- Open Access
A survey of best practices for RNA-seq data analysis
Genome Biology volume 17, Article number: 13 (2016)
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.
Transcript identification and the quantification of gene expression have been distinct core activities in molecular biology ever since the discovery of RNA’s role as the key intermediate between the genome and the proteome. The power of sequencing RNA lies in the fact that the twin aspects of discovery and quantification can be combined in a single high-throughput sequencing assay called RNA-sequencing (RNA-seq). The pervasive adoption of RNA-seq has spread well beyond the genomics community and has become a standard part of the toolkit used by the life sciences research community. Many variations of RNA-seq protocols and analyses have been published, making it challenging for new users to appreciate all of the steps necessary to conduct an RNA-seq study properly.
There is no optimal pipeline for the variety of different applications and analysis scenarios in which RNA-seq can be used. Scientists plan experiments and adopt different analysis strategies depending on the organism being studied and their research goals. For example, if a genome sequence is available for the studied organism, it should be possible to identify transcripts by mapping RNA-seq reads onto the genome. By contrast, for organisms without sequenced genomes, quantification would be achieved by first assembling reads de novo into contigs and then mapping these contigs onto the transcriptome. For well-annotated genomes such as the human genome, researchers may choose to base their RNA-seq analysis on the existing annotated reference transcriptome alone, or might try to identify new transcripts and their differential regulation. Furthermore, investigators might be interested only in messenger RNA isoform expression or microRNA (miRNA) levels or allele variant identification. Both the experimental design and the analysis procedures will vary greatly in each of these cases. RNA-seq can be used solo for transcriptome profiling or in combination with other functional genomics methods to enhance the analysis of gene expression. Finally, RNA-seq can be coupled with different types of biochemical assay to analyze many other aspects of RNA biology, such as RNA–protein binding, RNA structure, or RNA–RNA interactions. These applications are, however, beyond the scope of this review as we focus on ‘typical’ RNA-seq.
Every RNA-seq experimental scenario could potentially have different optimal methods for transcript quantification, normalization, and ultimately differential expression analysis. Moreover, quality control checks should be applied pertinently at different stages of the analysis to ensure both reproducibility and reliability of the results. Our focus is to outline current standards and resources for the bioinformatics analysis of RNA-seq data. We do not aim to provide an exhaustive compilation of resources or software tools nor to indicate one best analysis pipeline. Rather, we aim to provide a commented guideline for RNA-seq data analysis. Figure 1 depicts a generic roadmap for experimental design and analysis using standard Illumina sequencing. We also briefly list several data integration paradigms that have been proposed and comment on their potential and limitations. We finally discuss the opportunities as well as challenges provided by single-cell RNA-seq and long-read technologies when compared to traditional short-read RNA-seq.
A crucial prerequisite for a successful RNA-seq study is that the data generated have the potential to answer the biological questions of interest. This is achieved by first defining a good experimental design, that is, by choosing the library type, sequencing depth and number of replicates appropriate for the biological system under study, and second by planning an adequate execution of the sequencing experiment itself, ensuring that data acquisition does not become contaminated with unnecessary biases. In this section, we discuss both considerations.
One important aspect of the experimental design is the RNA-extraction protocol used to remove the highly abundant ribosomal RNA (rRNA), which typically constitutes over 90 % of total RNA in the cell, leaving the 1–2 % comprising messenger RNA (mRNA) that we are normally interested in. For eukaryotes, this involves choosing whether to enrich for mRNA using poly(A) selection or to deplete rRNA. Poly(A) selection typically requires a relatively high proportion of mRNA with minimal degradation as measured by RNA integrity number (RIN), which normally yields a higher overall fraction of reads falling onto known exons. Many biologically relevant samples (such as tissue biopsies) cannot, however, be obtained in great enough quantity or good enough mRNA integrity to produce good poly(A) RNA-seq libraries and therefore require ribosomal depletion. For bacterial samples, in which mRNA is not polyadenylated, the only viable alternative is ribosomal depletion. Another consideration is whether to generate strand-preserving libraries. The first generation of Illumina-based RNA-seq used random hexamer priming to reverse-transcribe poly(A)-selected mRNA. This methodology did not retain information contained on the DNA strand that is actually expressed  and therefore complicates the analysis and quantification of antisense or overlapping transcripts. Several strand-specific protocols , such as the widely used dUTP method, extend the original protocol by incorporating UTP nucleotides during the second cDNA synthesis step, prior to adapter ligation followed by digestion of the strand containing dUTP . In all cases, the size of the final fragments (usually less than 500 bp for Illumina) will be crucial for proper sequencing and subsequent analysis. Furthermore, sequencing can involve single-end (SE) or paired-end (PE) reads, although the latter is preferable for de novo transcript discovery or isoform expression analysis [4, 5]. Similarly, longer reads improve mappability and transcript identification [5, 6]. The best sequencing option depends on the analysis goals. The cheaper, short SE reads are normally sufficient for studies of gene expression levels in well-annotated organisms, whereas longer and PE reads are preferable to characterize poorly annotated transcriptomes.
Another important factor is sequencing depth or library size, which is the number of sequenced reads for a given sample. More transcripts will be detected and their quantification will be more precise as the sample is sequenced to a deeper level . Nevertheless, optimal sequencing depth again depends on the aims of the experiment. While some authors will argue that as few as five million mapped reads are sufficient to quantify accurately medium to highly expressed genes in most eukaryotic transcriptomes, others will sequence up to 100 million reads to quantify precisely genes and transcripts that have low expression levels . When studying single cells, which have limited sample complexity, quantification is often carried out with just one million reads but may be done reliably for highly expressed genes with as few as 50,000 reads ; even 20,000 reads have been used to differentiate cell types in splenic tissue . Moreover, optimal library size depends on the complexity of the targeted transcriptome. Experimental results suggest that deep sequencing improves quantification and identification but might also result in the detection of transcriptional noise and off-target transcripts . Saturation curves can be used to assess the improvement in transcriptome coverage to be expected at a given sequencing depth .
Finally, a crucial design factor is the number of replicates. The number of replicates that should be included in a RNA-seq experiment depends on both the amount of technical variability in the RNA-seq procedures and the biological variability of the system under study, as well as on the desired statistical power (that is, the capacity for detecting statistically significant differences in gene expression between experimental groups). These two aspects are part of power analysis calculations (Fig. 1a; Box 1).
The adequate planning of sequencing experiments so as to avoid technical biases is as important as good experimental design, especially when the experiment involves a large number of samples that need to be processed in several batches. In this case, including controls, randomizing sample processing and smart management of sequencing runs are crucial to obtain error-free data (Fig. 1a; Box 2).
Analysis of the RNA-seq data
The actual analysis of RNA-seq data has as many variations as there are applications of the technology. In this section, we address all of the major analysis steps for a typical RNA-seq experiment, which involve quality control, read alignment with and without a reference genome, obtaining metrics for gene and transcript expression, and approaches for detecting differential gene expression. We also discuss analysis options for applications of RNA-seq involving alternative splicing, fusion transcripts and small RNA expression. Finally, we review useful packages for data visualization.
The acquisition of RNA-seq data consists of several steps — obtaining raw reads, read alignment and quantification. At each of these steps, specific checks should be applied to monitor the quality of the data (Fig. 1a).
Quality control for the raw reads involves the analysis of sequence quality, GC content, the presence of adaptors, overrepresented k-mers and duplicated reads in order to detect sequencing errors, PCR artifacts or contaminations. Acceptable duplication, k-mer or GC content levels are experiment- and organism-specific, but these values should be homogeneous for samples in the same experiments. We recommend that outliers with over 30 % disagreement to be discarded. FastQC  is a popular tool to perform these analyses on Illumina reads, whereas NGSQC  can be applied to any platform. As a general rule, read quality decreases towards the 3’ end of reads, and if it becomes too low, bases should be removed to improve mappability. Software tools such as the FASTX-Toolkit  and Trimmomatic  can be used to discard low-quality reads, trim adaptor sequences, and eliminate poor-quality bases.
Reads are typically mapped to either a genome or a transcriptome, as will be discussed later. An important mapping quality parameter is the percentage of mapped reads, which is a global indicator of the overall sequencing accuracy and of the presence of contaminating DNA. For example, we expect between 70 and 90 % of regular RNA-seq reads to map onto the human genome (depending on the read mapper used) , with a significant fraction of reads mapping to a limited number of identical regions equally well (‘multi-mapping reads’). When reads are mapped against the transcriptome, we expect slightly lower total mapping percentages because reads coming from unannotated transcripts will be lost, and significantly more multi-mapping reads because of reads falling onto exons that are shared by different transcript isoforms of the same gene.
Other important parameters are the uniformity of read coverage on exons and the mapped strand. If reads primarily accumulate at the 3’ end of transcripts in poly(A)-selected samples, this might indicate low RNA quality in the starting material. The GC content of mapped reads may reveal PCR biases. Tools for quality control in mapping include Picard , RSeQC  and Qualimap .
Once actual transcript quantification values have been calculated, they should be checked for GC content and gene length biases so that correcting normalization methods can be applied if necessary. If the reference transcriptome is well annotated, researchers could analyze the biotype composition of the sample, which is indicative of the quality of the RNA purification step. For example, rRNA and small RNAs should not be present in regular polyA longRNA preparations [10, 19]. A number of R packages (such as NOISeq  or EDASeq ) provide useful plots for quality control of count data.
The quality-control steps described above involve individual samples. In addition, it is also crucial to assess the global quality of the RNA-seq dataset by checking on the reproducibility among replicates and for possible batch effects. Reproducibility among technical replicates should be generally high (Spearman R2 > 0.9) , but no clear standard exists for biological replicates, as this depends on the heterogeneity of the experimental system. If gene expression differences exist among experimental conditions, it should be expected that biological replicates of the same condition will cluster together in a principal component analysis (PCA).
When a reference genome is available, RNA-seq analysis will normally involve the mapping of the reads onto the reference genome or transcriptome to infer which transcripts are expressed. Mapping solely to the reference transcriptome of a known species precludes the discovery of new, unannotated transcripts and focuses the analysis on quantification alone. By contrast, if the organism does not have a sequenced genome, then the analysis path is first to assemble reads into longer contigs and then to treat these contigs as the expressed transcriptome to which reads are mapped back again for quantification. In either case, read coverage can be used to quantify transcript expression level (Fig. 1b). A basic choice is whether transcript identification and quantification are done sequentially or simultaneously.
Two alternatives are possible when a reference sequence is available: mapping to the genome or mapping to the annotated transcriptome (Fig. 2a, b; Box 3). Regardless of whether a genome or transcriptome reference is used, reads may map uniquely (they can be assigned to only one position in the reference) or could be multi-mapped reads (multireads). Genomic multireads are primarily due to repetitive sequences or shared domains of paralogous genes. They normally account for a significant fraction of the mapping output when mapped onto the genome and should not be discarded. When the reference is the transcriptome, multi-mapping arises even more often because a read that would have been uniquely mapped on the genome would map equally well to all gene isoforms in the transcriptome that share the exon. In either case — genome or transcriptome mapping — transcript identification and quantification become important challenges for alternatively expressed genes.
Identifying novel transcripts using the short reads provided by Illumina technology is one of the most challenging tasks in RNA-seq. Short reads rarely span across several splice junctions and thus make it difficult to directly infer all full-length transcripts. In addition, it is difficult to identify transcription start and end sites , and tools such as GRIT  that incorporate other data such as 5’ ends from CAGE or RAMPAGE typically have a better chance of annotating the major expressed isoforms correctly. In any case, PE reads and higher coverage help to reconstruct lowly expressed transcripts, and replicates are essential to resolve false-positive calls (that is, mapping artifacts or contaminations) at the low end of signal detection. Several methods, such as Cufflinks , iReckon , SLIDE  and StringTie , incorporate existing annotations by adding them to the possible list of isoforms. Montebello  couples isoform discovery and quantification using a likelihood-based Monte Carlo algorithm to boost performance. Gene-finding tools such as Augustus  can incorporate RNA-seq data to better annotate protein-coding transcripts, but perform worse on non-coding transcripts . In general, accurate transcript reconstruction from short reads is difficult, and methods typically show substantial disagreement .
De novo transcript reconstruction
When a reference genome is not available or is incomplete, RNA-seq reads can be assembled de novo (Fig. 2c) into a transcriptome using packages such as SOAPdenovo-Trans , Oases , Trans-ABySS  or Trinity . In general, PE strand-specific sequencing and long reads are preferred because they are more informative . Although it is impossible to assemble lowly expressed transcripts that lack enough coverage for a reliable assembly, too many reads are also problematic because they lead to potential misassembly and increased runtimes. Therefore, in silico reduction of the number of reads is recommended for deeply sequenced samples . For comparative analyses across samples, it is advisable to combine all reads from multiple samples into a single input in order to obtain a consolidated set of contigs (transcripts), followed by mapping back of the short reads for expression estimation .
Either with a reference or de novo, the complete reconstruction of transcriptomes using short-read Illumina technology remains a challenging problem, and in many cases de novo assembly results in tens or hundreds of contigs accounting for fragmented transcripts. Emerging long-read technologies, such as SMRT from Pacific Biosciences, provide reads that are long enough to sequence complete transcripts for most genes and are a promising alternative that is discussed further in the “Outlook” section below.
The most common application of RNA-seq is to estimate gene and transcript expression. This application is primarily based on the number of reads that map to each transcript sequence, although there are algorithms such as Sailfish that rely on k-mer counting in reads without the need for mapping . The simplest approach to quantification is to aggregate raw counts of mapped reads using programs such as HTSeq-count  or featureCounts . This gene-level (rather than transcript-level) quantification approach utilizes a gene transfer format (GTF) file  containing the genome coordinates of exons and genes, and often discard multireads. Raw read counts alone are not sufficient to compare expression levels among samples, as these values are affected by factors such as transcript length, total number of reads, and sequencing biases. The measure RPKM (reads per kilobase of exon model per million reads)  is a within-sample normalization method that will remove the feature-length and library-size effects. This measure and its subsequent derivatives FPKM (fragments per kilobase of exon model per million mapped reads), a within-sample normalized transcript expression measure analogous to RPKs, and TPM (transcripts per million) are the most frequently reported RNA-seq gene expression values. It should be noted that RPKM and FPKM are equivalent for SE reads and that FPKM can be converted into TPM using a simple formula . The dichotomy of within-sample and between-sample comparisons has led to a lot of confusion in the literature. Correcting for gene length is not necessary when comparing changes in gene expression within the same gene across samples, but it is necessary for correctly ranking gene expression levels within the sample to account for the fact that longer genes accumulate more reads. Furthermore, programs such as Cufflinks that estimate gene length from the data can find significant differences in gene length between samples that cannot be ignored. TPMs, which effectively normalize for the differences in composition of the transcripts in the denominator rather than simply dividing by the number of reads in the library, are considered more comparable between samples of different origins and composition but can still suffer some biases. These must be addressed with normalization techniques such as TMM.
Several sophisticated algorithms have been developed to estimate transcript-level expression by tackling the problem of related transcripts’ sharing most of their reads. Cufflinks  estimates transcript expression from a mapping to the genome obtained from mappers such as TopHat using an expectation-maximization approach that estimates transcript abundances. This approach takes into account biases such as the non-uniform read distribution along the gene length. Cufflinks was designed to take advantage of PE reads, and may use GTF information to identify expressed transcripts, or can infer transcripts de novo from the mapping data alone. Algorithms that quantify expression from transcriptome mappings include RSEM (RNA-Seq by Expectation Maximization) , eXpress , Sailfish  and kallisto  among others. These methods allocate multi-mapping reads among transcript and output within-sample normalized values corrected for sequencing biases [35, 41, 43]. Additionally, the RSEM algorithm uses an expectation maximization approach that returns TPM values . NURD  provides an efficient way of estimating transcript expression from SE reads with a low memory and computing cost.
Differential gene expression analysis
Differential expression analysis (Fig. 1b) requires that gene expression values should be compared among samples. RPKM, FPKM, and TPM normalize away the most important factor for comparing samples, which is sequencing depth, whether directly or by accounting for the number of transcripts, which can differ significantly between samples. These approaches rely on normalizing methods that are based on total or effective counts, and tend to perform poorly when samples have heterogeneous transcript distributions, that is, when highly and differentially expressed features can skew the count distribution [45, 46]. Normalization methods that take this into account are TMM , DESeq , PoissonSeq  and UpperQuartile , which ignore highly variable and/or highly expressed features. Additional factors that interfere with intra-sample comparisons include changes in transcript length across samples or conditions , positional biases in coverage along the transcript (which are accounted for in Cufflinks), average fragment size , and the GC contents of genes (corrected in the EDAseq package ). The NOISeq R package  contains a wide variety of diagnostic plots to identify sources of biases in RNA-seq data and to apply appropriate normalization procedures in each case. Finally, despite these sample-specific normalization methods, batch effects may still be present in the data. These effects can be minimized by appropriate experimental design  or, alternatively, removed by batch-correction methods such as COMBAT  or ARSyN [20, 53]. These approaches, although initially developed for microarray data, have been shown to work well with normalized RNA-seq data (STATegra project, unpublished).
As RNA-seq quantification is based on read counts that are absolutely or probabilistically assigned to transcripts, the first approaches to compute differential expression used discrete probability distributions, such as the Poisson or negative binomial [48, 54]. The negative binomial distribution (also known as the gamma-Poisson distribution) is a generalization of the Poisson distribution, allowing for additional variance (called overdispersion) beyond the variance expected from randomly sampling from a pool of molecules that are characteristic of RNA-seq data. However, the use of discrete distributions is not required for accurate analysis of differential expression as long as the sampling variance of small read counts is taken into account (most important for experiments with small numbers of replicates). Methods for transforming normalized counts of RNA-seq reads while learning the variance structure of the data have been shown to perform well in comparison to the discrete distribution approaches described above [55, 56]. Moreover, after extensive normalization (including TMM and batch removal), the data might have lost their discrete nature and be more akin to a continuous distribution.
Some methods, such as the popular edgeR , take as input raw read counts and introduce possible bias sources into the statistical model to perform an integrated normalization as well as a differential expression analysis. In other methods, the differential expression requires the data to be previously normalized to remove all possible biases. DESeq2, like edgeR, uses the negative binomial as the reference distribution and provides its own normalization approach [48, 58]. baySeq  and EBSeq  are Bayesian approaches, also based on the negative binomial model, that define a collection of models to describe the differences among experimental groups and to compute the posterior probability of each one of them for each gene. Other approaches include data transformation methods that take into account the sampling variance of small read counts and create discrete gene expression distributions that can be analyzed by regular linear models . Finally, non-parametric approaches such as NOISeq  or SAMseq  make minimal assumptions about the data and estimate the null distribution for inferential analysis from the actual data alone. For small-scale studies that compare two samples with no or few replicates, the estimation of the negative binomial distribution can be noisy. In such cases, simpler methods based on the Poisson distribution, such as DEGseq , or on empirical distributions (NOISeq ) can be an alternative, although it should be strongly stressed that, in the absence of biological replication, no population inference can be made and hence any p value calculation is invalid. Methods that analyze RNA-seq data without replicates therefore only have exploratory value. Considering the drop in price of sequencing, we recommend that RNA-seq experiments have a minimum of three biological replicates when sample availability is not limiting to allow all of the differential expression methods to leverage reproducibility between replicates.
Recent independent comparison studies have demonstrated that the choice of the method (or even the version of a software package) can markedly affect the outcome of the analysis and that no single method is likely to perform favorably for all datasets [56, 63, 64] (Box 4). We therefore recommend thoroughly documenting the settings and version numbers of programs used and considering the repetition of important analyses using more than one package.
Alternative splicing analysis
Transcript-level differential expression analysis can potentially detect changes in the expression of transcript isoforms from the same gene, and specific algorithms for alternative splicing-focused analysis using RNA-seq have been proposed. These methods fall into two major categories. The first approach integrates isoform expression estimation with the detection of differential expression to reveal changes in the proportion of each isoform within the total gene expression. One such early method, BASIS, used a hierarchical Bayesian model to directly infer differentially expressed transcript isoforms . CuffDiff2 estimates isoform expression first and then compares their differences. By integrating the two steps, the uncertainty in the first step is taken into consideration when performing the statistical analysis to look for differential isoform expression . The flow difference metric (FDM) uses aligned cumulative transcript graphs from mapped exon reads and junction reads to infer isoforms and the Jensen-Shannon divergence to measure the difference . Recently, Shi and Jiang  proposed a new method, rSeqDiff, that uses a hierarchical likelihood ratio test to detect differential gene expression without splicing change and differential isoform expression simultaneously. All these approaches are generally hampered by the intrinsic limitations of short-read sequencing for accurate identification at the isoform level, as discussed in the RNA-seq Genome Annotation Assessment Project paper .
The so-called ‘exon-based’ approach skips the estimation of isoform expression and detects signals of alternative splicing by comparing the distributions of reads on exons and junctions of the genes between the compared samples. This approach is based on the premise that differences in isoform expression can be tracked in the signals of exons and their junctions. DEXseq  and DSGSeq  adopt a similar idea to detect differentially spliced genes by testing for significant differences in read counts on exons (and junctions) of the genes. rMATS detects differential usage of exons by comparing exon-inclusion levels defined with junction reads . rDiff detects differential isoform expression by comparing read counts on alternative regions of the gene, either with or without annotated alternative isoforms . DiffSplice uses alignment graphs to identify alternative splicing modules (ASMs) and identifies differential splicing using signals of the ASMs . The advantage of exon or junction methods is their greater accuracy in identifying individual alternative splicing events. Exon-based methods are appropriate if the focus of the study is not on whole isoforms but on the inclusion and exclusion of specific exons and the functional protein domains (or regulatory features, in case of untranslated region exons) that they contain.
Visualization of RNA-seq data (Fig. 1c) is, in general terms, similar to that of any other type of genomic sequencing data, and it can be done at the level of reads (using ReadXplorer , for example) or at the level of processed coverage (read pileup), unnormalized (for example, total count) or normalized, using genome browsers such as the UCSC browser , Integrative Genomics Viewer (IGV)  (Figure S1a in Additional file 1), Genome Maps , or Savant . Some visualization tools are specifically designed for visualizing multiple RNA-seq samples, such as RNAseqViewer , which provides flexible ways to display the read abundances on exons, transcripts and junctions. Introns can be hidden to better display signals on the exons, and the heatmaps can help the visual comparison of signals on multiple samples (Figure S1b, c in Additional file 1). However, RNAseqViewer is slower than IGV.
Some of the software packages for differential gene expression analysis (such as DESeq2 or DEXseq in Bioconductor) have functions to enable the visualization of results, whereas others have been developed for visualization-exclusive purposes, such as CummeRbund (for CuffDiff ) or Sashimi plots, which can be used to visualize differentially spliced exons . The advantage of Sashimi plots is that their display of junction reads is more intuitive and aesthetically pleasing when the number of samples is small (Figure S1d in Additional file 1). Sashimi, structure, and hive plots for splicing quantitative trait loci (sQTL) can be obtained using SplicePlot . Splice graphs can be produced using SpliceSeq , and SplicingViewer  plots splice junctions and alternative splicing events. TraV  is a visualization tool that integrates data analysis, but its analytical methods are not applicable to large genomes.
Owing to the complexity of transcriptomes, efficient display of multiple layers of information is still a challenge. All of the tools are evolving rapidly and we can expect more comprehensive tools with desirable features to be available soon. Nevertheless, the existing tools are of great value for exploring results for individual genes of biological interest to assess whether particular analyses’ results can withstand detailed scrutiny or to reveal potential complications caused by artifacts, such as 3’ biases or complicated transcript structures. Users should visualize changes in read coverage for genes that are deemed important or interesting on the basis of their analysis results to evaluate the robustness of their conclusions.
Gene fusion discovery
The discovery of fused genes that can arise from chromosomal rearrangements is analogous to novel isoform discovery, with the added challenge of a much larger search space as we can no longer assume that the transcript segments are co-linear on a single chromosome. Artifacts are common even using state-of-the-art tools, which necessitates post-processing using heuristic filters . Artifacts primarily result from misalignment of read sequences due to polymorphisms, homology, and sequencing errors. Families of homologous genes, and highly polymorphic genes such as the HLA genes, produce reads that cannot be easily mapped uniquely to their location of origin in the reference genome. For genes with very high expression, the small but non-negligible sequencing error rate of RNA-seq will produce reads that map incorrectly to homologous loci. Filtering highly polymorphic genes and pairs of homologous genes is recommended [86, 87]. Also recommended is the filtering of highly expressed genes that are unlikely to be involved in gene fusions, such as ribosomal RNA . Finally, a low ratio of chimeric to wild-type reads in the vicinity of the fusion boundary may indicate spurious mis-mapping of reads from a highly expressed gene (the transcript allele fraction described by Yoshihara et al. ).
Given successful prediction of chimeric sequences, the next step is the prioritization of gene fusions that have biological impact over more expected forms of genomic variation. Examples of expected variation include immunoglobulin (IG) rearrangements in tumor samples infiltrated by immune cells, transiently expressed transposons and nuclear mitochondrial DNA, and read-through chimeras produced by co-transcription of adjacent genes . Care must be taken with filtering in order not to lose events of interest. For example, removing all fusions involving an IG gene may remove real IG fusions in lymphomas and other blood disorders; filtering fusions for which both genes are from the IG locus is preferred . Transiently expressed genomic breakpoint sequences that are associated with real gene fusions often overlap transposons; these should be filtered unless they are associated with additional fusion isoforms from the same gene pair . Read-through chimeras are easily identified as predictions involving alternative splicing between adjacent genes. Where possible, fusions should be filtered by their presence in a set of control datasets . When control datasets are not available, artifacts can be identified by their presence in a large number of unrelated datasets, after excluding the possibility that they represent true recurrent fusions [90, 91].
Strong fusion-sequence predictions are characterized by distinct subsequences that each align with high specificity to one of the fused genes. As alignment specificity is highly correlated with sequence length, a strong prediction sequence is longer, with longer subsequences from each gene. Longer reads and larger insert sizes produce longer predicted sequences; thus, we recommend PE RNA-seq data with larger insert size over SE datasets or datasets with short insert size. Another indicator of prediction strength is splicing. For most known fusions, the genomic breakpoint is located in an intron of each gene  and the fusion boundary coincides with a splice site within each gene. Furthermore, fusion isoforms generally follow the splicing patterns of wild-type genes. Thus, high confidence predictions have fusion boundaries coincident with exon boundaries and exons matching wild-type exons . Fusion discovery tools often incorporate some of the aforementioned ideas to rank fusion predictions [93, 94], though most studies apply additional custom heuristic filters to produce a list of high-quality fusion candidates [90, 91, 95].
Next-generation sequencing represents an increasingly popular method to address questions concerning the biological roles of small RNAs (sRNAs). sRNAs are usually 18–34 nucleotides in length, and they include miRNAs, short-interfering RNAs (siRNAs), PIWI-interacting RNAs (piRNAs), and other classes of regulatory molecules. sRNA-seq libraries are rarely sequenced as deeply as regular RNA-seq libraries because of a lack of complexity, with a typical range of 2–10 million reads. Bioinformatics analysis of sRNA-seq data differs from standard RNA-seq protocols (Fig. 1c). Ligated adaptor sequences are first trimmed and the resulting read-length distribution is computed. In animals, there are usually peaks for 22 and 23 nucleotides, whereas in plants there are peaks for 21- and 24-nucleotide redundant reads. For instance, miRTools 2.0 , a tool for prediction and profiling of sRNA species, uses by default reads that are 18–30 bases long. The threshold value depends on the application, and in case of miRNAs is usually in the range of 19–25 nucleotides.
As in standard RNA-seq, sRNA reads must then be aligned to a reference genome or transcriptome sequences using standard tools, such as Bowtie2 , STAR , or Burrows-Wheeler Aligner (BWA) . There are, however, some aligners (such as PatMaN  and MicroRazerS ) that have been designed to map short sequences with preset parameter value ranges suited for optimal alignment of short reads. The mapping itself may be performed with or without mismatches, the latter being used more commonly. In addition, reads that map beyond a predetermined set number of locations may be removed as putatively originating from repetitive elements. In the case of miRNAs, usually 5–20 distinct mappings per genome are allowed. sRNA reads are then simply counted to obtain expression values. However, users should also verify that their sRNA reads are not significantly contaminated by degraded mRNA, for example, by checking whether a miRNA library shows unexpected read coverage over the body of highly expressed genes such as GAPDH or ACTB.
Further analysis steps include comparison with known sRNAs and de novo identification of sRNAs. There are class-specific tools for this purpose, such as miRDeep  and miRDeep-P  for animal and plant miRNAs, respectively, or the trans-acting siRNA prediction tool at the UEA sRNA Workbench . Tools such as miRTools 2.0 , ShortStack , and iMir  also exist for comprehensive annotation of sRNA libraries and for identification of diverse classes of sRNAs.
Functional profiling with RNA-seq
The last step in a standard transcriptomics study (Fig. 1b) is often the characterization of the molecular functions or pathways in which differentially expressed genes (DEGs) are involved. The two main approaches to functional characterization that were developed first for microarray technology are (a) comparing a list of DEGs against the rest of the genome for overrepresented functions, and (b) gene set enrichment analysis (GSEA), which is based on ranking the transcriptome according to a measurement of differential expression. RNA-seq biases such as gene length complicate the direct applications of these methods for count data and hence RNA-seq-specific tools have been proposed. For example, GOseq  estimates a bias effect (such as gene length) on differential expression results and adapts the traditional hypergeometric statistic used in the functional enrichment test to account for this bias. Similarly, the Gene Set Variation Analysis (GSVA)  or SeqGSEA  packages also combine splicing and implement enrichment analyses similar to GSEA.
Functional analysis requires the availability of sufficient functional annotation data for the transcriptome under study. Resources such as Gene Ontology , Bioconductor , DAVID [111, 112] or Babelomics  contain annotation data for most model species. However, novel transcripts discovered during de novo transcriptome assembly or reconstruction would lack at least some functional information and therefore annotation is necessary for functional profiling of those results. Protein-coding transcripts can be functionally annotated using orthology by searching for similar sequences in protein databases such as SwissProt  and in databases that contain conserved protein domains such as Pfam  and InterPro . The use of standard vocabularies such as the Gene Ontology (GO) allows for some exchangeability of functional information across orthologs. Popular tools such as Blast2GO  allow massive annotation of complete transcriptome datasets against a variety of databases and controlled vocabularies. Typically, between 50 and 80 % of the transcripts reconstructed from RNA-seq data can be annotated with functional terms in this way. However, RNA-seq data also reveal that an important fraction of the transcriptome is lacking protein-coding potential. The functional annotation of these long non-coding RNAs is more challenging as their conservation is often less pronounced than that of protein-coding genes. The Rfam database  contains most well-characterized RNA families, such as ribosomal or transfer RNAs, while mirBase  or Miranda  are specialized in miRNAs. These resources can be used for similarity-based annotation of short non-coding RNAs, but no standard functional annotation procedures are available yet for other RNA types such as the long non-coding RNAs.
Integration with other data types
The integration of RNA-seq data with other types of genome-wide data (Fig. 1c) allows us to connect the regulation of gene expression with specific aspects of molecular physiology and functional genomics. Integrative analyses that incorporate RNA-seq data as the primary gene expression readout that is compared with other genomic experiments are becoming increasingly prevalent. Below, we discuss some of the additional challenges posed by such analyses.
The combination of RNA and DNA sequencing can be used for several purposes, such as single nucleotide polymorphism (SNP) discovery, RNA-editing analyses, or expression quantitative trait loci (eQTL) mapping. In a typical eQTL experiment, genotype and transcriptome profiles are obtained from the same tissue type across a relatively large number of individuals (>50) and correlations between genotype and expression levels are then detected. These associations can unravel the genetic basis of complex traits such as height , disease susceptibility  or even features of genome architecture [123, 124]. Large eQTL studies have shown that genetic variation affects the expression of most genes [125–128].
RNA-seq has two major advantages over array-based technologies for detecting eQTLs. First, it can identify variants that affect transcript processing. Second, reads that overlap heterozygous SNPs can be mapped to maternal and paternal chromosomes, enabling quantification of allele-specific expression within an individual . Allele-specific signals provide additional information about a genetic effect on transcription, and a number of computational methods have recently become available that leverage these signals to boost power for association mapping [130–132]. One challenge of this approach is the computational burden, as billions of gene–SNP associations need to be tested; bootstrapping or permutation-based approaches  are frequently used [134, 135]. Many studies have focused on testing only SNPs in the cis region surrounding the gene in question, and computationally efficient approaches have been developed recently to allow extremely swift mapping of eQTLs genome-wide . Moreover, the combination of RNA-seq and re-sequencing can be used both to remove false positives when inferring fusion genes  and to analyze copy number alterations .
