RNA-Seq gene profiling--a systematic empirical comparison

DOT1L promotes spermatid differentiation by regulating expression of genes required for histone-to-protamine replacement

Unique chromatin remodeling factors orchestrate dramatic changes in nuclear morphology during differentiation of the mature sperm head. A crucial step in this process is histone-to-protamine exchange, which must be executed correctly to avoid sperm DNA damage, embryonic lethality and male sterility. Here, we define an essential role for the histone methyltransferase DOT1L in the histone-to-protamine transition. We show that DOT1L is abundantly expressed in mouse meiotic and postmeiotic germ cells, and that methylation of histone H3 lysine 79 (H3K79), the modification catalyzed by DOT1L, is enriched in developing spermatids in the initial stages of histone replacement. Elongating spermatids lacking DOT1L fail to fully replace histones and exhibit aberrant protamine recruitment, resulting in deformed sperm heads and male sterility. Loss of DOT1L results in transcriptional dysregulation coinciding with the onset of histone replacement and affecting genes required for histone-to-protamine exchange. DOT1L also deposits H3K79me2 and promotes accumulation of elongating RNA Polymerase II at the testis-specific bromodomain gene Brdt. Together, our results indicate that DOT1L is an important mediator of transcription during spermatid differentiation and an indispensable regulator of male fertility.

Robust and rigorous identification of tissue-specific genes by statistically extending tau score

OBJECTIVES

In this study, we aimed to identify tissue-specific genes for various human tissues/organs more robustly and rigorously by extending the tau score algorithm.

INTRODUCTION

Tissue-specific genes are a class of genes whose functions and expressions are preferred in one or several tissues restrictedly. Identification of tissue-specific genes is essential for discovering multi-cellular biological processes such as tissue-specific molecular regulations, tissue development, physiology, and the pathogenesis of tissue-associated diseases.

MATERIALS AND METHODS

Gene expression data derived from five large RNA sequencing (RNA-seq) projects, spanning 96 different human tissues, were retrieved from ArrayExpress and ExpressionAtlas. The first step is categorizing genes using significant filters and tau score as a specificity index. After calculating tau for each gene in all datasets separately, statistical distance from the maximum expression level was estimated using a new meaningful procedure. Specific expression of a gene in one or several tissues was calculated after the integration of tau and statistical distance estimation, which is called as extended tau approach. Obtained tissue-specific genes for 96 different human tissues were functionally annotated, and some comparisons were carried out to show the effectiveness of the extended tau method.

RESULTS AND DISCUSSION

Categorization of genes based on expression level and identification of tissue-specific genes for a large number of tissues/organs were executed. Genes were successfully assigned to multiple tissues by generating the extended tau approach as opposed to the original tau score, which can assign tissue specificity to single tissue only.

The miR-429 suppresses proliferation and migration in glioblastoma cells and induces cell-cycle arrest and apoptosis via modulating several target genes of ERBB signaling pathway

BACKGROUND

Glioblastoma multiforme (GBM) is an aggressive and lethal brain cancer, which is incurable with standard cancer treatments. miRNAs have great potential to be used for gene therapy due to their ability to modulate several target genes simultaneously. We found miR-429 is downregulated in GBM and has several predicted target genes from the ERBB signaling pathway using bioinformatics tools. ERBB is the most over-activated genetic pathway in GBM patients, which is responsible for augmented cell proliferation and migration in GBM.

METHODS AND RESULTS

Here, miR-429 was overexpressed using lentiviral vectors in U-251 and U-87 GBM cells and it was observed that the expression level of several oncogenes of the ERBB pathway, EGFR, PIK3CA, PIK3CB, KRAS, and MYC significantly decreased, as shown by real-time PCR and western blotting. Using the luciferase assay, we showed that miR-429 directly targets MYC, BCL2, and EGFR. In comparison to scrambled control, miR-429 had a significant inhibitory effect on cell proliferation and migration as deduced from MTT and scratch wound assays and induced cell-cycle arrest and apoptosis in flow cytometry.

CONCLUSIONS

Altogether, miR-429 seems to be an efficient suppressor of the ERBB genetic signaling pathway and a potential therapeutic for GBM.

miR-424 induces apoptosis in glioblastoma cells and targets AKT1 and RAF1 oncogenes from the ERBB signaling pathway

Glioblastoma is a lethal and incurable cancer. Tumor suppressor miRNAs are promising gene therapy tools for cancer treatment. In silico, we predicted miR-424 as a tumor suppressor. It had several target genes from the epidermal growth factor receptor (ERBB) signaling pathway that are overactive in most glioblastoma cases. We overexpressed miR-424 by lentiviral transduction of U-251 and U-87 glioblastoma cells confirmed with fluorescent microscopy and real-time quantitative PCR (qRT-PCR). Then the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) proliferation assay and scratch wound migration assay were performed to investigate the miR-424 tumor suppressor effect in glioblastoma. miR-424's effect on glioblastoma apoptosis and cell-cycle arrest was verified using Annexin V- phosphatidylethanolamine (PE) and 7-minoactinomycin D (7-AAD) apoptosis assay and cell-cycle assay. miR-424 predicted target genes mRNA and protein level were measured after miR-424 overexpression in comparison to the control group by qRT-PCR and western blotting, respectively. We confirmed miR-424 direct target genes by dual-luciferase reporter assay. miR-424 overexpression significantly suppressed cell proliferation and migration rate in glioblastoma cells based on the MTT and scratch assays. Flow cytometry results confirmed that miR-424 promotes apoptosis and cell-cycle arrest in glioblastoma cells. Predicted target genes of miR-424 from the ERBB pathway were downregulated by miR-424 overexpression. qRT-PCR and western blotting showed that KRAS, RAF1, MAP2K1, EGFR, PDGFRA, AKT1, and mTOR mRNA expression levels and KRAS, RAF1, MAP2K1, EGFR, and AKT1 protein level, respectively, had significantly decreased as a result of miR-424 overexpression in comparison to the control group. Dual-luciferase reporter assay confirmed that miR-424 directly targets RAF1 and AKT1 oncogenes. Overall, miR-424 acts as tumor suppressor miRNA in glioblastoma cells.

Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures

RNA-seq is a modular experimental and computational approach aiming in identifying and quantifying RNA molecules. The modularity of the RNA-seq technology enables adaptation of the protocol to develop new ways to explore RNA biology, but this modularity also brings forth the importance of methodological thoroughness. Liberty of approach comes with the responsibility of choices, and such choices must be informed. Here, we present an approach that identifies gene group-specific quantification biases in current RNA-seq software and references by processing datasets using diverse RNA-seq computational pipelines, and by decomposing these expression datasets with an independent component analysis matrix factorization method. By exploring the RNA-seq pipeline using this systemic approach, we identify genome annotations as a design choice that affects to the same extent quantification results as does the choice of aligners and quantifiers. We also show that the different choices in RNA-seq methodology are not independent, identifying interactions between genome annotations and quantification software. Genes were mainly affected by differences in their sequence, by overlapping genes and genes with similar sequence. Our approach offers an explanation for the observed biases by identifying the common features used differently by the software and references, therefore providing leads for the betterment of RNA-seq methodology.

Strandedness during cDNA synthesis, the stranded parameter in htseq-count and analysis of RNA-Seq data

RNA sequencing (RNA-Seq) is a complicated protocol, both in the laboratory in generation of data and at the computer in analysis of results. Several decisions during RNA-Seq library construction have important implications for analysis, most notably strandedness during complementary DNA library construction. Here, we clarify bioinformatic decisions related to strandedness in both alignment of DNA sequencing reads to reference genomes and subsequent determination of transcript abundance.

Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Identification of novel semen and saliva specific methylation markers and its potential application in forensic analysis

Differential DNA methylation in human tissues has been widely used to develop markers for body fluid identification in forensics. In the present study, identification of potential tissue specific differentially methylated regions (tDMRs) was based on mining differentially expressed genes in surrogate tissues for blood, saliva, semen and vaginal fluid. Genes specifically over expressed in one of the surrogate tissues viz: blood, salivary glands, testis, prostrate, cervix, uterus and ovary were identified from genome wide expression datasets. We hypothesized that over expression in surrogate tissues for body fluids could be correlated with differential methylation. Methylation information from two methylation datasets, NGSmethDB and ENCODE were integrated and heavily methylated gene body CpG islands (CGI) representing the body fluids were extracted. From a total of 53 potential genes the present study reports, two genes, ZNF282 and HPCAL1 which were preferentially expressed in cervix with comparatively reduced expression in other surrogate tissues. Methylated CGIs were targeted to design primers for methylation specific PCR (MSP) and bisulphite sequencing (BS). The ZNF282 CpG sites displayed semen-specific hypomethylation while HPCAL1 CpGs showed saliva-specific hypomethylation. Clone-based bisulphite sequencing also revealed significant hypomethylation in the target body fluids. To evaluate the stability of methylation profiles, the ZNF282 tDMR was tested and each body fluid was subjected to five different forensic simulated conditions (dry at room temperature, wet in an exicator, outside on the ground, sprayed with alcohol and sprayed with bleach) for 50 days. Under the condition "outside on the ground", saliva showed a significant decrease in methylation level by bisulphite sequencing analysis over time. Complete methylation profiles were obtained only for vaginal fluid under all conditions and no differences in methylation levels were observed for this fluid after 50 days. Thus, ZNF282 and HPCAL1 tDMRs can be used as reliable semen and saliva identification markers respectively.

Short paired-end reads trump long single-end reads for expression analysis

Background

Typical experimental design advice for expression analyses using RNA-seq generally assumes that single-end reads provide robust gene-level expression estimates in a cost-effective manner, and that the additional benefits obtained from paired-end sequencing are not worth the additional cost. However, in many cases (e.g., with Illumina NextSeq and NovaSeq instruments), shorter paired-end reads and longer single-end reads can be generated for the same cost, and it is not obvious which strategy should be preferred. Using publicly available data, we test whether short-paired end reads can achieve more robust expression estimates and differential expression results than single-end reads of approximately the same total number of sequenced bases.

Results

At both the transcript and gene levels, 2 × 40 paired-end reads unequivocally provide expression estimates that are more highly correlated with 2 × 125 than 1 × 75 reads; in nearly all cases, those correlations are also greater than for 1 × 125, despite the greater total number of sequenced bases for the latter. Across an array of metrics, differential expression tests based upon 2 × 40 consistently outperform those using 1 × 75.

Conclusion

Researchers seeking a cost-effective approach for gene-level expression analysis should prefer short paired-end reads over a longer single-end strategy. Short paired-end reads will also give reasonably robust expression estimates and differential expression results at the isoform level.

Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model Plant Arabidopsis thaliana

Quantification of gene expression is crucial to connect genome sequences with phenotypic and physiological data. RNA-Sequencing (RNA-Seq) has taken a prominent role in the study of transcriptomic reactions of plants to various environmental and genetic perturbations. However, comparative tests of different tools for RNA-Seq read mapping and quantification have been mainly performed on data from animals or humans, which necessarily neglect, for example, the large genetic variability among natural accessions within plant species. Here, we compared seven computational tools for their ability to map and quantify Illumina single-end reads from the Arabidopsis thaliana accessions Columbia-0 (Col-0) and N14. Between 92.4% and 99.5% of all reads were mapped to the reference genome or transcriptome and the raw count distributions obtained from the different mappers were highly correlated. Using the software DESeq2 to determine differential gene expression (DGE) between plants exposed to 20 °C or 4 °C from these read counts showed a large pairwise overlap between the mappers. Interestingly, when the commercial CLC software was used with its own DGE module instead of DESeq2, strongly diverging results were obtained. All tested mappers provided highly similar results for mapping Illumina reads of two polymorphic Arabidopsis accessions to the reference genome or transcriptome and for the determination of DGE when the same software was used for processing.

Biosensor for the Characterization of Gene Expression in Cells

We developed a new label-free biosensor technique for the detection of messenger ribonucleic acid (mRNA) that can be used in the prognosis and diagnosis of certain diseases. We observed a dependence of the negative dielectrophoresis (DEP) force applied to polystyrene microspheres (PMs) in conjugation with different types of mRNA and the frequency of the electric field produced by interdigitated microelectrodes. Since the frequency dependence of the negative DEP force is an effective transduction mechanism for the detection of mRNA, this sensing technology has the potential to be used in the diagnosis and identification of gene expression that is associated with various human disease.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance

BACKGROUND

RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance characteristics. Although a number of workflows are emerging as high performers that are robust to diverse input types, the relative performance characteristics of these workflows when either read depth or sample number is limited-a common occurrence in real-world practice-remain unexplored.

