Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments

A reliable and quick method for screening alternative splicing variants for low-abundance genes

Alternative splicing (AS) is a universal phenomenon in eukaryotes, and it is still challenging to identify AS events. Several methods have been developed to identify AS events, such as expressed sequence tags (EST), microarrays and RNA-seq. However, EST has limitations in identifying low-abundance genes, while microarray and RNA-seq are high-throughput technologies, and PCR-based technology is needed for validation. To overcome the limitations of EST and shortcomings of high-throughput technologies, we established a method to identify AS events, especially for low-abundance genes, by reverse transcription (RT) PCR with gene-specific primers (GSPs) followed by nested PCR. This process includes two major steps: 1) the use of GSPs to amplify as long as the specific gene segment and 2) multiple rounds of nested PCR to screen the AS and confirm the unknown splicing variants. With this method, we successfully identified three new splicing variants, namely, GenBank Accession No. HM623886 for the bdnf gene (GenBank GeneID: 12064), GenBank Accession No. JF417977 for the trkc gene (GenBank GeneID: 18213) and GenBank Accession No. HM623888 for the glb-18 gene (GenBank GeneID: 172485). In addition to its reliability and simplicity, the method is also cost-effective and labor-intensive. In conclusion, we developed an RT-nested PCR method using gene-specific primers to efficiently identify known and novel AS variants. This approach overcomes the limitations of existing methods for detecting rare transcripts. By enabling the discovery of new isoforms, especially for low-abundance genes, this technique can aid research into aberrant splicing in disease. Future studies can apply this method to uncover AS variants involved in cancer, neurodegeneration, and other splicing-related disorders.

TiSA: TimeSeriesAnalysis-a pipeline for the analysis of longitudinal transcriptomics data

Improved transcriptomic sequencing technologies now make it possible to perform longitudinal experiments, thus generating a large amount of data. Currently, there are no dedicated or comprehensive methods for the analysis of these experiments. In this article, we describe our TimeSeries Analysis pipeline (TiSA) which combines differential gene expression, clustering based on recursive thresholding, and a functional enrichment analysis. Differential gene expression is performed for both the temporal and conditional axes. Clustering is performed on the identified differentially expressed genes, with each cluster being evaluated using a functional enrichment analysis. We show that TiSA can be used to analyse longitudinal transcriptomic data from both microarrays and RNA-seq, as well as small, large, and/or datasets with missing data points. The tested datasets ranged in complexity, some originating from cell lines while another was from a longitudinal experiment of severity in COVID-19 patients. We have also included custom figures to aid with the biological interpretation of the data, these plots include Principal Component Analyses, Multi Dimensional Scaling plots, functional enrichment dotplots, trajectory plots, and complex heatmaps showing the broad overview of results. To date, TiSA is the first pipeline to provide an easy solution to the analysis of longitudinal transcriptomics experiments.

Towards a Systems Immunology Approach to Understanding Correlates of Protective Immunity against HCV

Over the past decade, tremendous progress has been made in systems biology-based approaches to studying immunity to viral infections and responses to vaccines. These approaches that integrate multiple facets of the immune response, including transcriptomics, serology and immune functions, are now being applied to understand correlates of protective immunity against hepatitis C virus (HCV) infection and to inform vaccine development. This review focuses on recent progress in understanding immunity to HCV using systems biology, specifically transcriptomic and epigenetic studies. It also examines proposed strategies moving forward towards an integrated systems immunology approach for predicting and evaluating the efficacy of the next generation of HCV vaccines.

Alternatively Splicing Interactomes Identify Novel Isoform-Specific Partners for NSD2

Nuclear receptor SET domain protein (NSD2) plays a fundamental role in the pathogenesis of Wolf-Hirschhorn Syndrome (WHS) and is overexpressed in multiple human myelomas, but its protein-protein interaction (PPI) patterns, particularly at the isoform/exon levels, are poorly understood. We explored the subcellular localizations of four representative NSD2 transcripts with immunofluorescence microscopy. Next, we used label-free quantification to perform immunoprecipitation mass spectrometry (IP-MS) analyses of the transcripts. Using the interaction partners for each transcript detected in the IP-MS results, we identified 890 isoform-specific PPI partners (83% are novel). These PPI networks were further divided into four categories of the exon-specific interactome. In these exon-specific PPI partners, two genes, RPL10 and HSPA8, were successfully confirmed by co-immunoprecipitation and Western blotting. RPL10 primarily interacted with Isoforms 1, 3, and 5, and HSPA8 interacted with all four isoforms, respectively. Using our extended NSD2 protein interactions, we constructed an isoform-level PPI landscape for NSD2 to serve as reference interactome data for NSD2 spliceosome-level studies. Furthermore, the RNA splicing processes supported by these isoform partners shed light on the diverse roles NSD2 plays in WHS and myeloma development. We also validated the interactions using Western blotting, RPL10, and the three NSD2 (Isoform 1, 3, and 5). Our results expand gene-level NSD2 PPI networks and provide a basis for the treatment of NSD2-related developmental diseases.

Intrinsically disordered protein RBM14 plays a role in generation of RNA:DNA hybrids at double-strand break sites

Accumulating evidence suggests participation of RNA-binding proteins with intrinsically disordered domains (IDPs) in the DNA damage response (DDR). These IDPs form liquid compartments at DNA damage sites in a poly(ADP ribose) (PAR)-dependent manner. However, it is greatly unknown how the IDPs are involved in DDR. We have shown previously that one of the IDPs RBM14 is required for the canonical nonhomologous end joining (cNHEJ). Here we show that RBM14 is recruited to DNA damage sites in a PARP- and RNA polymerase II (RNAPII)-dependent manner. Both KU and RBM14 are required for RNAPII-dependent generation of RNA:DNA hybrids at DNA damage sites. In fact, RBM14 binds to RNA:DNA hybrids. Furthermore, RNA:DNA hybrids and RNAPII are detected at gene-coding as well as at intergenic areas when double-strand breaks (DSBs) are induced. We propose that the cNHEJ pathway utilizes damage-induced transcription and intrinsically disordered protein RBM14 for efficient repair of DSBs.