Pairwise DNA-methylation and RNA-seq integration, for the most part, has consisted of the analysis of the correlation between DEGs and methylation patterns [138–140]. General linear models [141–143], logistic regression models  and empirical Bayes model  have been attempted among other modeling approaches. The statistically significant correlations that were observed, however, accounted for relatively small effects. An interesting shift away from focusing on individual gene–CpG methylation correlations is to use a network-interaction-based approach to analyze RNA-seq in relation to DNA methylation. This approach identifies one or more sets of genes (also called modules) that have coordinated differential expression and differential methylation .
The combination of RNA-seq and transcription factor (TF) chromatin immunoprecipitation sequencing (ChIP-seq) data can be used to remove false positives in ChIP-seq analysis and to suggest the activating or repressive effect of a TF on its target genes. For example, BETA  uses differential gene expression in combination with peaks from ChIP-seq experiments to call TF targets. In addition, ChIP-seq experiments involving histone modifications have been used to understand the general role of these epigenomic changes on gene expression [147, 148]. Other RNA-ChIP-sequencing integrative approaches are reviewed in . Integration of open chromatin data such as that from FAIRE-seq and DNase-seq with RNA-seq has mostly been limited to verifying the expression status of genes that overlap a region of interest . DNase-seq can be used for genome-wide footprinting of DNA-binding factors, and this in combination with the actual expression of genes can be used to infer active transcriptional networks .
Integration of RNA-seq and miRNA-seq data has the potential to unravel the regulatory effects of miRNAs on transcript steady-state levels. This analysis is challenging, however, because of the very noisy nature of miRNA target predictions, which hampers analyses based on correlations between miRNAs and their target genes. Associations might be found in databases such as mirWalk  and miRBase  that offer target prediction according to various algorithms. Tools such as CORNA , MMIA [154, 155], MAGIA , and SePIA  refine predictions by testing for significant associations between genes, miRNAs, pathways and GO terms, or by testing the relatedness or anticorrelation of the expression profiles of both the target genes and the associated miRNAs. In general, we recommend using miRNA–mRNA associations that are predicted by several algorithms. For example, in mouse, we found that requiring miRNA–mRNA association in five databases resulted in about 50 target mRNA predictions per miRNA (STATegra observations).
Proteomics and metabolomics
Integration of RNA-seq with proteomics is controversial because the two measurements show generally low correlation (~0.40 [158, 159]). Nevertheless, pairwise integration of proteomics and RNA-seq can be used to identify novel isoforms. Unreported peptides can be predicted from RNA-seq data and then used to complement databases normally queried in mass spectrometry as done by Low et al. . Furthermore, post-translational editing events may be identified if peptides that are present in the mass spectrometry analysis are absent from the expressed genes of the RNA-seq dataset. Integration of transcriptomics with metabolomics data has been used to identify pathways that are regulated at both the gene expression and the metabolite level, and tools are available that visualize results within the pathway context (MassTRIX , Paintomics , VANTED v2 , and SteinerNet ).
Integration and visualization of multiple data types
Integration of more than two genomic data types is still at its infancy and not yet extensively applied to functional sequencing techniques, but there are already some tools that combine several data types. SNMNMF  and PIMiM  combine mRNA and miRNA expression data with protein–protein, DNA–protein, and miRNA–mRNA interaction networks to identify miRNA–gene regulatory modules. MONA  combines different levels of functional genomics data, including mRNA, miRNA, DNA methylation, and proteomics data to discover altered biological functions in the samples being studied. Paintomics can integrate any type of functional genomics data into pathway analysis, provided that the features can be mapped onto genes or metabolites . 3Omics  integrates transcriptomics, metabolomics and proteomics data into regulatory networks.
In all cases, integration of different datasets is rarely straightforward because each data type is analyzed separately with its own tailored algorithms that yield results in different formats. Tools that facilitate format conversions and the extraction of relevant results can help; examples of such workflow construction software packages include Anduril , Galaxy  and Chipster . Anduril was developed for building complex pipelines with large datasets that require automated parallelization. The strength of Galaxy and Chipster is their usability; visualization is a key component of their design. Simultaneous or integrative visualization of the data in a genome browser is extremely useful for both data exploration and interpretation of results. Browsers can display in tandem mappings from most next-generation sequencing technologies, while adding custom tracks such as gene annotation, nucleotide variation or ENCODE datasets. For proteomics integration, the PG Nexus pipeline  converts mass spectrometry data to mappings that are co-visualized with RNA-seq alignments.
RNA-seq has become the standard method for transcriptome analysis, but the technology and tools are continuing to evolve. It should be noted that the agreement between results obtained from different tools is still unsatisfactory and that results are affected by parameter settings, especially for genes that are expressed at low levels. The two major highlights in the current application of RNA-seq are the construction of transcriptomes from small amounts of starting materials and better transcript identification from longer reads. The state of the art in both of these areas is changing rapidly, but we will briefly outline what can be done now and what can be expected in the near future.
Single-cell RNA-seq (scRNA-seq) is one of the newest and most active fields of RNA-seq with its unique set of opportunities and challenges. Newer protocols such as Smart-seq  and Smart-seq2  have enabled us to work from very small amounts of starting mRNA that, with proper amplification, can be obtained from just a single cell. The resulting single-cell libraries enable the identification of new, uncharacterized cell types in tissues. They also make it possible to measure a fascinating phenomenon in molecular biology, the stochasticity of gene expression in otherwise identical cells within a defined population. In this context, single cell studies are meaningful only when a set of individual cell libraries are compared with the cell population, with the aim of identifying subgroups of multiple cells with distinct combinations of expressed genes. Differences may be due to naturally occurring factors such as stage of the cell cycle, or may reflect rare cell types such as cancer stem cells. Recent rapid progress in methodologies for single-cell preparation, including the availability of single-cell platforms such as the Fluidigm C1 , has increased the number of individual cells analyzed from a handful to 50–90 per condition up to 800 cells at a time. Other methods, such as DROP-seq , can profile more than 10,000 cells at a time. This increased number of single-cell libraries in each experiment directly allows for the identification of smaller subgroups within the population.
The small amount of starting material and the PCR amplification limit the depth to which single-cell libraries can be sequenced productively, often to less than a million reads. Deeper sequencing for scRNA-seq will do little to improve quantification as the number of individual mRNA molecules in a cell is small (in the order of 100–300,000 transcripts) and only a fraction of them are successfully reverse-transcribed to cDNA [8, 176]; but deeper sequencing is potentially useful for discovering and measuring allele-specific expression, as additional reads could provide useful evidence.
Single-cell transcriptomes typically include about 3000–8000 expressed genes, which is far fewer than are counted in the transcriptomes of the corresponding pooled populations. The challenge is to distinguish the technical noise that results from a lack of sensitivity at the single-molecule level  (where capture rates of around 10–50 % result in the frequent loss of the most lowly expressed transcripts) from true biological noise where a transcript might not be transcribed and present in the cell for a certain amount of time while the protein is still present. The inclusion of added reference transcripts and the use of unique molecule identifiers (UMIs) have been applied to overcome amplification bias and to improve gene quantification [177, 178]. Methods that can quantify gene-level technical variation allow us to focus on biological variation that is likely to be of interest . Typical quality-control steps involve setting aside libraries that contain few reads, libraries that have a low mapping rate, and libraries that have zero expression levels for housekeeping genes, such as GAPDH and ACTB, that are expected to be expressed at a detectable level.
Depending on the chosen single-cell protocol and the aims of the experiment, different bulk RNA-seq pipelines and tools can be used for different stages of the analysis as reviewed by Stegle et al. . Single-cell libraries are typically analyzed by mapping to a reference transcriptome (using a program such as RSEM) without any attempt at new transcript discovery, although at least one package maps to the genome (Monocle ). While mapping onto the genome does result in a higher overall read-mapping rate, studies that are focused on gene expression alone with fewer reads per cell tend to use mapping to the reference transcriptome for the sake of simplicity. Other single-cell methods have been developed to measure single-cell DNA methylation  and single-cell open chromatin using ATAC-seq [183, 184]. At present, we can measure only one functional genomic data-type at a time in the same single cell, but we can expect that in the near future we will be able to recover the transcriptome of a single cell simultaneously with additional functional data.
The major limitation of short-read RNA-seq is the difficulty in accurately reconstructing expressed full-length transcripts from the assembly of reads. This is particularly complicated in complex transcriptomes, where different but highly similar isoforms of the same gene are expressed, and for genes that have many exons and possible alternative promoters or 3’ ends. Long-read technologies, such as Pacific-Biosciences (PacBio) SMRT and Oxford Nanopore, that were initially applied to genome sequencing are now being used for transcriptomics and have the potential to overcome this assembly problem. Long-read sequencing provides amplification-free, single-molecule sequencing of cDNAs that enables recovery of full-length transcripts without the need for an assembly step. PacBio adds adapters to the cDNA molecule and creates a circularized structure that can be sequenced with multiple passes within one single long read. The Nanopore GridION system can directly sequence RNA strands by using RNA processive enzymes and RNA-specific bases. Another interesting technology was previously known as Moleculo (now Illumina’s TruSeq synthetic long-read technology), where Illumina library preparation is multiplexed and restricted to a limited number of long DNA molecules that are separately bar-coded and pooled back for sequencing. As one barcode corresponds to a limited number of molecules, assembly is greatly simplified and unambiguous reconstruction to long contigs is possible. This approach has recently been published for RNA-seq analysis .
PacBio RNA-seq is the long-read approach with the most publications to date. The technology has proven useful for unraveling isoform diversity at complex loci , and for determining allele-specific expression from single reads . Nevertheless, long-read sequencing has its own set of limitations, such as a still high error rate that limits de novo transcript identifications and forces the technology to leverage the reference genome . Moreover, the relatively low throughput of SMRT cells hampers the quantification of transcript expression. These two limitations can be addressed by matching PacBio experiments with regular, short-read RNA-seq. The accurate and abundant Illumina reads can be used both to correct long-read sequencing errors and to quantify transcript levels . Updates in PacBio chemistry are increasing sequencing lengths to produce reads with a sufficient number of passes over the cDNA molecule to autocorrect sequencing errors. This will eventually improve sequencing accuracy and allow for genome-free determination of isoform-resolved transcriptomes.
Alternative splicing module
Chromatin immunoprecipitation sequencing
Differentially expressed genes
Expression quantitative loci
False discovery rate
Fragments per kilobase of exon model per million mapped reads
Gene set enrichment analysis
Gene transfer format
Integrative Genomics Viewer
Principal component analysis
- PE read:
Reads per kilobase of exon model per million reads
RNA-Seq by Expectation Maximization
- SE read:
Single nucleotide polymorphism
Splicing quantitative trait loci
Transcripts per million
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:1–8.
Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N, et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods. 2010;7:709–15.
Parkhomchuk D, Borodina T, Amstislavskiy V, Banaru M, Hallen L, Krobitsch S, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009;37:e123.
Katz Y, Wang ET, Airoldi EM, Burge CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010;7:1009–15.
Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011;8:469–77.
Łabaj PP, Leparc GG, Linggi BE, Markillie LM, Wiley HS, Kreil DP. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics. 2011;27:i383–91.
Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014;15:121–32.
Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014;32:1053–8.
Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343:776–9.
Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome Res. 2011;21:2213–23.
Andrews S. FASTQC. A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 29 September 2014.
Dai M, Thompson RC, Maher C, Contreras-Galindo R, Kaplan MH, Markovitz DM, et al. NGSQC: cross-platform quality analysis pipeline for deep sequencing data. BMC Genomics. 2010;11 Suppl 4:S7.
FASTX-Toolkit. http://hannonlab.cshl.edu/fastx_toolkit/. Accessed 12 January 2016.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
Picard. http://picard.sourceforge.net/. Accessed 12 January 2016.
Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28:2184–5.
García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28:2678–9.
Tarazona S, Furió-Tarí P, Turrà D, Pietro AD, Nueda MJ, Ferrer A, et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. 2015;43:e140.
Risso D, Schwartz K, Sherlock G, Dudoit S. GC-content normalization for RNA-seq data. BMC Bioinformatics. 2011;12:480.
Steijger T, Abril JF, Engström PG, Kokocinski F, Hubbard TJ, Guigó R, et al. Assessment of transcript reconstruction methods for RNA-seq. Nat Methods. 2013;10:1177–84.
Boley N, Stoiber MH, Booth BW, Wan KH, Hoskins RA, Bickel PJ, et al. Genome-guided transcript assembly by integrative analysis of RNA sequence data. Nat Biotechnol. 2014;32:341–6.
Roberts A, Pimentel H, Trapnell C, Pachter L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics. 2011;27:2325–9.
Mezlini AM, Smith EJ, Fiume M, Buske O, Savich GL, Shah S, et al. iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data. Genome Res. 2013;23:519–29.
Li JJ, Jiang CR, Brown JB, Huang H, Bickel PJ. Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation. Proc Natl Acad Sci U S A. 2011;108:19867–72.
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5.
Hiller D, Wong WH. Simultaneous isoform discovery and quantification from RNA-Seq. Stat Biosci. 2013;5:100–18.
Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34:W435–9.
Engström PG, Steijger T, Sipos B, Grant GR, Kahles A, Rätsch G, et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods. 2013;10:1185–91.
Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30:1660–6.
Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28:1086–92.
Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.
Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novotranscript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.
Patro R, Mount SM, Kingsford C. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol. 2014;32:462–4.
Anders S, Pyl PT, Huber W. HTSeq - a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.
UCSC Genome Bioinformatics: Frequently Asked Questions: Data File Formats. https://genome.ucsc.edu/FAQ/FAQformat.html#format4. Accessed on 12 January 2016.
Pachter L. Models for transcript quantification from RNA-seq. arXiv.org. 2011. http://arxiv.org/abs/1104.3889. Accessed 6 January 2016.
Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
Roberts A, Pachter L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat Methods. 2013;10:71–3.
Bray N, Pimentel H, Melsted P, Pachter L. Near-optimal RNA-Seq quantification with kallisto. https://liorpachter.wordpress.com/2015/05/10/near-optimal-rna-seq-quantification-with-kallisto/. Accessed 6 January 2016.
Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-seq expression estimates by correcting for fragment bias. Genome Biol. 2011;12:R22.
Ma X, Zhang X. NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data. BMC Bioinformatics. 2013;14:220.
Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010;11:94.
Hansen K, Brenner S, Dudoit S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 2010;38:e131.
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.
Li J, Witten DM, Johnstone IM, Tibshirani R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics. 2012;13:523–38.
Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2012;31:46–53.
Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics. 2010;185:405–16.
Johnson WE, Rabinovic A, Li C. Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics. 2007;8:118–27.
Nueda MJ, Ferrer A, Conesa A. ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics. 2012;13:553–66.
Robinson MD, Smyth GK. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics. 2007;23:2881–7.
Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29.
Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:91.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics. 2010;11:422.
Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013;29:1035–43.
Li J, Tibshirani R. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res. 2013;22:519–36.
Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136–8.
Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 2013;14:R95.
Seyednasrollah F, Laiho A, Elo LL. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. 2015;16:59–70.
Zheng S, Chen L. A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level. Nucleic Acids Res. 2009;37:e75.
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–78.
Singh D, Orellana CF, Hu Y, Jones CD, Liu Y, Chiang DY, et al. FDM: a graph-based statistical method to detect differential transcription using RNA-seq data. Bioinformatics. 2011;27:2633–40.
Shi Y, Jiang H. rSeqDiff: detecting differential isoform expression from RNA-Seq data using hierarchical likelihood ratio test. PLoS One. 2013;8:e79448.
Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22:2008–17.
Wang W, Qin Z, Feng Z, Wang X, Zhang X. Identifying differentially spliced genes from two groups of RNA-seq samples. Gene. 2013;518:164–70.
Shen S, Park JW, Lu ZX, Lin L, Henry MD, Wu YN, et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc Natl Acad Sci U S A. 2014;111:E5593–601.
Drewe P, Stegle O, Hartmann L, Kahles A, Bohnert R, Wachter A, et al. Accurate detection of differential RNA processing. Nucleic Acids Res. 2013;41:5189–98.
Hu Y, Huang Y, Du Y, Orellana CF, Singh D, Johnson AR, et al. DissSplice: the genome-wide detection of differential splicing events with RNA-seq. Nucleic Acids Res. 2013;41:e39.
Hilker R, Stadermann KB, Doppmeier D, Kalinowski J, Stoye J, Straube J, et al. ReadXplorer - visualization and analysis of mapped sequences. Bioinformatics. 2014;30:2247–54.
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The Human Genome Browser at UCSC. Genome Res. 2002;12:996–1006.
Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high- performance genomics data visualization and exploration. Brief Bioinformatics. 2012;14:178–92.
Medina I, Salavert F, Sanchez R, de Maria A, Alonso R, Escobar P, et al. Genome Maps, a new generation genome browser. Nucleic Acids Res. 2013;41(Web Server issue):W41–6.
Fiume M, Williams V, Brook A, Brudno M. Savant: genome browser for high-throughput sequencing data. Bioinformatics. 2010;26:1938–44.
Rogé X, Zhang X. RNAseqViewer: visualization tool for RNA-Seq data. Bioinformatics. 2013;30:891–2.
Katz Y, Wang ET, Silterra J, Schwartz S, Wong B, Thorvaldsdóttir H, et al. Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics. 2015;31:2400–2.
Wu E, Nance T, Montgomery SB. SplicePlot: a utility for visualizing splicing quantitative trait loci. Bioinformatics. 2014;30:1025–6.
Ryan MC, Cleland J, Kim R, Wong WC, Weinstein JN. SpliceSeq: a resource for analysis and visualization of RNA-Seq data on alternative splicing and its functional impacts. Bioinformatics. 2012;28:2385–7.
Liu Q, Chen C, Shen E, Zhao F, Sun Z, Wu J. Detection, annotation and visualization of alternative splicing from RNA-Seq data with SplicingViewer. Genomics. 2012;99:178–82.
Dietrich S, Wiegand S, Liesegang H. TraV: a genome context sensitive transcriptome browser. PLoS One. 2014;9:e93677.
Carrara M, Beccuti M, Lazzarato F, Cavallo F, Cordero F, Donatelli S, et al. State-of-the-art fusion-finder algorithms sensitivity and specificity. BioMed Res Int. 2013;15:340620.
Maher CA, Palanisamy N, Brenner JC, Cao X, Kalyana-Sundaram S, Luo S, et al. Chimeric transcript discovery by paired-end transcriptome sequencing. Proc Natl Acad Sci U S A. 2009;106:12353–8.
Yoshihara K, Wang Q, Torres-Garcia W, Zheng S, Vegesna R, Kim H, et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene. 2015;34:4845–54.
McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MG, et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-seq data. PLoS Comput Biol. 2011;7:e1001138.
Wu C, Wyatt AW, McPherson A, Lin D, McConeghy BJ, Mo F, et al. Poly-gene fusion transcripts and chromothripsis in prostate cancer. Gene Chromosomes Cancer. 2012;51:1144–53.
Wyatt AW, Mo F, Wang K, McConeghy B, Brahmbhatt S, Jong L, et al. Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer. Genome Biol. 2014;15:426.
Stransky N, Cerami E, Schalm S, Kim JL, Lengauer C. The landscape of kinase fusions in cancer. Nat Commun. 2014;5:4846.
Rabbitts TH. Commonality but diversity in cancer gene fusions. Cell. 2009;137:391–5.
McPherson A, Wu C, Hajirasouliha I, Hormozdiari F, Hach F, Lapuk A, et al. Comrad: detection of expressed rearrangements by integrated analysis of RNA-Seq and low coverage genome sequence data. Bioinformatics. 2011;27:1481–8.
Iyer MK, Chinnaiyan AM, Maher CA. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics. 2011;27:2903–4.
Pflueger D, Terry S, Sboner A, Habegger L, Esgueva R, Lin PC, et al. Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing. Genome Res. 2011;21:56–67.
Wu J, Liu Q, Wang X, Zheng J, Wang T, You M, et al. mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol. 2013;10:1087–92.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.
Prüfer K, Stenzel U, Dannemann M, Green RE, Lachmann M, Kelso J. PatMaN: rapid alignment of short sequences to large databases. Bioinformatics. 2008;24:1530–1.
Emde AK, Grunert M, Weese D, Reinert K, Sperling SR. MicroRazerS: rapid alignment of small RNA reads. Bioinformatics. 2010;26:123–4.
An J, Lai J, Lehman ML, Nelson CC. miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res. 2013;41:727–37.
Yang X, Li L. miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics. 2011;27:2614–5.
Stocks MB, Moxon S, Mapleson D, Woolfenden HC, Mohorianu I, Folkes L, et al. The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics. 2012;28:2059–61.
Axtell MJ. ShortStack: comprehensive annotation and quantification of small RNA genes. RNA. 2013;19:740–51.
Giurato G, De Filippo MR, Rinaldi A, Hashim A, Nassa G, Ravo M, et al. iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq. BMC Bioinformatics. 2013;14:362.
Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:1–12.
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7.
Wang X, Cairns MJ. Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing. BMC Bioinformatics. 2013;14 Suppl 5:S16.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protocols. 2009;4:44–57.
Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13.
Medina I, Carbonell J, Pulido L, Madeira SC, Goetz S, Conesa A, et al. Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res. 2010;38 suppl 2:W210–3.
Bairoch A, Boeckmann B, Ferro S, Gasteiger E. Swiss-Prot: juggling between evolution and stability. Brief Bioinformatics. 2004;5:39–55.
Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. The Pfam protein families database. Nucleic Acids Res. 2014;42(Database issue):D222–30.
Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 2011;40(Database issue):D306–12.
Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–6.
Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, et al. Rfam: updates to the RNA families database. Nucleic Acids Res. 2009;37 suppl 1:D136–40.
Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42(Database issue):D68–73.
Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in Drosophila. Genome Biol. 2003;5:R1.
Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.
Moffatt MF, Kabesch M, Liang L, Dixon AL, Strachan D, Heath S, et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature. 2007;448:470–3.
Gilad Y, Rifkin S, Pritchard J. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet. 2008;24:408–15.
Gaffney D. Global properties and functional complexity of human gene regulatory variation. PLoS Genet. 2013;9:e1003501.
Montgomery S, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010;464:773–7.
Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464:768–72.
Lappalainen T, Sammeth M, Friedlander M, ‘t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–11.
Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, Shi J, et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 2014;24:14–24.
Pastinen T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet. 2010;11:533–8.
Sun W. A statistical framework for eQTL mapping using RNA-seq data. Biometrics. 2012;68:1–11.
van de Geijn B, McVicker G, Gilad Y, Pritchard JK. WASP: allele-specific for robust molecular quantitative trait locus discovery. Nat Methods. 2015;12:1061–3.
Kumasaka N, Knights AJ, Gaffney DJ. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat Genet. 2015. doi: 10.1038/ng.3467.
Storey JD, Tibshirani R. Statistical significance for genome-wide studies. Proc Natl Acad Sci U S A. 2003;100:9440–5.
Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, et al. Genome-wide associations of gene expression variation in humans. PLoS Genet. 2005;1:e78.
Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, Replogle JM, et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344:519–23.
Shabalin A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28:1353–8.
Louhimo R, Lepikhova T, Monni O, Hautaniemi S. Comparative analysis of algorithms for integration of copy number and expression data. Nat Methods. 2012;9:351–5.
Kim JH, Dhanasekaran SM, Prensner JR, Cao X, Robinson D, Kalyana-Sundaram S, et al. Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer. Genome Res. 2011;21:1028–41.
Li JL, Mazar J, Zhong C, Faulkner GJ, Govindarajan SS, Zhang Z, et al. Genome-wide methylated CpG island profiles of melanoma cells reveal a melanoma coregulation network. Sci Rep. 2013;3:2962.
Xie L, Weichel B, Ohm JE, Zhang K. An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver. BMC Syst Biol. 2011;5 Suppl 3:S4.
Van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics. 2012;13:636.
Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31:142–7.
Yeang C-H. An integrated analysis of molecular aberrations in NCI-60 cell lines. BMC Bioinformatics. 2010;11:495.
Jeong J, Li L, Liu Y, Nephew KP, Huang YHM, Shen C. An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer. BMC Med Genomics. 2010;3:55.
Jiao Y, Widschwendter M, Teschendorff AE. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics. 2014;30:2360–6.
Wang S, Sun H, Ma J, Zang C, Wang C, Wang J, et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc. 2013;8:2502–15.
Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30.
Madrigal P, Krajewski P. Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform. BioData Min. 2015;8:20.
Angelini C, Costa V. Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems. Front Cell Dev Biol. 2014;2:51.
Neph S, Stergachis AB, Reynolds A, Sandstrom R, Borenstein E, Stamatoyannopoulos JA. Circuitry and dynamics of human transcription factor regulatory networks. Cell. 2012;150:1274–86.
Dweep H, Sticht C, Pandey P, Gretz N. miRWalk - database: prediction of possible miRNA binding sites by ‘walking’ the genes of 3 genomes. J Biomed Inform. 2011;44:839–47.
Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36:D154–8.
Wu X, Watson M. CORNA: testing gene lists for regulation by microRNAs. Bioinformatics. 2009;25:832–3.
Lee H, Yang Y, Chae H, Nam S, Choi D, Tangchaisin P, et al. BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2. IEEE Trans Nanobiosci. 2012;11:266–72.
Nam S, Li M, Choi K, Balch C, Kim S, Nephew KP. MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res. 2009;37:W356–62.
Sales G, Coppe A, Bisognin A, Bortoluzzi S, Romualdi C. MAGIA, a web-based tool for miRNA and Genes Integrated Analysis. Nucleic Acids Res. 2010;38:W352–9.
Icay K, Chen P, Cervera C, Lehtonen R, Hautaniemi S. SePIA: RNA and smallRNA-sequence processing, integration, and analysis. 2015. http://anduril.org/sepia. Accessed 6 Jan 2016.
de Sousa AR, Penalva LO, Marcotte EM, Vogel C. Global signatures of protein and mRNA expression levels. Mol Biosyst. 2009;5:1512–26.
Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32.
Low TY, van Heesch S, van den Toorn H, Giansanti P, Cristobal A, Toonen P. Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis. Cell Rep. 2013;5:1469–78.
Suhre K, Schmitt-Kopplin P. MassTRIX: mass translator into pathways. Nucleic Acids Res. 2008;36(Web Server issue):W481–4.
García-Alcalde F, García-López F, Dopazo J, Conesa A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics. 2011;27:137–9.
Rohn H, Junker A, Hartmann A, Grafahrend-Belau E, Treutler H, Klapperstück M, et al. VANTED v2: a framework for systems biology applications. BMC Syst Biol. 2012;6:139.
Tuncbag N, McCallum S, Huang SS, Fraenkel E. SteinerNet: a web server for integrating ‘omic’ data to discover hidden components of response pathways. Nucleic Acids Res. 2012;40:W505–9.
Zhang S, Li Q, Liu J, Zhou XJ. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011;27:i401–9.
Le H-S, Bar-Joseph Z. Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation. Bioinformatics. 2013;29:i89–97.
Sass S, Buettner F, Mueller NS, Theis FJ. A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Res. 2013;41:9622–33.
Kuo TC, Tian TF, Tseng YJ. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol. 2013;7:64.
Ovaska K, Laakso M, Haapa-Paananen S, Louhimo R, Chen P, Aittomäki V, et al. Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Med. 2010;2:65.
Goecks J, Nekrutenko A, Taylor J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11:R86.
Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Gentile M, Scheinin I, et al. Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics. 2011;12:507.
Pang CNI, Tay AP, Aya C. Tools to covisualize and coanalyze proteomic data with genomes and transcriptomes: validation of genes and alternative mRNA splicing. J Proteome Res. 2014;13:84–98.
Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82.
Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8.
Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.
Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014;24:496–510.
Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2014;11:163–6.
Kivioja T, Vähärautio A, Karlsson K, Bonke M, Enge M, Linnarsson S, et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods. 2011;9:72–4.
Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013;10:1093–5.
Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015;16:133–45.
Trapnell C, Cacchiarelli D. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6.
Lorthongpanich C, Cheow LF, Balu S, Quake SR, Knowles BB, Burkholder WF, et al. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science. 2013;341:1110–2.
Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523:486–90.
Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015;348:910–4.
Tilgner H, Jahanbani F, Blauwkamp T, Moshrefi A, Jaeger E, Chen F, et al. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat Biotechnol. 2015;33:736–42.
Au KF, Sebastiano V, Afshar PT, Durruthy JD, Lee L, Williams BA, et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc Natl Acad Sci U S A. 2013;110:E4821–30.
Tilgner H, Grubert F, Sharon D, Snyder MP. Defining a personal, allele-specific, and single-molecule long-read transcriptome. Proc Natl Acad Sci U S A. 2014;111:9869–74.
Au KF, Underwood JG, Lee L, Wong WH. Improving PacBio long read accuracy by short read alignment. PLoS One. 2012;7:e46679.
Hansen KD, Wu Z, Irizarry RA, Leek JT. Sequencing technology does not eliminate biological variability. Nat Biotechnol. 2011;29:572–3.
Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher JP. Calculating sample size estimates for RNA sequencing data. J Comput Biol. 2013;20:970–8.
Busby MA, Stewart C, Miller CA, Grzeda KR, Marth GT. Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics. 2013;29:656–7.
Oshlack A, Wakefield MJ. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct. 2009;4:14.
Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27:1135–7.
Robinson DG, Storey JD. subSeq: determining appropriate sequencing depth through efficient read subsampling. Bioinformatics. 2014;30:3424–6.
Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2013;30:301–4.
SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol. 2014;32:903–14.
Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011;21:1543–51.
Kouzine F, Wojtowicz D, Yamane A, Resch W, Kieffer-Kwon KR, Bandle R, et al. Global regulation of promoter melting in naive lymphocytes. Cell. 2013;153:988–99.
Van Dijk EL, Jaszczyszyn Y, Thermes C. Library preparation methods for next-generation sequencing: tone down the bias. Exp Cell Res. 2014;322:12–20.
Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36.
Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81.
Jean G, Kahles A, Sreedharan VT, De Bona F, Rätsch G. RNA-Seq read alignments with PALMapper. Curr Protoc Bioinformatics. 2010;11(6).
Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38:e178.
Marco-Sola S, Sammeth M, Guigó R, Ribeca P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat Methods. 2012;9:1185–8.
Zhao S, Zhang B. A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification. BMC Genomics. 2015;16:97.
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.
Kvam VM, Liu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot. 2012;99:248–56.
Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics. 2012;13:484.
Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlén M, et al. 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. Nucleic Acids Res. 2012;40:10084–97.
Seyednasrollah F, Rantanen K, Jaakkola P, Elo LL. ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer. Nucleic Acids Res. 2016;44(1):e1. doi:10.1093/nar/gkv806.
Bi Y, Davuluri RV. NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:262.
Nueda MJ, Tarazona S, Conesa A. Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics. 2014;30:2598–602.
GTEx Consortium. The Genotype-Tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.
The authors would like to thank Michael Love and Harold Pimentel for helpful suggestions on the initial draft of the manuscript. AC, ST, AM, DGC were supported by the FP7 STATegra project (grant 36000). Research in AC’s laboratory was supported by MINECO grant BIO2012-40244 and co-funded with European Regional Development Funds (ERDF). Research in PM’s laboratory is supported by ERC starting grant Relieve-IMDs and by a core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute. XZ was supported by the National Basic Research Program of China (2012CB316504). LLE was supported by JDRF (grant number 2-2013-32) and by the Sigrid Juselius Foundation. ACe was supported by the Academy of Finland (Center of Excellence in Cancer Genetics Research).
The authors declare that they have no competing interests.
ACo, PM and AM conceived the idea and shaped the structure of the manuscript. ACo drafted the experimental design, alignment and functional profiling sections and integrated contributions from all authors. PM drafted the visualization and de novo transcript reconstruction sections, and coordinated author contributions. ST drafted the quality-control and differential expression sections. DGC drafted the experimental design and integration sections. ACe contributed to drafting the integration section. AMP drafted the transcript fusion section. MWS drafted the small RNA section. DG drafted the eQTL section. LLE drafted the software comparison for differential expression section. LLE and XZ drafted the transcript isoform analysis sections. XZ contributed to drafting the visualization section. AM drafted the introduction and outlook sections and globally edited the manuscript. All authors read and approved the final manuscript.
Additional file 1: Figure S1.
Screenshots of RNA-seq data visualization. a Integrative Genomics Viewer (IGV)  display of a gene detected as differentially expressed between the two groups of samples by DEGseq . The bottom track in the right panel is the gene annotation. The tracks are five samples from each group. b RNAseqViewer  display of the same data as in (a). c RNAseqViewer heatmap display of a gene detected as differentially spliced between two groups by both DSGSeq  and DEXSeq . Introns are hidden in the display to emphasize the signals on the exons. d MISO  display of another gene detected as differentially spliced, with junction reads illustrated. (PDF 1152 kb)
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Conesa, A., Madrigal, P., Tarazona, S. et al. A survey of best practices for RNA-seq data analysis. Genome Biol 17, 13 (2016). https://0-doi-org.brum.beds.ac.uk/10.1186/s13059-016-0881-8
- Differential Expression Analysis
- Reference Transcriptome
- Transcript Identification
- Transcript Discovery
- Gene Transfer Format