Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)

Gibberellin Signaling Requires Chromatin Remodeler PICKLE to Promote Vegetative Growth and Phase Transitions

PICKLE (PKL) is an ATP-dependent chromodomain-helicase-DNA-binding domain (CHD3) chromatin remodeling enzyme in Arabidopsis (Arabidopsis thaliana). Previous studies showed that PKL promotes embryonic-to-vegetative transition by inhibiting expression of seed-specific genes during seed germination. The pkl mutants display a low penetrance of the "pickle root" phenotype, with a thick and green primary root that retains embryonic characteristics. The penetrance of this pickle root phenotype in pkl is dramatically increased in gibberellin (GA)-deficient conditions. At adult stages, the pkl mutants are semidwarfs with delayed flowering time, which resemble reduced GA-signaling mutants. These findings suggest that PKL may play a positive role in regulating GA signaling. A recent biochemical analysis further showed that PKL and GA signaling repressors DELLAs antagonistically regulate hypocotyl cell elongation genes by direct protein-protein interaction. To elucidate further the role of PKL in GA signaling and plant development, we studied the genetic interaction between PKL and DELLAs using the hextuple mutant containing pkl and della pentuple (dP) mutations. Here, we show that PKL is required for most of GA-promoted developmental processes, including vegetative growth such as hypocotyl, leaf, and inflorescence stem elongation, and phase transitions such as juvenile-to-adult leaf and vegetative-to-reproductive phase. The removal of all DELLA functions (in the dP background) cannot rescue these phenotypes in pkl RNA-sequencing analysis using the ga1 (a GA-deficient mutant), pkl, and the ga1 pkl double mutant further shows that expression of 80% of GA-responsive genes in seedlings is PKL dependent, including genes that function in cell elongation, cell division, and phase transitions. These results indicate that the CHD3 chromatin remodeler PKL is required for regulating gene expression during most of GA-regulated developmental processes.

Single cell transcriptomic profiling of mouse pancreatic progenitors

The heterogeneity of the developing pancreatic epithelium and low abundance of endocrine progenitors limit the information derived from traditional expression studies. To identify genes that characterize early developmental tissues composed of multiple progenitor lineages, we applied single-cell RNA-Seq to embryonic day (e)13.5 mouse pancreata and performed integrative analysis with single cell data from mature pancreas. We identified subpopulations expressing macrophage or endothelial markers and new pancreatic progenitor markers. We also identified potential α-cell precursors expressing glucagon (Gcg) among the e13.5 pancreatic cells. Despite their high Gcg expression levels, these cells shared greater transcriptomic similarity with other e13.5 cells than with adult α-cells, indicating their immaturity. Comparative analysis identified the sodium-dependent neutral amino acid transporter, Slc38a5, as a characteristic gene expressed in α-cell precursors but not mature cells. By immunofluorescence analysis, we observed SLC38A5 expression in pancreatic progenitors, including in a subset of NEUROG3+ endocrine progenitors and MAFB+ cells and in all GCG+ cells. Expression declined in α-cells during late gestation and was absent in the adult islet. Our results suggest SLC38A5 as an early marker of α-cell lineage commitment.

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.

LIM domain-binding 1 maintains the terminally differentiated state of pancreatic β cells

The recognition of β cell dedifferentiation in type 2 diabetes raises the translational relevance of mechanisms that direct and maintain β cell identity. LIM domain-binding protein 1 (LDB1) nucleates multimeric transcriptional complexes and establishes promoter-enhancer looping, thereby directing fate assignment and maturation of progenitor populations. Many terminally differentiated endocrine cell types, however, remain enriched for LDB1, but its role is unknown. Here, we have demonstrated a requirement for LDB1 in maintaining the terminally differentiated status of pancreatic β cells. Inducible ablation of LDB1 in mature β cells impaired insulin secretion and glucose homeostasis. Transcriptomic analysis of LDB1-depleted β cells revealed the collapse of the terminally differentiated gene program, indicated by a loss of β cell identity genes and induction of the endocrine progenitor factor neurogenin 3 (NEUROG3). Lineage tracing confirmed that LDB1-depleted, insulin-negative β cells express NEUROG3 but do not adopt alternate endocrine cell fates. In primary mouse islets, LDB1 and its LIM homeodomain-binding partner islet 1 (ISL1) were coenriched at chromatin sites occupied by pancreatic and duodenal homeobox 1 (PDX1), NK6 homeobox 1 (NKX6.1), forkhead box A2 (FOXA2), and NK2 homeobox 2 (NKX2.2) - factors that co-occupy active enhancers in 3D chromatin domains in human islets. Indeed, LDB1 was enriched at active enhancers in human islets. Thus, LDB1 maintains the terminally differentiated state of β cells and is a component of active enhancers in both murine and human islets.

A high resolution map of the Arabidopsis thaliana developmental transcriptome based on RNA-seq profiling

Arabidopsis thaliana is a long established model species for plant molecular biology, genetics and genomics, and studies of A. thaliana gene function provide the basis for formulating hypotheses and designing experiments involving other plants, including economically important species. A comprehensive understanding of the A. thaliana genome and a detailed and accurate understanding of the expression of its associated genes is therefore of great importance for both fundamental research and practical applications. Such goal is reliant on the development of new genetic and genomic resources, involving new methods of data acquisition and analysis. We present here the genome-wide analysis of A. thaliana gene expression profiles across different organs and developmental stages using high-throughput transcriptome sequencing. The expression of 25 706 protein-coding genes, as well as their stability and their spatiotemporal specificity, was assessed in 79 organs and developmental stages. A search for alternative splicing events identified 37 873 previously unreported splice junctions, approximately 30% of them occurred in intergenic regions. These potentially represent novel spliced genes that are not included in the TAIR10 database. These data are housed in an open-access web-based database, TraVA (Transcriptome Variation Analysis, http://travadb.org/), which allows visualization and analysis of gene expression profiles and differential gene expression between organs and developmental stages.

Hyperglycaemia induces metabolic dysfunction and glycogen accumulation in pancreatic β-cells

Insulin secretion from pancreatic β-cells is impaired in all forms of diabetes. The resultant hyperglycaemia has deleterious effects on many tissues, including β-cells. Here we show that chronic hyperglycaemia impairs glucose metabolism and alters expression of metabolic genes in pancreatic islets. In a mouse model of human neonatal diabetes, hyperglycaemia results in marked glycogen accumulation, and increased apoptosis in β-cells. Sulphonylurea therapy rapidly normalizes blood glucose levels, dissipates glycogen stores, increases autophagy and restores β-cell metabolism. Insulin therapy has the same effect but with slower kinetics. Similar changes are observed in mice expressing an activating glucokinase mutation, in in vitro models of hyperglycaemia, and in islets from type-2 diabetic patients. Altered β-cell metabolism may underlie both the progressive impairment of insulin secretion and reduced β-cell mass in diabetes.

Single-Cell Transcriptomics of the Human Endocrine Pancreas

Human pancreatic islets consist of multiple endocrine cell types. To facilitate the detection of rare cellular states and uncover population heterogeneity, we performed single-cell RNA sequencing (RNA-seq) on islets from multiple deceased organ donors, including children, healthy adults, and individuals with type 1 or type 2 diabetes. We developed a robust computational biology framework for cell type annotation. Using this framework, we show that α- and β-cells from children exhibit less well-defined gene signatures than those in adults. Remarkably, α- and β-cells from donors with type 2 diabetes have expression profiles with features seen in children, indicating a partial dedifferentiation process. We also examined a naturally proliferating α-cell from a healthy adult, for which pathway analysis indicated activation of the cell cycle and repression of checkpoint control pathways. Importantly, this replicating α-cell exhibited activated Sonic hedgehog signaling, a pathway not previously known to contribute to human α-cell proliferation. Our study highlights the power of single-cell RNA-seq and provides a stepping stone for future explorations of cellular heterogeneity in pancreatic endocrine cells.

Web-based bioinformatics workflows for end-to-end RNA-seq data computation and analysis in agricultural animal species

Background

Remarkable advances in Next Generation Sequencing (NGS) technologies, bioinformatics algorithms and computational technologies have significantly accelerated genomic research. However, complicated NGS data analysis still remains as a major bottleneck. RNA-seq, as one of the major area in the NGS field, also confronts great challenges in data analysis.

Results

To address the challenges in RNA-seq data analysis, we developed a web portal that offers three integrated workflows that can perform end-to-end compute and analysis, including sequence quality control, read-mapping, transcriptome assembly, reconstruction and quantification, and differential analysis. The first workflow utilizes Tuxedo (Tophat, Cufflink, Cuffmerge and Cuffdiff suite of tools). The second workflow deploys Trinity for de novo assembly and uses RSEM for transcript quantification and EdgeR for differential analysis. The third combines STAR, RSEM, and EdgeR for data analysis. All these workflows support multiple samples and multiple groups of samples and perform differential analysis between groups in a single workflow job submission. The calculated results are available for download and post-analysis. The supported animal species include chicken, cow, duck, goat, pig, horse, rabbit, sheep, turkey, as well as several other model organisms including yeast, C. elegans, Drosophila, and human, with genomic sequences and annotations obtained from ENSEMBL.

The RNA-seq portal is freely available from http://weizhongli-lab.org/RNA-seq.

Conclusions

The web portal offers not only bioinformatics software, workflows, computation and reference data, but also an integrated environment for complex RNA-seq data analysis for agricultural animal species. In this project, our aim is not to develop new RNA-seq tools, but to build web workflows for using popular existing RNA-seq methods and make these tools more accessible to the communities.

RNA sequencing from neural ensembles activated during fear conditioning in the mouse temporal association cortex

The stable formation of remote fear memories is thought to require neuronal gene induction in cortical ensembles that are activated during learning. However, the set of genes expressed specifically in these activated ensembles is not known; knowledge of such transcriptional profiles may offer insights into the molecular program underlying stable memory formation. Here we use RNA-Seq to identify genes whose expression is enriched in activated cortical ensembles labeled during associative fear learning. We first establish that mouse temporal association cortex (TeA) is required for remote recall of auditory fear memories. We then perform RNA-Seq in TeA neurons that are labeled by the activity reporter Arc-dVenus during learning. We identify 944 genes with enriched expression in Arc-dVenus+ neurons. These genes include markers of L2/3, L5b, and L6 excitatory neurons but not glial or inhibitory markers, confirming Arc-dVenus to be an excitatory neuron-specific but non-layer-specific activity reporter. Cross comparisons to other transcriptional profiles show that 125 of the enriched genes are also activity-regulated in vitro or induced by visual stimulus in the visual cortex, suggesting that they may be induced generally in the cortex in an experience-dependent fashion. Prominent among the enriched genes are those encoding potassium channels that down-regulate neuronal activity, suggesting the possibility that part of the molecular program induced by fear conditioning may initiate homeostatic plasticity.

A Role for Bradykinin Signaling in Chronic Vulvar Pain

Chronic vulvar pain is alarmingly common in women of reproductive age and is often accompanied by psychological distress, sexual dysfunction, and a significant reduction in quality of life. Localized provoked vulvodynia (LPV) is associated with intense vulvar pain concentrated in the vulvar vestibule (area surrounding vaginal opening). To date, the origins of vulvodynia are poorly understood, and treatment for LPV manages pain symptoms, but does not resolve the root causes of disease. Until recently, no definitive disease mechanisms had been identified; our work indicates LPV has inflammatory origins, although additional studies are needed to understand LPV pain. Bradykinin signaling is one of the most potent inducers of inflammatory pain and is a candidate contributor to LPV. We report that bradykinin receptors are expressed at elevated levels in LPV patient versus healthy control vestibular fibroblasts, and patient vestibular fibroblasts produce elevated levels of proinflammatory mediators with bradykinin stimulation. Inhibiting expression of one or both bradykinin receptors significantly reduces proinflammatory mediator production. Finally, we determined that bradykinin activates nuclear factor (NF)κB signaling (a major inflammatory pathway), whereas inhibition of NFκB successfully ablates this response. These data suggest that therapeutic agents targeting bradykinin sensing and/or NFκB may represent new, more specific options for LPV therapy.

PERSPECTIVE

There is an unmet need for the development of more effective vulvodynia therapies. As we explore the mechanisms by which human vulvar fibroblasts respond to proinflammatory/propain stimuli, we move closer to understanding the origins of chronic vulvar pain and identifying new therapeutic targets, knowledge that could significantly improve patient care.

Targeting ACLY sensitizes castration-resistant prostate cancer cells to AR antagonism by impinging on an ACLY-AMPK-AR feedback mechanism

The androgen receptor (AR) plays a central role in prostate tumor growth. Inappropriate reactivation of the AR after androgen deprivation therapy promotes development of incurable castration-resistant prostate cancer (CRPC). In this study, we provide evidence that metabolic features of prostate cancer cells can be exploited to sensitize CRPC cells to AR antagonism. We identify a feedback loop between ATP-citrate lyase (ACLY)-dependent fatty acid synthesis, AMPK, and the AR in prostate cancer cells that could contribute to therapeutic resistance by maintaining AR levels. When combined with an AR antagonist, ACLY inhibition in CRPC cells promotes energetic stress and AMPK activation, resulting in further suppression of AR levels and target gene expression, inhibition of proliferation, and apoptosis. Supplying exogenous fatty acids can restore energetic homeostasis; however, this rescue does not occur through increased β-oxidation to support mitochondrial ATP production. Instead, concurrent inhibition of ACLY and AR may drive excess ATP consumption as cells attempt to cope with endoplasmic reticulum (ER) stress, which is prevented by fatty acid supplementation. Thus, fatty acid metabolism plays a key role in coordinating ER and energetic homeostasis in CRPC cells, thereby sustaining AR action and promoting proliferation. Consistent with a role for fatty acid metabolism in sustaining AR levels in prostate cancer in vivo, AR mRNA levels in human prostate tumors correlate positively with expression of ACLY and other fatty acid synthesis genes. The ACLY-AMPK-AR network can be exploited to sensitize CRPC cells to AR antagonism, suggesting novel therapeutic opportunities for prostate cancer.