Transcriptomic Analysis of Testicular Gene Expression in Normal and Cryptorchid Horses

Simple Summary

Cryptorchidism is a common congenital malformation that results in impaired fertility in horses. The high abdominal temperature and the effects of this disease lead to differences in gene expression between retained testes and descended testes (DTs). Here, we focus on the genetic effects of cryptorchidism. All the differentially expressed genes (DEGs) between undescended testes (UDTs) and DTs were analyzed in this study. A total of 84 DEGs were associated with functions related to sperm development and male reproductive performance. Our study has provided fundamental transcriptomic data for future studies on equine testes and cryptorchidism.

Abstract

Testes produce sperm, and investigations into gene expression in the testes will enhance the understanding of the roles of testicular genes in male reproduction. Cryptorchidism, the failure of one or both testes to descend into the scrotal sac, is a common congenital malformation in horses. The major clinical consequence of this abnormality is impaired fertility. The aim of this study was to analyze the expression patterns of testicular genes and to identify the differentially expressed genes (DEGs) in testes between cryptorchid and normal horses. In this study, the gene expression patterns in equine testes and the DEGs between mature descended testes (DTs) and undescended testes (UDTs) were identified by RNA-seq and validated by real-time qPCR. Our results provide comprehensive transcriptomic data on equine testes. The transcriptomic analysis revealed 11 affected genes that were downregulated in UDTs, possibly as a result of the higher temperature in the abdomen than in the scrotal sac. These 11 genes have previously been associated with male reproduction, and their downregulation might explain the impaired fertility of cryptorchid horses. Two homozygous missense mutations detected in horses with cryptorchidism were absent in normal horses and were listed as potential pathogenic mutations; these mutations should be verified in the future.

Optimization of Insect Odorant Receptor Trafficking and Functional Expression Via Transient Transfection in HEK293 Cells

Insect odorant receptors (ORs) show a limited functional expression in various heterologous expression systems including insect and mammalian cells. This may be in part due to the absence of key components driving the release of these proteins from the endoplasmic reticulum and directing them to the plasma membrane. In order to mitigate this problem, we took advantage of small export signals within the human HCN1 and Rhodopsin that have been shown to promote protein release from the endoplasmic reticulum and the trafficking of post-Golgi vesicles, respectively. Moreover, we designed a new vector based on a bidirectional expression cassette to drive the functional expression of the insect odorant receptor coreceptor (Orco) and an odor-binding OR, simultaneously. We show that this new method can be used to reliably express insect ORs in HEK293 cells via transient transfection and that is highly suitable for downstream applications using automated and high-throughput imaging platforms.

Isoform-level gene expression patterns in single-cell RNA-sequencing data

Motivation

RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data.

Results

We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data.

Availability and implementation

The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

Temporal dynamics in meta longitudinal RNA-Seq data

Identification of differentially expressed genes has been a high priority task of downstream analyses to further advances in biomedical research. Investigators have been faced with an array of issues in dealing with more complicated experiments and metadata, including batch effects, normalization, temporal dynamics (temporally differential expression), and isoform diversity (isoform-level quantification and differential splicing events). To date, there are currently no standard approaches to precisely and efficiently analyze these moderate or large-scale experimental designs, especially with combined metadata. In this report, we propose comprehensive analytical pipelines to precisely characterize temporal dynamics in differential expression of genes and other genomic features, i.e., the variability of transcripts, isoforms and exons, by controlling batch effects and other nuisance factors that could have significant confounding effects on the main effects of interest in comparative models and may result in misleading interpretations.

Cytochrome P450 family proteins as potential biomarkers for ovarian granulosa cell damage in mice with premature ovarian failure

Premature ovarian failure (POF) is the pathological aging of ovarian tissue. We have previously established a cyclophosphamide-induced mouse POF model and found that cyclophosphamide caused significant damage and apoptosis of mouse ovarian granulosa cells (mOGCs). To systematically explore the molecular biologic evidence of cyclophosphamide-induced mOGC damage at the gene transcription level, RNA-Seqwas used to analyse the differences in mOGC transcriptomes between POF and control (PBS) mice. The sequencing results showed that there were 18765 differential transcription genes between the two groups, of which 192 were significantly up-regulated (log2 [POF/PBS] > 2.0) and 116 were significantly down-regulated (log2 [POF/PBS] < -4.0). Kyoto Encyclopedia of Genes and Genomes analysis found that the neuroactive ligand-receptor interaction pathway was significantly up-regulated and metabolic pathways were significantly down-regulated in the POF group. Gene Ontology analysis showed that the expression of plasma membrane, regulation of transcription and ion binding functions were significantly up-regulated in the POF group, while the expression of cell and cell parts, catalytic activity and single-organism process functions were significantly down-regulated. Finally, protein interaction analysis reveals that in the ovarian steroidogenesis pathway, three Cytochrome P450 family proteins-Cyp1a1, Cyp11a1 and Cyp2u1-interact with Fdx1 to form an interactive network. These three proteins were down-regulated in POF cells, suggesting that they are likely direct regulatory targets of cyclophosphamide. RNA-Seq high-throughput screening analysis demonstrated that cyclophosphamide damage to mOGCs was achieved through its impacts on multiple pathways and on the transcription activities of multiple target genes. Among them, the protein network consisting of the cytochrome P450 family Fdx1, Cyp17a1, Cyp11a1 and Cyp2u1 is a potential new biomarker of mOGC damage in POF in mice.