RESULTS

Here, we evaluate the impact of varying read depth and sample number on the performance of differential gene expression identification workflows, as measured by precision, or the fraction of genes correctly identified as differentially expressed, and by recall, or the fraction of differentially expressed genes identified. We focus our analysis on 30 high-performing workflows, systematically varying the read depth and number of biological replicates of patient monocyte samples provided as input. We find that, in general for most workflows, read depth has little effect on workflow performance when held above two million reads per sample, with reduced workflow performance below this threshold. The greatest impact of decreased sample number is seen below seven samples per group, when more heterogeneity in workflow performance is observed. The choice of differential expression identification tool, in particular, has a large impact on the response to limited inputs.

CONCLUSIONS

Among the tested workflows, the recall/precision balance remains relatively stable at a range of read depths and sample numbers, although some workflows are more sensitive to input restriction. At ranges typically recommended for biological studies, performance is more greatly impacted by the number of biological replicates than by read depth. Caution should be used when selecting analysis workflows and interpreting results from low sample number experiments, as all workflows exhibit poorer performance at lower sample numbers near typically reported values, with variable impact on recall versus precision. These analyses highlight the performance characteristics of common differential gene expression workflows at varying read depths and sample numbers, and provide empirical guidance in experimental and analytical design.

Comparative transcriptomics identifies genes differentially expressed in the intestine of a new fast-growing strain of common carp with higher unsaturated fatty acid content in muscle

We have created a new, fast-growing strain of common carp with higher unsaturated fatty acid content in muscle. To better understand the impacts of gene regulation in intestinal tissue on growth and unsaturated fatty acid content, we conducted a comparative RNA-Seq transcriptome analysis between intestine samples of Selected and Control groups (and corroborated selected results by PCR). After eight weeks of cage culture, weight gain of the Selected group was 20.84% higher. In muscles of the control group, monounsaturated fatty acids (FAs) were more abundant, whereas polyunsaturated FAs were more abundant in muscles of the Selected group. In total, we found 106 differentially expressed genes (DEGs) between the two groups. Only the endocytosis pathway was significantly enriched in DEGs, with two upregulated genes: il2rb and ehd1. The latter is involved in the growth hormone/insulin-like growth factor (Gh/Igf) axis, which plays a key role in the regulation of growth in animals. tll2, which is known to be associated with intestinal regeneration, was extremely highly upregulated in both transcriptomic (infinite) and qPCR (610.70) analyses. Two of the upregulated genes are associated with the fatty acid metabolism, several genes are likely to be indicators of heightened transcription levels, several are associated with metabolic and developmental roles, several with neuronal functions (including two with vision), several with the immune system, and two downregulated genes with the development of vasculature. The higher growth rate of the Selected group is likely to be at least partially attributed to increased endocytosis efficiency and genetically-driven behavioural differences (higher aggression levels). There are some indications that this new strain might have slightly impaired immune responses, and a higher propensity for inherited diseases leading to sight impairment, as well for neurodegenerative diseases in general, but these indications still need to be confirmed.

Common and differential transcriptional responses to different models of traumatic stress exposure in rats

The effect of six different traumatic stress protocols on the transcriptome of the rat adrenal gland was examined using RNA sequencing. These protocols included chronic variable stress, chronic shock, social defeat and social isolation. The response of the transcriptome to stress suggested that there are genes that respond in a universal or stress modality-independent manner, as well as genes that respond in a stress modality-specific manner. Using a small number of the genes selected from the modality-independent set of stress-sensitive genes, a sensitive and robust measure of chronic stress exposure was developed. This stress-sensitive gene expression (SSGE) index could detect chronic traumatic stress exposure in a wide range of different stress models in a manner that was relatively independent of the modality of stress exposure and that paralleled the intensity of stress exposure in a dose-dependent manner. This measure could reliably distinguish control and stressed individuals in the case of animals exposed to the most intense stress protocols. The response of a subset of the modality-specific genes could also distinguish some types of stress exposure, based solely on changes in the pattern of gene expression. The results suggest that it is possible to develop diagnostic measures of traumatic stress exposure based solely on changes in the level of expression of a relatively small number of genes.

Studying Smaller and Neglected Organisms in Modern Evolutionary Venomics Implementing RNASeq (Transcriptomics)-A Critical Guide

Venoms are evolutionary key adaptations that species employ for defense, predation or competition. However, the processes and forces that drive the evolution of venoms and their toxin components remain in many aspects understudied. In particular, the venoms of many smaller, neglected (mostly invertebrate) organisms are not characterized in detail, especially with modern methods. For the majority of these taxa, even their biology is only vaguely known. Modern evolutionary venomics addresses the question of how venoms evolve by applying a plethora of -omics methods. These recently became so sensitive and enhanced that smaller, neglected organisms are now more easily accessible to comparatively study their venoms. More knowledge about these taxa is essential to better understand venom evolution in general. The methodological core pillars of integrative evolutionary venomics are genomics, transcriptomics and proteomics, which are complemented by functional morphology and the field of protein synthesis and activity tests. This manuscript focuses on transcriptomics (or RNASeq) as one toolbox to describe venom evolution in smaller, neglected taxa. It provides a hands-on guide that discusses a generalized RNASeq workflow, which can be adapted, accordingly, to respective projects. For neglected and small taxa, generalized recommendations are difficult to give and conclusions need to be made individually from case to case. In the context of evolutionary venomics, this overview highlights critical points, but also promises of RNASeq analyses. Methodologically, these concern the impact of read processing, possible improvements by perfoming multiple and merged assemblies, and adequate quantification of expressed transcripts. Readers are guided to reappraise their hypotheses on venom evolution in smaller organisms and how robustly these are testable with the current transcriptomics toolbox. The complementary approach that combines particular proteomics but also genomics with transcriptomics is discussed as well. As recently shown, comparative proteomics is, for example, most important in preventing false positive identifications of possible toxin transcripts. Finally, future directions in transcriptomics, such as applying 3rd generation sequencing strategies to overcome difficulties by short read assemblies, are briefly addressed.