Human islets contain four distinct subtypes of β cells

Human pancreatic islets of Langerhans contain five distinct endocrine cell types, each producing a characteristic hormone. The dysfunction or loss of the insulin-producing β cells causes diabetes mellitus, a disease that harms millions. Until now, β cells were generally regarded as a single, homogenous cell population. Here we identify four antigenically distinct subtypes of human β cells, which we refer to as β1–4, and which are distinguished by differential expression of ST8SIA1 and CD9. These subpopulations are always present in normal adult islets and have diverse gene expression profiles and distinct basal and glucose-stimulated insulin secretion. Importantly, the β cell subtype distribution is profoundly altered in type 2 diabetes. These data suggest that this antigenically defined β cell heterogeneity is functionally and likely medically relevant.

Dysfunction or loss of insulin-secreting β cells in the pancreas is a hallmark of diabetes. Here, Dorrell et al. identify four subpopulations of β cells in humans, which differ in gene expression and insulin secretion kinetics, and the abundance of which is altered in patients with type 2 diabetes.

Innate immune defects in HIV permissive cell lines

Background

Primary CD4+ T cells and cell lines differ in their permissiveness to HIV infection. Impaired innate immunity may contribute to this different phenotype.

Findings

We used transcriptome profiling of 1503 innate immunity genes in primary CD4+ T cells and permissive cell lines. Two clusters of differentially expressed genes were identified: a set of 249 genes that were highly expressed in primary cells and minimally expressed in cell lines and a set of 110 genes with the opposite pattern. Specific to HIV, HEK293T, Jurkat, SupT1 and CEM cell lines displayed unique patterns of downregulation of genes involved in viral sensing and restriction. Activation of primary CD4+ T cells resulted in reversal of the pattern of expression of those sets of innate immunity genes. Functional analysis of prototypical innate immunity pathways of permissive cell lines confirmed impaired responses identified in transcriptome analyses.

Conclusion

Integrity of innate immunity genes and pathways needs to be considered in designing gain/loss functional genomic screens of viral infection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-016-0275-8) contains supplementary material, which is available to authorized users.

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.

Cis-regulatory architecture of a brain signaling center predates the origin of chordates

Genomic approaches have predicted hundreds of thousands of tissue-specific cis-regulatory sequences, but the determinants critical to their function and evolutionary history are mostly unknown. Here we systematically decode a set of brain enhancers active in the zona limitans intrathalamica (zli), a signaling center essential for vertebrate forebrain development via the secreted morphogen Sonic hedgehog (Shh). We apply a de novo motif analysis tool to identify six position-independent sequence motifs together with their cognate transcription factors that are essential for zli enhancer activity and Shh expression in the mouse embryo. Using knowledge of this regulatory lexicon, we discover new Shh zli enhancers in mice and a functionally equivalent element in hemichordates, indicating an ancient origin of the Shh zli regulatory network that predates the chordate phylum. These findings support a strategy for delineating functionally conserved enhancers in the absence of overt sequence homologies and over extensive evolutionary distances.

mTORC1 is necessary but mTORC2 and GSK3β are inhibitory for AKT3-induced axon regeneration in the central nervous system

Injured mature CNS axons do not regenerate in mammals. Deletion of PTEN, the negative regulator of PI3K, induces CNS axon regeneration through the activation of PI3K-mTOR signaling. We have conducted an extensive molecular dissection of the cross-regulating mechanisms in axon regeneration that involve the downstream effectors of PI3K, AKT and the two mTOR complexes (mTORC1 and mTORC2). We found that the predominant AKT isoform in CNS, AKT3, induces much more robust axon regeneration than AKT1 and that activation of mTORC1 and inhibition of GSK3β are two critical parallel pathways for AKT-induced axon regeneration. Surprisingly, phosphorylation of T308 and S473 of AKT play opposite roles in GSK3β phosphorylation and inhibition, by which mTORC2 and pAKT-S473 negatively regulate axon regeneration. Thus, our study revealed a complex neuron-intrinsic balancing mechanism involving AKT as the nodal point of PI3K, mTORC1/2 and GSK3β that coordinates both positive and negative cues to regulate adult CNS axon regeneration.

DOI:http://dx.doi.org/10.7554/eLife.14908.001

The central nervous system consists of the neurons that make up the brain and spinal cord. An important part of a neuron is the long, slender projection along which electrical signals travel, called the axon. In the central nervous system of mammals, damaged axons cannot regrow, which is why spinal injuries or optic nerve injuries can result in life-long neuronal deficits.

Recent studies have found that activating a particular signaling pathway in central nervous system neurons causes their axons to regenerate. A key protein in this pathway is called AKT. Several signaling cascades are triggered by AKT to regulate cell survival and growth, but it was not known how the different branches of the AKT pathway are involved in axon regeneration.

Miao, Yang et al. have now investigated AKT’s role in axon regeneration using a range of approaches to manipulate signaling in damaged mouse neurons. This revealed that a particular form of AKT (called AKT3) causes damaged axons to regenerate to a greater extent than other forms of this protein. This response depends on two parallel pathways: one in which AKT3 activates a protein complex called mTORC1, and one where AKT3 inhibits a protein called GSK3β. In addition, another protein complex called mTORC2, which is closely related to mTORC1, helps to inhibit the activity of AKT3 on GSK3β and hence inhibits axon regeneration.

These findings reveal that a complex balancing mechanism, with AKT at its center, coordinates the many signals that regulate axon regeneration. Future studies into this system could ultimately help to develop new treatments for brain and spinal injuries.

DOI:http://dx.doi.org/10.7554/eLife.14908.002

jSplice: a high-performance method for accurate prediction of alternative splicing events and its application to large-scale renal cancer transcriptome data

MOTIVATION

Alternative splicing represents a prime mechanism of post-transcriptional gene regulation whose misregulation is associated with a broad range of human diseases. Despite the vast availability of transcriptome data from different cell types and diseases, bioinformatics-based surveys of alternative splicing patterns remain a major challenge due to limited availability of analytical tools that combine high accuracy and rapidity.

RESULTS

We describe here a novel junction-centric method, jSplice, that enables de novo extraction of alternative splicing events from RNA-sequencing data with high accuracy, reliability and speed. Application to clear cell renal carcinoma (ccRCC) cell lines and 65 ccRCC patients revealed experimentally validatable alternative splicing changes and signatures able to prognosticate ccRCC outcome. In the aggregate, our results propose jSplice as a key analytic tool for the derivation of cell context-dependent alternative splicing patterns from large-scale RNA-sequencing datasets.

AVAILABILITY AND IMPLEMENTATION

jSplice is a standalone Python application freely available at http://www.mhs.biol.ethz.ch/research/krek/jsplice

CONTACT

wilhelm.krek@biol.ethz.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NCLscan: accurate identification of non-co-linear transcripts (fusion, trans-splicing and circular RNA) with a good balance between sensitivity and precision

Analysis of RNA-seq data often detects numerous 'non-co-linear' (NCL) transcripts, which comprised sequence segments that are topologically inconsistent with their corresponding DNA sequences in the reference genome. However, detection of NCL transcripts involves two major challenges: removal of false positives arising from alignment artifacts and discrimination between different types of NCL transcripts (trans-spliced, circular or fusion transcripts). Here, we developed a new NCL-transcript-detecting method ('NCLscan'), which utilized a stepwise alignment strategy to almost completely eliminate false calls (>98% precision) without sacrificing true positives, enabling NCLscan outperform 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. With the high accuracy, NCLscan was applied to distinguishing between trans-spliced, circular and fusion transcripts on the basis of poly(A)- and nonpoly(A)-selected RNA-seq data. We showed that circular RNAs were expressed more ubiquitously, more abundantly and less cell type-specifically than trans-spliced and fusion transcripts. Our study thus describes a robust pipeline for the discovery of NCL transcripts, and sheds light on the fundamental biology of these non-canonical RNA events in human transcriptome.

Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data

RNA-Seq made possible the global identification of fusion transcripts, i.e. "chimeric RNAs". Even though various software packages have been developed to serve this purpose, they behave differently in different datasets provided by different developers. It is important for both users, and developers to have an unbiased assessment of the performance of existing fusion detection tools. Toward this goal, we compared the performance of 12 well-known fusion detection software packages. We evaluated the sensitivity, false discovery rate, computing time, and memory usage of these tools in four different datasets (positive, negative, mixed, and test). We conclude that some tools are better than others in terms of sensitivity, positive prediction value, time consumption and memory usage. We also observed small overlaps of the fusions detected by different tools in the real dataset (test dataset). This could be due to false discoveries by various tools, but could also be due to the reason that none of the tools are inclusive. We have found that the performance of the tools depends on the quality, read length, and number of reads of the RNA-Seq data. We recommend that users choose the proper tools for their purpose based on the properties of their RNA-Seq data.

Highly sensitive and ultrafast read mapping for RNA-seq analysis

As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.

GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality

The programs GMAP and GSNAP, for aligning RNA-Seq and DNA-Seq datasets to genomes, have evolved along with advances in biological methodology to handle longer reads, larger volumes of data, and new types of biological assays. The genomic representation has been improved to include linear genomes that can compare sequences using single-instruction multiple-data (SIMD) instructions, compressed genomic hash tables with fast access using SIMD instructions, handling of large genomes with more than four billion bp, and enhanced suffix arrays (ESAs) with novel data structures for fast access. Improvements to the algorithms have included a greedy match-and-extend algorithm using suffix arrays, segment chaining using genomic hash tables, diagonalization using segmental hash tables, and nucleotide-level dynamic programming procedures that use SIMD instructions and eliminate the need for F-loop calculations. Enhancements to the functionality of the programs include standardization of indel positions, handling of ambiguous splicing, clipping and merging of overlapping paired-end reads, and alignments to circular chromosomes and alternate scaffolds. The programs have been adapted for use in pipelines by integrating their usage into R/Bioconductor packages such as gmapR and HTSeqGenie, and these pipelines have facilitated the discovery of numerous biological phenomena.

SpliceJumper: a classification-based approach for calling splicing junctions from RNA-seq data

BACKGROUND

Next-generation RNA sequencing technologies have been widely applied in transcriptome profiling. This facilitates further studies of gene structure and expression on the genome wide scale. It is an important step to align reads to the reference genome and call out splicing junctions for the following analysis, such as the analysis of alternative splicing and isoform construction. However, because of the existence of introns, when RNA-seq reads are aligned to the reference genome, reads can not be fully mapped at splicing sites. Thus, it is challenging to align reads and call out splicing junctions accurately.

RESULTS

In this paper, we present a classification based approach for calling splicing junctions from RNA-seq data, which is implemented in the program SpliceJumper. SpliceJumper uses a machine learning approach which combines multiple features extracted from RNA-seq data. We compare SpliceJumper with two existing RNA-seq analysis approaches, TopHat2 and MapSplice2, on both simulated and real data. Our results show that SpliceJumper outperforms TopHat2 and MapSplice2 in accuracy. The program SpliceJumper can be downloaded at https://github.com/Reedwarbler/SpliceJumper.

Designing alternative splicing RNA-seq studies. Beyond generic guidelines

MOTIVATION

Designing an RNA-seq study depends critically on its specific goals, technology and underlying biology, which renders general guidelines inadequate. We propose a Bayesian framework to customize experiments so that goals can be attained and resources are not wasted, with a focus on alternative splicing.

RESULTS

We studied how read length, sequencing depth, library preparation and the number of replicates affects cost-effectiveness of single-sample and group comparison studies. Optimal settings varied strongly according to the target organism or tissue (potential 50-500% cost cuts) and, interestingly, short reads outperformed long reads for standard analyses. Our framework learns key characteristics for study design from the data, and predicts if and how to continue experimentation. These predictions matched several follow-up experimental datasets that were used for validation. We provide default pipelines, but the framework can be combined with other data analysis methods and can help assess their relative merits.

AVAILABILITY AND IMPLEMENTATION

casper package at www.bioconductor.org/packages/release/bioc/html/casper.html, Supplementary Manual by typing casperDesign() at the R prompt.

CONTACT

rosselldavid@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

EBF2 promotes the recruitment of beige adipocytes in white adipose tissue

Objective

The induction of beige/brite adipose cells in white adipose tissue (WAT) is associated with protection against high fat diet-induced obesity and insulin resistance in animals. The helix-loop-helix transcription factor Early B-Cell Factor-2 (EBF2) regulates brown adipose tissue development. Here, we asked if EBF2 regulates beige fat cell biogenesis and protects animals against obesity.

Methods

In addition to primary cell culture studies, we used ​Ebf2 knockout mice and mice overexpressing EBF2 in the adipose tissue to study the necessity and sufficiency of EBF2 to induce beiging in vivo.

Results

We found that EBF2 is required for beige adipocyte development in mice. Subcutaneous WAT or primary adipose cell cultures from Ebf2 knockout mice did not induce Uncoupling Protein 1 (UCP1) or a thermogenic program following adrenergic stimulation. Conversely, over-expression of EBF2 in adipocyte cultures induced UCP1 expression and a brown-like/beige fat-selective differentiation program. Transgenic expression of Ebf2 in adipose tissues robustly stimulated beige adipocyte development in the WAT of mice, even while housed at thermoneutrality. EBF2 overexpression was sufficient to increase mitochondrial function in WAT and protect animals against high fat diet-induced weight gain.

Conclusions

Taken together, our results demonstrate that EBF2 controls the beiging process and suggest that activation of EBF2 in WAT could be used to reduce obesity.

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Loss of Ebf2 prevents induction of beige adipocytes in inguinal WAT.

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Ectopic expression of Ebf2 promotes beige fat induction in inguinal WAT.

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Ectopic Ebf2 expression protects against high fat diet-induced obesity.

Antisense Transcription of Retrotransposons in Drosophila: An Origin of Endogenous Small Interfering RNA Precursors

Movement of transposons causes insertions, deletions, and chromosomal rearrangements potentially leading to premature lethality in Drosophila melanogaster. To repress these elements and combat genomic instability, eukaryotes have evolved several small RNA-mediated defense mechanisms. Specifically, in Drosophila somatic cells, endogenous small interfering (esi)RNAs suppress retrotransposon mobility. EsiRNAs are produced by Dicer-2 processing of double-stranded RNA precursors, yet the origins of these precursors are unknown. We show that most transposon families are transcribed in both the sense (S) and antisense (AS) direction in Dmel-2 cells. LTR retrotransposons Dm297, mdg1, and blood, and non-LTR retrotransposons juan and jockey transcripts, are generated from intraelement transcription start sites with canonical RNA polymerase II promoters. We also determined that retrotransposon antisense transcripts are less polyadenylated than sense. RNA-seq and small RNA-seq revealed that Dicer-2 RNA interference (RNAi) depletion causes a decrease in the number of esiRNAs mapping to retrotransposons and an increase in expression of both S and AS retrotransposon transcripts. These data support a model in which double-stranded RNA precursors are derived from convergent transcription and processed by Dicer-2 into esiRNAs that silence both sense and antisense retrotransposon transcripts. Reduction of sense retrotransposon transcripts potentially lowers element-specific protein levels to prevent transposition. This mechanism preserves genomic integrity and is especially important for Drosophila fitness because mobile genetic elements are highly active.

Genomic limitations to RNA sequencing expression profiling

The field of genomics has grown rapidly with the advent of massively parallel sequencing technologies, allowing for novel biological insights with regards to genomic, transcriptomic, and epigenomic variation. One widely utilized application of high-throughput sequencing is transcriptional profiling using RNA sequencing (RNAseq). Understanding the limitations of a technology is critical for accurate biological interpretations, and clear interpretation of RNAseq data can be difficult in species with complex genomes. To understand the limitations of accurate profiling of expression levels we simulated RNAseq reads from annotated gene models in several plant species including Arabidopsis, brachypodium, maize, potato, rice, soybean, and tomato. The simulated reads were aligned using various parameters such as unique versus multiple read alignments. This allowed the identification of genes recalcitrant to RNAseq analyses by having over- and/or under-estimated expression levels. In maize, over 25% of genes deviated by more than 20% from the expected count values, suggesting the need for cautious interpretation of RNAseq data for certain genes. The reasons identified for deviation from expected expression varied between species due to differences in genome structure including, but not limited to, genes encoding short transcripts, overlapping gene models, and gene family size. Utilizing existing empirical datasets we demonstrate the potential for biological misinterpretation resulting from inclusion of 'flagged genes' in analyses. While RNAseq is a powerful tool for understanding biology, there are limitations to this technology that need to be understood in order to improve our biological interpretations.

Transgenerational epigenetic programming via sperm microRNA recapitulates effects of paternal stress

Epigenetic signatures in germ cells, capable of both responding to the parental environment and shaping offspring neurodevelopment, are uniquely positioned to mediate transgenerational outcomes. However, molecular mechanisms by which these marks may communicate experience-dependent information across generations are currently unknown. In our model of chronic paternal stress, we previously identified nine microRNAs (miRs) that were increased in the sperm of stressed sires and associated with reduced hypothalamic-pituitary-adrenal (HPA) stress axis reactivity in offspring. In the current study, we rigorously examine the hypothesis that these sperm miRs function postfertilization to alter offspring stress responsivity and, using zygote microinjection of the nine specific miRs, demonstrated a remarkable recapitulation of the offspring stress dysregulation phenotype. Further, we associated long-term reprogramming of the hypothalamic transcriptome with HPA axis dysfunction, noting a marked decreased in the expression of extracellular matrix and collagen gene sets that may reflect an underlying change in blood-brain barrier permeability. We conclude by investigating the developmental impact of sperm miRs in early zygotes with single-cell amplification technology, identifying the targeted degradation of stored maternal mRNA transcripts including sirtuin 1 and ubiquitin protein ligase E3a, two genes with established function in chromatin remodeling, and this potent regulatory function of miRs postfertilization likely initiates a cascade of molecular events that eventually alters stress reactivity. Overall, these findings demonstrate a clear mechanistic role for sperm miRs in the transgenerational transmission of paternal lifetime experiences.

CRCDA--Comprehensive resources for cancer NGS data analysis

Next generation sequencing (NGS) innovations put a compelling landmark in life science and changed the direction of research in clinical oncology with its productivity to diagnose and treat cancer. The aim of our portal comprehensive resources for cancer NGS data analysis (CRCDA) is to provide a collection of different NGS tools and pipelines under diverse classes with cancer pathways and databases and furthermore, literature information from PubMed. The literature data was constrained to 18 most common cancer types such as breast cancer, colon cancer and other cancers that exhibit in worldwide population. NGS-cancer tools for the convenience have been categorized into cancer genomics, cancer transcriptomics, cancer epigenomics, quality control and visualization. Pipelines for variant detection, quality control and data analysis were listed to provide out-of-the box solution for NGS data analysis, which may help researchers to overcome challenges in selecting and configuring individual tools for analysing exome, whole genome and transcriptome data. An extensive search page was developed that can be queried by using (i) type of data [literature, gene data and sequence read archive (SRA) data] and (ii) type of cancer (selected based on global incidence and accessibility of data). For each category of analysis, variety of tools are available and the biggest challenge is in searching and using the right tool for the right application. The objective of the work is collecting tools in each category available at various places and arranging the tools and other data in a simple and user-friendly manner for biologists and oncologists to find information easier. To the best of our knowledge, we have collected and presented a comprehensive package of most of the resources available in cancer for NGS data analysis. Given these factors, we believe that this website will be an useful resource to the NGS research community working on cancer.

Database URL: http://bioinfo.au-kbc.org.in/ngs/ngshome.html.

Gene activity in primary T cells infected with HIV89.6: intron retention and induction of genomic repeats

Background

HIV infection has been reported to alter cellular gene activity, but published studies have commonly assayed transformed cell lines and lab-adapted HIV strains, yielding inconsistent results. Here we carried out a deep RNA-Seq analysis of primary human T cells infected with the low passage HIV isolate HIV_89.6.