Delineating inflammatory bowel disease through transcriptomic studies: current review of progress and evidence

Inflammatory bowel disease (IBD), which comprises of Crohn's disease and ulcerative colitis, is an idiopathic relapsing and remitting disease in which the interplay of different environment, microbial, immunological and genetic factors that attribute to the progression of the disease. Numerous studies have been conducted in multiple aspects including clinical, endoscopy and histopathology for the diagnostics and treatment of IBD. However, the molecular mechanism underlying the aetiology and pathogenesis of IBD is still poorly understood. This review tries to critically assess the scientific evidence at the transcriptomic level as it would help in the discovery of RNA molecules in tissues or serum between the healthy and diseased or different IBD subtypes. These molecular signatures could potentially serve as a reliable diagnostic or prognostic biomarker. Researchers have also embarked on the study of transcriptome to be utilized in targeted therapy. We focus on the evaluation and discussion related to the publications reporting the different approaches and techniques used in investigating the transcriptomic changes in IBD with the intention to offer new perspectives to the landscape of the disease.

Gene co-expression analysis for functional classification and gene-disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Paradigm for disease deconvolution in rare neurodegenerative disorders in Indian population: insights from studies in cerebellar ataxias

Cerebellar ataxias are a group of rare progressive neurodegenerative disorders with an average prevalence ranges from 4.8 to 13.8 in 100,000 individuals. The inherited disorders affect multiple members of the families, or a community that is endogamous or consanguineous. Presence of more than 3000 mutations in different genes with overlapping clinical symptoms, genetic anticipation and pleiotropy, as well as incomplete penetrance and variable expressivity due to modifiers pose challenges in genotype-phenotype correlation. Development of a diagnostic algorithm could reduce the time as well as cost in clinicogenetic diagnostics and also help in reducing the economic and social burden of the disease. In a unique research collaboration spanning over 20 years, we have been able to develop a paradigm for studying cerebellar ataxias in the Indian population which would also be relevant in other rare diseases. This has involved clinical and genetic analysis of thousands of families from diverse Indian populations. The extensive resource on ataxia has led to the development of a clinicogenetic algorithm for cost-effective screening of ataxia and a unique ataxia clinic in the tertiary referral centre in All India Institute of Medical Sciences. Utilizing a population polymorphism scanning approach, we have been able to dissect the mechanisms of repeat instability and expansion in many ataxias, and also identify founders, and trace the mutational histories in the Indian population. This provides information for genetic testing of at-risk as well as protected individuals and populations. To dissect uncharacterized cases which comprises more than 50% of the cases, we have explored the potential of next-generation sequencing technologies coupled with the extensive resource of baseline data generated in-house and other public domains. We have also developed a repository of patient-derived peripheral blood mononuclear cells, lymphoblastoid cell lines and neuronal lineages (derived from iPSCs) for ascribing functionality to novel genes/mutations. Through integrating these technologies, novel genes have been identified that has broadened the diagnostic panel, increased the diagnostic yield to over 75%, helped in ascribing pathogenicity to novel mutations and enabled understanding of disease mechanisms. It has also provided a platform for testing novel molecules for amelioration of pathophysiological phenotypes. This review through a perspective on CAs suggests a generic paradigm fromdiagnostics to therapeutic interventions for rare disorders in the context of heterogeneous Indian populations.

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83-88% accuracy). The results are also consistent with the actual data and the "leave-one-out" analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.

Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest

In bioinformatics, exon skipping (ES) event prediction is an essential part of alternative splicing (AS) event analysis. Although many methods have been developed to predict ES events, a solution has yet to be found. In this study, given the limitations of machine learning algorithms with RNA-Seq data or genome sequences, a new feature, called RS (RNA-seq and sequence) features, was constructed. These features include RNA-Seq features derived from the RNA-Seq data and sequence features derived from genome sequences. We propose a novel Rotation Forest classifier to predict ES events with the RS features (RotaF-RSES). To validate the efficacy of RotaF-RSES, a dataset from two human tissues was used, and RotaF-RSES achieved an accuracy of 98.4%, a specificity of 99.2%, a sensitivity of 94.1%, and an area under the curve (AUC) of 98.6%. When compared to the other available methods, the results indicate that RotaF-RSES is efficient and can predict ES events with RS features.

RNA-seq Reveals Transcriptomic Differences in Inflamed and Noninflamed Intestinal Mucosa of Crohn's Disease Patients Compared with Normal Mucosa of Healthy Controls

BACKGROUND

Aberrant gene expression in the gut mucosa might contribute to the initiation and progression of Crohn's disease (CD). RNA sequencing (RNA-seq) provides precise measurements of expression levels of transcripts and their isoforms. The aim of this study was to use RNA-seq to investigate transcriptomic differences and identify significantly differentially expressed transcripts in inflamed and noninflamed intestinal mucosa of CD patients.

METHODS

RNA-seq was performed on 13 pairs of inflamed and noninflamed intestinal mucosa from 13 CD patients and on sex-matched normal mucosa of 13 healthy controls. Significantly differentially expressed transcripts were validated by immunohistochemistry, quantitative reverse transcriptase polymerase chain reaction, and enzyme-linked immunosorbent assay.

RESULTS

RNA-seq revealed genome-wide transcriptomic differences between normal mucosa, noninflamed, and inflamed CD mucosa. Among 950 differentially expressed genes, 19 were up- or downregulated (upregulation: ANGPT2, CHN1, CPXM1, CPZ, CXCL1, FCN3, GJC1, HSD11B1, LZTS1, MEOX1, MMP12, PLA1A, SERPINE1, SGIP1, and TRPC4; downregulation: FAM189A1, PDE6A, SLC38A4, and HMGCS2) with statistical significance (p < 0.01 and q < 0.05). Among them, CXCL1 exhibited the highest fold change between groups. Immunohistochemistry for CXCL1 revealed no expression in normal mucosa, slightly increased expression in noninflamed CD mucosa, and highly increased expression in inflamed CD mucosa. Quantitative reverse transcriptase polymerase chain reaction showed that CXCL1 expression was significantly associated with epithelial damage, increased infiltration of polymorphonuclear leukocytes, and submucosal fibrosis. Serum CXCL1 concentration measured by enzyme-linked immunosorbent assay was better correlated with CD activity index (r = 0.660) than with C-reactive protein (r = 0.204).

CONCLUSIONS

RNA-seq revealed transcriptomic differences between normal mucosa, noninflamed CD mucosa, and inflamed CD mucosa. Intestinal and serum CXCL1 was substantially increased with CD activity and can be used as a potential biomarker of CD.