Mononuclear cell transcriptome changes associated with dimethyl fumarate in MS

Objective

To identify short-term changes in gene expression in peripheral blood mononuclear cells (PBMCs) associated with treatment response to dimethyl fumarate (DMF, Tecfidera) in patients with relapsing-remitting MS (RRMS).

Methods

Blood samples were collected from 24 patients with RRMS (median Expanded Disability Status Scale score, 2.0; range 1–7) at baseline, 6 weeks, and 15 months after the initiation of treatment with DMF (BG-12; Tecfidera). Seven healthy controls were also recruited, and blood samples were collected over the same time intervals. PBMCs were extracted from blood samples and sequenced using next-generation RNA sequencing. Treatment responders were defined using the composite outcome measure “no evidence of disease activity” (NEDA-4). Time-course and cross-sectional differential expression analyses were performed to identify transcriptomic markers of treatment response.

Results

Treatment responders (NEDA-4 positive, 8/24) over the 15-month period had 478 differentially expressed genes (DEGs) 6 weeks after the start of treatment. These were enriched for nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and inhibition of nuclear factor κB (NFκB) pathway transcripts. For patients who showed signs of disease activity, there were no DEGs at 6 weeks relative to their (untreated) baseline. Contrasting transcriptomes expressed at 6 weeks with those at 15 months of treatment, 0 and 1,264 DEGs were found in the responder and nonresponder groups, respectively. Transcripts in the nonresponder group (NEDA-4 negative, 18/24) were enriched for T-cell signaling genes.

Conclusion

Short-term PBMC transcriptome changes reflecting activation of the Nrf2 and inhibition of NFκB pathways distinguish patients who subsequently show a medium-term treatment response with DMF. Relative stabilization of gene expression patterns may accompany treatment-associated suppression of disease activity.

GPCRomics: GPCR Expression in Cancer Cells and Tumors Identifies New, Potential Biomarkers and Therapeutic Targets

G protein-coupled receptors (GPCRs), the largest family of targets for approved drugs, are rarely targeted for cancer treatment, except for certain endocrine and hormone-responsive tumors. Limited knowledge regarding GPCR expression in cancer cells likely has contributed to this lack of use of GPCR-targeted drugs as cancer therapeutics. We thus undertook GPCRomic studies to define the expression of endoGPCRs (which respond to endogenous molecules such as hormones, neurotransmitters and metabolites) in multiple types of cancer cells. Using TaqMan qPCR arrays to quantify the mRNA expression of ∼340 such GPCRs, we found that human chronic lymphocytic leukemia (CLL) cells/stromal cells associated with CLL, breast cancer cell lines, colon cancer cell lines, pancreatic ductal adenocarcinoma (PDAC) cells, cancer associated fibroblasts (CAFs), and PDAC tumors express 50 to >100 GPCRs, including many orphan GPCRs (which lack known physiologic agonists). Limited prior data exist regarding the expression or function of most of the highly expressed GPCRs in these cancer cells and tumors. Independent results from public cancer gene expression databases confirm the expression of such GPCRs. We propose that highly expressed GPCRs in cancer cells (for example, GPRC5A in PDAC and colon cancer cells and GPR68 in PDAC CAFs) may contribute to the malignant phenotype, serve as biomarkers and/or may be novel therapeutic targets for the treatment of cancer.

Guanosine tetra- and pentaphosphate increase antibiotic tolerance by reducing reactive oxygen species production in Vibrio cholerae

The pathogen Vibrio cholerae is the causative agent of cholera. Emergence of antibiotic-resistant V. cholerae strains is increasing, but the underlying mechanisms remain unclear. Herein, we report that the stringent response regulator and stress alarmone guanosine tetra- and pentaphosphate ((p)ppGpp) significantly contributes to antibiotic tolerance in V. cholerae We found that N16961, a pandemic V. cholerae strain, and its isogenic (p)ppGpp-overexpressing mutant ΔrelAΔspoT are both more antibiotic-resistant than (p)ppGpp⁰ (ΔrelAΔrelVΔspoT) and ΔdksA mutants, which cannot produce or utilize (p)ppGpp, respectively. We also found that additional disruption of the aconitase B-encoding and tricarboxylic acid (TCA) cycle gene acnB in the (p)ppGpp⁰ mutant increases its antibiotic tolerance. Moreover, expression of TCA cycle genes, including acnB, was increased in (p)ppGpp⁰, but not in the antibiotic-resistant ΔrelAΔspoT mutant, suggesting that (p)ppGpp suppresses TCA cycle activity, thereby entailing antibiotic resistance. Importantly, when grown anaerobically or incubated with an iron chelator, the (p)ppGpp⁰ mutant became antibiotic-tolerant, suggesting that reactive oxygen species (ROS) are involved in antibiotic-mediated bacterial killing. Consistent with that hypothesis, tetracycline treatment markedly increased ROS production in the antibiotic-susceptible mutants. Interestingly, expression of the Fe(III) ABC transporter substrate-binding protein FbpA was increased 10-fold in (p)ppGpp⁰, and fbpA gene deletion restored viability of tetracycline-exposed (p)ppGpp⁰ cells. Of note, FbpA expression was repressed in the (p)ppGpp-accumulating mutant, resulting in a reduction of intracellular free iron, required for the ROS-generating Fenton reaction. Our results indicate that (p)ppGpp-mediated suppression of central metabolism and iron uptake reduces antibiotic-induced oxidative stress in V. cholerae.

