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Background Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). Materials and methods Patients were retrospectively enrolled between April 2007 and December 2018 from two institutions. We extracted deep learning features of six pre-trained convolutional neural networks, respectively, from pretreatment CT images in the training cohort (n = 161). Support vector machine was adopted as the classifier. Validation was performed in an external testing cohort (n = 70). We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model consisting of clinical factors only was also built for baseline comparison. We further conducted a radiogenomics analysis using gene expression profiles to reveal underlying biology associated with radiological prediction. Results The optimal model with features extracted from ResNet50 achieved an AUC and accuracy of 0.805 (95% CI, 0.696–0.913) and 77.1% (65.6%-86.3%) in the testing cohort, compared with 0.725 (0.605–0.846)) and 67.1% (54.9%-77.9%) for the radiomics model. All the radiological models showed better predictive performance than the clinical model. Radiogenomics analysis suggested a potential association mainly with WNT signaling pathway and tumor microenvironment. Conclusions The novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.

Cancer growth is usually accompanied by metastasis which kills most cancer patients. Here we aim to study the effect of cisplatin at different doses on breast cancer growth and metastasis. Methods: We used cisplatin to treat breast cancer cells, then detected the migration of cells and the changes of epithelial-mesenchymal transition (EMT) markers by migration assay, Western blot, and immunofluorescent staining. Next, we analyzed the changes of RNA expression of genes by RNA-seq and confirmed the binding of activating transcription factor 3 (ATF3) to cytoskeleton related genes by ChIP-seq. Thereafter, we combined cisplatin and paclitaxel in a neoadjuvant setting to treat xenograft mouse models. Furthermore, we analyzed the association of disease prognosis with cytoskeletal genes and ATF3 by clinical data analysis. Results: When administered at a higher dose (6 mg/kg), cisplatin inhibits both cancer growth and metastasis, yet with strong side effects, whereas a lower dose (2 mg/kg) cisplatin blocks cancer metastasis without obvious killing effects. Cisplatin inhibits cancer metastasis through blocking early steps of EMT. It antagonizes transforming growth factor beta (TGFβ) signaling through suppressing transcription of many genes involved in cytoskeleton reorganization and filopodia formation which occur early in EMT and are responsible for cancer metastasis. Mechanistically, TGFβ and fibronectin-1 (FN1) constitute a positive reciprocal regulation loop that is critical for activating TGFβ/SMAD3 signaling, which is repressed by cisplatin induced expression of ATF3. Furthermore, neoadjuvant administration of cisplatin at 2 mg/kg in conjunction with paclitaxel inhibits cancer growth and blocks metastasis without causing obvious side effects by inhibiting colonization of cancer cells in the target organs. Conclusion: Thus, cisplatin prevents breast cancer metastasis through blocking early EMT, and the combination of cisplatin and paclitaxel represents a promising therapy for killing breast cancer and blocking tumor metastasis.

Short-term antibiotics exposure is associated with alterations in microbiota and antibiotic resistance genes (ARGs) in the human gut. While antibiotics are critical in the successful eradication of Helicobacter pylori, the short-term and long-term impacts on the composition and quantity of antibiotics resistance genes after H. pylori eradication are unclear. This study used whole-genome shotgun metagenomic of stool samples to characterize the gut microbiota and ARGs, before and after H. pylori eradication therapy.Forty-four H. pylori-infected patients were recruited, including 21 treatment naïve patients who received clarithromycin-based triple therapy (CLA group) and 23 patients who failed previous therapies, in which 10 received levofloxacin-based quadruple therapy (LEVO group) and 13 received other combinations (OTHER group). Stool samples were collected at baseline (before current treatment), 6 week and 6 month after eradication therapy. At baseline, there was only a slight difference among the three groups on ARGs and gut microbiota. After eradication therapy, there was a transient but significant increase in gut ARGs 6 week post-therapy, among which the LEVO group had the most significant ARGs alteration compared to other two groups. For treatment naïve patients, those with higher ErmF abundance were prone to fail CLA eradication and gain more ARGs after treatment. For gut microbiota, the bacteria richness decreased at 6 week and there was a significant difference in microbiota community among the three groups at 6 week.Our findings demonstrated the dynamic alterations in gut microbiota and ARGs induced by different eradication therapies, which could influence the choices of antibiotics in eradication therapy.

This cohort study assesses the incidence of emergency department (ED) visits in Hong Kong, China, for sexual abuse among youth before and during the COVID-19 pandemic.

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset., Scalable trajectory inference for multi-omic single cell datasets is challenging in terms of capturing non-tree complex topologies. Here the authors present a method, VIA, that scales to millions of cells across multiple omic modalities using lazy-teleporting random walks.

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9’s activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.5-fold in comparison to the null model. Using this approach and followed by structure-guided engineering, we identify the N888R/A889Q variant conferring increased editing activity on the protospacer adjacent motif-relaxed KKH variant of Cas9 nuclease from Staphylococcus aureus (KKH-SaCas9) and its derived base editor in human cells. Our work validates a readily applicable workflow to enable resource-efficient high-throughput engineering of genome editor’s activity.

Importance: Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clinical image analysis can be used for diagnosis and prognosis in cancers, including oral SCC; its use in these areas can improve patient care and outcome. Observations: This review is a summary of the use of deep learning models for diagnosis, prognosis, and metastasis detection for oral SCC by analyzing information from pathological and radiographic images. Specifically, deep learning has been used to classify different cell types, to differentiate cancer cells from nonmalignant cells, and to identify oral SCC from other cancer types. It can also be used to predict survival, to differentiate between tumor grades, and to detect lymph node metastasis. In general, the performance of these deep learning models has an accuracy ranging from 77.89% to 97.51% and 76% to 94.2% with the use of pathological and radiographic images, respectively. The review also discusses the importance of using good-quality clinical images in sufficient quantity on model performance. Conclusions: and Relevance Applying pathological and radiographic images in deep learning models for diagnosis and prognosis of oral SCC has been explored, and most studies report results showing good classification accuracy. The successful use of deep learning in these areas has a high clinical translatability in the improvement of patient care.

Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9–95.8%; Sensitivity: 55.5–77.8%; Specificity: 91.5–98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

Background Read alignment and transcript assembly are the core of RNA-seq analysis for transcript isoform discovery. Nonetheless, current tools are not designed to be scalable for analysis of full-length bulk or single cell RNA-seq (scRNA-seq) data. The previous version of our cloud-based tool Falco only focuses on RNA-seq read counting, but does not allow for more flexible steps such as alignment and read assembly. Results The Falco framework can harness the parallel and distributed computing environment in modern cloud platforms to accelerate read alignment and transcript assembly of full-length bulk RNA-seq and scRNA-seq data. There are two new modes in Falco: alignment-only and transcript assembly. In the alignment-only mode, Falco can speed up the alignment process by 2.5–16.4x based on two public scRNA-seq datasets when compared to alignment on a highly optimised standalone computer. Furthermore, it also provides a 10x average speed-up compared to alignment using published cloud-enabled tool for read alignment, Rail-RNA. In the transcript assembly mode, Falco can speed up the transcript assembly process by 1.7–16.5x compared to performing transcript assembly on a highly optimised computer. Conclusion Falco is a significantly updated open source big data processing framework that enables scalable and accelerated alignment and assembly of full-length scRNA-seq data on the cloud. The source code can be found at https://github.com/VCCRI/Falco.

Predicting where transcription factors bind in the genome from their in vitro DNA-binding affinity is confounded by the large number of possible interactions with nearby transcription factors. To characterize the in vivo binding logic for the Wnt effector Tcf7l2, we developed a high-throughput screening platform in which thousands of synthesized DNA phrases are inserted into a specific genomic locus, followed by measurement of Tcf7l2 binding by DamID. Using this platform at two genomic loci in mouse embryonic stem cells, we show that while the binding of Tcf7l2 closely follows the in vitro motif-binding strength and is influenced by local chromatin accessibility, it is also strongly affected by the surrounding 99 bp of sequence. Through controlled sequence perturbation, we show that Oct4 and Klf4 motifs promote Tcf7l2 binding, particularly in the adjacent ∼50 bp and oscillating with a 10.8-bp phasing relative to these cofactor motifs, which matches the turn of a DNA helix. © 2020 The Authors Transcription factor binding in cells depends on interactions with other proteins. We have developed a high-throughput screening platform to study transcription factor binding strength at thousands of variable sequences in a fixed genomic locus. We find that binding of the Wnt effector Tcf7l2 in mouse embryonic stem cells depends on proximity and phasing that matches the turn of the DNA helix relative to its cofactors Oct4 and Klf4. © 2020 The Authors, Cell Systems, 11 (3), ISSN:2405-4720

Background Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors. Results Here, we present a method to simultaneously predict the set of active signaling pathways, together with their pathway structure, by integrating protein-protein interaction network and gene expression data. We evaluated the capacity for our method to predict active signaling pathways for dental epithelial cells, ocular lens epithelial cells, human pluripotent stem cell-derived lens epithelial cells, and lens fiber cells. This analysis showed our approach could identify all the known active pathways that are associated with tooth formation and lens development. Conclusions The results suggest that SPAGI can be a useful approach to identify the potential active signaling pathways given a gene expression profile. Our method is implemented as an open source R package, available via https://github.com/VCCRI/SPAGI/. Electronic supplementary material The online version of this article (10.1186/s12918-018-0655-x) contains supplementary material, which is available to authorized users.

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp, while it failed for datasets with

Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches.

Background High-throughput single-cell RNA-seq (scRNA-seq) is a powerful technology for studying gene expression variability in single cells; however, standard analysis approaches only consider the overall expression of each gene, masking additional heterogeneity that could exist through cell type-specific expression of alternative mRNA transcripts. Results Here we show that differential transcript usage (DTU) can be readily detected in data-sets generated from commonly used polyA-captured nanodroplet scRNA-seq technology. Our computational pipeline, Sierra, detects and quantifies polyadenylation sites in scRNA-seq data-sets, which are utilised to evaluate DTU between single-cell populations. We validate our approach by comparing cardiac cell populations derived from scRNA-seq to bulk RNA-seq of matched populations obtained through fluorescent activated cell sorting (FACS), demonstrating that we detect a significant overlap in cell-type DTU between the congruous data-sets. We further illustrate the utility of our method by detecting alternative transcript usage in human peripheral blood mononuclear cells (PBMCs), 3’UTR shortening in activated and proliferating cardiac fibroblasts from injured mouse hearts and finally by building an initial atlas of cell type-specific transcript usage across 12 mouse tissues. Conclusions We anticipate that Sierra will enable new avenues of transcriptional complexity and regulation to be explored in single-cell transcriptomic experiments. Sierra is available at https://github.com/VCCRI/Sierra .

Motivation In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery. Results To further improve DeepVariant’s variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant. Availability and implementation dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/). Contact e.giannoulatou@victorchang.edu.au Supplementary information Supplementary data are available at Bioinformatics online.

There exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI’s Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO’s free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention.In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network.GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.

Key Points Question Are peritumoral radiomics features extracted from pretreatment computed tomography images predictive of pathological complete response following neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma? Findings In this diagnostic study of 231 patients, the developed model integrating intratumoral and peritumoral radiomics features achieved improvement of predictive performance (area under the receiver operating characteristic curve, 0.852) compared with the conventionally constructed model merely using intratumoral radiomics features (area under the receiver operating characteristic curve, 0.730). Meaning Peritumoral radiomics may provide additional predictive value for treatment response estimation in esophageal squamous cell carcinoma and thus benefit individualized therapeutic strategies., This diagnostic study assesses the use of intratumoral and peritumoral computed tomography (CT) radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma (ESCC)., Importance For patients with locally advanced esophageal squamous cell carcinoma, neoadjuvant chemoradiation has been shown to improve long-term outcomes, but the treatment response varies among patients. Accurate pretreatment prediction of response remains an urgent need. Objective To determine whether peritumoral radiomics features derived from baseline computed tomography images could provide valuable information about neoadjuvant chemoradiation response and enhance the ability of intratumoral radiomics to estimate pathological complete response. Design, Setting, and Participants A total of 231 patients with esophageal squamous cell carcinoma, who underwent baseline contrast-enhanced computed tomography and received neoadjuvant chemoradiation followed by surgery at 2 institutions in China, were consecutively included. This diagnostic study used single-institution data between April 2007 and December 2018 to extract radiomics features from intratumoral and peritumoral regions and established intratumoral, peritumoral, and combined radiomics models using different classifiers. External validation was conducted using independent data collected from another hospital during the same period. Radiogenomics analysis using gene expression profile was done in a subgroup of the training set for pathophysiological explanation. Data were analyzed from June to December 2019. Exposures Computed tomography–based radiomics. Main Outcomes and Measures The discriminative performances of radiomics models were measured by area under the receiver operating characteristic curve. Results Among the 231 patients included (192 men [83.1%]; mean [SD] age, 59.8 [8.7] years), the optimal intratumoral and peritumoral radiomics models yielded similar areas under the receiver operating characteristic curve of 0.730 (95% CI, 0.609-0.850) and 0.734 (0.613-0.854), respectively. The combined model was composed of 7 intratumoral and 6 peritumoral features and achieved better discriminative performance, with an area under the receiver operating characteristic curve of 0.852 (95% CI, 0.753-0.951), accuracy of 84.3%, sensitivity of 90.3%, and specificity of 79.5% in the test set. Gene sets associated with the combined model mainly involved lymphocyte-mediated immunity. The association of peritumoral area with response identification might be partially attributed to type I interferon–related biological process. Conclusions and Relevance A combination of peritumoral radiomics features appears to improve the predictive performance of intratumoral radiomics to estimate pathological complete response after neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. This study underlines the significant application of peritumoral radiomics to assess treatment response in clinical practice.

Congenital heart disease (CHD) affects up to 1% of live births. However, a genetic diagnosis is not made in most cases. The purpose of this study was to assess the outcomes of genome sequencing (GS) of a heterogeneous cohort of CHD patients.Ninety-seven families with probands born with CHD requiring surgical correction were recruited for genome sequencing. At minimum, a proband-parents trio was sequenced per family. GS data were analyzed via a two-tiered method: application of a high-confidence gene screen (hcCHD), and comprehensive analysis. Identified variants were assessed for pathogenicity using the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines.Clinically relevant genetic variants in known and emerging CHD genes were identified. The hcCHD screen identified a clinically actionable variant in 22% of families. Subsequent comprehensive analysis identified a clinically actionable variant in an additional 9% of families in genes with recent disease associations. Overall, this two-tiered approach provided a clinically relevant variant for 31% of families.Interrogating GS data using our two-tiered method allowed identification of variants with high clinical utility in a third of our heterogeneous cohort. However, association of emerging genes with CHD etiology, and development of novel technologies for variant assessment and interpretation, will increase diagnostic yield during future reassessment of our GS data.

Cell type identification from an unknown sample can often be done by comparing its gene expression profile against a gene expression database containing profiles of a large number of cell-types. This type of compendium-based cell-type identification strategy is particularly successful for human and mouse samples because a large volume of data exists for these organisms. However, such rich data repositories often do not exist for most non-model organisms. This makes transcriptome-based sample classification in these species challenging. We propose to overcome this challenge by performing a cross-species compendium comparison. The key is to utilise a recently published cross-species gene set analysis (XGSA) framework to correct for biases that may arise due to potentially complex homologous gene mapping between two species. The framework is implemented as an open source R package called C3. We have evaluated the performance of C3 using a variety of public data in NCBI Gene Expression Omnibus. We also compared the functionality and performance of C3 against some similar gene expression profile matching tools. Our evaluation shows that C3 is a simple and effective method for cell type identification. C3 is available at https://github.com/VCCRI/C3.

Background It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs – a motif grammar – located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model. Results We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar. Conclusions Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific. Electronic supplementary material The online version of this article (10.1186/s12864-017-4340-z) contains supplementary material, which is available to authorized users.

Motivation: Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal enrichment and co-occurrence patterns of histone modifications. Results: Here, we present a new Bayesian non-parametric method called hierarchically linked infinite HMM (hiHMM) to jointly infer chromatin state maps in multiple genomes (different species, cell types and developmental stages) using genome-wide histone modification data. This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them. We demonstrate the utility of this method using synthetic data as well as multiple modENCODE ChIP-seq datasets. Conclusion: The hierarchical and Bayesian non-parametric formulation in our approach is an important extension to the current set of methodologies for comparative chromatin landscape analysis. Availability and implementation: Source codes are available at https://github.com/kasohn/hiHMM. Chromatin data are available at http://encode-x.med.harvard.edu/data_sets/chromatin/. Contact: peter_park@harvard.edu or juhan@snu.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.

Embryoid bodies (EBs) can serve as a system for evaluating pluripotency, cellular differentiation, and tissue morphogenesis. In this study, we use EBs derived from mouse embryonic stem cells (mESCs) and human amniocyte–derived induced pluripotent stem cells (hAdiPSCs) as a model for ovarian granulosa cell (GC) development and steroidogenic cell commitment. We demonstrated that spontaneously differentiated murine EBs (mEBs) and human EBs (hEBs) displayed ovarian GC markers, such as aromatase (CYP19A1), FOXL2, AMHR2, FSHR, and GJA1. Comparative microarray analysis identified both shared and unique gene expression between mEBs and the maturing mouse ovary. Gene sets related to gonadogenesis, lipid metabolism, and ovarian development were significantly overrepresented in EBs. Of the 29 genes, 15 that were differentially regulated in steroidogenic mEBs displayed temporal expression changes between embryonic, postnatal, and mature ovarian tissues by polymerase chain reaction. Importantly, both mEBs and hEBs were capable of gonadotropin-responsive estradiol (E2) synthesis in vitro (217-759 pg/mL). Live fluorescence-activated cell sorting–sorted AMHR2(+) granulosa-like cells from mEBs continued to produce E2 after purification (15.3 pg/mL) and secreted significantly more E2 than AMHR2(−) cells (8.6 pg/mL, P < .05). We conclude that spontaneously differentiated EBs of both mESC and hAdiPSC origin can serve as a biologically relevant model for ovarian GC differentiation and steroidogenic cell commitment. These cells should be further investigated for therapeutic uses, such as stem cell–based hormone replacement therapy and in vitro maturation of oocytes.

The benefits of adult stem cells for repair of the heart have been attributed to the repertoire of salutary paracrine activities they appear to exert. We previously isolated human W8B2+ cardiac stem cells (CSCs) and found they powerfully influence cardiomyocytes and endothelial cells to collectively promote cardiac repair and regeneration. Here, the complexity of the W8B2+ CSC secretomes was characterised and examined in more detail. Using ion exchange chromatography to separate soluble proteins based on their net surface charge, the secreted factors responsible for the pro-survival activity of W8B2+ CSCs were found within the low and medium cation fractions. In addition to the soluble proteins, extracellular vesicles generated from W8B2+ CSCs not only exhibited pro-survival and pro-angiogenic activities, but also promoted proliferation of neonatal cardiomyocytes. These extracellular vesicles contain a cargo of proteins, mRNA and primary microRNA precursors that are enriched in exosomes and are capable of modulating collectively many of the cellular pathways involved in protein metabolism, cell growth, as well as cellular responses to stress and organisation of the extracellular matrix. Thus the W8B2+ CSC secretome contains a multitude of bioactive paracrine factors we have now characterised, that might well be harnessed for therapeutic application for cardiac repair and regeneration.

The lizards are evolutionarily the closest vertebrates to humans that demonstrate the ability to regenerate entire appendages containing cartilage, muscle, skin, and nervous tissue. We previously isolated PAX7-positive cells from muscle of the green anole lizard, Anolis carolinensis, that can differentiate into multinucleated myotubes and express the muscle structural protein, myosin heavy chain. Studying gene expression in these satellite/progenitor cell populations from A. carolinensis can provide insight into the mechanisms regulating tissue regeneration. We generated a transcriptome from proliferating lizard myoprogenitor cells and compared them to transcriptomes from the mouse and human tissues from the ENCODE project using XGSA, a statistical method for cross-species gene set analysis. These analyses determined that the lizard progenitor cell transcriptome was most similar to mammalian satellite cells. Further examination of specific GO categories of genes demonstrated that among genes with the highest level of expression in lizard satellite cells were an increased number of genetic regulators of chondrogenesis, as compared to mouse satellite cells. In micromass culture, lizard PAX7-positive cells formed Alcian blue and collagen 2a1 positive nodules, without the addition of exogenous morphogens, unlike their mouse counterparts. Subsequent quantitative RT-PCR confirmed up-regulation of expression of chondrogenic regulatory genes in lizard cells, including bmp2, sox9, runx2, and cartilage specific structural genes, aggrecan and collagen 2a1. Taken together, these data suggest that tail regeneration in lizards involves significant alterations in gene regulation with expanded musculoskeletal potency.