RNA-Seq技术及其在胃肠肿瘤研究中的应用现状

转录组是特定的细胞或组织在特定的时间或状态下转录出来的RNA集合, 转录组研究能够从整体水平研究基因功能以及基因结构, 并能很好的显示处于表达状态的基因数量和活跃程度. 作为转录组学新一代高通量测序技术之一, RNA-Seq技术能够更为快速、准确地为人们提供更多的生物体转录信息, 在生物医学研究中已经得到广泛应用. 随着全球胃肠肿瘤发病率的逐年提高, RNA-Seq技术在胃肠肿瘤研究领域进行全转录组测序分析的应用越来越多, 并取得了一些新的进展. 本文将就RNA-Seq技术原理、优势及其在胃肠肿瘤研究中的具体应用进行论述.

Structural basis for spumavirus GAG tethering to chromatin

The interactions between a retrovirus and host cell chromatin that underlie integration and provirus expression are poorly understood. The prototype foamy virus (PFV) structural protein GAG associates with chromosomes via a chromatin-binding sequence (CBS) located within its C-terminal region. Here, we show that the PFV CBS is essential and sufficient for a direct interaction with nucleosomes and present a crystal structure of the CBS bound to a mononucleosome. The CBS interacts with the histone octamer, engaging the H2A-H2B acidic patch in a manner similar to other acidic patch-binding proteins such as herpesvirus latency-associated nuclear antigen (LANA). Substitutions of the invariant arginine anchor residue in GAG result in global redistribution of PFV and macaque simian foamy virus (SFV_mac) integration sites toward centromeres, dampening the resulting proviral expression without affecting the overall efficiency of integration. Our findings underscore the importance of retroviral structural proteins for integration site selection and the avoidance of genomic junkyards.

Androgen receptor-deficient islet β-cells exhibit alteration in genetic markers of insulin secretion and inflammation. A transcriptome analysis in the male mouse

AIMS

Testosterone action is mediated via the androgen receptor (AR). We have reported that male mice lacking AR selectively in β-cells (βARKO^-/y) develop decreased glucose-stimulated insulin secretion (GSIS), producing glucose intolerance. We showed that testosterone action on AR in β-cells amplifies the insulinotropic action of GLP-1 on its receptor via a cAMP-dependent protein kinase-A pathway.

METHODS

To investigate AR-dependent gene networks in β-cells, we performed a high throughput whole transcriptome sequencing (RNA-Seq) in islets from male βARKO^-/y and control mice.

RESULTS

We identified 214 differentially expressed genes (DEGs) (158 up- and 56 down-regulated) with a false discovery rate (FDR) < 0.05 and a fold change (FC) > 2. Our analysis of individual transcripts revealed alterations in β-cell genes involved in cellular inflammation/stress and insulin secretion. Based on 312 DEGs with an FDR < 0.05, the pathway analysis revealed 23 significantly enriched pathways, including cytokine-cytokine receptor interaction, Jak-STAT signaling, insulin signaling, MAPK signaling, type 2 diabetes (T2D) and pancreatic secretion. The gene ontology analysis confirmed the results of the individual DEGs and the pathway analysis in showing enriched biological processes encompassing inflammation, ion transport, exocytosis and insulin secretion.

CONCLUSIONS

AR-deficient islets exhibit altered expression of genes involved in inflammation and insulin secretion demonstrating the importance of androgen action in β-cell health in the male with implications for T2D development in men.

Clustering of mRNA-Seq data based on alternative splicing patterns

Sequencing of messenger RNA (mRNA) can provide estimates of the levels of individual isoforms within the cell. It remains to adapt many standard statistical methods commonly used for analyzing gene expression levels to take advantage of this additional information. One novel question is whether we can find clusters of samples that are distinguished not by their gene expression but by their isoform usage. We propose a novel approach for clustering mRNA-Seq data that identifies such clusters. We show via simulation that our methods are more sensitive to finding clusters based on isoform usage than standard clustering techniques. We demonstrate its performance by finding a technical artifact that resulted in different batches having different isoform usage patterns, and illustrate its usage on several The Cancer Genome Atlas datasets.

A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets

A Gene Regulatory Network (GRN) is a collection of interactions between molecular regulators and their targets in cells governing gene expression level. Omics data explosion generated from high-throughput genomic assays such as microarray and RNA-Seq technologies and the emergence of a number of pre-processing methods demands suitable guidelines to determine the impact of transcript data platforms and normalization procedures on describing associations in GRNs. In this study exploiting publically available microarray and RNA-Seq datasets and a gold standard of transcriptional interactions in Arabidopsis, we performed a comparison between six GRNs derived by RNA-Seq and microarray data and different normalization procedures. As a result we observed that compared algorithms were highly data-specific and Networks reconstructed by RNA-Seq data revealed a considerable accuracy against corresponding networks captured by microarrays. Topological analysis showed that GRNs inferred from two platforms were similar in several of topological features although we observed more connectivity in RNA-Seq derived genes network. Taken together transcriptional regulatory networks obtained by Robust Multiarray Averaging (RMA) and Variance-Stabilizing Transformed (VST) normalized data demonstrated predicting higher rate of true edges over the rest of methods used in this comparison.

Effect of Temperature Downshift on the Transcriptomic Responses of Chinese Hamster Ovary Cells Using Recombinant Human Tissue Plasminogen Activator Production Culture

Recombinant proteins are widely used as biopharmaceuticals, but their production by mammalian cell culture is expensive. Hence, improvement of bioprocess productivity is greatly needed. A temperature downshift (TDS) from 37°C to 28–34°C is an effective strategy to expand the productive life period of cells and increase their productivity (q_p). Here, TDS in Chinese hamster ovary (CHO) cell cultures, initially grown at 37°C and switched to 30°C during the exponential growth phase, resulted in a 1.6-fold increase in the q_p of recombinant human tissue plasminogen activator (rh-tPA). The transcriptomic response using next-generation sequencing (NGS) was assessed to characterize the cellular behavior associated with TDS. A total of 416 (q > 0.8) and 3,472 (q > 0.9) differentially expressed transcripts, with more than a 1.6-fold change at 24 and 48 h post TDS, respectively, were observed in cultures with TDS compared to those at constant 37°C. In agreement with the extended cell survival resulting from TDS, transcripts related to cell growth arrest that controlled cell proliferation without the activation of the DNA damage response, were differentially expressed. Most upregulated genes were related to energy metabolism in mitochondria, mitochondrial biogenesis, central metabolism, and avoidance of apoptotic cell death. The gene coding for rh-tPA was not differentially expressed, but fluctuations were detected in the transcripts encoding proteins involved in the secretory machinery, particularly in glycosylation. Through NGS the dynamic processes caused by TDS were assessed in this biological system.

IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity

We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, e.g., comparing the paternal and maternal alleles of one individual or comparing tumor and normal samples of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on the mouse transcriptome and identify a group of genes whose isoform usages respond to haloperidol treatment.

Use of next-generation DNA sequencing to analyze genetic variants in rheumatic disease

Next-generation DNA sequencing has revolutionized the field of genetics and genomics, providing researchers with the tools to efficiently identify novel rare and low frequency risk variants, which was not practical with previously available methodologies. These methods allow for the sequence capture of a specific locus or small genetic region all the way up to the entire six billion base pairs of the diploid human genome.

Rheumatic diseases are a huge burden on the US population, affecting more than 46 million Americans. Those afflicted suffer from one or more of the more than 100 diseases characterized by inflammation and loss of function, mainly of the joints, tendons, ligaments, bones, and muscles. While genetics studies of many of these diseases (for example, systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease) have had major successes in defining their genetic architecture, causal alleles and rare variants have still been elusive. This review describes the current high-throughput DNA sequencing methodologies commercially available and their application to rheumatic diseases in both case–control as well as family-based studies.

Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization

To improve the applicability of RNA-seq technology, a large number of RNA-seq data analysis methods and correction algorithms have been developed. Although these new methods and algorithms have steadily improved transcriptome analysis, greater prediction accuracy is needed to better guide experimental designs with computational results. In this study, a new tool for the identification of differentially expressed genes with RNA-seq data, named GExposer, was developed. This tool introduces a local normalization algorithm to reduce the bias of nonrandomly positioned read depth. The naive Bayes classifier is employed to integrate fold change, transcript length, and GC content to identify differentially expressed genes. Results on several independent tests show that GExposer has better performance than other methods. The combination of the local normalization algorithm and naive Bayes classifier with three attributes can achieve better results; both false positive rates and false negative rates are reduced. However, only a small portion of genes is affected by the local normalization and GC content correction.

Transcriptome sequencing of microglial cells stimulated with TLR3 and TLR4 ligands

Background

Resident macrophages in the CNS microglia become activated and produce proinflammatory molecules upon encountering bacteria or viruses. TLRs are a phylogenetically conserved diverse family of sensors that drive innate immune responses following interactions with PAMPs. TLR3 and TLR4 recognize viral dsRNA Poly (I:C) and bacterial endotoxin LPS, respectively. Importantly, these receptors differ in their downstream adaptor molecules. Thus far, only a few studies have investigated the effects of TLR3 and TLR4 in macrophages. However, a genome-wide search for the effects of these TLRs has not been performed in microglia using RNA-seq. Gene expression patterns were determined for the BV-2 microglial cell line when stimulated with viral dsRNA Poly (I:C) or bacterial endotoxin LPS to identify novel transcribed genes, as well as investigate how differences in downstream signaling could influence gene expression in innate immunity.

Results

Sequencing assessment and quality evaluation revealed that common and unique patterns of proinflammatory genes were significantly up-regulated in response to TLR3 and TLR4 stimulation. However, the IFN/viral response gene showed a stronger response to TLR3 stimulation than to TLR4 stimulation. Unexpectedly, TLR3 and TLR4 stimulation did not activate IFN-ß and IRF3 in BV-2 microglia. Most importantly, we observed that previously unidentified transcription factors (TFs) (i.e., IRF1, IRF7, and IRF9) and the epigenetic regulators KDM4A and DNMT3L were significantly up-regulated in both TLR3- and TLR4-stimulated microglia. We also identified 29 previously unidentified genes that are important in immune regulation. In addition, we confirmed the expressions of key inflammatory genes as well as pro-inflammatory mediators in the supernatants were significantly induced in TLR3-and TLR4-stimulated primary microglial cells. Moreover, transcriptional start sites (TSSs) and isoforms, as well as differential promoter usage, revealed a complex pattern of transcriptional and post-transcriptional gene regulation upon infection with TLR3 and TLR4. Furthermore, TF motif analysis (-950 to +50 bp of the 5′ upstream promoters) revealed that the DNA sequences for NF-κB, IRF1, and STAT1 were significantly enriched in TLR3- and TLR4-stimulated microglia.

Conclusions

These unprecedented findings not only permit a comparison of TLR3-and TLR4-stimulated genes but also identify new genes that have not been previously implicated in innate immunity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1728-5) contains supplementary material, which is available to authorized users.

SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice

Spinal muscular atrophy (SMA), which results from the loss of expression of the survival of motor neuron-1 (SMN1) gene, represents the most common genetic cause of pediatric mortality. A duplicate copy (SMN2) is inefficiently spliced, producing a truncated and unstable protein. We describe herein a potent, orally active, small-molecule enhancer of SMN2 splicing that elevates full-length SMN protein and extends survival in a severe SMA mouse model. We demonstrate that the molecular mechanism of action is via stabilization of the transient double-strand RNA structure formed by the SMN2 pre-mRNA and U1 small nuclear ribonucleic protein (snRNP) complex. The binding affinity of U1 snRNP to the 5' splice site is increased in a sequence-selective manner, discrete from constitutive recognition. This new mechanism demonstrates the feasibility of small molecule-mediated, sequence-selective splice modulation and the potential for leveraging this strategy in other splicing diseases.

A serine-arginine-rich (SR) splicing factor modulates alternative splicing of over a thousand genes in Toxoplasma gondii

Single genes are often subject to alternative splicing, which generates alternative mature mRNAs. This phenomenon is widespread in animals, and observed in over 90% of human genes. Recent data suggest it may also be common in Apicomplexa. These parasites have small genomes, and economy of DNA is evolutionarily favoured in this phylum. We investigated the mechanism of alternative splicing in Toxoplasma gondii, and have identified and localized TgSR3, a homologue of ASF/SF2 (alternative-splicing factor/splicing factor 2, a serine-arginine–rich, or SR protein) to a subnuclear compartment. In addition, we conditionally overexpressed this protein, which was deleterious to growth. qRT-PCR was used to confirm perturbation of splicing in a known alternatively-spliced gene. We performed high-throughput RNA-seq to determine the extent of splicing modulated by this protein. Current RNA-seq algorithms are poorly suited to compact parasite genomes, and hence we complemented existing tools by writing a new program, GeneGuillotine, that addresses this deficiency by segregating overlapping reads into distinct genes. In order to identify the extent of alternative splicing, we released another program, JunctionJuror, that detects changes in intron junctions. Using this program, we identified about 2000 genes that were constitutively alternatively spliced in T. gondii. Overexpressing the splice regulator TgSR3 perturbed alternative splicing in over 1000 genes.

Dual RNA sequencing reveals the expression of unique transcriptomic signatures in lipopolysaccharide-induced BV-2 microglial cells

Microglial cells become rapidly activated through interactions with pathogens, and the persistent activation of these cells is associated with various neurodegenerative diseases. Previous studies have investigated the transcriptomic signatures in microglia or macrophages using microarray technologies. However, this method has numerous restrictions, such as spatial biases, uneven probe properties, low sensitivity, and dependency on the probes spotted. To overcome this limitation and identify novel transcribed genes in response to LPS, we used RNA Sequencing (RNA-Seq) to determine the novel transcriptomic signatures in BV-2 microglial cells. Sequencing assessment and quality evaluation showed that approximately 263 and 319 genes (≥ 1.5 log₂-fold), such as cytokines and chemokines, were strongly induced after 2 and 4 h, respectively, and the induction of several genes with unknown immunological functions was also observed. Importantly, we observed that previously unidentified transcription factors (TFs) (irf1, irf7, and irf9), histone demethylases (kdm4a) and DNA methyltransferases (dnmt3l) were significantly and selectively expressed in BV-2 microglial cells. The gene expression levels, transcription start sites (TSS), isoforms, and differential promoter usage revealed a complex pattern of transcriptional and post-transcriptional gene regulation upon infection with LPS. In addition, gene ontology, molecular networks and pathway analyses identified the top significantly regulated functional classification, canonical pathways and network functions at each activation status. Moreover, we further analyzed differentially expressed genes to identify transcription factor (TF) motifs (−950 to +50 bp of the 5’ upstream promoters) and epigenetic mechanisms. Furthermore, we confirmed that the expressions of key inflammatory genes as well as pro-inflammatory mediators in the supernatants were significantly induced in LPS treated primary microglial cells. This transcriptomic analysis is the first to show a comparison of the family-wide differential expression of most known immune genes and also reveal transcription evidence of multiple gene families in BV-2 microglial cells. Collectively, these findings reveal unique transcriptomic signatures in BV-2 microglial cells required for homeostasis and effective immune responses.

Profiling status epilepticus-induced changes in hippocampal RNA expression using high-throughput RNA sequencing

Status epilepticus (SE) is a life-threatening condition that can give rise to a number of neurological disorders, including learning deficits, depression, and epilepsy. Many of the effects of SE appear to be mediated by alterations in gene expression. To gain deeper insight into how SE affects the transcriptome, we employed the pilocarpine SE model in mice and Illumina-based high-throughput sequencing to characterize alterations in gene expression from the induction of SE, to the development of spontaneous seizure activity. While some genes were upregulated over the entire course of the pathological progression, each of the three sequenced time points (12-hour, 10-days and 6-weeks post-SE) had a largely unique transcriptional profile. Hence, genes that regulate synaptic physiology and transcription were most prominently altered at 12-hours post-SE; at 10-days post-SE, marked changes in metabolic and homeostatic gene expression were detected; at 6-weeks, substantial changes in the expression of cell excitability and morphogenesis genes were detected. At the level of cell signaling, KEGG analysis revealed dynamic changes within the MAPK pathways, as well as in CREB-associated gene expression. Notably, the inducible expression of several noncoding transcripts was also detected. These findings offer potential new insights into the cellular events that shape SE-evoked pathology.

Heterogeneity of glycolysis in cancers and therapeutic opportunities

Upregulated glycolysis, both in normoxic and hypoxic environments, is a nearly universal trait of cancer cells. The enormous difference in glucose metabolism offers a target for therapeutic intervention with a potentially low toxicity profile. The past decade has seen a steep rise in the development and clinical assessment of small molecules that target glycolysis. The enzymes in glycolysis have a highly heterogeneous nature that allows for the different bioenergetic, biosynthetic, and signaling demands needed for various tissue functions. In cancers, these properties enable them to respond to the variable requirements of cell survival, proliferation and adaptation to nutrient availability. Heterogeneity in glycolysis occurs through the expression of different isoforms, posttranslational modifications that affect the kinetic and regulatory properties of the enzyme. In this review, we will explore this vast heterogeneity of glycolysis and discuss how this information might be exploited to better target glucose metabolism and offer possibilities for biomarker development.

RNA sequencing: current and prospective uses in metabolic research

Previous global RNA analysis was restricted to known transcripts in species with a defined transcriptome. Next generation sequencing has transformed transcriptomics by making it possible to analyse expressed genes with an exon level resolution from any tissue in any species without any a priori knowledge of which genes that are being expressed, splice patterns or their nucleotide sequence. In addition, RNA sequencing is a more sensitive technique compared with microarrays with a larger dynamic range, and it also allows for investigation of imprinting and allele-specific expression. This can be done for a cost that is able to compete with that of a microarray, making RNA sequencing a technique available to most researchers. Therefore RNA sequencing has recently become the state of the art with regards to large-scale RNA investigations and has to a large extent replaced microarrays. The only drawback is the large data amounts produced, which together with the complexity of the data can make a researcher spend far more time on analysis than performing the actual experiment.

RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis

RNA-seq is widely used to determine differential expression of genes or transcripts as well as identify novel transcripts, identify allele-specific expression, and precisely measure translation of transcripts. Thoughtful experimental design and choice of analysis tools are critical to ensure high-quality data and interpretable results. Important considerations for experimental design include number of replicates, whether to collect paired-end or single-end reads, sequence length, and sequencing depth. Common analysis steps in all RNA-seq experiments include quality control, read alignment, assigning reads to genes or transcripts, and estimating gene or transcript abundance. Our aims are two-fold: to make recommendations for common components of experimental design and assess tool capabilities for each of these steps. We also test tools designed to detect differential expression, since this is the most widespread application of RNA-seq. We hope that these analyses will help guide those who are new to RNA-seq and will generate discussion about remaining needs for tool improvement and development.

Influence of RNA extraction methods and library selection schemes on RNA-seq data

BACKGROUND

Gene expression analysis by RNA sequencing is now widely used in a number of applications surveying the whole transcriptomes of cells and tissues. The recent introduction of ribosomal RNA depletion protocols, such as RiboZero, has extended the view of the polyadenylated transcriptome to the poly(A)- fraction of the RNA. However, substantial amounts of intronic transcriptional activity has been reported in RiboZero protocols, raising issues regarding their potential nuclear origin and the impact on the actual sequence depth in exonic regions.

RESULTS

Using HEK293 human cells as source material, we assessed here the impact of the two commonly used RNA extraction methods and of the library construction protocols (rRNA depletion versus mRNA) on 1) the relative abundance of intronic reads and 2) on the estimation of gene expression values. We benchmarked the rRNA depletion-based sequencing with a specific analysis of the cytoplasmic and nuclear transcriptome fractions, suggesting that the large majority of the intronic reads correspond to unprocessed nuclear transcripts rather than to independent transcriptional units. We show that Qiagen or TRIzol extraction methods retain differentially nuclear RNA species, and that consequently, rRNA depletion-based RNA sequencing protocols are particularly sensitive to the extraction methods.

CONCLUSIONS

We could show that the combination of Trizol-based RNA extraction with rRNA depletion sequencing protocols led to the largest fraction of intronic reads, after the sequencing of the nuclear transcriptome. We discuss here the impact of the various strategies on gene expression and alternative splicing estimation measures. Further, we propose guidelines and a double selection strategy for minimizing the expression biases, without loss of information.

A novel multi-alignment pipeline for high-throughput sequencing data

Mapping reads to a reference sequence is a common step when analyzing allele effects in high-throughput sequencing data. The choice of reference is critical because its effect on quantitative sequence analysis is non-negligible. Recent studies suggest aligning to a single standard reference sequence, as is common practice, can lead to an underlying bias depending on the genetic distances of the target sequences from the reference. To avoid this bias, researchers have resorted to using modified reference sequences. Even with this improvement, various limitations and problems remain unsolved, which include reduced mapping ratios, shifts in read mappings and the selection of which variants to include to remove biases. To address these issues, we propose a novel and generic multi-alignment pipeline. Our pipeline integrates the genomic variations from known or suspected founders into separate reference sequences and performs alignments to each one. By mapping reads to multiple reference sequences and merging them afterward, we are able to rescue more reads and diminish the bias caused by using a single common reference. Moreover, the genomic origin of each read is determined and annotated during the merging process, providing a better source of information to assess differential expression than simple allele queries at known variant positions. Using RNA-seq of a diallel cross, we compare our pipeline with the single-reference pipeline and demonstrate our advantages of more aligned reads and a higher percentage of reads with assigned origins. Database URL: http://csbio.unc.edu/CCstatus/index.py?run=Pseudo.

ARH-seq: identification of differential splicing in RNA-seq data

The computational prediction of alternative splicing from high-throughput sequencing data is inherently difficult and necessitates robust statistical measures because the differential splicing signal is overlaid by influencing factors such as gene expression differences and simultaneous expression of multiple isoforms amongst others. In this work we describe ARH-seq, a discovery tool for differential splicing in case–control studies that is based on the information-theoretic concept of entropy. ARH-seq works on high-throughput sequencing data and is an extension of the ARH method that was originally developed for exon microarrays. We show that the method has inherent features, such as independence of transcript exon number and independence of differential expression, what makes it particularly suited for detecting alternative splicing events from sequencing data. In order to test and validate our workflow we challenged it with publicly available sequencing data derived from human tissues and conducted a comparison with eight alternative computational methods. In order to judge the performance of the different methods we constructed a benchmark data set of true positive splicing events across different tissues agglomerated from public databases and show that ARH-seq is an accurate, computationally fast and high-performing method for detecting differential splicing events.

RNA-sequence analysis of primary alveolar macrophages after in vitro infection with porcine reproductive and respiratory syndrome virus strains of differing virulence

Porcine reproductive and respiratory syndrome virus (PRRSV) mainly infects porcine alveolar macrophages (PAMs), resulting in porcine reproductive and respiratory syndrome (PRRS) in pigs. Most of the transcriptomic studies on PAMs infected with PRRSV conducted thus far have made use of microarray technology. Here, we investigated the transcriptome of PAMs in vitro at 12 h post-infection with two European PRRSV strains characterized by low (Lelystad, LV) and high (Lena) virulence through RNA-Seq. The expression levels of genes, isoforms, alternative transcription start sites (TSS) and differential promoter usage revealed a complex pattern of transcriptional and post-transcriptional gene regulation upon infection with the two strains. Gene ontology analysis confirmed that infection of PAMs with both the Lena and LV strains affected signaling pathways directly linked to the innate immune response, including interferon regulatory factors (IRF), RIG1-like receptors, TLRs and PKR pathways. The results confirmed that interferon signaling is crucial for transcriptional regulation during PAM infection. IFN-β1 and IFN-αω, but not IFN-α, were up-regulated following infection with either the LV or Lena strain. The down-regulation of canonical pathways, such as the interplay between the innate and adaptive immune responses, cell death and TLR3/TLR7 signaling, was observed for both strains, but Lena triggered a stronger down-regulation than LV. This analysis contributes to a better understanding of the interactions between PRRSV and PAMs and outlines the differences in the responses of PAMs to strains with different levels of virulence, which may lead to the development of new PRRSV control strategies.

