RNA sequencing: advances, challenges and opportunities

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

Characterization of the transcriptome of an ecologically important avian species, the Vinous-throated Parrotbill Paradoxornis webbianus bulomachus (Paradoxornithidae; Aves)

Background

Adaptive divergence driven by environmental heterogeneity has long been a fascinating topic in ecology and evolutionary biology. The study of the genetic basis of adaptive divergence has, however, been greatly hampered by a lack of genomic information. The recent development of transcriptome sequencing provides an unprecedented opportunity to generate large amounts of genomic data for detailed investigations of the genetics of adaptive divergence in non-model organisms. Herein, we used the Illumina sequencing platform to sequence the transcriptome of brain and liver tissues from a single individual of the Vinous-throated Parrotbill, Paradoxornis webbianus bulomachus, an ecologically important avian species in Taiwan with a wide elevational range of sea level to 3100 m.

Results

Our 10.1 Gbp of sequences were first assembled based on Zebra Finch (Taeniopygia guttata) and chicken (Gallus gallus) RNA references. The remaining reads were then de novo assembled. After filtering out contigs with low coverage (<10X), we retained 67,791 of 487,336 contigs, which covered approximately 5.3% of the P. w. bulomachus genome. Of 7,779 contigs retained for a top-hit species distribution analysis, the majority (about 86%) were matched to known Zebra Finch and chicken transcripts. We also annotated 6,365 contigs to gene ontology (GO) terms: in total, 122 GO-slim terms were assigned, including biological process (41%), molecular function (32%), and cellular component (27%). Many potential genetic markers for future adaptive genomic studies were also identified: 8,589 single nucleotide polymorphisms, 1,344 simple sequence repeats and 109 candidate genes that might be involved in elevational or climate adaptation.

Conclusions

Our study shows that transcriptome data can serve as a rich genetic resource, even for a single run of short-read sequencing from a single individual of a non-model species. This is the first study providing transcriptomic information for species in the avian superfamily Sylvioidea, which comprises more than 1,000 species. Our data can be used to study adaptive divergence in heterogeneous environments and investigate other important ecological and evolutionary questions in parrotbills from different populations and even in other species in the Sylvioidea.

Complex changes in alternative pre-mRNA splicing play a central role in the epithelial-to-mesenchymal transition (EMT)

The epithelial-to-mesenchymal transition (EMT) is an important developmental process that is also implicated in disease pathophysiology, such as cancer progression and metastasis. A wealth of literature in recent years has identified important transcriptional regulators and large-scale changes in gene expression programs that drive the phenotypic changes that occur during the EMT. However, in the past couple of years it has become apparent that extensive changes in alternative splicing also play a profound role in shaping the changes in cell behavior that characterize the EMT. While long known splicing switches in FGFR2 and p120-catenin provided hints of a larger program of EMT-associated alternative splicing, the recent identification of the epithelial splicing regulatory proteins 1 and 2 (ESRP1 and ESRP2) began to reveal this genome-wide post-transcriptional network. Several studies have now demonstrated the truly vast extent of this alternative splicing program. The global switches in splicing associated with the EMT add an important additional layer of post-transcriptional control that works in harmony with transcriptional and epigenetic regulation to effect complex changes in cell shape, polarity, and behavior that mediate transitions between epithelial and mesenchymal cell states. Future challenges include the need to investigate the functional consequences of these splicing switches at both the individual gene as well as systems level.

SAVoR: a server for sequencing annotation and visualization of RNA structures

RNA secondary structure is required for the proper regulation of the cellular transcriptome. This is because the functionality, processing, localization and stability of RNAs are all dependent on the folding of these molecules into intricate structures through specific base pairing interactions encoded in their primary nucleotide sequences. Thus, as the number of RNA sequencing (RNA-seq) data sets and the variety of protocols for this technology grow rapidly, it is becoming increasingly pertinent to develop tools that can analyze and visualize this sequence data in the context of RNA secondary structure. Here, we present Sequencing Annotation and Visualization of RNA structures (SAVoR), a web server, which seamlessly links RNA structure predictions with sequencing data and genomic annotations to produce highly informative and annotated models of RNA secondary structure. SAVoR accepts read alignment data from RNA-seq experiments and computes a series of per-base values such as read abundance and sequence variant frequency. These values can then be visualized on a customizable secondary structure model. SAVoR is freely available at http://tesla.pcbi.upenn.edu/savor.

Averaged differential expression for the discovery of biomarkers in the blood of patients with prostate cancer

BACKGROUND

The identification of a blood-based diagnostic marker is a goal in many areas of medicine, including the early diagnosis of prostate cancer. We describe the use of averaged differential display as an efficient mechanism for biomarker discovery in whole blood RNA. The process of averaging reduces the problem of clinical heterogeneity while simultaneously minimizing sample handling.

METHODOLOGY/PRINCIPAL FINDINGS

RNA was isolated from the blood of prostate cancer patients and healthy controls. Samples were pooled and subjected to the averaged differential display process. Transcripts present at different levels between patients and controls were purified and sequenced for identification. Transcript levels in the blood of prostate cancer patients and controls were verified by quantitative RT-PCR. Means were compared using a t-test and a receiver-operating curve was generated. The Ring finger protein 19A (RNF19A) transcript was identified as having higher levels in prostate cancer patients compared to healthy men through the averaged differential display process. Quantitative RT-PCR analysis confirmed a more than 2-fold higher level of RNF19A mRNA levels in the blood of patients with prostate cancer than in healthy controls (p = 0.0066). The accuracy of distinguishing cancer patients from healthy men using RNF19A mRNA levels in blood as determined by the area under the receiving operator curve was 0.727.