New extension software modules to enhance searching and display of transcriptome data in Tripal databases

Tripal is an open source software package for developing biological databases with a focus on genetic and genomic data. It consists of a set of core modules that deliver essential functions for loading and displaying data records and associated attributes including organisms, sequence features and genetic markers. Beyond the core modules, community members are encouraged to contribute extension modules to build on the Tripal core and to customize Tripal for individual community needs. To expand the utility of the Tripal software system, particularly for RNASeq data, we developed two new extension modules. Tripal Elasticsearch enables fast, scalable searching of the entire content of a Tripal site as well as the construction of customized advanced searches of specific data types. We demonstrate the use of this module for searching assembled transcripts by functional annotation. A second module, Tripal Analysis Expression, houses and displays records from gene expression assays such as RNA sequencing. This includes biological source materials (biomaterials), gene expression values and protocols used to generate the data. In the case of an RNASeq experiment, this would reflect the individual organisms and tissues used to produce sequencing libraries, the normalized gene expression values derived from the RNASeq data analysis and a description of the software or code used to generate the expression values. The module will load data from common flat file formats including standard NCBI Biosample XML. Data loading, display options and other configurations can be controlled by authorized users in the Drupal administrative backend. Both modules are open source, include usage documentation, and can be found in the Tripal organization’s GitHub repository.

Database URL: Tripal Elasticsearch module:https://github.com/tripal/tripal_elasticsearch

Tripal Analysis Expression module:https://github.com/tripal/tripal_analysis_expression

AP-2α and AP-2β cooperatively orchestrate homeobox gene expression during branchial arch patterning

The evolution of a hinged moveable jaw with variable morphology is considered a major factor behind the successful expansion of the vertebrates. DLX homeobox transcription factors are crucial for establishing the positional code that patterns the mandible, maxilla and intervening hinge domain, but how the genes encoding these proteins are regulated remains unclear. Herein, we demonstrate that the concerted action of the AP-2α and AP-2β transcription factors within the mouse neural crest is essential for jaw patterning. In the absence of these two proteins, the hinge domain is lost and there are alterations in the size and patterning of the jaws correlating with dysregulation of homeobox gene expression, with reduced levels of Emx, Msx and Dlx paralogs accompanied by an expansion of Six1 expression. Moreover, detailed analysis of morphological features and gene expression changes indicate significant overlap with various compound Dlx gene mutants. Together, these findings reveal that the AP-2 genes have a major function in mammalian neural crest development, influencing patterning of the craniofacial skeleton via the DLX code, an effect that has implications for vertebrate facial evolution, as well as for human craniofacial disorders.

CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates

BACKGROUND

In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis.

RESULTS

We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.

CONCLUSIONS

Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .

Complete sequence-based pathway analysis by differential on-chip DNA and RNA extraction from a single cell

We demonstrate on-chip, differential DNA and RNA extraction from a single cell using a microfluidic chip and a two-stage lysis protocol. This method enables direct use of the whole extract, without additional washing steps, reducing sample loss. Using this method, the tumor driving pathway in individual cells from a colorectal cancer cell line was determined by applying a Bayesian computational pathway model to sequences obtained from the RNA fraction of a single cell and, the mutations driving the pathway were determined by analyzing sequences obtained from the DNA fraction of the same single cell. This combined functional and mutational pathway assessment of a single cell could be of significant value for dissecting cellular heterogeneity in tumors and analyzing single circulating tumor cells.

Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

RNA-sequencing (RNA-seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA-seq. Here we conduct an extensive study analysing a broad spectrum of RNA-seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant-calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in ~120 combinations, and ~490 analyses involving 15 samples with a variety of germline, cancer and stem cell data sets. We report the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy. Validation on different samples reveals that our proposed protocol could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.

RNA-seq is widely used for transcriptome analysis. Here, the authors analyse a wide spectrum of RNA-seq workflows and present a comprehensive analysis protocol named RNACocktail as well as a computational pipeline leveraging the widely used tools for accurate RNA-seq analysis.

A Four-Biomarker Blood Signature Discriminates Systemic Inflammation Due to Viral Infection Versus Other Etiologies

The innate immune system of humans and other mammals responds to pathogen-associated molecular patterns (PAMPs) that are conserved across broad classes of infectious agents such as bacteria and viruses. We hypothesized that a blood-based transcriptional signature could be discovered indicating a host systemic response to viral infection. Previous work identified host transcriptional signatures to individual viruses including influenza, respiratory syncytial virus and dengue, but the generality of these signatures across all viral infection types has not been established. Based on 44 publicly available datasets and two clinical studies of our own design, we discovered and validated a four-gene expression signature in whole blood, indicative of a general host systemic response to many types of viral infection. The signature’s genes are: Interferon Stimulated Gene 15 (ISG15), Interleukin 16 (IL16), 2′,5′-Oligoadenylate Synthetase Like (OASL), and Adhesion G Protein Coupled Receptor E5 (ADGRE5). In each of 13 validation datasets encompassing human, macaque, chimpanzee, pig, mouse, rat and all seven Baltimore virus classification groups, the signature provides statistically significant (p < 0.05) discrimination between viral and non-viral conditions. The signature may have clinical utility for differentiating host systemic inflammation (SI) due to viral versus bacterial or non-infectious causes.

Comprehensive evaluation of RNA-seq quantification methods for linearity

BACKGROUND

Deconvolution is a mathematical process of resolving an observed function into its constituent elements. In the field of biomedical research, deconvolution analysis is applied to obtain single cell-type or tissue specific signatures from a mixed signal and most of them follow the linearity assumption. Although recent development of next generation sequencing technology suggests RNA-seq as a fast and accurate method for obtaining transcriptomic profiles, few studies have been conducted to investigate best RNA-seq quantification methods that yield the optimum linear space for deconvolution analysis.

RESULTS

Using a benchmark RNA-seq dataset, we investigated the linearity of abundance estimated from seven most popular RNA-seq quantification methods both at the gene and isoform levels. Linearity is evaluated through parameter estimation, concordance analysis and residual analysis based on a multiple linear regression model. Results show that count data gives poor parameter estimations, large intercepts and high inter-sample variability; while TPM value from Kallisto and Salmon shows high linearity in all analyses.

CONCLUSIONS

Salmon and Kallisto TPM data gives the best fit to the linear model studied. This suggests that TPM values estimated from Salmon and Kallisto are the ideal RNA-seq measurements for deconvolution studies.