Results

Seventeen percent of cellular genes showed altered activity 48 h after infection. In a meta-analysis including four other studies, our data differed from studies of HIV infection in cell lines but showed more parallels with infections of primary cells. We found a global trend toward retention of introns after infection, suggestive of a novel cellular response to infection. HIV_89.6 infection was also associated with activation of several human endogenous retroviruses (HERVs) and retrotransposons, of interest as possible novel antigens that could serve as vaccine targets. The most highly activated group of HERVs was a subset of the ERV-9. Analysis showed that activation was associated with a particular variant of ERV-9 long terminal repeats that contains an indel near the U3-R border. These data also allowed quantification of >70 splice forms of the HIV_89.6 RNA and specified the main types of chimeric HIV_89.6-host RNAs. Comparison to over 100,000 integration site sequences from the same infected cell populations allowed quantification of authentic versus artifactual chimeric reads, showing that 5′ read-in, splicing out of HIV_89.6 from the D4 donor and 3′ read-through were the most common HIV_89.6-host cell chimeric RNA forms.

Conclusions

Analysis of RNA abundance after infection of primary T cells with the low passage HIV_89.6 isolate disclosed multiple novel features of HIV-host interactions, notably intron retention and induction of transcription of retrotransposons and endogenous retroviruses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-015-0205-1) contains supplementary material, which is available to authorized users.

The impact of RNA-seq aligners on gene expression estimation

While numerous RNA-seq data analysis pipelines are available, research has shown that the choice of pipeline influences the results of differentially expressed gene detection and gene expression estimation. Gene expression estimation is a key step in RNA-seq data analysis, since the accuracy of gene expression estimates profoundly affects the subsequent analysis. Generally, gene expression estimation involves sequence alignment and quantification, and accurate gene expression estimation requires accurate alignment. However, the impact of aligners on gene expression estimation remains unclear. We address this need by constructing nine pipelines consisting of nine spliced aligners and one quantifier. We then use simulated data to investigate the impact of aligners on gene expression estimation. To evaluate alignment, we introduce three alignment performance metrics, (1) the percentage of reads aligned, (2) the percentage of reads aligned with zero mismatch (ZeroMismatchPercentage), and (3) the percentage of reads aligned with at most one mismatch (ZeroOneMismatchPercentage). We then evaluate the impact of alignment performance on gene expression estimation using three metrics, (1) gene detection accuracy, (2) the number of genes falsely quantified (FalseExpNum), and (3) the number of genes with falsely estimated fold changes (FalseFcNum). We found that among various pipelines, FalseExpNum and FalseFcNum are correlated. Moreover, FalseExpNum is linearly correlated with the percentage of reads aligned and ZeroMismatchPercentage, and FalseFcNum is linearly correlated with ZeroMismatchPercentage. Because of this correlation, the percentage of reads aligned and ZeroMismatchPercentage may be used to assess the performance of gene expression estimation for all RNA-seq datasets.

Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data

MOTIVATION

Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy.

RESULTS

We conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development.

AVAILABILITY AND IMPLEMENTATION

Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Concurrent and Accurate Short Read Mapping on Multicore Processors

We introduce a parallel aligner with a work-flow organization for fast and accurate mapping of RNA sequences on servers equipped with multicore processors. Our software, HPG Aligner SA (HPG Aligner SA is an open-source application. The software is available at http://www.opencb.org, exploits a suffix array to rapidly map a large fraction of the RNA fragments (reads), as well as leverages the accuracy of the Smith-Waterman algorithm to deal with conflictive reads. The aligner is enhanced with a careful strategy to detect splice junctions based on an adaptive division of RNA reads into small segments (or seeds), which are then mapped onto a number of candidate alignment locations, providing crucial information for the successful alignment of the complete reads. The experimental results on a platform with Intel multicore technology report the parallel performance of HPG Aligner SA, on RNA reads of 100-400 nucleotides, which excels in execution time/sensitivity to state-of-the-art aligners such as TopHat 2+Bowtie 2, MapSplice, and STAR.

RASER: reads aligner for SNPs and editing sites of RNA

MOTIVATION

Accurate identification of genetic variants such as single-nucleotide polymorphisms (SNPs) or RNA editing sites from RNA-Seq reads is important, yet challenging, because it necessitates a very low false-positive rate in read mapping. Although many read aligners are available, no single aligner was specifically developed or tested as an effective tool for SNP and RNA editing prediction.

RESULTS

We present RASER, an accurate read aligner with novel mapping schemes and index tree structure that aims to reduce false-positive mappings due to existence of highly similar regions. We demonstrate that RASER shows the best mapping accuracy compared with other popular algorithms and highest sensitivity in identifying multiply mapped reads. As a result, RASER displays superb efficacy in unbiased mapping of the alternative alleles of SNPs and in identification of RNA editing sites.

AVAILABILITY AND IMPLEMENTATION

RASER is written in C++ and freely available for download at https://github.com/jaegyoonahn/RASER.

Heterogeneous lineage marker expression in naive embryonic stem cells is mostly due to spontaneous differentiation

Populations of cultured mouse embryonic stem cells (ESCs) exhibit a subfraction of cells expressing uncharacteristically low levels of pluripotency markers such as Nanog. Yet, the extent to which individual Nanog-negative cells are differentiated, both from ESCs and from each other, remains unclear. Here, we show the transcriptome of Nanog-negative cells exhibits expression of classes of genes associated with differentiation that are not yet active in cells exposed to differentiation conditions for one day. Long non-coding RNAs, however, exhibit more changes in expression in the one-day-differentiated cells than in Nanog-negative cells. These results are consistent with the concept that Nanog-negative cells may contain subpopulations of both lineage-primed and differentiated cells. Single cell analysis showed that Nanog-negative cells display substantial and coherent heterogeneity in lineage marker expression in progressively nested subsets of cells exhibiting low levels of Nanog, then low levels of Oct4, and then a set of lineage markers, which express intensely in a small subset of these more differentiated cells. Our results suggest that the observed enrichment of lineage-specific marker gene expression in Nanog-negative cells is associated with spontaneous differentiation of a subset of these cells rather than the more random expression that may be associated with reversible lineage priming.