CONCLUSIONS/SIGNIFICANCE

Averaged differential display offers a simplified approach for the comprehensive screening of body fluids, such as blood, to identify biomarkers in patients with prostate cancer. Furthermore, this proof-of-concept study warrants further analysis of RNF19A as a clinically relevant biomarker for prostate cancer detection.

Deep sequencing the circadian and diurnal transcriptome of Drosophila brain

Eukaryotic circadian clocks include transcriptional/translational feedback loops that drive 24-h rhythms of transcription. These transcriptional rhythms underlie oscillations of protein abundance, thereby mediating circadian rhythms of behavior, physiology, and metabolism. Numerous studies over the last decade have used microarrays to profile circadian transcriptional rhythms in various organisms and tissues. Here we use RNA sequencing (RNA-seq) to profile the circadian transcriptome of Drosophila melanogaster brain from wild-type and period-null clock-defective animals. We identify several hundred transcripts whose abundance oscillates with 24-h periods in either constant darkness or 12 h light/dark diurnal cycles, including several noncoding RNAs (ncRNAs) that were not identified in previous microarray studies. Of particular interest are U snoRNA host genes (Uhgs), a family of diurnal cycling noncoding RNAs that encode the precursors of more than 50 box-C/D small nucleolar RNAs, key regulators of ribosomal biogenesis. Transcriptional profiling at the level of individual exons reveals alternative splice isoforms for many genes whose relative abundances are regulated by either period or circadian time, although the effect of circadian time is muted in comparison to that of period. Interestingly, period loss of function significantly alters the frequency of RNA editing at several editing sites, suggesting an unexpected link between a key circadian gene and RNA editing. We also identify tens of thousands of novel splicing events beyond those previously annotated by the modENCODE Consortium, including several that affect key circadian genes. These studies demonstrate extensive circadian control of ncRNA expression, reveal the extent of clock control of alternative splicing and RNA editing, and provide a novel, genome-wide map of splicing in Drosophila brain.

Lessons from eQTL mapping studies: non-coding regions and their role behind natural phenotypic variation in plants

Even if considerable progress has been achieved towards the understanding of natural variation in plant systems, the contribution of transcript abundance variation to phenotypic diversity remains unappreciated. Over the last decade, efforts to characterise the genome-wide expression variation in natural accessions, structured populations and hybrids have improved our knowledge of the contribution of non-coding polymorphisms to gene expression regulation. Moreover, new studies are helping to unravel the role of expression polymorphisms and their orchestrated performance. Recent advances involving classical linkage analysis, GWAS and improved eQTL mapping strategies will provide a greater resolution to determine the genetic variants shaping the broad diversity in plant systems.

Response to copper stress in Streptomyces lividans extends beyond genes under direct control of a copper-sensitive operon repressor protein (CsoR)

A copper-sensitive operon repressor protein (CsoR) has been identified in Streptomyces lividans (CsoR(Sl)) and found to regulate copper homeostasis with attomolar affinity for Cu(I). Solution studies reveal apo- and Cu(I)-CsoR(Sl) to be a tetramer assembly, and a 1.7-Å resolution crystal structure of apo-CsoR(Sl) reveals that a significant conformational change is necessary to enable Cu(I) binding. In silico prediction of the CsoR regulon was confirmed in vitro (EMSA) and in vivo (RNA-seq), which highlighted that next to the csoR gene itself, the regulon consists of two Cu(I) efflux systems involving a CopZ-like copper metallochaperone protein and a CopA P(1)-type ATPase. Although deletion of csoR has only minor effects on S. lividans development when grown under high copper concentrations, mutations of the Cu(I) ligands decrease tolerance to copper as a result of the Cu(I)-CsoR mutants failing to disengage from the DNA targets, thus inhibiting the derepression of the regulon. RNA-seq experiments carried out on samples incubated with exogenous copper and a ΔcsoR strain showed that the set of genes responding to copper stress is much wider than anticipated and largely extends beyond genes targeted by CsoR. This suggests more control levels are operating and directing other regulons in copper homeostasis beside the CsoR regulon.

High through-put sequencing of the Parhyale hawaiensis mRNAs and microRNAs to aid comparative developmental studies

Understanding the genetic and evolutionary basis of animal morphological diversity will require comparative developmental studies that use new model organisms. This necessitates development of tools for the study of genetics and also the generation of sequence information of the organism to be studied. The development of next generation sequencing technology has enabled quick and cost effective generation of sequence information. Parhyale hawaiensis has emerged as a model organism of choice due to the development of advanced molecular tools, thus P. hawaiensis genetic information will help drive functional studies in this organism.

Here we present a transcriptome and miRNA collection generated using next generation sequencing platforms. We generated approximately 1.7 million reads from a P. hawaiensis cDNA library constructed from embryos up to the germ band stage. These reads were assembled into a dataset comprising 163,501 transcripts.

