RNA sequencing: advances, challenges and opportunities

The possibility of de novo assembly of the genome and population genomics of the mangrove rivulus, Kryptolebias marmoratus

How organisms adapt to the range of environments they encounter is a fundamental question in biology. Elucidating the genetic basis of adaptation is a difficult task, especially when the targets of selection are not known. Emerging sequencing technologies and assembly algorithms facilitate the genomic dissection of adaptation and population differentiation in a vast array of organisms. Here we describe the attributes of Kryptolebias marmoratus, one of two known self-fertilizing hermaphroditic vertebrates that make this fish an attractive genetic system and a model for understanding the genomics of adaptation. Long periods of selfing have resulted in populations composed of many distinct naturally homozygous strains with a variety of identifiable, and apparently heritable, phenotypes. There also is strong population genetic structure across a diverse range of mangrove habitats, making this a tractable system in which to study differentiation both within and among populations. The ability to rear K. marmoratus in the laboratory contributes further to its value as a model for understanding the genetic drivers for adaptation. To date, microsatellite markers distinguish wild isogenic strains but the naturally high homozygosity improves the quality of de novo assembly of the genome and facilitates the identification of genetic variants associated with phenotypes. Gene annotation can be accomplished with RNA-sequencing data in combination with de novo genome assembly. By combining genomic information with extensive laboratory-based phenotyping, it becomes possible to map genetic variants underlying differences in behavioral, life-history, and other potentially adaptive traits. Emerging genomic technologies provide the required resources for establishing K. marmoratus as a new model organism for behavioral genetics and evolutionary genetics research.

Medulloblastomics: the end of the beginning

The division of medulloblastoma into different subgroups by microarray expression profiling has dramatically changed our perspective of this malignant childhood brain tumour. Now, the availability of next-generation sequencing and complementary high-density genomic technologies has unmasked novel driver mutations in each medulloblastoma subgroup. The implications of these findings for the management of patients are readily apparent, pinpointing previously unappreciated diagnostic and therapeutic targets. In this Review, we summarize the 'explosion' of data emerging from the application of modern genomics to medulloblastoma, and in particular the recurrent targets of mutation in medulloblastoma subgroups. These data are currently making their way into clinical trials as we seek to integrate conventional and molecularly targeted therapies.

Gene profiling in the avian embryo using laser capture microdissection and RT-qPCR

The dynamic nature of the developing embryo makes it challenging to understand complex morphogenetic events using information from large-scale gene expression patterns. What would be more insightful is molecular profiling of small numbers of cells selectively surveyed at specific developmental stages. However, detecting gene expression profile information from small numbers of cells (<10) in homogenous tissue has remained a major challenge. Here, we describe the use of laser capture microdissection (LCM), immunohistochemistry (IHC), and RT-qPCR to extract gene profile information in distinct embryo tissue more precisely than is possible with any other method. We use the chick embryo model system and combine electroporation and dual-label IHC to specifically identify cells for harvest by LCM without significant degradation of total RNA. We describe the development of a pre-amplification protocol for small subpopulations of cells to produce sensitive RT-qPCR results. The gene-specific pre-amplification efficiently and linearly amplifies only gene transcripts of interest from the harvested material without the need for RNA isolation. By combining the above techniques with microfluidic RT-qPCR, we robustly analyze the expression of ∼300 genes from as few as 10 cells harvested by LCM. Together, this protocol presents a confident isolation and means of sensitive expression analysis of small cell numbers from tissues and overcomes a technical hurdle that limits gene profiling.

Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples

Measures of RNA abundance are important for many areas of biology and often obtained from high-throughput RNA sequencing methods such as Illumina sequence data. These measures need to be normalized to remove technical biases inherent in the sequencing approach, most notably the length of the RNA species and the sequencing depth of a sample. These biases are corrected in the widely used reads per kilobase per million reads (RPKM) measure. Here, we argue that the intended meaning of RPKM is a measure of relative molar RNA concentration (rmc) and show that for each set of transcripts the average rmc is a constant, namely the inverse of the number of transcripts mapped. Further, we show that RPKM does not respect this invariance property and thus cannot be an accurate measure of rmc. We propose a slight modification of RPKM that eliminates this inconsistency and call it TPM for transcripts per million. TPM respects the average invariance and eliminates statistical biases inherent in the RPKM measure.

Accurate diagnostics for Bovine tuberculosis based on high-throughput sequencing

Background

Bovine tuberculosis (bTB) is an enduring contagious disease of cattle that has caused substantial losses to the global livestock industry. Despite large-scale eradication efforts, bTB continues to persist. Current bTB tests rely on the measurement of immune responses in vivo (skin tests), and in vitro (bovine interferon-γ release assay). Recent developments are characterized by interrogating the expression of an increasing number of genes that participate in the immune response. Currently used assays have the disadvantages of limited sensitivity and specificity, which may lead to incomplete eradication of bTB. Moreover, bTB that reemerges from wild disease reservoirs requires early and reliable diagnostics to prevent further spread. In this work, we use high-throughput sequencing of the peripheral blood mononuclear cells (PBMCs) transcriptome to identify an extensive panel of genes that participate in the immune response. We also investigate the possibility of developing a reliable bTB classification framework based on RNA-Seq reads.

Methodology/Principal Findings

Pooled PBMC mRNA samples from unaffected calves as well as from those with disease progression of 1 and 2 months were sequenced using the Illumina Genome Analyzer II. More than 90 million reads were splice-aligned against the reference genome, and deposited to the database for further expression analysis and visualization. Using this database, we identified 2,312 genes that were differentially expressed in response to bTB infection (p<10⁻⁸). We achieved a bTB infected status classification accuracy of more than 99% with split-sample validation on newly designed and learned mixtures of expression profiles.

Conclusions/Significance

We demonstrated that bTB can be accurately diagnosed at the early stages of disease progression based on RNA-Seq high-throughput sequencing. The inclusion of multiple genes in the diagnostic panel, combined with the superior sensitivity and broader dynamic range of RNA-Seq, has the potential to improve the accuracy of bTB diagnostics. The computational pipeline used for the project is available from http://code.google.com/p/bovine-tb-prediction.

Identification of the highly accumulated microRNA*s in Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa)

Plant microRNAs (miRNAs) are crucial for the regulation of gene expression, which is involved in almost all the important biological processes. In the cytoplasm, the miRNA strand is selectively incorporated into a specific Argonaute (AGO)-associated gene silencing complex, while the miRNA* is degraded rapidly. Thus, most miRNA*s were thought to be biologically meaningless. Interestingly, several recent reports in both plants and animals have shaken this notion. Many miRNA*s were demonstrated to possess regulatory roles in gene expression. However, the low accumulation levels of most miRNA*s raise the question whether the activities of this small RNA (sRNA) species are widespread in plants. Here, by using publicly available sRNA high-throughput sequencing data, we found that the accumulation levels of several miRNA*s could be much higher than those of their miRNA partners in certain organs, mutants and/or AGO-associated silencing complexes of both Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa). Based on target prediction and degradome sequencing data-based validation, some of these highly accumulated miRNA*s were indicated to possess cleavage-based potential regulatory role on certain targets. Besides, some interesting biological interpretations were obtained based on the accumulation patterns of the miRNA*s, the annotations of the target genes, and literature mining. Taken together, the expanded list of the highly accumulated miRNA*s along with their potential target genes discovered in this study further strengthened the current notion that certain members of the miRNA* species are biologically relevant, which needs further inspection.

ASGARD: an open-access database of annotated transcriptomes for emerging model arthropod species

The increased throughput and decreased cost of next-generation sequencing (NGS) have shifted the bottleneck genomic research from sequencing to annotation, analysis and accessibility. This is particularly challenging for research communities working on organisms that lack the basic infrastructure of a sequenced genome, or an efficient way to utilize whatever sequence data may be available. Here we present a new database, the Assembled Searchable Giant Arthropod Read Database (ASGARD). This database is a repository and search engine for transcriptomic data from arthropods that are of high interest to multiple research communities but currently lack sequenced genomes. We demonstrate the functionality and utility of ASGARD using de novo assembled transcriptomes from the milkweed bug Oncopeltus fasciatus, the cricket Gryllus bimaculatus and the amphipod crustacean Parhyale hawaiensis. We have annotated these transcriptomes to assign putative orthology, coding region determination, protein domain identification and Gene Ontology (GO) term annotation to all possible assembly products. ASGARD allows users to search all assemblies by orthology annotation, GO term annotation or Basic Local Alignment Search Tool. User-friendly features of ASGARD include search term auto-completion suggestions based on database content, the ability to download assembly product sequences in FASTA format, direct links to NCBI data for predicted orthologs and graphical representation of the location of protein domains and matches to similar sequences from the NCBI non-redundant database. ASGARD will be a useful repository for transcriptome data from future NGS studies on these and other emerging model arthropods, regardless of sequencing platform, assembly or annotation status. This database thus provides easy, one-stop access to multi-species annotated transcriptome information. We anticipate that this database will be useful for members of multiple research communities, including developmental biology, physiology, evolutionary biology, ecology, comparative genomics and phylogenomics.

Database URL:asgard.rc.fas.harvard.edu

Methods, Challenges and Potentials of Single Cell RNA-seq

RNA-sequencing (RNA-seq) has become the tool of choice for transcriptomics. Several recent studies demonstrate its successful adaption to single cell analysis. This allows new biological insights into cell differentiation, cell-to-cell variation and gene regulation, and how these aspects depend on each other. Here, I review the current single cell RNA-seq (scRNA-seq) efforts and discuss experimental protocols, challenges and potentials.

Deep sequencing reveals differences in the transcriptional landscapes of fibers from two cultivated species of cotton

Cotton (Gossypium) fiber is the most prevalent natural product used in the textile industry. The two major cultivated species, G. hirsutum (Gh) and G. barbadense (Gb), are allotetraploids with contrasting fiber quality properties. To better understand the molecular basis for their fiber differences, EST pyrosequencing was used to document the fiber transcriptomes at two key development stages, 10 days post anthesis (dpa), representing the peak of fiber elongation, and 22 dpa, representing the transition to secondary cell wall synthesis. The 617,000 high quality reads (89% of the total 692,000 reads) from 4 libraries were assembled into 46,072 unigenes, comprising 38,297 contigs and 7,775 singletons. Functional annotation of the unigenes together with comparative digital gene expression (DGE) revealed a diverse set of functions and processes that were partly linked to specific fiber stages. Globally, 2,770 contigs (7%) showed differential expression (>2-fold) between 10 and 22 dpa (irrespective of genotype), with 70% more highly expressed at 10 dpa, while 2,248 (6%) were differentially expressed between the genotypes (irrespective of stage). The most significant genes with differential DGE at 10 dpa included expansins and lipid transfer proteins (higher in Gb), while at 22 dpa tubulins, cellulose, and sucrose synthases showed higher expression in Gb. DGE was compared with expression data of 10 dpa-old fibers from Affymetrix microarrays. Among 543 contigs showing differential expression on both platforms, 74% were consistent in being either over-expressed in Gh (242 genes) or in Gb (161 genes). Furthermore, the unigene set served to identify 339 new SSRs and close to 21,000 inter-genotypic SNPs. Subsets of 88 SSRs and 48 SNPs were validated through mapping and added 65 new loci to a RIL genetic map. The new set of fiber ESTs and the gene-based markers complement existing available resources useful in basic and applied research for crop improvement in cotton.

Individual RNA base recognition in immobilized oligonucleotides using a protein nanopore

Protein nanopores are under investigation as key components of rapid, low-cost platforms to sequence DNA molecules. Previously, it has been shown that the α-hemolysin (αHL) nanopore contains three recognition sites, capable of discriminating between individual DNA bases when oligonucleotides are immobilized within the nanopore. However, the direct sequencing of RNA is also of critical importance. Here, we achieve sharply defined current distributions that enable clear discrimination of the four nucleobases, guanine, cytosine, adenine, and uracil, in RNA. Further, the modified bases, inosine, N(6)-methyladenosine, and N(5)-methylcytosine, can be distinguished.

Targeted transcript profiling by sequencing

In this work we present a targeted gene expression strategy employing trinucleotide threading (TnT) amplification and massive parallel sequencing. We have previously shown that TnT combined with array readout accurately monitors expression levels. However, with this detection strategy spurious products go undetected. Accordingly, we adapted the TnT protocol to massive parallel sequencing to acquire an unbiased view of the entire TnT-generated product population. In this manner we investigated the identity of undesired products, their extent at different oligonucleotide:RNA ratios and their effect on the expression levels. We demonstrate that TnT gene expression profiling with massive sequencing readout renders reliable expression data from as low as 3.5 ng of total RNA. Moreover, using 350 ng of total RNA results in only 0.7% to 1.1% undesired products. When lowering the amount of input material, the undesired product fraction increases but this does not influence the expression profiles.

Omics and drug response

A new generation of technologies commonly named omics permits assessment of the entirety of the components of biological systems and produces an explosion of data and a major shift in our concepts of disease. These technologies will likely shape the future of health care. One aspect of these advances is that the data generated document the uniqueness of each human being in regard to disease risk and treatment response. These developments have reemphasized the concept of personalized medicine. Here we review the impact of omics technologies on one key aspect of personalized medicine: the individual drug response. We describe how knowledge of different omics may affect treatment decisions, namely drug choice and drug dose, and how it can be used to improve clinical outcomes.

Glucosylated hydroxymethyluracil, DNA base J, prevents transcriptional readthrough in Leishmania

Some Ts in nuclear DNA of trypanosomes and Leishmania are hydroxylated and glucosylated to yield base J (β-D-glucosyl-hydroxymethyluracil). In Leishmania, about 99% of J is located in telomeric repeats. We show here that most of the remaining J is located at chromosome-internal RNA polymerase II termination sites. This internal J and telomeric J can be reduced by a knockout of J-binding protein 2 (JBP2), an enzyme involved in the first step of J biosynthesis. J levels are further reduced by growing Leishmania JBP2 knockout cells in BrdU-containing medium, resulting in cell death. The loss of internal J in JBP2 knockout cells is accompanied by massive readthrough at RNA polymerase II termination sites. The readthrough varies between transcription units but may extend over 100 kb. We conclude that J is required for proper transcription termination and infer that the absence of internal J kills Leishmania by massive readthrough of transcriptional stops.

Revisiting global gene expression analysis

Gene expression analysis is a widely used and powerful method for investigating the transcriptional behavior of biological systems, for classifying cell states in disease, and for many other purposes. Recent studies indicate that common assumptions currently embedded in experimental and analytical practices can lead to misinterpretation of global gene expression data. We discuss these assumptions and describe solutions that should minimize erroneous interpretation of gene expression data from multiple analysis platforms.