Circular RNAs (circRNA) are a unique class of transcripts that can only be identified from sequence alignments spanning discordant junctions, commonly referred to as backsplice junctions (BSJ). Canonical splicing is also linked with circRNA biogenesis either from the parental transcript or internal to the circRNA, and is not fully utilized in circRNA software. Here we present Ularcirc, a software tool that integrates the visualization of both BSJ and forward splicing junctions and provides downstream analysis of selected circRNA candidates. Ularcirc utilizes the output of CIRI, circExplorer, or raw chimeric output of the STAR aligner and assembles BSJ count table to allow multi-sample analysis. We used Ularcirc to identify and characterize circRNA from public and in-house generated data sets and demonstrate how it can be used to (i) discover novel splicing patterns of parental transcripts, (ii) detect internal splicing patterns of circRNA, and (iii) reveal the complexity of BSJ formation. Furthermore, we identify circRNA that have potential open reading frames longer than their linear sequence. Finally, we detected and validated the presence of a novel class of circRNA generated from ApoA4 transcripts whose BSJ derive from multiple non-canonical splicing sites within coding exons. Ularcirc is accessed via https://github.com/VCCRI/Ularcirc.

Most existing dimensionality reduction and clustering packages for single-cell RNA-Seq (scRNA-Seq) data deal with dropouts by heavy modelling and computational machinery. Here we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm which uses a novel yet very simple ‘implicit imputation’ approach to alleviate the impact of dropouts in scRNA-Seq data in a principled manner. Using a range of simulated and real data, we have shown that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds for processing a data set of hundreds of cells, and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.org/VCCRI/CIDR.

SummarySingle-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellisation of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq data sets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6 – 145.4 times faster using Falco than running on a highly optimised single node analysis. Falco also allows user to the utilise low-cost spot instances of Amazon Web Services (AWS), providing a 65% reduction in cost of analysis.AvailabilityFalco is available via a GNU General Public License at https://github.com/VCCRI/Falco/Contactj.ho@victorchang.edu.auSupplementary informationSupplementary data are available at BioRXiv online.

SummaryThe central task of a genome browser is to enable easy visual exploration of large genomic data to gain biological insight. Most existing genome browsers were designed for data exploration by individual users, while a few allow some limited forms of collaboration among multiple users, such as file sharing and wiki-style collaborative editing of gene annotations. Our work’s premise is that allowing sharing of genome browser views instantaneously in real-time enables the exchange of ideas and insight in a collaborative project, thus harnessing the wisdom of the crowd. PBrowse is a parallel-access real-time collaborative web-based genome browser that provides both an integrated, real-time collaborative platform and a comprehensive file sharing system. PBrowse also allows real-time track comment and has integrated group chat to facilitate interactive discussion among multiple users. Through the Distributed Annotation Server protocol, PBrowse can easily access a wide range of publicly available genomic data, such as the ENCODE data sets. We argue that PBrowse, with the re-designed user management, data management and novel collaborative layer based on Biodalliance, represents a paradigm shift from seeing genome browser merely as a tool of data visualisation to a tool that enables real-time human-human interaction and knowledge exchange in a collaborative setting.AvailabilityPBrowse is available at http://pbrowse.victorchang.edu.au, and its source code is available via the open source BSD 3 license at http://github.com/VCCRI/PBrowse.Contactj.ho@victorchang.edu.auSupplementary InformationSupplementary video demonstrating collaborative feature of pbrowse is available in https://www.youtube.com/watch?v=ROvKXZoXiIc.