Using the combined annotation of Annot8r and pfam2go, Gene Ontology classifications was assigned to 20,597 transcripts. Annot8r was used to provide KEGG orthology to our transcript dataset. A total of 25,292 KEGG pathway assignments were defined and further confirmed with reciprocal blast against the NCBI nr protein database. This has identified many P. hawaiensis gene orthologs of key conserved signalling pathways involved in development. We also generated small RNA sequences from P. hawaiensis, identifying 55 conserved miRNAs. Sequenced small RNAs that were not annotated by stringent comparison to mirBase were used to search the Daphnia pulex for possible novel miRNAs. Using a conservative approach, we have identified 51 possible miRNA candidates conserved in the Daphnia pulex genome, which could be potential crustacean/arthropod specific miRNAs. Our study presents gene and miRNA discovery in a new model organism that does not have a sequenced genome. The data provided by our work will be valuable for the P. hawaiensis community as well as the wider evolutionary developmental biology community.

Considerations for creating and annotating the budding yeast Genome Map at SGD: a progress report

The Saccharomyces Genome Database (SGD) is compiling and annotating a comprehensive catalogue of functional sequence elements identified in the budding yeast genome. Recent advances in deep sequencing technologies have enabled for example, global analyses of transcription profiling and assembly of maps of transcription factor occupancy and higher order chromatin organization, at nucleotide level resolution. With this growing influx of published genome-scale data, come new challenges for their storage, display, analysis and integration. Here, we describe SGD's progress in the creation of a consolidated resource for genome sequence elements in the budding yeast, the considerations taken in its design and the lessons learned thus far. The data within this collection can be accessed at http://browse.yeastgenome.org and downloaded from http://downloads.yeastgenome.org.

Database URL:http://www.yeastgenome.org

Newborn screening for primary immunodeficiencies: beyond SCID and XLA

Primary immunodeficiencies (PID) encompass more than 250 disease entities, including phagocytic disorders, complement deficiencies, T cell defects, and antibody deficiencies. While differing in clinical severity, early diagnosis and treatment is of considerable importance for all forms of PID to prevent organ damage and life-threatening infections. During the past few years, neonatal screening assays have been developed to detect diseases hallmarked by the absence of T or B lymphocytes, classically seen in severe combined immunodeficiencies (SCID) and X-linked agammaglobulinemia (XLA). As described in this review, a reduction or lack of T and B cells in newborns is also frequently found in several other forms of PID, requiring supplemental investigation and involving the development of additional technical platforms in order to help classify abnormal screening results.

Molecular Functions of Long Non-Coding RNAs in Plants

The past decade has seen dramatic changes in our understanding of the scale and complexity of eukaryotic transcriptome owing to the discovery of diverse types of short and long non-protein-coding RNAs (ncRNAs). While short ncRNA-mediated gene regulation has been extensively studied and the mechanisms well understood, the function of long ncRNAs remains largely unexplored, especially in plants. Nevertheless, functional insights generated in recent studies with mammalian systems have indicated that long ncRNAs are key regulators of a variety of biological processes. They have been shown to act as transcriptional regulators and competing endogenous RNAs (ceRNAs), to serve as molecular cargos for protein re-localization and as modular scaffolds to recruit the assembly of multiple protein complexes for chromatin modifications. Some of these functions have been found to be conserved in plants. Here, we review our current understanding of long ncRNA functions in plants and discuss the challenges in functional characterization of plant long ncRNAs.

Identification of microRNA-regulated gene networks by expression analysis of target genes

MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGFβ signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs.

RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings

There are remarkable disparities among patients of different races with prostate cancer; however, the mechanism underlying this difference remains unclear. Here, we present a comprehensive landscape of the transcriptome profiles of 14 primary prostate cancers and their paired normal counterparts from the Chinese population using RNA-seq, revealing tremendous diversity across prostate cancer transcriptomes with respect to gene fusions, long noncoding RNAs (long ncRNA), alternative splicing and somatic mutations. Three of the 14 tumors (21.4%) harbored a TMPRSS2-ERG fusion, and the low prevalence of this fusion in Chinese patients was further confirmed in an additional tumor set (10/54=18.5%). Notably, two novel gene fusions, CTAGE5-KHDRBS3 (20/54=37%) and USP9Y-TTTY15 (19/54=35.2%), occurred frequently in our patient cohort. Further systematic transcriptional profiling identified numerous long ncRNAs that were differentially expressed in the tumors. An analysis of the correlation between expression of long ncRNA and genes suggested that long ncRNAs may have functions beyond transcriptional regulation. This study yielded new insights into the pathogenesis of prostate cancer in the Chinese population.

Transcriptomic landscape of breast cancers through mRNA sequencing

Breast cancer is a heterogeneous disease with a poorly defined genetic landscape, which poses a major challenge in diagnosis and treatment. By massively parallel mRNA sequencing, we obtained 1.2 billion reads from 17 individual human tissues belonging to TNBC, Non-TNBC, and HER2-positive breast cancers and defined their comprehensive digital transcriptome for the first time. Surprisingly, we identified a high number of novel and unannotated transcripts, revealing the global breast cancer transcriptomic adaptations. Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. Our study also identified common transcriptional regulatory elements, such as highly abundant primary transcripts, including osteonectin, RACK1, calnexin, calreticulin, FTL, and B2M, and "genomic hotspots" enriched in primary transcripts between the three groups. Thus, our study opens previously unexplored niches that could enable a better understanding of the disease and the development of potential intervention strategies.

Clinical review: sepsis and septic shock--the potential of gene arrays

Over the past decade several investigators have applied microarray technology and related bioinformatic approaches to clinical sepsis and septic shock, thus allowing for an assessment of how, or if, this branch of genomic medicine has meaningfully impacted the field of sepsis research. The ability to simultaneously and efficiently measure the steady-state mRNA abundance of thousands of transcripts from a given tissue source (that is, 'transcriptomics') has provided an unprecedented opportunity to gain a broader, genome-level 'picture' of complex and heterogeneous clinical syndromes such as sepsis. A trancriptomic approach to sepsis and septic shock is technically challenging on multiple levels, but nonetheless modest, tangible advances are being realized. These include a genome-level understanding of the complexity of sepsis and septic shock, identification of novel candidate pathways and targets for potential intervention, discovery of novel, candidate diagnostic and stratification biomarkers, and the ability to stratify patients into clinically relevant, expression-based subclasses. The challenges moving forward include robust validation studies, standardization of technical approaches, standardization and further development of analytical algorithms, and large-scale collaborations.

EST and transcriptome analysis of cephalochordate amphioxus--past, present and future

The cephalochordates, commonly known as amphioxus or lancelets, are now considered the most basal chordate group, and the studies of these organisms therefore offer important insights into various levels of evolutionary biology. In the past two decades, the investigation of amphioxus developmental biology has provided key knowledge for understanding the basic patterning mechanisms of chordates. Comparative genome studies of vertebrates and amphioxus have uncovered clear evidence supporting the hypothesis of two-round whole-genome duplication thought to have occurred early in vertebrate evolution and have shed light on the evolution of morphological novelties in the complex vertebrate body plan. Complementary to the amphioxus genome-sequencing project, a large collection of expressed sequence tags (ESTs) has been generated for amphioxus in recent years; this valuable collection represents a rich resource for gene discovery, expression profiling and molecular developmental studies in the amphioxus model. Here, we review previous EST analyses and available cDNA resources in amphioxus and discuss their value for use in evolutionary and developmental studies. We also discuss the potential advantages of applying high-throughput, next-generation sequencing (NGS) technologies to the field of amphioxus research.

Third-generation sequencing techniques and applications to drug discovery

INTRODUCTION

There is an immediate need for functional and molecular studies to decipher differences between disease and 'normal' settings to identify large quantities of validated targets with the highest therapeutic utilities. Furthermore, drug mechanism of action and biomarkers to predict drug efficacy and safety need to be identified for effective design of clinical trials, decreasing attrition rates, regulatory agency approval process and drug repositioning. By expanding the power of genetics and pharmacogenetics studies, next-generation nucleic acid sequencing technologies have started to play an important role in all stages of drug discovery.

AREAS COVERED

This article reviews the first- and second-generation sequencing technologies (SGSTs) and challenges they pose to biomedicine. The article then focuses on the emerging third-generation sequencing technologies (TGSTs), their technological foundations and potential contributions to drug discovery.

EXPERT OPINION

Despite the scientific and commercial success of SGSTs, the goal of rapid, comprehensive and unbiased sequencing of nucleic acids has not been achieved. TGSTs promise to increase sequencing throughput and read lengths, decrease costs, run times and error rates, eliminate biases inherent in SGSTs and offer capabilities beyond nucleic acid sequencing. Such changes will have positive impact on all sequencing applications to drug discovery.

New technologies for 21st century plant science

Plants are one of the most fascinating and important groups of organisms living on Earth. They serve as the conduit of energy into the biosphere, provide food, and shape our environment. If we want to make headway in understanding how these essential organisms function and build the foundation for a more sustainable future, then we need to apply the most advanced technologies available to the study of plant life. In 2009, a committee of the National Academy highlighted the "understanding of plant growth" as one of the big challenges for society and part of a new era which they termed "new biology." The aim of this article is to identify how new technologies can and will transform plant science to address the challenges of new biology. We assess where we stand today regarding current technologies, with an emphasis on molecular and imaging technologies, and we try to address questions about where we may go in the future and whether we can get an idea of what is at and beyond the horizon.

Genomic basis for light control of plant development

Light is one of the key environmental signals regulating plant growth and development. Therefore, understanding the mechanisms by which light controls plant development has long been of great interest to plant biologists. Traditional genetic and molecular approaches have successfully identified key regulatory factors in light signaling, but recent genomic studies have revealed massive reprogramming of plant transcriptomes by light, identified binding sites across the entire genome of several pivotal transcription factors in light signaling, and discovered the involvement of epigenetic regulation in light-regulated gene expression. This review summarizes the key genomic work conducted in the last decade which provides new insights into light control of plant development.

Targeted sequence capture as a powerful tool for evolutionary analysis

Next-generation sequencing technologies (NGS) have revolutionized biological research by significantly increasing data generation while simultaneously decreasing the time to data output. For many ecologists and evolutionary biologists, the research opportunities afforded by NGS are substantial; even for taxa lacking genomic resources, large-scale genome-level questions can now be addressed, opening up many new avenues of research. While rapid and massive sequencing afforded by NGS increases the scope and scale of many research objectives, whole genome sequencing is often unwarranted and unnecessarily complex for specific research questions. Recently developed targeted sequence enrichment, coupled with NGS, represents a beneficial strategy for enhancing data generation to answer questions in ecology and evolutionary biology. This marriage of technologies offers researchers a simple method to isolate and analyze a few to hundreds, or even thousands, of genes or genomic regions from few to many samples in a relatively efficient and effective manner. These strategies can be applied to questions at both the infra- and interspecific levels, including those involving parentage, gene flow, divergence, phylogenetics, reticulate evolution, and many more. Here we provide a brief overview of targeted sequence enrichment, and emphasize the power of this technology to increase our ability to address a wide range of questions of interest to ecologists and evolutionary biologists, particularly for those working with taxa for which few genomic resources are available.

Genomics and transcriptomics across the diversity of the Nematoda

The diversity of biology in nematodes is reflected in the diversity of their genomes. Parasitic species in particular have evolved mechanisms to invade and outwit their hosts, and these offer opportunities for the development of control measures. Genomic analyses can reveal the molecular underpinnings of phenotypes such as parasitism and thus, initiate and support research programmes that explore the manipulation of host and parasite physiologies to achieve favourable outcomes. Wide sampling across nematode diversity allows phylogenetically informed formulation of research hypotheses, identification of core features shared by all species or important evolutionary novelties present in isolated clades. Many nematode species have been investigated through the use of the expressed sequence tag approach, which samples from the transcribed genome. Gene catalogues generated in this way can be explored to reveal the patterns of expression associated with parasitism and candidates for testing as drug targets or vaccine components. Analysis environments, such as NEMBASE facilitate exploitation of these data. The development of new high-throughput DNA-sequencing technologies has facilitated transcriptomic and genomic approaches to parasite biology. Whole genome sequencing offers more complete catalogues of genes and assists a systems approach to phenotype dissection. These efforts are being coordinated through the 959 Nematode Genomes initiative.

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data

The rapid expansion in the quantity and quality of RNA-Seq data requires the development of sophisticated high-performance bioinformatics tools capable of rapidly transforming this data into meaningful information that is easily interpretable by biologists. Currently available analysis tools are often not easily installed by the general biologist and most of them lack inherent parallel processing capabilities widely recognized as an essential feature of next-generation bioinformatics tools. We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets. R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading. In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.

Single-tube linear DNA amplification for genome-wide studies using a few thousand cells

Linear amplification of DNA (LinDA) by T7 polymerase is a versatile and robust method for generating sufficient amounts of DNA for genome-wide studies with minute amounts of cells. LinDA can be coupled to a great number of global profiling technologies. Indeed, chromatin immunoprecipitation coupled to massive parallel sequencing (ChIP-seq) has been achieved for transcription factors and epigenetic modification of chromatin histones with 1,000 to 5,000 cells. LinDA largely simplifies reChIP-seq experiments to monitor co-binding at chromatin target sites. The single-tube design of LinDA is ideal for handling ultrasmall amounts of DNA (<30 pg) and is compatible with automation. The actual hands-on working time is less than 6 h with one overnight reaction. The present protocol describes all materials and critical steps, and provides examples and controls for LinDA. Applications of LinDA for genome-wide analyses of biobank samples and for the study of chromatin conformation and nuclear architecture are in progress.

mRNA-Seq reveals complex patterns of gene regulation and expression in the mouse skeletal muscle transcriptome associated with calorie restriction

Sarcopenia is an age-associated loss of skeletal muscle mass and strength that increases the risk of disability. Calorie restriction (CR), the consumption of fewer calories while maintaining adequate nutrition, mitigates sarcopenia and many other age-related diseases. To identify potential mechanisms by which CR preserves skeletal muscle integrity during aging, we used mRNA-Seq for deep characterization of gene regulation and mRNA abundance in skeletal muscle of old mice compared with old mice subjected to CR. mRNA-Seq revealed complex CR-associated changes in expression of mRNA isoforms, many of which occur without a change in total message abundance and thus would not be detected by methods other than mRNA-Seq. Functional annotation of differentially expressed genes reveals CR-associated upregulation of pathways involved in energy metabolism and lipid biosynthesis, and downregulation of pathways mediating protein breakdown and oxidative stress, consistent with earlier microarray-based studies. CR-associated changes not noted in previous studies involved downregulation of genes controlling actin cytoskeletal structures and muscle development. These CR-associated changes reflect generally healthier muscle, consistent with CR's mitigation of sarcopenia. mRNA-Seq generates a rich picture of the changes in gene expression associated with CR, and may facilitate identification of genes that are primary mediators of CR's effects.

Proteomics in evolutionary ecology: linking the genotype with the phenotype

The study of the proteome (proteomics), which includes the dynamics of protein expression, regulation, interactions and its function, has played a less prominent role in evolutionary and ecological investigations in comparison with the study of the genome and transcriptome. There are, however, a number of arguments suggesting that this situation should change. First, the proteome is closer to the phenotype than the genome or the transcriptome, and as such may be more directly responsive to natural selection, and thus closely linked to adaptation. Second, there is evidence of a low correlation between protein and transcript expression levels across genes in many different organisms. Finally, there have been some recent important technological improvements in proteomics methods that make them feasible, practical and useful to address a wide range of evolutionary questions even in nonmodel organisms. The different proteomic methods, their limitations and problems when interpreting empirical data are described and discussed. In addition, the proteomic literature pertaining to evolutionary ecology is reviewed with examples, and potential applications of proteomics in a variety of evolutionary contexts are outlined. New proteomic research trends such as the study of posttranslational modifications and protein-protein interactions, as well as the combined use of the different -omics approaches, are discussed in relation to the development of a more functional and integrated perspective, needed for achieving a more comprehensive knowledge of evolutionary change.

RNA therapeutics: beyond RNA interference and antisense oligonucleotides

Here, we discuss three RNA-based therapeutic technologies exploiting various oligonucleotides that bind to RNA by base pairing in a sequence-specific manner yet have different mechanisms of action and effects. RNA interference and antisense oligonucleotides downregulate gene expression by inducing enzyme-dependent degradation of targeted mRNA. Steric-blocking oligonucleotides block the access of cellular machinery to pre-mRNA and mRNA without degrading the RNA. Through this mechanism, steric-blocking oligonucleotides can redirect alternative splicing, repair defective RNA, restore protein production or downregulate gene expression. Moreover, they can be extensively chemically modified to acquire more drug-like properties. The ability of RNA-blocking oligonucleotides to restore gene function makes them best suited for the treatment of genetic disorders. Positive results from clinical trials for the treatment of Duchenne muscular dystrophy show that this technology is close to achieving its clinical potential.

Pharmacogenomics: what is next?

Pharmacogenomics is moving from a candidate gene strategy to large scale approaches. This is in line with the new paradigm of linking a trait to (a) pathway(s) rather than to single genes. In addition, breakthroughs in genomics offer a non-a priori assessment of implicated genes, expanding the possibilities in pharmacogenomics research. In this review, we discuss the pros and cons of new concepts in study design and on high throughput approaches to be implemented in the near future.

Genome-wide analytical approaches for reverse metabolic engineering of industrially relevant phenotypes in yeast

Successful reverse engineering of mutants that have been obtained by nontargeted strain improvement has long presented a major challenge in yeast biotechnology. This paper reviews the use of genome-wide approaches for analysis of Saccharomyces cerevisiae strains originating from evolutionary engineering or random mutagenesis. On the basis of an evaluation of the strengths and weaknesses of different methods, we conclude that for the initial identification of relevant genetic changes, whole genome sequencing is superior to other analytical techniques, such as transcriptome, metabolome, proteome, or array-based genome analysis. Key advantages of this technique over gene expression analysis include the independency of genome sequences on experimental context and the possibility to directly and precisely reproduce the identified changes in naive strains. The predictive value of genome-wide analysis of strains with industrially relevant characteristics can be further improved by classical genetics or simultaneous analysis of strains derived from parallel, independent strain improvement lineages.

Overview of available methods for diverse RNA-Seq data analyses

RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. With the development of high-throughput sequencing technologies, the sequencing cost is dropping dramatically with the sequencing output increasing sharply. However, the sequencing reads are still short in length and contain various sequencing errors. Moreover, the intricate transcriptome is always more complicated than we expect. These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies. This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junction detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction.

Transcriptional and epigenetic control of T helper cell specification: molecular mechanisms underlying commitment and plasticity

T helper cell differentiation occurs in the context of the extracellular cytokine milieu evoked by diverse microbes and other pathogenic stimuli along with T cell receptor stimulation. The culmination of these signals results in specification of T helper lineages, which occurs through the combinatorial action of multiple transcription factors that establish distinctive transcriptomes. In this manner, inducible, but constitutively active, master regulators work in conjunction with factors such as the signal transducer and activator of transcriptions (STATs) that sense the extracellular environment. The acquisition of a distinctive transcriptome also depends on chromatin modifications that impact key cis elements as well as the changes in global genomic organization. Thus, signal transduction and epigenetics are linked in these processes of differentiation. In this review, recent advances in understanding T helper lineage specification and deciphering the action of transcription factors are summarized with emphasis on comprehensive views of the dynamic T cell epigenome.

Systems biology approach for new target and biomarker identification

The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.

A High-Throughput Method for Illumina RNA-Seq Library Preparation

With the introduction of cost effective, rapid, and superior quality next generation sequencing techniques, gene expression analysis has become viable for labs conducting small projects as well as large-scale gene expression analysis experiments. However, the available protocols for construction of RNA-sequencing (RNA-Seq) libraries are expensive and/or difficult to scale for high-throughput applications. Also, most protocols require isolated total RNA as a starting point. We provide a cost-effective RNA-Seq library synthesis protocol that is fast, starts with tissue, and is high-throughput from tissue to synthesized library. We have also designed and report a set of 96 unique barcodes for library adapters that are amenable to high-throughput sequencing by a large combination of multiplexing strategies. Our developed protocol has more power to detect differentially expressed genes when compared to the standard Illumina protocol, probably owing to less technical variation amongst replicates. We also address the problem of gene-length biases affecting differential gene expression calls and demonstrate that such biases can be efficiently minimized during mRNA isolation for library preparation.

Whole genome studies of Tetrahymena

Within the past decade, genomic studies have emerged as essential and highly productive tools to explore the biology of Tetrahymena thermophila. The current major resources, which have been extensively mined by the research community, are the annotated macronuclear genome assembly, transcriptomic data and the databases that house this information. Efforts in progress will soon improve these data sources and expand their scope, including providing annotated micronuclear and comparative genomic sequences. Future studies of Tetrahymena cell and molecular biology, development, physiology, evolution and ecology will benefit greatly from these resources and the advanced genomic technologies they enable.

Assessing the genetic risk for alcohol use disorders

The past two decades have witnessed a revolution in the field of genetics which has led to a rapid evolution in the tools and techniques available for mapping genes that contribute to genetically complex disorders such as alcohol dependence. Research in humans and in animal models of human disease has provided important new information. Among the most commonly applied approaches used in human studies are family studies, case-control studies, and genome-wide association studies. Animal models have been aimed at identifying genetic regions or individual genes involved in different aspects of alcoholism, using such approaches as quantitative trait locus analysis, genome sequencing, knockout animals, and other sophisticated molecular genetic techniques. All of these approaches have led to the identification of several genes that seem to influence the risk for alcohol dependence, which are being further analyzed. Newer studies, however, also are attempting to look at the genetic basis of alcoholism at the level of the entire genome, moving beyond the study of individual genes toward analyses of gene interactions and gene networks in the development of this devastating disease.

Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets

Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, "-omics" methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments "constraints" using previously reported protein-protein and protein-DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions "optimization". A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.

Interpreting gene-expression profiles in transplantation: a critical appraisal

Oligonucleotide microarray technology has created a small revolution in the transplant community because it has helped to decipher previously unknown molecular processes involved in allograft pathology, redefined molecular patterns of diseases that are indistinguishable at the pathological level and made possible the definition of new prognostic factors for long-term graft outcomes. However, given the tremendous complexity of the biological processes that are involved in the pathology of a transplanted organ, the interpretation of transcriptomic data can be speculative and oversimplified. Here, we discuss critical considerations regarding the nature of the object studied by cDNA microarray technology, the means by which it is observed, the interpretation of the observations, and whether the observations make sense in the context of transplant-related scientific questions. Given these limitations, we believe that global approaches based on more functional biological intermediates are necessary for a better understanding of the molecular processes that regulate the physiopathology of the transplanted organ.

Exploring long non-coding RNAs through sequencing

Long non-coding RNAs (lncRNAs) are emerging as an important class of regulatory transcripts that are implicated in a variety of biological functions. RNA-sequencing, along with other next-generation sequencing-based approaches, enables their study on a genome-wide scale, at maximal resolution, and across multiple conditions. This review discusses how sequencing-based studies are providing global insights into lncRNA transcription, post-transcriptional processing, expression regulation and sites of function. The next few years will deepen our insight into the overall contribution of lncRNAs to genome function and to the information flow from genotype to phenotype.

Cryptococcus gattii comparative genomics and transcriptomics: a NIH/NIAID White Paper

Cryptococcus gattii is an emerging global pathogen. Recent reports suggest that C. gattii cryptococcosis is more common in immunocompetent as well as HIV-infected AIDS patients than earlier estimated. An ongoing outbreak of C. gattii in Vancouver, Canada, and the US Pacific Northwest has heightened public health awareness in North America. We have few clues as to what causes emergence or re-emergence of highly pathogenic strains, why C. gattii split up from its sibling pathogen C. neoformans, why it thrives in trees instead, and why immunocompetent individuals are vulnerable to this pathogen? C. gattii comprises of four distinct lineages, but the information on the genome of C. gattii is inadequate and unrepresentative as it is limited to two strains, R265 and WM276, which are MATα, serotype B, genotype VGII/VGI from Canada and Australia, respectively. There is a wide gap in knowledge about the genomes of VGIII and VGIV strains, serotype C strains, and MATa strains. The geographical representation is inadequate in the absence of strains from California, South America, Asia, and Africa. Additional obstacles to work with this pathogen are the following: (a) complex molecular typing schemes and (b) lack of functional genomics analyses. We propose to complete genome sequencing of 12 reference strains by next-generation sequencing technology and to map their transcriptomes by RNA-Seq technology. This effort would lead to new resources for the scientific community including (1) insight from additional C. gattii genomes to anchor future research studies, (2) validation of single-nucleotide polymorphisms (SNPs) for molecular typing to improve epidemiology studies, and (3) transcript analyses from strains under relevant pathogenic and non-pathogenic conditions to accelerate the discovery of proteins for diagnostics, drug targets, and vaccines.

RNA-Seq-based monitoring of infection-linked changes in Vibrio cholerae gene expression

Pathogens adapt to the host environment by altering their patterns of gene expression. Microarray-based and genetic techniques used to characterize bacterial gene expression during infection are limited in their ability to comprehensively and simultaneously monitor genome-wide transcription. We used massively parallel cDNA sequencing (RNA-seq) techniques to quantitatively catalog the transcriptome of the cholera pathogen, Vibrio cholerae, derived from two animal models of infection. Transcripts elevated in infected rabbits and mice relative to laboratory media derive from the major known V. cholerae virulence factors and also from genes and small RNAs not previously linked to virulence. The RNA-seq data was coupled with metabolite analysis of cecal fluid from infected rabbits to yield insights into the host environment encountered by the pathogen and the mechanisms controlling pathogen gene expression. RNA-seq-based transcriptome analysis of pathogens during infection produces a robust, sensitive, and accessible data set for evaluation of regulatory responses driving pathogenesis.

A microRNA guide for clinicians and basic scientists: background and experimental techniques

MicroRNAs (miRNAs) are short non-coding RNA molecules that are approximately 22 nucleotides in length. In the last 10 years, miRNA research and discovery has advanced at a rapid rate. This review provides a brief overview of the discovery and biology of miRNAs, and summarises some of the experimental techniques used for isolation, detection, target prediction, and regulation of miRNAs. We also outline experimental workflows for investigators new to the field, and discuss the diagnostic and therapeutic application of miRNAs.