Pivots of pluripotency: the roles of non-coding RNA in regulating embryonic and induced pluripotent stem cells

BACKGROUND

Induced pluripotent stem cells (iPSC) derived from reprogrammed patient somatic cells possess enormous therapeutic potential. However, unlocking the full capabilities of iPSC will require an improved understanding of the molecular mechanisms which govern the induction and maintenance of pluripotency, as well as directed differentiation to clinically relevant lineages. Induced pluripotency of a differentiated cell is mediated by sequential cascades of genetic and epigenetic reprogramming of somatic histone and DNA CpG methylation marks. These genome-wide changes are mediated by a coordinated activity of transcription factors and epigenetic modifying enzymes. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), are now recognized as an important third class of regulators of the pluripotent state.

SCOPE OF REVIEW

This review surveys the currently known roles and mechanisms of ncRNAs in regulating the embryonic and induced pluripotent states.

MAJOR CONCLUSIONS

Through a variety of mechanisms, ncRNAs regulate constellations of key pluripotency genes and epigenetic regulators, and thus critically determine induction and maintenance of the pluripotent state.

GENERAL SIGNIFICANCE

A further understanding of the roles of ncRNAs in regulating pluripotency may help assess the quality of human iPSC reprogramming. Additionally, ncRNA biology may help decipher potential transcriptional and epigenetic commonalities between the self renewal processes that govern both ESC and tumor initiating cancer stem cells (CSC). This article is part of a Special Issue entitled Biochemistry of Stem Cells.

Characterization of in vitro transcription amplification linearity and variability in the low copy number regime using External RNA Control Consortium (ERCC) spike-ins

Using spike-in controls designed to mimic mammalian mRNA species, we used the quantitative reverse transcription polymerase chain reaction (RT-qPCR) to assess the performance of in vitro transcription (IVT) amplification process of small samples. We focused especially on the confidence of the transcript level measurement, which is essential for differential gene expression analyses. IVT reproduced gene expression profiles down to approximately 100 absolute input copies. However, a RT-qPCR analysis of the antisense RNA showed a systematic bias against low copy number transcripts, regardless of sequence. Experiments also showed that noise increases with decreasing copy number. First-round IVT preserved the gene expression information within a sample down to the 100 copy level, regardless of total input sample amount. However, the amplification was nonlinear under low total RNA input/long IVT conditions. Variability of the amplification increased predictably with decreasing input copy number. For the small enrichments of interest in typical differential gene expression studies (e.g., twofold changes), the bias from IVT reactions is unlikely to affect the results. In limited cases, some transcript-specific differential gene expression values will need adjustment to reflect this bias. Proper experimental design with reasonable detection limits will yield differential gene expression capability even between low copy number transcripts.

Recurrent R-spondin fusions in colon cancer

Identifying and understanding changes in cancer genomes is essential for the development of targeted therapeutics. Here we analyse systematically more than 70 pairs of primary human colon tumours by applying next-generation sequencing to characterize their exomes, transcriptomes and copy-number alterations. We have identified 36,303 protein-altering somatic changes that include several new recurrent mutations in the Wnt pathway gene TCF7L2, chromatin-remodelling genes such as TET2 and TET3 and receptor tyrosine kinases including ERBB3. Our analysis for significantly mutated cancer genes identified 23 candidates, including the cell cycle checkpoint kinase ATM. Copy-number and RNA-seq data analysis identified amplifications and corresponding overexpression of IGF2 in a subset of colon tumours. Furthermore, using RNA-seq data we identified multiple fusion transcripts including recurrent gene fusions involving R-spondin family members RSPO2 and RSPO3 that together occur in 10% of colon tumours. The RSPO fusions were mutually exclusive with APC mutations, indicating that they probably have a role in the activation of Wnt signalling and tumorigenesis. Consistent with this we show that the RSPO fusion proteins were capable of potentiating Wnt signalling. The R-spondin gene fusions and several other gene mutations identified in this study provide new potential opportunities for therapeutic intervention in colon cancer.

Genome-wide ribosome profiling reveals complex translational regulation in response to oxidative stress

Information on unique and coordinated regulation of transcription and translation in response to stress is central to the understanding of cellular homeostasis. Here we used ribosome profiling coupled with next-generation sequencing to examine the interplay between transcription and translation under conditions of hydrogen peroxide treatment in Saccharomyces cerevisiae. Hydrogen peroxide treatment led to a massive and rapid increase in ribosome occupancy of short upstream ORFs, including those with non-AUG translational starts, and of the N-terminal regions of ORFs that preceded the transcriptional response. In addition, this treatment induced the synthesis of N-terminally extended proteins and elevated stop codon read-through and frameshift events. It also increased ribosome occupancy at the beginning of ORFs and potentially the duration of the elongation step. We identified proteins whose synthesis was regulated rapidly by hydrogen peroxide posttranscriptionally; however, for the majority of genes increased protein synthesis followed transcriptional regulation. These data define the landscape of genome-wide regulation of translation in response to hydrogen peroxide and suggest that potentiation (coregulation of the transcript level and translation) is a feature of oxidative stress.

Cephalopod genomics: A plan of strategies and organization

The Cephalopod Sequencing Consortium (CephSeq Consortium) was established at a NESCent Catalysis Group Meeting, “Paths to Cephalopod Genomics- Strategies, Choices, Organization,” held in Durham, North Carolina, USA on May 24-27, 2012. Twenty-eight participants representing nine countries (Austria, Australia, China, Denmark, France, Italy, Japan, Spain and the USA) met to address the pressing need for genome sequencing of cephalopod mollusks. This group, drawn from cephalopod biologists, neuroscientists, developmental and evolutionary biologists, materials scientists, bioinformaticians and researchers active in sequencing, assembling and annotating genomes, agreed on a set of cephalopod species of particular importance for initial sequencing and developed strategies and an organization (CephSeq Consortium) to promote this sequencing. The conclusions and recommendations of this meeting are described in this white paper.

Current state-of-art of sequencing technologies for plant genomics research

A number of next-generation sequencing (NGS) technologies such as Roche/454, Illumina and AB SOLiD have recently become available. These technologies are capable of generating hundreds of thousands or tens of millions of short DNA sequence reads at a relatively low cost. These NGS technologies, now referred as second-generation sequencing (SGS) technologies, are being utilized for de novo sequencing, genome re-sequencing, and whole genome and transcriptome analysis. Now, new generation of sequencers, based on the 'next-next' or third-generation sequencing (TGS) technologies like the Single-Molecule Real-Time (SMRT™) Sequencer, Heliscope™ Single Molecule Sequencer, and the Ion Personal Genome Machine™ are becoming available that are capable of generating longer sequence reads in a shorter time and at even lower costs per instrument run. Ever declining sequencing costs and increased data output and sample throughput for NGS and TGS sequencing technologies enable the plant genomics and breeding community to undertake genotyping-by-sequencing (GBS). Data analysis, storage and management of large-scale second or TGS projects, however, are essential. This article provides an overview of different sequencing technologies with an emphasis on forthcoming TGS technologies and bioinformatics tools required for the latest evolution of DNA sequencing platforms.

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation ≥0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation ≥0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data.

Wavelet-based detection of transcriptional activity on a novel Staphylococcus aureus tiling microarray

BACKGROUND

High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the genome.Transcriptome analysis of tiling microarray data facilitates the discovery of novel transcripts and the assessment of differential expression in diverse experimental conditions. Although new technologies such as next-generation sequencing have appeared, microarrays might still be useful for the study of small genomes or for the analysis of genomic regions with custom microarrays due to their lower price and good accuracy in expression quantification.

RESULTS

Here, we propose a novel wavelet-based method, named ZCL (zero-crossing lines), for the combined denoising and segmentation of tiling signals. The denoising is performed with the classical SUREshrink method and the detection of transcriptionally active regions is based on the computation of the Continuous Wavelet Transform (CWT). In particular, the detection of the transitions is implemented as the thresholding of the zero-crossing lines. The algorithm described has been applied to the public Saccharomyces cerevisiae dataset and it has been compared with two well-known algorithms: pseudo-median sliding window (PMSW) and the structural change model (SCM). As a proof-of-principle, we applied the ZCL algorithm to the analysis of the custom tiling microarray hybridization results of a S. aureus mutant deficient in the sigma B transcription factor. The challenge was to identify those transcripts whose expression decreases in the absence of sigma B.

CONCLUSIONS

The proposed method archives the best performance in terms of positive predictive value (PPV) while its sensitivity is similar to the other algorithms used for the comparison. The computation time needed to process the transcriptional signals is low as compared with model-based methods and in the same range to those based on the use of filters. Automatic parameter selection has been incorporated and moreover, it can be easily adapted to a parallel implementation. We can conclude that the proposed method is well suited for the analysis of tiling signals, in which transcriptional activity is often hidden in the noise. Finally, the quantification and differential expression analysis of S. aureus dataset have demonstrated the valuable utility of this novel device to the biological analysis of the S. aureus transcriptome.

Next generation deep sequencing and vaccine design: today and tomorrow

Next generation sequencing (NGS) technologies have redefined the modus operandi in both human and microbial genetics research, allowing the unprecedented generation of very large sequencing datasets on a short time scale and at affordable costs. Vaccine development research is rapidly taking full advantage of the advent of NGS. This review provides a concise summary of the current applications of NGS in relation to research seeking to develop vaccines for human infectious diseases, incorporating studies of both the pathogen and the host. We focus on rapidly mutating viral pathogens, which are major targets in current vaccine research. NGS is unraveling the complex dynamics of viral evolution and host responses against these viruses, thus contributing substantially to the likelihood of successful vaccine development.

Application of metatranscriptomics to soil environments

The activities of soil microbial communities are of critical importance to terrestrial ecosystem functioning. The mechanisms that determine the interactions between soil microorganisms, their environment and neighbouring organisms, however, are poorly understood. Due to advances in sequencing technologies, an increasing number of metagenomics studies are being conducted on samples from diverse environments including soils. This information has not only increased our awareness of the functional potential of soil microbial communities, but also constitutes powerful reference material for soil metatranscriptomics studies. Metatranscriptomics provides a snapshot of transcriptional profiles that correspond to discrete populations within a microbial community at the time of sampling. This information can indicate the potential activities of complex microbial communities and the mechanisms that regulate them. Here we summarise the technical challenges for metatranscriptomics applied to soil environments and discuss approaches for gaining biologically meaningful insight into these datasets.

Transcriptome profiling of the cancer, adjacent non-tumor and distant normal tissues from a colorectal cancer patient by deep sequencing

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the world. A genome-wide screening of transcriptome dysregulation between cancer and normal tissue would provide insight into the molecular basis of CRC initiation and progression. Compared with microarray technology, which is commonly used to identify transcriptional changes, the recently developed RNA-seq technique has the ability to detect other abnormal regulations in the cancer transcriptome, such as alternative splicing, novel transcripts or gene fusion. In this study, we performed high-throughput transcriptome sequencing at ∼50× coverage on CRC, adjacent non-tumor and distant normal tissue. The results revealed cancer-specific, differentially expressed genes and differential alternative splicing, suggesting that the extracellular matrix and metabolic pathways are activated and the genes related to cell homeostasis are suppressed in CRC. In addition, one tumor-restricted gene fusion, PRTEN-NOTCH2, was also detected and experimentally confirmed. This study reveals some common features in tumor invasion and provides a comprehensive survey of the CRC transcriptome, which provides better insight into the complexity of regulatory changes during tumorigenesis.

Extensive terminal and asymmetric processing of small RNAs from rRNAs, snoRNAs, snRNAs, and tRNAs

Deep sequencing studies frequently identify small RNA fragments of abundant RNAs. These fragments are thought to represent degradation products of their precursors. Using sequencing, computational analysis, and sensitive northern blot assays, we show that constitutively expressed non-coding RNAs such as tRNAs, snoRNAs, rRNAs and snRNAs preferentially produce small 5' and 3' end fragments. Similar to that of microRNA processing, these terminal fragments are generated in an asymmetric manner that predominantly favors either the 5' or 3' end. Terminal-specific and asymmetric processing of these small RNAs occurs in both mouse and human cells. In addition to the known processing of some 3' terminal tRNA-derived fragments (tRFs) by the RNase III endonuclease Dicer, we show that several RNase family members can produce tRFs, including Angiogenin that cleaves the TψC loop to generate 3' tRFs. The 3' terminal tRFs but not the 5' tRFs are highly complementary to human endogenous retroviral sequences in the genome. Despite their independence from Dicer processing, these tRFs associate with Ago2 and are capable of down regulating target genes by transcript cleavage in vitro. We suggest that endogenous 3' tRFs have a role in regulating the unwarranted expression of endogenous viruses through the RNA interference pathway.

Statistical methods for identifying differentially expressed genes in RNA-Seq experiments

RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses.

Deep sequencing-based transcriptome analysis of chicken spleen in response to avian pathogenic Escherichia coli (APEC) infection

Avian pathogenic Escherichia coli (APEC) leads to economic losses in poultry production and is also a threat to human health. The goal of this study was to characterize the chicken spleen transcriptome and to identify candidate genes for response and resistance to APEC infection using Solexa sequencing. We obtained 14422935, 14104324, and 14954692 Solexa read pairs for non-challenged (NC), challenged-mild pathology (MD), and challenged-severe pathology (SV), respectively. A total of 148197 contigs and 98461 unigenes were assembled, of which 134949 contigs and 91890 unigenes match the chicken genome. In total, 12272 annotated unigenes take part in biological processes (11664), cellular components (11927), and molecular functions (11963). Summing three specific contrasts, 13650 significantly differentially expressed unigenes were found in NC Vs. MD (6844), NC Vs. SV (7764), and MD Vs. SV (2320). Some unigenes (e.g. CD148, CD45 and LCK) were involved in crucial pathways, such as the T cell receptor (TCR) signaling pathway and microbial metabolism in diverse environments. This study facilitates understanding of the genetic architecture of the chicken spleen transcriptome, and has identified candidate genes for host response to APEC infection.

Global impact of RNA splicing on transcriptome remodeling in the heart

In the eukaryotic transcriptome, both the numbers of genes and different RNA species produced by each gene contribute to the overall complexity. These RNA species are generated by the utilization of different transcriptional initiation or termination sites, or more commonly, from different messenger RNA (mRNA) splicing events. Among the 30,000+ genes in human genome, it is estimated that more than 95% of them can generate more than one gene product via alternative RNA splicing. The protein products generated from different RNA splicing variants can have different intracellular localization, activity, or tissue-distribution. Therefore, alternative RNA splicing is an important molecular process that contributes to the overall complexity of the genome and the functional specificity and diversity among different cell types. In this review, we will discuss current efforts to unravel the full complexity of the cardiac transcriptome using a deep-sequencing approach, and highlight the potential of this technology to uncover the global impact of RNA splicing on the transcriptome during development and diseases of the heart.

Application of next generation qPCR and sequencing platforms to mRNA biomarker analysis

Recent years have seen the emergence of new high-throughput PCR and sequencing platforms with the potential to bring analysis of transcriptional biomarkers to a broader range of clinical applications and to provide increasing depth to our understanding of the transcriptome. We present an overview of how to process clinical samples for RNA biomarker analysis in terms of RNA extraction and mRNA enrichment, and guidelines for sample analysis by RT-qPCR and digital PCR using nanofluidic real-time PCR platforms. The options for quantitative gene expression profiling and whole transcriptome sequencing by next generation sequencing are reviewed alongside the bioinformatic considerations for these approaches. Considering the diverse technologies now available for transcriptome analysis, methods for standardising measurements between platforms will be paramount if their diagnostic impact is to be maximised. Therefore, the use of RNA standards and other reference materials is also discussed.