Background— Alternative mRNA splicing is an important mechanism for regulation of gene expression. Altered mRNA splicing occurs in association with several types of cancer, and a small number of disease-associated changes in splicing have been reported in heart disease. However, genome-wide approaches have not been used to study splicing changes in heart disease. We hypothesized that mRNA splicing is different in diseased hearts compared with control hearts. Methods and Results— We used the Affymetrix Exon array to globally evaluate mRNA splicing in left ventricular myocardial RNA from controls (n=15) and patients with ischemic cardiomyopathy (n=15). We observed a broad and significant decrease in mRNA splicing efficiency in heart failure, which affected some introns to a greater extent than others. The profile of mRNA splicing separately clustered ischemic cardiomyopathy and control samples, suggesting distinct changes in mRNA splicing between groups. Reverse transcription–polymerase chain reaction validated 9 previously unreported alternative splicing events. Furthermore, we demonstrated that splicing of 4 key sarcomere genes, cardiac troponin T ( TNNT2 ), cardiac troponin I ( TNNI3 ), myosin heavy chain 7 ( MYH7 ), and filamin C, gamma ( FLNC ), was significantly altered in ischemic cardiomyopathy and in dilated cardiomyopathy and aortic stenosis. In aortic stenosis samples, these differences preceded the onset of heart failure. Remarkably, the ratio of minor to major splice variants of TNNT2 , MYH7 , and FLNC classified independent test samples as control or disease with >98% accuracy. Conclusions— Our data indicate that mRNA splicing is broadly altered in human heart disease and that patterns of aberrant RNA splicing accurately assign samples to control or disease classes.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has heterogeneous clinical manifestation with diverse patterns of organ involvement, autoantibody profiles and varying degrees of severity of disease. Research and clinical experience indicate that different subtypes of SLE patients will likely benefit from more tailored treatment regimes, but we currently lack a fast and objective test with high enough sensitivity to enable us to perform such sub-grouping for clinical use. In this article, we review how proteomic technologies could be used as such an objective test. In particular, we extensively review many leukocyte surface markers that are known to have an association with the pathogenesis of SLE, and we discuss how these markers can be used in the further development of a novel SLE-specific antibody leukocyte capture microarray. In addition, we review some bioinformatics challenges and current methods for using the data generated by these cell-capture microarrays in clinical use. In a broader context, we hope our experience in developing a disease specific cell-capture microarray for clinical application can be a guide to other proteomic practitioners who intend to extend their technologies to develop clinical diagnostic and prognostic tests for complex diseases.

Background Bioinformatics software quality assurance is essential in genomic medicine. Systematic verification and validation of bioinformatics software is difficult because it is often not possible to obtain a realistic "gold standard" for systematic evaluation. Here we apply a technique that originates from the software testing literature, namely Metamorphic Testing (MT), to systematically test three widely used short-read sequence alignment programs. Results MT alleviates the problems associated with the lack of gold standard by checking that the results from multiple executions of a program satisfy a set of expected or desirable properties that can be derived from the software specification or user expectations. We tested BWA, Bowtie and Bowtie2 using simulated data and one HapMap dataset. It is interesting to observe that multiple executions of the same aligner using slightly modified input FASTQ sequence file, such as after randomly re-ordering of the reads, may affect alignment results. Furthermore, we found that the list of variant calls can be affected unless strict quality control is applied during variant calling. Conclusion Thorough testing of bioinformatics software is important in delivering clinical genomic medicine. This paper demonstrates a different framework to test a program that involves checking its properties, thus greatly expanding the number and repertoire of test cases we can apply in practice.

Genome function is dynamically regulated in part by chromatin, which consists of the histones, non-histone proteins and RNA molecules that package DNA. Studies in Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular mechanisms of genome function in humans, and have revealed conservation of chromatin components and mechanisms. Nevertheless, the three organisms have markedly different genome sizes, chromosome architecture and gene organization. On human and fly chromosomes, for example, pericentric heterochromatin flanks single centromeres, whereas worm chromosomes have dispersed heterochromatin-like regions enriched in the distal chromosomal ‘arms’, and centromeres distributed along their lengths. To systematically investigate chromatin organization and associated gene regulation across species, we generated and analysed a large collection of genome-wide chromatin data sets from cell lines and developmental stages in worm, fly and human. Here we present over 800 new data sets from our ENCODE and modENCODE consortia, bringing the total to over 1,400. Comparison of combinatorial patterns of histone modifications, nuclear lamina-associated domains, organization of large-scale topological domains, chromatin environment at promoters and enhancers, nucleosome positioning, and DNA replication patterns reveals many conserved features of chromatin organization among the three organisms. We also find notable differences in the composition and locations of repressive chromatin. These data sets and analyses provide a rich resource for comparative and species-specific investigations of chromatin composition, organization and function., National Science Foundation (U.S.) (1122374)

The diagnosis of Systemic Lupus Erythematosus (SLE) is challenging due to its heterogeneous clinical presentation and the lack of robust biomarkers to distinguish it from other autoimmune diseases. Further, currently used laboratory tests do not readily distinguish active and inactive disease. Several groups have attempted to apply emerging high throughput profiling technologies to diagnose and monitor SLE. Despite showing promise, many are expensive and technically challenging for routine clinical use. The goal of this work is to develop a better diagnostic and monitoring tool for SLE. We report a highly customisable antibody microarray that consists of a duplicate arrangement of 82 antibodies directed against surface antigens on peripheral blood mononuclear cells (PMBCs). This high-throughput array was used to profile SLE patients (n = 60) with varying disease activity, compared to healthy controls (n = 24), patients with rheumatoid arthritis (n = 25), and other autoimmune diseases (n = 28). We used a computational algorithm to calculate a score from the entire microarray profile and correlated it with SLE disease activity. Our results demonstrate that leukocyte-capture microarray profiles can readily distinguish active SLE patients from healthy controls (AUROC = 0.84). When combined with the standard laboratory tests (serum anti-dsDNA, complements C3 and C4), the microarrays provide significantly increased discrimination. The antibody microarrays can be enhanced by the addition of other markers for potential application to the diagnosis and stratification of SLE, paving the way for the customised and accurate diagnosis and monitoring of SLE.

Recent development of cluster of differentiation (CD) antibody arrays has enabled expression levels of many leukocyte surface CD antigens to be monitored simultaneously. Such membrane-proteome surveys have provided a powerful means to detect changes in leukocyte activity in various human diseases, such as cancer and cardiovascular diseases. The challenge is to devise a computational method to infer differential leukocyte activity among multiple biological states based on antigen expression profiles. Standard DNA microarray analysis methods cannot accurately infer differential leukocyte activity because they often fail to take the cell-to-antigen relationships into account. Here we present a novel latent variable model (LVM) approach to tackle this problem. The idea is to model each cell type as a latent variable, and represent the class-to-cell and cell-to-antigen relationships as a LVM. Once the parameters of the LVM are learned from the data, differentially active leukocytes can be easily identified from the model. We describe the model formulation and assumptions which lead to an efficient expectation-maximization algorithm. Our LVM method was applied to re-analyze two cardiovascular disease datasets. We show that our results match existing biological knowledge better than other methods such as gene set enrichment analysis. Furthermore, we discuss how our approach can be extended to become a general framework for gene set analysis for DNA microarrays.

Background Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage. Results By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer. Conclusion We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.

Background It has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify "interesting" genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns. Results Here we present a novel model selection approach to discover genes with linear or non-linear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models. Conclusion In this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks.

BackgroundMany genes have been implicated in the development of congenital heart disease (CHD). Next-generation sequencing offers opportunities for genetic testing but is often complicated by logistic and interpretative hurdles.ObjectivesThis study sought to apply next-generation sequencing technology to CHD families with multiple affected members using a purpose-designed gene panel to assess diagnostic potential for future clinical applications.MethodsWe designed a targeted next-generation sequencing gene panel for 57 genes previously implicated in CHD. Probands were screened in 16 families with strong CHD histories and in 15 control subjects. Variants affecting protein-coding regions were classified in silico using prediction programs and filtered according to predicted mode of inheritance, minor allele frequencies, and presence in databases such as dbSNP (Single Nucleotide Polymorphism Database) and ESP (Exome Sequencing Project). Disease segregation studies were conducted in variants identified in CHD cases predicted to be deleterious and with minor allele frequencies