Identifying novel transcriptional components controlling energy metabolism

The investigation of metabolic regulation at the transcriptional level presents different challenges than those encountered in the study of other important problems like development or cancer. Levels of key components like glucose, insulin, and lipids can be modulated but rarely change in an all-or-none fashion, necessitating quantitative techniques that can be applied to multiple tissues and systems. This review examines recent advances in methods for studying transcriptional regulation, with special emphasis on metabolic science. We compare these methods for investigators trying to decide on the best approach for their particular physiological paradigm or model system.

Revealing the missing expressed genes beyond the human reference genome by RNA-Seq

BACKGROUND

The complete and accurate human reference genome is important for functional genomics researches. Therefore, the incomplete reference genome and individual specific sequences have significant effects on various studies.

RESULTS

we used two RNA-Seq datasets from human brain tissues and 10 mixed cell lines to investigate the completeness of human reference genome. First, we demonstrated that in previously identified ~5 Mb Asian and ~5 Mb African novel sequences that are absent from the human reference genome of NCBI build 36, ~211 kb and ~201 kb of them could be transcribed, respectively. Our results suggest that many of those transcribed regions are not specific to Asian and African, but also present in Caucasian. Then, we found that the expressions of 104 RefSeq genes that are unalignable to NCBI build 37 in brain and cell lines are higher than 0.1 RPKM. 55 of them are conserved across human, chimpanzee and macaque, suggesting that there are still a significant number of functional human genes absent from the human reference genome. Moreover, we identified hundreds of novel transcript contigs that cannot be aligned to NCBI build 37, RefSeq genes and EST sequences. Some of those novel transcript contigs are also conserved among human, chimpanzee and macaque. By positioning those contigs onto the human genome, we identified several large deletions in the reference genome. Several conserved novel transcript contigs were further validated by RT-PCR.

CONCLUSION

Our findings demonstrate that a significant number of genes are still absent from the incomplete human reference genome, highlighting the importance of further refining the human reference genome and curating those missing genes. Our study also shows the importance of de novo transcriptome assembly. The comparative approach between reference genome and other related human genomes based on the transcriptome provides an alternative way to refine the human reference genome.

Crop improvement in the era of climate change: an integrated, multi-disciplinary approach for common bean (Phaseolus vulgaris)

Climate change and global population increase are two converging forces that will jointly challenge researchers to design programs that ensure crop production systems meet the world's food demand. Climate change will potentially reduce productivity while a global population increase will require more food. If productivity is not improved for future climatic conditions, food insecurity may foster major economic and political uncertainty. Given the importance of grain legumes in general - common bean (Phaseolus vulgaris L.) in particular - a workshop entitled 'Improving Tolerance of Common Bean to Abiotic Stresses' was held with the goal of developing an interdisciplinary research agenda designed to take advantage of modern genotyping and breeding approaches that are coupled with large scale phenotyping efforts to improve common bean. Features of the program included a multinational phenotyping effort to evaluate the major common bean core germplasm collections and appropriate genetic populations. The phenotyping effort will emphasise the response of root and shoot traits to individual and combined stress conditions. These populations would also be genotyped using newly emerging high density single nucleotide polymorphism (SNP) marker arrays or next generation sequencing technology. Association analysis of the core collections aims to identify key loci associated with the response to the stress conditions. Companion bi-parental quantitative trait loci (QTL) experiments will act as confirmation experiments for the association analysis. The upcoming release of the genome sequence of common bean will be leveraged by utilising population genomic approaches to discover genomic regions that differentiate stress-responsive and non-responsive genotypes. The genome sequence will also enable global gene expression studies that will highlight specific molecular-based stress responses. This collective knowledge will inform the selection of parental lines to improve the efficiency of common bean improvement programs.

Directional gene expression and antisense transcripts in sexual and asexual stages of Plasmodium falciparum

BACKGROUND

It has been shown that nearly a quarter of the initial predicted gene models in the Plasmodium falciparum genome contain errors. Although there have been efforts to obtain complete cDNA sequences to correct the errors, the coverage of cDNA sequences on the predicted genes is still incomplete, and many gene models for those expressed in sexual or mosquito stages have not been validated. Antisense transcripts have widely been reported in P. falciparum; however, the extent and pattern of antisense transcripts in different developmental stages remain largely unknown.

RESULTS

We have sequenced seven bidirectional libraries from ring, early and late trophozoite, schizont, gametocyte II, gametocyte V, and ookinete, and four strand-specific libraries from late trophozoite, schizont, gametocyte II, and gametocyte V of the 3D7 parasites. Alignment of the cDNA sequences to the 3D7 reference genome revealed stage-specific antisense transcripts and novel intron-exon splicing junctions. Sequencing of strand-specific cDNA libraries suggested that more genes are expressed in one direction in gametocyte than in schizont. Alternatively spliced genes, antisense transcripts, and stage-specific expressed genes were also characterized.

CONCLUSIONS

It is necessary to continue to sequence cDNA from different developmental stages, particularly those of non-erythrocytic stages. The presence of antisense transcripts in some gametocyte and ookinete genes suggests that these antisense RNA may play an important role in gene expression regulation and parasite development. Future gene expression studies should make use of directional cDNA libraries. Antisense transcripts may partly explain the observed discrepancy between levels of mRNA and protein expression.