Kassiopeia: a database and web application for the analysis of mutually exclusive exomes of eukaryotes

BACKGROUND

Alternative splicing is an important process in higher eukaryotes that allows obtaining several transcripts from one gene. A specific case of alternative splicing is mutually exclusive splicing, in which exactly one exon out of a cluster of neighbouring exons is spliced into the mature transcript. Recently, a new algorithm for the prediction of these exons has been developed based on the preconditions that the exons of the cluster have similar lengths, sequence homology, and conserved splice sites, and that they are translated in the same reading frame.

DESCRIPTION

In this contribution we introduce Kassiopeia, a database and web application for the generation, storage, and presentation of genome-wide analyses of mutually exclusive exomes. Currently, Kassiopeia provides access to the mutually exclusive exomes of twelve Drosophila species, the thale cress Arabidopsis thaliana, the flatworm Caenorhabditis elegans, and human. Mutually exclusive spliced exons (MXEs) were predicted based on gene reconstructions from Scipio. Based on the standard prediction values, with which 83.5% of the annotated MXEs of Drosophila melanogaster were reconstructed, the exomes contain surprisingly more MXEs than previously supposed and identified. The user can search Kassiopeia using BLAST or browse the genes of each species optionally adjusting the parameters used for the prediction to reveal more divergent or only very similar exon candidates.

CONCLUSIONS

We developed a pipeline to predict MXEs in the genomes of several model organisms and a web interface, Kassiopeia, for their visualization. For each gene Kassiopeia provides a comprehensive gene structure scheme, the sequences and predicted secondary structures of the MXEs, and, if available, further evidence for MXE candidates from cDNA/EST data, predictions of MXEs in homologous genes of closely related species, and RNA secondary structure predictions. Kassiopeia can be accessed at http://www.motorprotein.de/kassiopeia.

Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models

MOTIVATION

The most common RNA-Seq strategy consists of random shearing, amplification and high-throughput sequencing of the RNA fraction. Methods to analyze transcription level variations along the genome from the read count profiles generated by the RNA-Seq protocol are needed.

RESULTS

We developed a statistical approach to estimate the local transcription levels and to identify transcript borders. This transcriptional landscape reconstruction relies on a state-space model to describe transcription level variations in terms of abrupt shifts and more progressive drifts. A new emission model is introduced to capture not only the read count variance inside a transcript but also its short-range autocorrelation and the fraction of positions with zero counts. The estimation relies on a particle Gibbs algorithm whose running time makes it more suited to microbial genomes. The approach outperformed read-overlapping strategies on synthetic and real microbial datasets.

AVAILABILITY

A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/.

CONTACT

bodgan.mirauta@upmc.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rare and private variations in neural crest, apoptosis and sarcomere genes define the polygenic background of isolated Tetralogy of Fallot

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease. Its genetic basis is demonstrated by an increased recurrence risk in siblings and familial cases. However, the majority of TOF are sporadic, isolated cases of undefined origin and it had been postulated that rare and private autosomal variations in concert define its genetic basis. To elucidate this hypothesis, we performed a multilevel study using targeted re-sequencing and whole-transcriptome profiling. We developed a novel concept based on a gene's mutation frequency to unravel the polygenic origin of TOF. We show that isolated TOF is caused by a combination of deleterious private and rare mutations in genes essential for apoptosis and cell growth, the assembly of the sarcomere as well as for the neural crest and secondary heart field, the cellular basis of the right ventricle and its outflow tract. Affected genes coincide in an interaction network with significant disturbances in expression shared by cases with a mutually affected TOF gene. The majority of genes show continuous expression during adulthood, which opens a new route to understand the diversity in the long-term clinical outcome of TOF cases. Our findings demonstrate that TOF has a polygenic origin and that understanding the genetic basis can lead to novel diagnostic and therapeutic routes. Moreover, the novel concept of the gene mutation frequency is a versatile measure and can be applied to other open genetic disorders.

A robust method for transcript quantification with RNA-seq data

The advent of high throughput RNA-seq technology allows deep sampling of the transcriptome, making it possible to characterize both the diversity and the abundance of transcript isoforms. Accurate abundance estimation or transcript quantification of isoforms is critical for downstream differential analysis (e.g., healthy vs. diseased cells) but remains a challenging problem for several reasons. First, while various types of algorithms have been developed for abundance estimation, short reads often do not uniquely identify the transcript isoforms from which they were sampled. As a result, the quantification problem may not be identifiable, i.e., lacks a unique transcript solution even if the read maps uniquely to the reference genome. In this article, we develop a general linear model for transcript quantification that leverages reads spanning multiple splice junctions to ameliorate identifiability. Second, RNA-seq reads sampled from the transcriptome exhibit unknown position-specific and sequence-specific biases. We extend our method to simultaneously learn bias parameters during transcript quantification to improve accuracy. Third, transcript quantification is often provided with a candidate set of isoforms, not all of which are likely to be significantly expressed in a given tissue type or condition. By resolving the linear system with LASSO, our approach can infer an accurate set of dominantly expressed transcripts while existing methods tend to assign positive expression to every candidate isoform. Using simulated RNA-seq datasets, our method demonstrated better quantification accuracy and the inference of dominant set of transcripts than existing methods. The application of our method on real data experimentally demonstrated that transcript quantification is effective for differential analysis of transcriptomes.

Methods to study splicing from high-throughput RNA sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data, which could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.