Properties of human disease genes and the role of genes linked to Mendelian disorders in complex disease aetiology

Do genes presenting variation that has been linked to human disease have different biological properties than genes that have never been related to disease? What is the relationship between disease and fitness? Are the evolutionary pressures that affect genes linked to Mendelian diseases the same to those acting on genes whose variation contributes to complex disorders? The answers to these questions could shed light on the architecture of human genetic disorders and may have relevant implications when designing mapping strategies in future genetic studies. Here we show that, relative to non-disease genes, human disease (HD) genes have specific evolutionary profiles and protein network properties. Additionally, our results indicate that the mutation-selection balance renders an insufficient account of the evolutionary history of some HD genes and that adaptive selection could also contribute to shape their genetic architecture. Notably, several biological features of HD genes depend on the type of pathology (complex or Mendelian) with which they are related. For example, genes harbouring both causal variants for Mendelian disorders and risk factors for complex disease traits (Complex-Mendelian genes), tend to present higher functional relevance in the protein network and higher expression levels than genes associated only with complex disorders. Moreover, risk variants in Complex-Mendelian genes tend to present higher odds ratios than those on genes associated with the same complex disorders but with no link to Mendelian diseases. Taken together, our results suggest that genetic variation at genes linked to Mendelian disorders plays an important role in driving susceptibility to complex disease.

Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq

BACKGROUND

RNA-Seq has supplanted microarrays as the preferred method of transcriptome-wide identification of differentially expressed genes. However, RNA-Seq analysis is still rapidly evolving, with a large number of tools available for each of the three major processing steps: read alignment, expression modeling, and identification of differentially expressed genes. Although some studies have benchmarked these tools against gold standard gene expression sets, few have evaluated their performance in concert with one another. Additionally, there is a general lack of testing of such tools on real-world, physiologically relevant datasets, which often possess qualities not reflected in tightly controlled reference RNA samples or synthetic datasets.

RESULTS

Here, we evaluate 219 combinatorial implementations of the most commonly used analysis tools for their impact on differential gene expression analysis by RNA-Seq. A test dataset was generated using highly purified human classical and nonclassical monocyte subsets from a clinical cohort, allowing us to evaluate the performance of 495 unique workflows, when accounting for differences in expression units and gene- versus transcript-level estimation. We find that the choice of methodologies leads to wide variation in the number of genes called significant, as well as in performance as gauged by precision and recall, calculated by comparing our RNA-Seq results to those from four previously published microarray and BeadChip analyses of the same cell populations. The method of differential gene expression identification exhibited the strongest impact on performance, with smaller impacts from the choice of read aligner and expression modeler. Many workflows were found to exhibit similar overall performance, but with differences in their calibration, with some biased toward higher precision and others toward higher recall.

CONCLUSIONS

There is significant heterogeneity in the performance of RNA-Seq workflows to identify differentially expressed genes. Among the higher performing workflows, different workflows exhibit a precision/recall tradeoff, and the ultimate choice of workflow should take into consideration how the results will be used in subsequent applications. Our analyses highlight the performance characteristics of these workflows, and the data generated in this study could also serve as a useful resource for future development of software for RNA-Seq analysis.

The first international workshop on the epigenetics of osteoarthritis

Osteoarthritis (OA) is a major clinical problem across the world, in part due to the lack of disease-modifying drugs resulting, to a significant degree, from our incomplete understanding of the underlying molecular mechanisms of the disease. Emerging evidence points to a role of epigenetics in the pathogenesis of OA, but research in this area is still in its early stages. In order to summarize current knowledge and to facilitate the potential coordination of future research activities, the first international workshop on the epigenetics of OA was held in Amsterdam in October 2015. Recent findings on DNA methylation and hydroxymethylation, histone modifications, noncoding RNAs, and other epigenetic mechanisms were presented and discussed. The workshop demonstrated the advantage of bringing together those working in this nascent field and highlights from the event are summarized in this report in the form of summaries from invited speakers and organizers.

Selection and validation of reference genes for RT-qPCR analysis in potato under abiotic stress

BACKGROUND

Real-time quantitative PCR (RT-qPCR) is the most commonly used method for accurately detecting gene expression patterns. As part of RT-qPCR analysis, normalization of the data requires internal control gene(s) that display uniform expression under different biological conditions. However, no invariable internal control gene exists, and therefore more than one reference gene is needed to normalize RT-qPCR results. Identification of stable reference genes in potato will improve assay accuracy for selecting stress-tolerance genes and identifying molecular mechanisms conferring stress tolerance in this species.

RESULTS

In the experiment, we assessed the expression of eight candidate internal control genes, namely elongation factor-1alpha (EF1α), actin, tubulin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), adenine phosphoribosyl transferase (APRT), 60S ribosomal protein L8 (L8), Cullin 3A (CUL3A), and exocyst complex component sec3 (sec3), in a diverse set of potato samples representing drought stress and osmotic stress challenges, and using geNorm, NormFinder, BestKeeper and RefFinder softwares.

CONCLUSIONS

The results indicated that EF1α and sec3 were the most stably expressed genes in the potato under drought and osmotic stress conditions. This work will facilitate future work on gene expression studies in potato and also benefit other species of the Solanaceae, such as tomato.

Simulation-based comprehensive benchmarking of RNA-seq aligners

Alignment is the first step in most RNA-seq analysis pipelines, and the accuracy of downstream analyses depends heavily on it. Unlike most steps in the pipeline, alignment is particularly amenable to benchmarking with simulated data. We performed a comprehensive benchmarking of 14 common splice-aware aligners for base, read, and exon junction-level accuracy and compared default with optimized parameters. We found that performance varied by genome complexity, and accuracy and popularity were poorly correlated. The most widely cited tool underperforms for most metrics, particularly when using default settings.