DIS3 shapes the RNA polymerase II transcriptome in humans by degrading a variety of unwanted transcripts

Human DIS3, the nuclear catalytic subunit of the exosome complex, contains exonucleolytic and endonucleolytic active domains. To identify DIS3 targets genome-wide, we combined comprehensive transcriptomic analyses of engineered HEK293 cells that expressed mutant DIS3, with Photoactivatable Ribonucleoside-Enhanced Cross-Linking and Immunoprecipitation (PAR-CLIP) experiments. In cells expressing DIS3 with both catalytic sites mutated, RNAs originating from unannotated genomic regions increased ∼2.5-fold, covering ∼70% of the genome and allowing for thousands of novel transcripts to be discovered. Previously described pervasive transcription products, such as Promoter Upstream Transcripts (PROMPTs), accumulated robustly upon DIS3 dysfunction, representing a significant fraction of PAR-CLIP reads. We have also detected relatively long putative premature RNA polymerase II termination products of protein-coding genes whose levels in DIS3 mutant cells can exceed the mature mRNAs, indicating that production of such truncated RNA is a common phenomenon. In addition, we found DIS3 to be involved in controlling the formation of paraspeckles, nuclear bodies that are organized around NEAT1 lncRNA, whose short form was overexpressed in cells with mutated DIS3. Moreover, the DIS3 mutations resulted in misregulation of expression of ∼50% of transcribed protein-coding genes, probably as a secondary effect of accumulation of various noncoding RNA species. Finally, cells expressing mutant DIS3 accumulated snoRNA precursors, which correlated with a strong PAR-CLIP signal, indicating that DIS3 is the main snoRNA-processing enzyme. EXOSC10 (RRP6) instead controls the levels of the mature snoRNAs. Overall, we show that DIS3 has a major nucleoplasmic function in shaping the human RNA polymerase II transcriptome.

Spontaneous Pancreatitis Caused by Tissue-Specific Gene Ablation of Hhex in Mice

BACKGROUND & AIMS

Perturbations in pancreatic ductal bicarbonate secretion cause chronic pancreatitis. The physiologic mechanism of ductal secretion is known, but its transcriptional control is not. We determine the role of the transcription factor hematopoietically expressed homeobox protein (Hhex) in ductal secretion and pancreatitis.

METHODS

We derived mice with pancreas-specific, Cremediated Hhex gene ablation to determine the requirement of Hhex in the pancreatic duct in early life and in adult stages. Histologic and immunostaining analyses were used to detect the presence of pathology. Pancreatic primary ductal cells were isolated to discover differentially expressed transcripts upon acute Hhex ablation on a cell autonomous level.

RESULTS

Hhex protein was detected throughout the embryonic and adult ductal trees. Ablation of Hhex in pancreatic progenitors resulted in postnatal ductal ectasia associated with acinar-to-ductal metaplasia, a progressive phenotype that ultimately resulted in chronic pancreatitis. Hhex ablation in adult mice, however, did not cause any detectable pathology. Ductal ectasia in young mice did not result from perturbation of expression of Hnf6, Hnf1β, or the primary cilia genes. RNA-seq analysis of Hhex-ablated pancreatic primary ductal cells showed mRNA levels of the G-protein coupled receptor natriuretic peptide receptor 3 (Npr3), implicated in paracrine signaling, up-regulated by 4.70-fold.

CONCLUSIONS

Although Hhex is dispensable for ductal cell function in the adult, ablation of Hhex in pancreatic progenitors results in pancreatitis. Our data highlight the critical role of Hhex in maintaining ductal homeostasis in early life and support ductal hypersecretion as a novel etiology of pediatric chronic pancreatitis.

RNASequel: accurate and repeat tolerant realignment of RNA-seq reads

RNA-seq is a key technology for understanding the biology of the cell because of its ability to profile transcriptional and post-transcriptional regulation at single nucleotide resolutions. Compared to DNA sequencing alignment algorithms, RNA-seq alignment algorithms have a diminished ability to accurately detect and map base pair substitutions, gaps, discordant pairs and repetitive regions. These shortcomings adversely affect experiments that require a high degree of accuracy, notably the ability to detect RNA editing. We have developed RNASequel, a software package that runs as a post-processing step in conjunction with an RNA-seq aligner and systematically corrects common alignment artifacts. Its key innovations are a two-pass splice junction alignment system that includes de novo splice junctions and the use of an empirically determined estimate of the fragment size distribution when resolving read pairs. We demonstrate that RNASequel produces improved alignments when used in conjunction with STAR or Tophat2 using two simulated datasets. We then show that RNASequel improves the identification of adenosine to inosine RNA editing sites on biological datasets. This software will be useful in applications requiring the accurate identification of variants in RNA sequencing data, the discovery of RNA editing sites and the analysis of alternative splicing.

RNA-sequencing reveals transcriptional up-regulation of Trem2 in response to bexarotene treatment

We have recently demonstrated that short term bexarotene treatment of APP/PS1 mice significantly improves their cognitive performance. While there were no changes in plaque load, or insoluble Aβ levels in brain, biochemical analysis strongly suggested improved clearance of soluble Aβ, including Aβ oligomers. To get further insight into molecular mechanisms underlying this therapeutic effect, we explored genome-wide differential gene expression in brain of bexarotene and control treated APP/PS1 mice. We performed high throughput massively parallel sequencing on mRNA libraries generated from cortices of bexarotene or vehicle treated APP/PS1 mice and compared the expression profiles for differential gene expression. Gene Ontology (GO) Biological Process categories with the highest fold enrichment and lowest False Discovery Rate (FDR) are clustered in GO terms immune response, inflammatory response, oxidation-reduction and immunoglobulin mediated immune response. Chromatin immunoprecipitation (ChIP) followed by ChIP-QPCR, and RT-QPCR expression assays were used to validate select genes, including Trem2, Tyrobp, Apoe and Ttr, differentially expressed in response to Retinoid X Receptor (RXR) activation. We found that bexarotene significantly increased the phagocytosis of soluble and insoluble Aβ in BV2 cells. The results of our study demonstrate that in AD model mice expressing human APP, gene networks up-regulated in response to RXR activation by the specific, small molecule, ligand bexarotene may influence diverse regulatory pathways that are considered critical for cognitive performance, inflammatory response and Aβ clearance, and may provide an explanation of the bexarotene therapeutic effect at the molecular level. This study also confirms that unbiased massive parallel sequencing approaches are useful and highly informative for revealing brain molecular and cellular mechanisms underlying responses to activated nuclear hormone receptors in AD animal models.

Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation

BACKGROUND

Differentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question.

RESULTS

We present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved.

CONCLUSIONS

Single-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.

The exonuclease Nibbler regulates age-associated traits and modulates piRNA length in Drosophila

Nibbler (Nbr) is a 3'-to-5' exonuclease that trims the 3'end of microRNAs (miRNAs) to generate different length patterns of miRNAs in Drosophila. Despite its effect on miRNAs, we lack knowledge of its biological significance and whether Nbr affects other classes of small RNAs such as piRNAs and endo-siRNAs. Here, we characterized the in vivo function of nbr by defining the Nbr protein expression pattern and loss-of-function effects. Nbr protein is enriched in the ovary and head. Analysis of nbr null animals reveals adult-stage defects that progress with age, including held-up wings, decreased locomotion, and brain vacuoles, indicative of accelerated age-associated processes upon nbr loss. Importantly, these effects depend on catalytic residues in the Nbr exonuclease domain, indicating that the catalytic activity is responsible for these effects. Given the impact of nbr on miRNAs, we also analyzed the effect of nbr on piRNA and endo-siRNA lengths by deep-sequence analysis of libraries from ovaries. As with miRNAs, nbr mutation led to longer length piRNAs - an effect that was dependent on the catalytic residues of the exonuclease domain. These analyses indicate a role of nbr on age-associated processes and to modulate length of multiple classes of small RNAs including miRNAs and piRNAs in Drosophila.