Recent developments of genomic research in soybean

Soybean is an important cash crop with unique and important traits such as the high seed protein and oil contents, and the ability to perform symbiotic nitrogen fixation. A reference genome of cultivated soybeans was established in 2010, followed by whole-genome re-sequencing of wild and cultivated soybean accessions. These efforts revealed unique features of the soybean genome and helped to understand its evolution. Mapping of variations between wild and cultivated soybean genomes were performed. These genomic variations may be related to the process of domestication and human selection. Wild soybean germplasms exhibited high genomic diversity and hence may be an important source of novel genes/alleles. Accumulation of genomic data will help to refine genetic maps and expedite the identification of functional genes. In this review, we summarize the major findings from the whole-genome sequencing projects and discuss the possible impacts on soybean researches and breeding programs. Some emerging areas such as transcriptomic and epigenomic studies will be introduced. In addition, we also tabulated some useful bioinformatics tools that will help the mining of the soybean genomic data.

Chips and tags suggest plant-environment interactions differ for two alpine Pachycladon species

BACKGROUND

Expression profiling has been proposed as a means for screening non-model organisms in their natural environments to identify genes potentially important in adaptive diversification. Tag profiling using high throughput sequencing is a relatively low cost means of expression profiling with deep coverage. However the extent to which very short cDNA sequences can be effectively used in screening for candidate genes is unclear. Here we investigate this question using an evolutionarily distant as well as a closely related transcriptome for referencing tags. We do this by comparing differentially expressed genes and processes between two closely related allopolyploid species of Pachycladon which have distinct altitudinal preferences in the New Zealand Southern Alps. We validate biological inferences against earlier microarray analyses.

RESULTS

Statistical and gene annotation enrichment analyses of tag profiles identified more differentially expressed genes of potential adaptive significance than previous analyses of array-based expression profiles. These include genes involved in glucosinolate metabolism, flowering time, and response to cold, desiccation, fungi and oxidation. In addition, despite the short length of 20mer tags, we were able to infer patterns of homeologous gene expression for 700 genes in our reference library of 7,128 full-length Pachycladon ESTs. We also demonstrate that there is significant information loss when mapping tags to the non-conspecific reference transcriptome of A. thaliana as opposed to P. fastigiatum ESTs but also describe mapping strategies by which the larger collection of A. thaliana ESTs can be used as a reference.

CONCLUSION

When coupled with a reference transcriptome generated using RNA-seq, tag sequencing offers a promising approach for screening natural populations and identifying candidate genes of potential adaptive significance. We identify computational issues important for the successful application of tag profiling in a non-model allopolyploid plant species.

Whole transcriptome RNA-Seq analysis of breast cancer recurrence risk using formalin-fixed paraffin-embedded tumor tissue

RNA biomarkers discovered by RT-PCR-based gene expression profiling of archival formalin-fixed paraffin-embedded (FFPE) tissue form the basis for widely used clinical diagnostic tests; however, RT-PCR is practically constrained in the number of transcripts that can be interrogated. We have developed and optimized RNA-Seq library chemistry as well as bioinformatics and biostatistical methods for whole transcriptome profiling from FFPE tissue. The chemistry accommodates low RNA inputs and sample multiplexing. These methods both enable rediscovery of RNA biomarkers for disease recurrence risk that were previously identified by RT-PCR analysis of a cohort of 136 patients, and also identify a high percentage of recurrence risk markers that were previously discovered using DNA microarrays in a separate cohort of patients, evidence that this RNA-Seq technology has sufficient precision and sensitivity for biomarker discovery. More than two thousand RNAs are strongly associated with breast cancer recurrence risk in the 136 patient cohort (FDR <10%). Many of these are intronic RNAs for which corresponding exons are not also associated with disease recurrence. A number of the RNAs associated with recurrence risk belong to novel RNA networks. It will be important to test the validity of these novel associations in whole transcriptome RNA-Seq screens of other breast cancer cohorts.

From single cells to deep phenotypes in cancer

In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.

Single-neuron RNA-Seq: technical feasibility and reproducibility

Understanding brain function involves improved knowledge about how the genome specifies such a large diversity of neuronal types. Transcriptome analysis of single neurons has been previously described using gene expression microarrays. Using high-throughput transcriptome sequencing (RNA-Seq), we have developed a method to perform single-neuron RNA-Seq. Following electrophysiology recording from an individual neuron, total RNA was extracted by aspirating the cellular contents into a fine glass electrode tip. The mRNAs were reverse transcribed and amplified to construct a single-neuron cDNA library, and subsequently subjected to high-throughput sequencing. This approach was applied to both individual neurons cultured from embryonic mouse hippocampus, as well as neocortical neurons from live brain slices. We found that the average pairwise Spearman’s rank correlation coefficient of gene expression level expressed as RPKM (reads per kilobase of transcript per million mapped reads) was 0.51 between five cultured neuronal cells, whereas the same measure between three cortical layer 5 neurons in situ was 0.25. The data suggest that there may be greater heterogeneity of the cortical neurons, as compared to neurons in vitro. The results demonstrate the technical feasibility and reproducibility of RNA-Seq in capturing a part of the transcriptome landscape of single neurons, and confirmed that morphologically identical neurons, even from the same region, have distinct gene expression patterns.

Alternative splicing: a potential source of functional innovation in the eukaryotic genome

Alternative splicing (AS) is a common posttranscriptional process in eukaryotic organisms, by which multiple distinct functional transcripts are produced from a single gene. The release of the human genome draft revealed a much smaller number of genes than anticipated. Because of its potential role in expanding protein diversity, interest in alternative splicing has been increasing over the last decade. Although recent studies have shown that 94% human multiexon genes undergo AS, evolution of AS and thus its potential role in functional innovation in eukaryotic genomes remain largely unexplored. Here we review available evidence regarding the evolution of AS prevalence and functional role. In addition we stress the need to correct for the strong effect of transcript coverage in AS detection and set out a strategy to ultimately elucidate the extent of the role of AS in functional innovation on a genomic scale.

Identification of aberrant pathways and network activities from high-throughput data

Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

The availability of the human genome sequence has allowed identification of disease-causing mutations in many Mendelian disorders, and detection of significant associations of nucleotide polymorphisms to complex diseases and traits. Despite these progresses, finding the causative variations for most of the common diseases remains a complex task. Several studies have shown gene expression analyses provide a quite unbiased way to investigate complex traits and common disorders' pathogenesis. Therefore, whole-transcriptome analysis is increasingly acquiring a key role in the knowledge of mechanisms responsible for complex diseases. Hybridization- and tag-based technologies have elucidated the involvement of multiple genes and pathways in pathological conditions, providing insights into the expression of thousand of coding and noncoding RNAs, such as microRNAs. However, the introduction of Next-Generation Sequencing, particularly of RNA-Seq, has overcome some drawbacks of previously used technologies. Identifying, in a single experiment, potentially novel genes/exons and splice isoforms, RNA editing, fusion transcripts and allele-specific expression are some of its advantages. RNA-Seq has been fruitfully applied to study cancer and host-pathogens interactions, and it is taking first steps for studying neurodegenerative diseases (ND) as well as neuropsychiatric diseases. In addition, it is emerging as a very powerful tool to study quantitative trait loci associated with gene expression in complex diseases. This paper provides an overview on gene expression profiling of complex diseases, with emphasis on RNA-Seq, its advantages over conventional technologies for studying cancer and ND, and for linking nucleotide variations to gene expression changes, also discussing its limitations.

Global Approaches to the Role of miRNAs in Drug-Induced Changes in Gene Expression

Neurons modulate gene expression with subcellular precision through excitation-coupled local protein synthesis, a process that is regulated in part through the involvement of microRNAs (miRNAs), a class of small non-coding RNAs. The biosynthesis of miRNAs is reviewed, with special emphasis on miRNA families, the subcellular localization of specific miRNAs in neurons, and their potential roles in the response to drugs of abuse. For over a decade, DNA microarrays have dominated genome-wide gene expression studies, revealing widespread effects of drug exposure on neuronal gene expression. We review a number of recent studies that explore the emerging role of miRNAs in the biochemical and behavioral responses to cocaine. The more powerful next-generation sequencing technology offers certain advantages and is supplanting microarrays for the analysis of complex transcriptomes. Next-generation sequencing is unparalleled in its ability to identify and quantify low-abundance transcripts without prior sequence knowledge, facilitating the accurate detection and quantification of miRNAs expressed in total tissue and miRNAs localized to postsynaptic densities (PSDs). We previously identified cocaine-responsive miRNAs, synaptically enriched and depleted miRNA families, and confirmed cocaine-induced changes in protein expression for several bioinformatically predicted target genes. The miR-8 family was found to be highly enriched and cocaine-regulated at the PSD, where its members may modulate expression of cell adhesion molecules. An integrative approach that combines mRNA, miRNA, and protein expression profiling in combination with focused single gene studies and innovative behavioral paradigms should facilitate the development of more effective therapeutic approaches to treat addiction.

Conserved and quickly evolving immunome genes have different evolutionary paths

Genetic, transcript, and protein level variations have important functional and evolutionary consequences. We performed systematic data collection and analysis of copy-number variations, single-nucleotide polymorphisms, disease-causing variations, messenger RNA splicing variants, and protein posttranslational modifications for the genes and proteins essential for human immune system. Information about polymorphic and evolutionarily fixed genetic variations was used to group immunome genes to the most conserved and the most quickly changing ones under directed selection during the recent immunome evolution. Gene Ontology terms related to adaptive immunity are associated with gene groups subject to recent directing selection. In addition, several other characteristics of the immunome genes and proteins in these two categories have statistically significant differences. The presented findings question the usability of directed mouse genes as models for human diseases and conditions and shed light on the fine tuning of human immunity and its diverse functions.