p53 Specifically Binds Triplex DNA In Vitro and in Cells

Triplex DNA is implicated in a wide range of biological activities, including regulation of gene expression and genomic instability leading to cancer. The tumor suppressor p53 is a central regulator of cell fate in response to different type of insults. Sequence and structure specific modes of DNA recognition are core attributes of the p53 protein. The focus of this work is the structure-specific binding of p53 to DNA containing triplex-forming sequences in vitro and in cells and the effect on p53-driven transcription. This is the first DNA binding study of full-length p53 and its deletion variants to both intermolecular and intramolecular T.A.T triplexes. We demonstrate that the interaction of p53 with intermolecular T.A.T triplex is comparable to the recognition of CTG-hairpin non-B DNA structure. Using deletion mutants we determined the C-terminal DNA binding domain of p53 to be crucial for triplex recognition. Furthermore, strong p53 recognition of intramolecular T.A.T triplexes (H-DNA), stabilized by negative superhelicity in plasmid DNA, was detected by competition and immunoprecipitation experiments, and visualized by AFM. Moreover, chromatin immunoprecipitation revealed p53 binding T.A.T forming sequence in vivo. Enhanced reporter transactivation by p53 on insertion of triplex forming sequence into plasmid with p53 consensus sequence was observed by luciferase reporter assays. In-silico scan of human regulatory regions for the simultaneous presence of both consensus sequence and T.A.T motifs identified a set of candidate p53 target genes and p53-dependent activation of several of them (ABCG5, ENOX1, INSR, MCC, NFAT5) was confirmed by RT-qPCR. Our results show that T.A.T triplex comprises a new class of p53 binding sites targeted by p53 in a DNA structure-dependent mode in vitro and in cells. The contribution of p53 DNA structure-dependent binding to the regulation of transcription is discussed.

Single-Cell Transcriptomics Bioinformatics and Computational Challenges

The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.

Here We Are, But Where Do We Go? A Systematic Review of Crustacean Transcriptomic Studies from 2014-2015

Despite their economic, ecological, and experimental importance, genomic resources remain scarce for crustaceans. In lieu of genomes, many researchers have taken advantage of technological advancements to instead sequence and assemble crustacean transcriptomes de novo However, there is little consensus on what standard operating procedures are, or should be, for the field. Here, we systematically reviewed 53 studies published during 2014-2015 that utilized transcriptomic resources from this taxonomic group in an effort to identify commonalities as well as potential weaknesses that have applicability beyond just crustaceans. In general, these studies utilized RNA-Seq data, both novel and publicly available, to characterize transcriptomes and/or identify differentially expressed genes (DEGs) between treatments. Although the software suite Trinity was popular in assembly pipelines and other programs were also commonly employed, many studies failed to report crucial details regarding bioinformatic methodologies, including read mappers and the utilized parameters in identifying and characterizing DEGs. Annotation percentages for assembled transcriptomic contigs were low, averaging 32% overall. While other metrics, such as numbers of contigs and DEGs reported, correlated with the number of sequence reads utilized per sample, these did reach apparent saturation with increasing sequencing depth. Most disturbingly, a number of studies (55%) reported DEGs based on non-replicated experimental designs and single biological replicates for each treatment. Given this, we suggest future RNA-Seq experiments targeting transcriptome characterization conduct deeper (i.e., 50-100 M reads) sequencing while those examining differential expression instead focus more on increased biological replicates at shallower (i.e., ∼10-20 M reads/sample) sequencing depths. Moreover, the community must avoid submitting for review, or accepting for publication, non-replicated differential expression studies. Finally, mining the ever growing publicly available transcriptomic data from crustaceans will allow future studies to focus on hypothesis-driven research instead of continuing to simply characterize transcriptomes. As an example of this, we utilized neurotoxin sequences from the recently described remipede venom gland transcriptome in conjunction with publicly available crustacean transcriptomic data to derive preliminary results and hypotheses regarding the evolution of venom in crustaceans.

From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.

From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data. Rigorous standardization and extensive validation are required to guarantee reliability, reproducibility and comparability of research findings. Hypotheses based on flawed experimental conditions can be inconsistent and even misleading. Comparable to the well-established MIQE guidelines for qPCR experiments, this work aims at establishing guidelines for experimental design and pre-analytical sample processing, standardization of library preparation and sequencing reactions, as well as facilitating data analysis. We highlight bottlenecks in small RNA-Seq experiments, point out the importance of stringent quality control and validation, and provide a primer for differential expression analysis and biomarker discovery. Following our recommendations will encourage better sequencing practice, increase experimental transparency and lead to more reproducible small RNA-Seq results. This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions.

The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data

The quantification of transcriptomic features is the basis of the analysis of RNA-seq data. We present an integrated alignment workflow and a simple counting-based approach to derive estimates for gene, exon and exon–exon junction expression. In contrast to previous counting-based approaches, EQP takes into account only reads whose alignment pattern agrees with the splicing pattern of the features of interest. This leads to improved gene expression estimates as well as to the generation of exon counts that allow disambiguating reads between overlapping exons. Unlike other methods that quantify skipped introns, EQP offers a novel way to compute junction counts based on the agreement of the read alignments with the exons on both sides of the junction, thus providing a uniformly derived set of counts. We evaluated the performance of EQP on both simulated and real Illumina RNA-seq data and compared it with other quantification tools. Our results suggest that EQP provides superior gene expression estimates and we illustrate the advantages of EQP's exon and junction counts. The provision of uniformly derived high-quality counts makes EQP an ideal quantification tool for differential expression and differential splicing studies. EQP is freely available for download at https://github.com/Novartis/EQP-cluster.

miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis

Large-scale RNAseq has substantially changed the transcriptomics field, as it enables an unprecedented amount of high resolution data to be acquired. However, the analysis of these data still poses a challenge to the research community. Many tools have been developed to overcome this problem, and to facilitate the study of miRNA expression profiles and those of their target genes. While a few of these enable both kinds of analysis to be performed, they also present certain limitations in terms of their requirements and/or the restrictions on data uploading. To avoid these restraints, we have developed a suite that offers the identification of miRNA, mRNA and circRNAs that can be applied to any sequenced organism. Additionally, it enables differential expression, miRNA-mRNA target prediction and/or functional analysis. The miARma-Seq pipeline is presented as a stand-alone tool that is both easy to install and flexible in terms of its use, and that brings together well-established software in a single bundle. Our suite can analyze a large number of samples due to its multithread design. By testing miARma-Seq in validated datasets, we demonstrate here the benefits that can be gained from this tool by making it readily accessible to the research community.