JAFFA: High sensitivity transcriptome-focused fusion gene detection

Genomic instability is a hallmark of cancer and, as such, structural alterations and fusion genes are common events in the cancer landscape. RNA sequencing (RNA-Seq) is a powerful method for profiling cancers, but current methods for identifying fusion genes are optimised for short reads. JAFFA (https://github.com/Oshlack/JAFFA/wiki) is a sensitive fusion detection method that outperforms other methods with reads of 100 bp or greater. JAFFA compares a cancer transcriptome to the reference transcriptome, rather than the genome, where the cancer transcriptome is inferred using long reads directly or by de novo assembling short reads.

Vision from next generation sequencing: multi-dimensional genome-wide analysis for producing gene regulatory networks underlying retinal development, aging and disease

Genomics and genetics have invaded all aspects of biology and medicine, opening uncharted territory for scientific exploration. The definition of "gene" itself has become ambiguous, and the central dogma is continuously being revised and expanded. Computational biology and computational medicine are no longer intellectual domains of the chosen few. Next generation sequencing (NGS) technology, together with novel methods of pattern recognition and network analyses, has revolutionized the way we think about fundamental biological mechanisms and cellular pathways. In this review, we discuss NGS-based genome-wide approaches that can provide deeper insights into retinal development, aging and disease pathogenesis. We first focus on gene regulatory networks (GRNs) that govern the differentiation of retinal photoreceptors and modulate adaptive response during aging. Then, we discuss NGS technology in the context of retinal disease and develop a vision for therapies based on network biology. We should emphasize that basic strategies for network construction and analyses can be transported to any tissue or cell type. We believe that specific and uniform guidelines are required for generation of genome, transcriptome and epigenome data to facilitate comparative analysis and integration of multi-dimensional data sets, and for constructing networks underlying complex biological processes. As cellular homeostasis and organismal survival are dependent on gene-gene and gene-environment interactions, we believe that network-based biology will provide the foundation for deciphering disease mechanisms and discovering novel drug targets for retinal neurodegenerative diseases.

Polyester: simulating RNA-seq datasets with differential transcript expression

MOTIVATION

Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data.

RESULTS

Polyester is an R package designed to simulate RNA-seq data, beginning with an experimental design and ending with collections of RNA-seq reads. Its main advantage is the ability to simulate reads indicating isoform-level differential expression across biological replicates for a variety of experimental designs. Data generated by Polyester is a reasonable approximation to real RNA-seq data and standard differential expression workflows can recover differential expression set in the simulation by the user.

AVAILABILITY AND IMPLEMENTATION

Polyester is freely available from Bioconductor (http://bioconductor.org/).

CONTACT

jtleek@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ContextMap 2: fast and accurate context-based RNA-seq mapping

Background

Mapping of short sequencing reads is a crucial step in the analysis of RNA sequencing (RNA-seq) data. ContextMap is an RNA-seq mapping algorithm that uses a context-based approach to identify the best alignment for each read and allows parallel mapping against several reference genomes.

Results

In this article, we present ContextMap 2, a new and improved version of ContextMap. Its key novel features are: (i) a plug-in structure that allows easily integrating novel short read alignment programs with improved accuracy and runtime; (ii) context-based identification of insertions and deletions (indels); (iii) mapping of reads spanning an arbitrary number of exons and indels. ContextMap 2 using Bowtie, Bowtie 2 or BWA was evaluated on both simulated and real-life data from the recently published RGASP study.

Conclusions

We show that ContextMap 2 generally combines similar or higher recall compared to other state-of-the-art approaches with significantly higher precision in read placement and junction and indel prediction. Furthermore, runtime was significantly lower than for the best competing approaches. ContextMap 2 is freely available at http://www.bio.ifi.lmu.de/ContextMap.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0557-5) contains supplementary material, which is available to authorized users.

Zebrafish foxc1a drives appendage-specific neural circuit development

Neural connectivity between the spinal cord and paired appendages is key to the superior locomotion of tetrapods and aquatic vertebrates. In contrast to nerves that innervate axial muscles, those innervating appendages converge at a specialized structure, the plexus, where they topographically reorganize before navigating towards their muscle targets. Despite its importance for providing appendage mobility, the genetic program that drives nerve convergence at the plexus, as well as the functional role of this convergence, are not well understood. Here, we show that in zebrafish the transcription factor foxc1a is dispensable for trunk motor nerve guidance but is required to guide spinal nerves innervating the pectoral fins, equivalent to the tetrapod forelimbs. In foxc1a null mutants, instead of converging with other nerves at the plexus, pectoral fin nerves frequently bypass the plexus. We demonstrate that foxc1a expression in muscle cells delineating the nerve path between the spinal cord and the plexus region restores convergence at the plexus. By labeling individual fin nerves, we show that mutant nerves bypassing the plexus enter the fin at ectopic positions, yet innervate their designated target areas, suggesting that motor axons can select their appropriate fin target area independently of their migration through the plexus. Although foxc1a mutants display topographically correct fin innervation, mutant fin muscles exhibit a reduction in the levels of pre- and postsynaptic structures, concomitant with reduced pectoral fin function. Combined, our results reveal foxc1a as a key player in the development of connectivity between the spinal cord and paired appendages, which is crucial for appendage mobility.

RNA Sequencing and Analysis

RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.

HISAT: a fast spliced aligner with low memory requirements

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

Long non-coding RNAs control hematopoietic stem cell function

Hematopoietic stem cells (HSCs) possess unique gene expression programs that enforce their identity and regulate lineage commitment. Long non-coding RNAs (lncRNAs) have emerged as important regulators of gene expression and cell fate decisions, although their functions in HSCs are unclear. Here we profiled the transcriptome of purified HSCs by deep sequencing and identified 323 unannotated lncRNAs. Comparing their expression in differentiated lineages revealed 159 lncRNAs enriched in HSCs, some of which are likely HSC specific (LncHSCs). These lncRNA genes share epigenetic features with protein-coding genes, including regulated expression via DNA methylation, and knocking down two LncHSCs revealed distinct effects on HSC self-renewal and lineage commitment. We mapped the genomic binding sites of one of these candidates and found enrichment for key hematopoietic transcription factor binding sites, especially E2A. Together, these results demonstrate that lncRNAs play important roles in regulating HSCs, providing an additional layer to the genetic circuitry controlling HSC function.