accuracy of the pipeline designed to identify secreted proteins. Expression profiles at 0, 2, 5, 7, 14, 21, and 28 dpi verified the involvement of some of the SSPbPs in secondary infection. For seven of the SSPbPs, a functional domain was identified using Blast2GO and 3D structure analysis and domain functionality was confirmed for SSPbP22, a kinase localized to the cytoplasm and nucleus.

Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior. Therefore, an unbiased quantitative framework for evaluations of gene set analysis methods will be valuable. Such a framework requires gene expression datasets where enrichment status of gene sets is known a priori. In the absence of such gold standard datasets, artificial datasets are commonly used for evaluations of gene set analysis methods, however, they often rely on oversimplifying assumptions that make them biased in favor of or against a given method. In this paper, we propose a quantitative framework for evaluation of gene set analysis methods by synthesizing expression datasets using real data, without relying on oversimplifying or unrealistic assumptions, while preserving complex gene–gene correlations and retaining the distribution of expression values. The utility of the quantitative approach is shown by evaluating ten widely used gene set analysis methods. An implementation of the proposed method is publicly available. We suggest using Silver to evaluate existing and new gene set analysis methods. Evaluation using Silver provides a better understanding of current methods and can aid in the development of gene set analysis methods to achieve higher specificity without sacrificing sensitivity.

We have developed a graphical user interface for our Gen2Epi computational pipeline named Gen2EpiGUI. A total of 594 published whole-genome sequence datasets of Neisseria gonorrhoeae were used to validate the program. Gen2Epi facilitates an understandable analysis of N. gonorrhoeae whole-genome sequence data for users with limited bioinformatics skills.

Objective To identify discrete clusters comprising clinical features and inflammatory biomarkers in children with JIA and to determine cluster alignment with JIA categories. Methods A Canadian prospective inception cohort comprising 150 children with JIA was evaluated at baseline (visit 1) and after six months (visit 2). Data included clinical manifestations and inflammation-related biomarkers. Probabilistic principal component analysis identified sets of composite variables, or principal components, from 191 original variables. To discern new clinical-biomarker clusters (clusters), Gaussian mixture models were fit to the data. Newly-defined clusters and JIA categories were compared. Agreement between the two was assessed using Kruskal–Wallis analyses and contingency plots. Results Three principal components recovered 35% (three clusters) and 40% (five clusters) of the variance in patient profiles in visits 1 and 2, respectively. None of the clusters aligned precisely with any of the seven JIA categories but rather spanned multiple categories. Results demonstrated that the newly defined clinical-biomarker lustres are more homogeneous than JIA categories. Conclusion Applying unsupervised data mining to clinical and inflammatory biomarker data discerns discrete clusters that intersect multiple JIA categories. Results suggest that certain groups of patients within different JIA categories are more aligned pathobiologically than their separate clinical categorizations suggest. Applying data mining analyses to complex datasets can generate insights into JIA pathogenesis and could contribute to biologically based refinements in JIA classification.

BackgroundSeven structurally related β-lactamase-producing plasmids have been characterized in penicillinase-producing Neisseria gonorrhoeae (PPNG) isolates. We characterized a variant (i.e. pJRD20, Canada type) of the Africa-type (pJD5) plasmid isolated from N. gonorrhoeae strain 8903.ObjectivesTo compare the DNA sequence of pJRD20 with that of pJD5 and pJD4 (Asia-type) and their TEM-1 β-lactamases.MethodsN. gonorrhoeae 8903 was identified as part of the Gonococcal Antimicrobial Surveillance Program in Canada. β-Lactamase production was assessed using nitrocefin. MICs were determined by agar dilution and Etest methods (CLSI). The DNA sequences of pJRD20, pJD5 and pJD4 were assembled and annotated. The structure of TEM-1 and its penicillin-binding properties were determined by in silico molecular modelling and docking. TEM-1 proteins were characterized by western blot, mass spectrometry and ampicillin hydrolysis assays.ResultsN. gonorrhoeae 8903 exhibited intermediate susceptibility to penicillin with slow β-lactamase activity (i.e. 35 min to hydrolyse nitrocefin). Except for a novel 6 bp deletion starting at the G of the ATG start codon of blaTEM-1, the DNA sequence of pJRD20 was identical to that of pJD5. The TEM-1 β-lactamase produced by pJRD20 is 24 kDa and hydrolyses ampicillin only after several hours.ConclusionsThis unusual PPNG isolate might have been characterized as a non-PPNG owing to its low MIC of penicillin and its very slow hydrolysis of nitrocefin. Given the unusual nature of its TEM-1 β-lactamase, laboratories might consider extending the duration of nitrocefin hydrolysis assays.

Background Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. Methods We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. Results Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. Conclusions This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions. Electronic supplementary material The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.

Antibodies are critical effector molecules of the humoral immune system. Upon infection or vaccination, populations of antibodies are generated which bind to various regions of the invading pathogen or exogenous agent. Defining the reactivity and breadth of this antibody response provides an understanding of the antigenic determinants and enables the rational development and assessment of vaccine candidates. High-resolution analysis of these populations typically requires advanced techniques such as B cell receptor repertoire sequencing, mass spectrometry of isolated immunoglobulins, or phage display libraries that are dependent upon equipment and expertise which are prohibitive for many labs. High-density peptide microarrays representing diverse populations of putative linear epitopes (immunoarrays) are an effective alternative for high-throughput examination of antibody reactivity and diversity. While a promising technology, widespread adoption of immunoarrays has been limited by the need for, and relative absence of, user-friendly tools for consideration and visualization of the emerging data. To address this limitation, we developed EPIphany, a software platform with a simple web-based user interface, aimed at biological users, that provides access to important analysis parameters, data normalization options, and a variety of unique data visualization options. This platform provides researchers the greatest opportunity to extract biologically meaningful information from the immunoarray data, thereby facilitating the discovery and development of novel immuno-therapeutics.

Whole-genome sequencing (WGS) data are well established for the investigation of gonococcal transmission, antimicrobial resistance prediction, population structure determination and population dynamics. A variety of bioinformatics tools, repositories, services and platforms have been applied to manage and analyze Neisseria gonorrhoeae WGS datasets. This review provides an overview of the various bioinformatics approaches and resources used in 105 published studies (as of 30 April 2021). The challenges in the analysis of N. gonorrhoeae WGS datasets, as well as future bioinformatics requirements, are also discussed.

Peptide microarrays consisting of defined phosphorylation target sites are an effective approach for high throughput analysis of cellular kinase (kinome) activity. Kinome peptide arrays are highly customizable and do not require species-specific reagents to measure kinase activity, making them amenable for kinome analysis in any species. Our group developed software, Platform for Integrated, Intelligent Kinome Analysis (PIIKA), to enable more effective extraction of meaningful biological information from kinome peptide array data. A subsequent version, PIIKA2, unveiled new statistical tools and data visualization options. Here we introduce PIIKA 2.5 to provide two essential quality control metrics and a new background correction technique to increase the accuracy and consistency of kinome results. The first metric alerts users to improper spot size and informs them of the need to perform manual resizing to enhance the quality of the raw intensity data. The second metric uses inter-array comparisons to identify outlier arrays that sometimes emerge as a consequence of technical issues. In addition, a new background correction method, background scaling, can sharply reduce spatial biases within a single array in comparison to background subtraction alone. Collectively, the modifications of PIIKA 2.5 enable identification and correction of technical issues inherent to the technology and better facilitate the extraction of meaningful biological information. We show that these metrics demonstrably enhance kinome analysis by identifying low quality data and reducing batch effects, and ultimately improve clustering of treatment groups and enhance reproducibility. The web-based and stand-alone versions of PIIKA 2.5 are freely accessible at via http://saphire.usask.ca.

The K-homology (KH) domain is a nucleic acid–binding domain present in many proteins. Recently, we found that the DEAD-box helicase DDX43 contains a KH domain in its N-terminus; however, its function remains unknown. Here, we purified recombinant DDX43 KH domain protein and found that it prefers binding ssDNA and ssRNA. Electrophoretic mobility shift assay and NMR revealed that the KH domain favors pyrimidines over purines. Mutational analysis showed that the GXXG loop in the KH domain is involved in pyrimidine binding. Moreover, we found that an alanine residue adjacent to the GXXG loop is critical for binding. Systematic evolution of ligands by exponential enrichment, chromatin immunoprecipitation–seq, and cross-linking immunoprecipitation–seq showed that the KH domain binds C-/T-rich DNA and U-rich RNA. Bioinformatics analysis suggested that the KH domain prefers to bind promoters. Using 15N-heteronuclear single quantum coherence NMR, the optimal binding sequence was identified as TTGT. Finally, we found that the full-length DDX43 helicase prefers DNA or RNA substrates with TTGT or UUGU single-stranded tails and that the KH domain is critically important for sequence specificity and unwinding processivity. Collectively, our results demonstrated that the KH domain facilitates the substrate specificity and processivity of the DDX43 helicase.

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

Like humans, canines spontaneously develop lymphomas that are treated by chemotherapy cocktails and frequently develop multiple drug resistance (MDR). Their shortened clinical timelines and tumor accessibility make them excellent models to study MDR mechanisms. We previously demonstrated that adjunct treatment ofin vitroMDR cell lines with insulin-sensitizers effectively restored MDR chemosensitivity and prevented MDR development. This study extends the use of an insulin-sensitizer to clinical and tumor responsesin vivoin volunteer canines with MDR lymphoma, including assessing changes in MDR protein biomarkers and global gene expression. Longitudinal tumor sampling and analysis of MDR cases throughout treatment allowed a correlation betweenin vivomolecular mechanisms and clinical responsiveness. We found reduced MDR biomarkers within all tumors, yet only one canine entered clinical remission. Analysis of tumor samples during remission and relapse allowed comparison of gene expression profiles. This revealed the Anaphase Promoting Complex (APC), a ubiquitin-E3 ligase regulating cell cycle progression, was impaired during chemoresistance/MDR and restored during remission. Validatingin vitrotests restored MDR chemosensitivity upon APC activation, supporting the idea that APC activity is an important underlying cellular mechanism associated with treatment resistance, and a novel potential therapeutic target.

Within human health research, the remarkable utility of kinase inhibitors as therapeutics has motivated efforts to understand biology at the level of global cellular kinase activity (the kinome). In contrast, the diminished potential for using kinase inhibitors in food animals has dampened efforts to translate this research approach to livestock species. This, in our opinion, was a lost opportunity for livestock researchers given the unique potential of kinome analysis to offer insight into complex biology. To remedy this situation, our lab developed user-friendly, cost-effective approaches for kinome analysis that can be readily incorporated into most research programs but with a specific priority to enable the technology to livestock researchers. These contributions include the development of custom software programs for the creation of species-specific kinome arrays as well as comprehensive deconvolution and analysis of kinome array data. Presented in this review are examples of the application of kinome analysis to highlight the utility of the technology to further our understanding of two key complex biological events of priority to the livestock industry: host immune responses to infectious diseases and animal stress responses. These advances and examples of application aim to provide both mechanisms and motivation for researchers, particularly livestock researchers, to incorporate kinome analysis into their research programs.

Clubroot is an economically important disease affecting Brassica plants worldwide. Plasmodiophora brassicae is the protist pathogen associated with the disease, and its soil-borne obligate parasitic nature has impeded studies related to its biology and the mechanisms involved in its infection of the plant host. The identification of effector proteins is key to understanding how the pathogen manipulates the plant’s immune response and the genes involved in resistance. After more than 140 years studying clubroot and P. brassicae, very little is known about the effectors playing key roles in the infection process and subsequent disease progression. Here we analyze the information available for identified effectors and suggest several features of effector genes that can be used in the search for others. Based on the information presented in this review, we propose a comprehensive bioinformatics pipeline for effector identification and provide a list of the bioinformatics tools available for such.

Triple-negative breast cancer (TNBC) tumours that lack expression of oestrogen, and progesterone receptors, and do not overexpress the HER2 receptor represent the most aggressive breast cancer subtype, which is characterised by the resistance to therapy in frequently relapsing tumours and a high rate of patient mortality. This is likely due to the resistance of slowly proliferating tumour-initiating cells (TICs), and understanding molecular mechanisms that control TICs behaviour is crucial for the development of effective therapeutic approaches. Here, we present our novel findings, indicating that an intrinsically catalytically inactive member of the Eph group of receptor tyrosine kinases, EPHB6, partially suppresses the epithelial–mesenchymal transition in TNBC cells, while also promoting expansion of TICs. Our work reveals that EPHB6 interacts with the GRB2 adapter protein and that its effect on enhancing cell proliferation is mediated by the activation of the RAS-ERK pathway, which allows it to elevate the expression of the TIC-related transcription factor, OCT4. Consistent with this, suppression of either ERK or OCT4 activities blocks EPHB6-induced pro-proliferative responses. In line with its ability to trigger propagation of TICs, EPHB6 accelerates tumour growth, potentiates tumour initiation and increases TIC populations in xenograft models of TNBC. Remarkably, EPHB6 also suppresses tumour drug resistance to DNA-damaging therapy, probably by forcing TICs into a more proliferative, drug-sensitive state. In agreement, patients with higher EPHB6 expression in their tumours have a better chance for recurrence-free survival. These observations describe an entirely new mechanism that governs TNBC and suggest that it may be beneficial to enhance EPHB6 action concurrent with applying a conventional DNA-damaging treatment, as it would decrease drug resistance and improve tumour elimination.

A real-time PCR (RT-PCR) assay was designed for the simultaneous identification of Neisseria gonorrhoeae and its ciprofloxacin susceptibility status. A SYBR green-based multiplex RT-PCR format was used; it comprised two different forward primers and a common reverse primer to detect single nucleotide polymorphisms (SNPs) in gyrA of N. gonorrhoeae . The primer pairs were evaluated for their sensitivity and specificity using genomic DNA from 254 N. gonorrhoeae isolates (82 were ciprofloxacin susceptible and 172 were ciprofloxacin resistant) and 23 non- N. gonorrhoeae species isolates. The performance of the primers was validated using genomic DNA from 100 different N. gonorrhoeae isolates (46 were ciprofloxacin susceptible and 54 were ciprofloxacin resistant) and 52 non- N. gonorrhoeae isolates. The latter panel was revalidated by testing 99 (46 isolates were ciprofloxacin susceptible and 53 isolates were ciprofloxacin resistant) of the N. gonorrhoeae isolates and 23 non- N. gonorrhoeae isolates. These primers detected N. gonorrhoeae and its ciprofloxacin susceptibility status with over 99% sensitivity and specificity for all panels tested. This assay has the potential to be an inexpensive and rapid test for the simultaneous identification of N. gonorrhoeae and its ciprofloxacin susceptibility status.

De novo peptide sequencing using tandem mass spectrometry (MS/MS) data has become a major computational method for sequence identification in recent years. With the development of new instruments and technology, novel computational methods have emerged with enhanced performance. However, there are only a few methods focusing on ECD/ETD spectra, which mainly contain variants of $c$ -ions and $z$ -ions. Here, a de novo sequencing method for ECD/ETD spectra, NovoExD, is presented. NovoExD applies a new form of spectrum graph with multiple edge types (called a GMET), considers multiple peptide tags, and integrates amino acid combination (AAC) and fragment ion charge information. Its performance is compared with another successful de novo sequencing method, pNovo+, which has an option for ECD/ETD spectra. Experiments conducted on three different datasets show that the average full length peptide identification accuracy of NovoExD is as high as 88.70 percent, and that NovoExD's average accuracy is more than 20 percent greater on all datasets than that of pNovo+.

There is increasing appreciation among researchers and clinicians of the value of investigating biology and pathobiology at the level of cellular kinase (kinome) activity. Kinome analysis provides valuable opportunity to gain insights into complex biology (including disease pathology), identify biomarkers of critical phenotypes (including disease prognosis and evaluation of therapeutic efficacy), and identify targets for therapeutic intervention through kinase inhibitors. The growing interest in kinome analysis has fueled efforts to develop and optimize technologies that enable characterization of phosphorylation-mediated signaling events in a cost-effective, high-throughput manner. In this review, we highlight recent advances to the central technologies currently available for kinome profiling and offer our perspectives on the key challenges remaining to be addressed.

In order to comprehensively understand the structure-function relationship of the genome, 3D genome structures must first be predicted from biological data (like Hi-C) using computational tools. Many of these existing tools rely partially or completely on multi-dimensional scaling (MDS) to embed predicted structures in 3D space. MDS is known to have inherent problems when applied to high-dimensional datasets like Hi-C. Alternatively, t-Distributed Stochastic Neighbor Embedding (t-SNE) is able to overcome these problems but has not been applied to predict 3D genome structures. In this manuscript, we present a new workflow called StoHi-C (pronounced "stoic") that uses t-SNE to predict 3D genome structure from Hi-C data. StoHi-C was used to predict 3D genome structures for multiple, independent existing fission yeast Hi-C datasets. Overall, StoHi-C was able to generate 3D genome structures that more clearly exhibit the established principles of fission yeast 3D genomic organization.

Objective To identify early predictors of disease activity at 18 months in JIA using clinical and biomarker profiling. Methods Clinical and biomarker data were collected at JIA diagnosis in a prospective longitudinal inception cohort of 82 children with non-systemic JIA, and their ability to predict an active joint count of 0, a physician global assessment of disease activity of ≤1 cm, and inactive disease by Wallace 2004 criteria 18 months later was assessed. Correlation-based feature selection and ReliefF were used to shortlist predictors and random forest models were trained to predict outcomes. Results From the original 112 features, 13 effectively predicted 18-month outcomes. They included age, number of active/effused joints, wrist, ankle and/or knee involvement, ESR, ANA positivity and plasma levels of five inflammatory biomarkers (IL-10, IL-17, IL-12p70, soluble low-density lipoprotein receptor-related protein 1 and vitamin D), at enrolment. The clinical plus biomarker panel predicted active joint count = 0, physician global assessment ≤ 1, and inactive disease after 18 months with 0.79, 0.80 and 0.83 accuracy and 0.84, 0.83, 0.88 area under the curve, respectively. Using clinical features alone resulted in 0.75, 0.72 and 0.80 accuracy, and area under the curve values of 0.81, 0.78 and 0.83, respectively. Conclusion A panel of five plasma biomarkers combined with clinical features at the time of diagnosis more accurately predicted short-term disease activity in JIA than clinical characteristics alone. If validated in external cohorts, such a panel may guide more rationally conceived, biologically based, personalized treatment strategies in early JIA.

Whole-genome sequencing was used to identify mutations in antibiotic resistance-conferring genes to compare susceptibility predictions with MICs and to ascertain strain types in 99 isolates of Neisseria gonorrhoeae . Genotypes associated with susceptibility, as well as MIC creep or emerging resistance, were noted. Phylogenomic analysis revealed three distinctive clades and putative gonococcal transmission linkages involving a tetracycline-resistant N. gonorrhoeae outbreak and the clonal spread of susceptible isolates in men.

An effective publication-quality visualization tells a concise story from data. Methods and tools that facilitate making such visualizations are valuable to the scientific community. In this paper, we introduce pineplot, an R package for generating insightful visualizations called pine plots. Pine plots are applicable to a wide variety of datasets and create a holistic picture of the relationship between variables across different experimental conditions. A pine plot provides a means to visualize a group of symmetric matrices, each represented by triangular heat maps. Pine plots can be used to visualize large datasets for exploratory data analysis while controlling for different potentially confounding factors. The utility of the package is demonstrated by visualizing gene expression values of tissue-specific genes from RNA-seq data and the clinical factors in a liver disease and a heart disease dataset. The implementation of pineplot offers a straightforward procedure for generating pine plots; full control of the aesthetic elements of generated plots; and the possibility of augmenting generated plots with extra layers of graphical elements to further extend their usability.

Background Gene set analysis is a well-established approach for interpretation of data from high-throughput gene expression studies. Achieving reproducible results is an essential requirement in such studies. One factor of a gene expression experiment that can affect reproducibility is the choice of sample size. However, choosing an appropriate sample size can be difficult, especially because the choice may be method-dependent. Further, sample size choice can have unexpected effects on specificity. Results In this paper, we report on a systematic, quantitative approach to study the effect of sample size on the reproducibility of the results from 13 gene set analysis methods. We also investigate the impact of sample size on the specificity of these methods. Rather than relying on synthetic data, the proposed approach uses real expression datasets to offer an accurate and reliable evaluation. Conclusion Our findings show that, as a general pattern, the results of gene set analysis become more reproducible as sample size increases. However, the extent of reproducibility and the rate at which it increases vary from method to method. In addition, even in the absence of differential expression, some gene set analysis methods report a large number of false positives, and increasing sample size does not lead to reducing these false positives. The results of this research can be used when selecting a gene set analysis method from those available.

Gene set analysis is a well-established approach for analyzing high-throughput gene expression data. The choice of gene set database used for gene set analysis may affect the outcome of the analysis. Therefore, understanding characteristics of these databases is vital to the success of gene set analysis. Due to the sheer size of the gene set databases, a comprehensive qualitative evaluation of them is impractical. In this paper, we quantitatively study several well-established gene set databases. We propose and use a quantitative measure for assessing the similarity between gene set databases. Also, we introduce presence score, for quantifying the degree to which a given gene is represented in a database, and permeability score, for quantifying the degree to which genes in a given list co-occur in the gene sets of a database. A maximum achievable coverage score is defined based on the permeability score. Using the maximum achievable coverage score, we propose a methodology to statistically determine whether a phenotype of interest is well-represented in a given database. To study the effect of the choice of gene set database on the result of gene set analysis and show the utility of the maximum achievable coverage score, we conduct an experiment using two widely used gene set analysis methods and three expression datasets. The results suggest that the choice of gene set database might profoundly affect the outcome of the analysis. Also, our findings show that the permeability score and maximum achievable coverage can be used to guide the selection of an appropriate gene set database for a given study.