MECHANISMS IN ENDOCRINOLOGY: Alternative splicing: the new frontier in diabetes research

Type 1 diabetes (T1D) is a chronic autoimmune disease in which pancreatic β cells are killed by infiltrating immune cells and by cytokines released by these cells. This takes place in the context of a dysregulated dialogue between invading immune cells and target β cells, but the intracellular signals that decide β cell fate remain to be clarified. Alternative splicing (AS) is a complex post-transcriptional regulatory mechanism affecting gene expression. It regulates the inclusion/exclusion of exons into mature mRNAs, allowing individual genes to produce multiple protein isoforms that expand the proteome diversity. Functionally related transcript populations are co-ordinately spliced by master splicing factors, defining regulatory networks that allow cells to rapidly adapt their transcriptome in response to intra and extracellular cues. There is a growing interest in the role of AS in autoimmune diseases, but little is known regarding its role in T1D. In this review, we discuss recent findings suggesting that splicing events occurring in both immune and pancreatic β cells contribute to the pathogenesis of T1D. Splicing switches in T cells and in lymph node stromal cells are involved in the modulation of the immune response against β cells, while β cells exposed to pro-inflammatory cytokines activate complex splicing networks that modulate β cell viability, expression of neoantigens and susceptibility to immune-induced stress. Unveiling the role of AS in β cell functional loss and death will increase our understanding of T1D pathogenesis and may open new avenues for disease prevention and therapy.

Codon-Driven Translational Efficiency Is Stable across Diverse Mammalian Cell States

Whether codon usage fine-tunes mRNA translation in mammals remains controversial, with recent papers suggesting that production of proteins in specific Gene Ontological (GO) pathways can be regulated by actively modifying the codon and anticodon pools in different cellular conditions. In this work, we compared the sequence content of genes in specific GO categories with the exonic genome background. Although a substantial fraction of variability in codon usage could be explained by random sampling, almost half of GO sets showed more variability in codon usage than expected by chance. Nevertheless, by quantifying translational efficiency in healthy and cancerous tissues in human and mouse, we demonstrated that a given tRNA pool can equally well translate many different sets of mRNAs, irrespective of their cell-type specificity. This disconnect between variations in codon usage and the stability of translational efficiency is best explained by differences in GC content between gene sets. GC variation across the mammalian genome is most likely a result of the interplay between genome repair and gene duplication mechanisms, rather than selective pressures caused by codon-driven translational rates. Consequently, codon usage differences in mammalian transcriptomes are most easily explained by well-understood mutational biases acting on the underlying genome.

The potential role of PHF6 as an oncogene: a genotranscriptomic/proteomic meta-analysis

Epigenetic complexes control various pathways within the cells. Their abnormalities can be involved in the initiation and the progression of different types of cancer. Nucleosome remodeling and deacetylase (NuRD) is an epigenetic complex that comprises several subunits such as PHF6. Although PHF6 is reported as a tumor suppressor in some of the hematopoietic malignancies, its function is still challenging in other cancers. Our study aimed at investigating the role of PHF6 in different types of cancer. We conducted a meta-analysis of PHF6 in human cancers at genomic, transcriptomic, and proteomic levels. For this purpose, we acquired the data from several databases, and tried to statistically integrate and analyze the data in order to find the potential role of PHF6 in different tumors. The results demonstrated that although PHF6 has been previously known as a tumor suppressor gene, it was remarkably overexpressed in many cancer types such as breast and colorectal cancers. Notably, PHF6 was under-expressed in a few types of cancer, including esophageal tumors. Moreover, the results indicated that although the mutation rate of PHF6 is relatively low, it is mutated in some tumor types.  In addition, our data for 40 epigenetic genes showed that missense and nonsense mutations were associated with overexpression and under-expression, respectively. Our results suggest that PHF6 may function as an oncogenic factor in several types of cancer. We also hypothesize that PHF6 may also play its role in a tissue-specific manner. Our findings suggest further investigations regarding the exact role of PHF6 in tumor types.

Classification of low quality cells from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.

Transcriptome Analysis in Domesticated Species: Challenges and Strategies

Domesticated species occupy a special place in the human world due to their economic and cultural value. In the era of genomic research, domesticated species provide unique advantages for investigation of diseases and complex phenotypes. RNA sequencing, or RNA-seq, has recently emerged as a new approach for studying transcriptional activity of the whole genome, changing the focus from individual genes to gene networks. RNA-seq analysis in domesticated species may complement genome-wide association studies of complex traits with economic importance or direct relevance to biomedical research. However, RNA-seq studies are more challenging in domesticated species than in model organisms. These challenges are at least in part associated with the lack of quality genome assemblies for some domesticated species and the absence of genome assemblies for others. In this review, we discuss strategies for analyzing RNA-seq data, focusing particularly on questions and examples relevant to domesticated species.

MERAV: a tool for comparing gene expression across human tissues and cell types

The oncogenic transformation of normal cells into malignant, rapidly proliferating cells requires major alterations in cell physiology. For example, the transformed cells remodel their metabolic processes to supply the additional demand for cellular building blocks. We have recently demonstrated essential metabolic processes in tumor progression through the development of a methodological analysis of gene expression. Here, we present the Metabolic gEne RApid Visualizer (MERAV, http://merav.wi.mit.edu), a web-based tool that can query a database comprising ∼4300 microarrays, representing human gene expression in normal tissues, cancer cell lines and primary tumors. MERAV has been designed as a powerful tool for whole genome analysis which offers multiple advantages: one can search many genes in parallel; compare gene expression among different tissue types as well as between normal and cancer cells; download raw data; and generate heatmaps; and finally, use its internal statistical tool. Most importantly, MERAV has been designed as a unique tool for analyzing metabolic processes as it includes matrixes specifically focused on metabolic genes and is linked to the Kyoto Encyclopedia of Genes and Genomes pathway search.

ChIP-Seq Data Analysis to Define Transcriptional Regulatory Networks

The first step in the definition of transcriptional regulatory networks is to establish correct relationships between transcription factors (TFs) and their target genes, together with the effect of their regulatory activity (activator or repressor). Fundamental advances in this direction have been made possible by the introduction of experimental techniques such as Chromatin Immunoprecipitation, which, coupled with next-generation sequencing technologies (ChIP-Seq), permit the genome-wide identification of TF binding sites. This chapter provides a survey on how data of this kind are to be processed and integrated with expression and other types of data to infer transcriptional regulatory rules and codes.

B-Cell and Monocyte Contribution to Systemic Lupus Erythematosus Identified by Cell-Type-Specific Differential Expression Analysis in RNA-Seq Data

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for cell-type-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE.