Sailfish Research Paper

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Sailfish Research Paper

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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.

Some visualization tools are specifically designed for visualizing multiple RNA-seq samples, such as RNAseqViewer [ 79 ], 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. 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 [ 66 ] or Sashimi plots, which can be used to visualize differentially spliced exons [ 80 ].

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 [ 81 ]. Splice graphs can be produced using SpliceSeq [ 82 ], and SplicingViewer [ 83 ] plots splice junctions and alternative splicing events. TraV [ 84 ] 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. 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.

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 [ 85 ]. 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 [ 86 ]. 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 [ 88 ]. 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 [ 88 ].

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 [ 89 ]. 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 [ 87 ]. 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 [ 92 ] 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 [ 91 ]. 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. 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 and nucleotide redundant reads. For instance, miRTools 2. The threshold value depends on the application, and in case of miRNAs is usually in the range of 19—25 nucleotides. There are, however, some aligners such as PatMaN [ 99 ] 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. Tools such as miRTools 2. The last step in a standard transcriptomics study Fig. 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.

Functional analysis requires the availability of sufficient functional annotation data for the transcriptome under study. 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. 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.

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. The integration of RNA-seq data with other types of genome-wide data Fig. 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. These associations can unravel the genetic basis of complex traits such as height [ ], disease susceptibility [ ] or even features of genome architecture [ , ]. Large eQTL studies have shown that genetic variation affects the expression of most genes [ — ].

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 [ — ]. 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 [ , ].

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 [ 88 ] 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 [ — ]. General linear models [ — ], 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.

In addition, ChIP-seq experiments involving histone modifications have been used to understand the general role of these epigenomic changes on gene expression [ , ]. 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 [ ]. 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. 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 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. Paintomics can integrate any type of functional genomics data into pathway analysis, provided that the features can be mapped onto genes or metabolites [ ]. 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. 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 [ 8 ], has increased the number of individual cells analyzed from a handful to 50—90 per condition up to cells at a time.

Other methods, such as DROP-seq [ ], can profile more than 10, 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 —, transcripts and only a fraction of them are successfully reverse-transcribed to cDNA [ 8 , ]; 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 — expressed genes, which is far fewer than are counted in the transcriptomes of the corresponding pooled populations. 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 [ , ]. 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 [ , ].

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. 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. 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. Three factors determine the number of replicates required in a RNA-seq experiment. The first factor is the variability in the measurements, which is influenced by the technical noise and the biological variation. While reproducibility in RNA-seq is usually high at the level of sequencing [ 1 , 45 ], other steps such as RNA extraction and library preparation are noisier and may introduce biases in the data that can be minimized by adopting good experimental procedures Box 2.

Biological variability is particular to each experimental system and is harder to control [ ]. Nevertheless, biological replication is required if inference on the population is to be made, with three replicates being the minimum for any inferential analysis. For a proper statistical power analysis, estimates of the within-group variance and gene expression levels are required. This information is typically not available beforehand but can be obtained from similar experiments. The exact power will depend on the method used for differential expression analysis, and software packages exist that provide a theoretical estimate of power over a range of variables, given the within-group variance of the samples, which is intrinsic to the experiment [ , ].

Table 1 shows an example of statistical power calculations over a range of fold-changes or effect sizes and number of replicates in a human blood RNA-seq sample sequenced at 30 million mapped reads. It should be noted that these estimates apply to the average gene expression level, but as dynamic ranges in RNA-seq data are large, the probability that highly expressed genes will be detected as differentially expressed is greater than that for low-count genes [ ].

For methods that return a false discovery rate FDR , the proportion of genes that are highly expressed out of the total set of genes being tested will also influence the power of detection after multiple testing correction [ ]. Filtering out genes that are expressed at low levels prior to differential expression analysis reduces the severity of the correction and may improve the power of detection [ 20 ]. Increasing sequencing depth also can improve statistical power for lowly expressed genes [ 10 , ], and for any given sample there exists a level of sequencing at which power improvement is best achieved by increasing the number of replicates [ ]. Tools such as Scotty are available to calculate the best trade-off between sequencing depth and replicate number given some budgetary constraints [ ].

RNA-seq library preparation and sequencing procedures include a number of steps RNA fragmentation, cDNA synthesis, adapter ligation, PCR amplification, bar-coding, and lane loading that might introduce biases into the resulting data [ ]. For bias minimization, we recommend following the suggestions made by Van Dijk et al. Another option, when samples are individually barcoded and multiple Illumina lanes are needed to achieve the desired sequencing depth, is to include all samples in each lane, which would minimize any possible lane effect.

Mapping to a reference genome allows for the identification of novel genes or transcripts, and requires the use of a gapped or spliced mapper as reads may span splice junctions. The challenge is to identify splice junctions correctly, especially when sequencing errors or differences with the reference exist or when non-canonical junctions and fusion transcripts are sought. One of the most popular RNA-seq mappers, TopHat, follows a two-step strategy in which unspliced reads are first mapped to locate exons, then unmapped reads are split and aligned independently to identify exon junctions [ , ].

Important parameters to consider during mapping are the strandedness of the RNA-seq library, the number of mismatches to accept, the length and type of reads SE or PE , and the length of sequenced fragments. In addition, existing gene models can be leveraged by supplying an annotation file to some read mapper in order to map exon coordinates accurately and to help in identifying splicing events. The choice of gene model can also have a strong impact on the quantification and differential expression analysis [ ]. We refer the reader to [ 30 ] for a comprehensive comparison of RNA-seq mappers. If the transcriptome annotation is comprehensive for example, in mouse or human , researchers may choose to map directly to a Fasta-format file of all transcript sequences for all genes of interests.

In this case, no gapped alignment is needed and unspliced mappers such as Bowtie [ ] can be used Fig. Mapping to the transcriptome is generally faster but does not allow de novo transcript discovery. Many statistical methods are available for detecting differential gene or transcript expression from RNA-seq data, and a major practical challenge is how to choose the most suitable tool for a particular data analysis job.

This enables a direct assessment of the sensitivity and specificity of the methods as well as their FDR control. As simulations typically rely on specific statistical distributions or on limited experimental datasets and as spike-in datasets represent only technical replicates with minimal variation, comparisons using simulated datasets have been complemented with more practical comparisons in real datasets with true biological replicates [ 64 , , ].

As yet, no clear consensus has been reached regarding the best practices and the field is continuing to evolve rapidly. However, some common findings have been made in multiple comparison studies and in different study settings. First, specific caution is needed with all the methods when the number of replicate samples is very small or for genes that are expressed at very low levels [ 55 , 64 , ]. Among the tools, limma has been shown to perform well under many circumstances and it is also the fastest to run [ 56 , 63 , 64 ].

DESeq and edgeR perform similarly in ranking genes but are often relatively conservative or too liberal, respectively, in controlling FDR [ 63 , , ]. SAMseq performs well in terms of FDR but presents an acceptable sensitivity when the number of replicates is relatively high, at least 10 [ 20 , 55 , ]. Cuffdiff and Cuffdiff2 have performed surprisingly poorly in the comparisons [ 56 , 63 ]. This probably reflects the fact that detecting differential expression at the transcript level remains challenging and involves uncertainties in assigning the reads to alternative isoforms. In a recent comparison, BitSeq compared favorably to other transcript-level packages such as Cuffdiff2 [ ]. Besides the actual performance, other issues affecting the choice of the tool include ease of installation and use, computational requirements, and quality of documentation and instructions.

Finally, an important consideration when choosing an analysis method is the experimental design. While some of the differential expression tools can only perform a pair-wise comparison, others such as edgeR [ 57 ], limma-voom [ 55 ], DESeq [ 48 ], DESeq2 [ 58 ], and maSigPro [ ] can perform multiple comparisons, include different covariates or analyze time-series data. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Computational methods for transcriptome annotation and quantification using RNA-seq.

Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.

Differential expression in RNA-seq: a matter of depth. Genome Res. Andrews S. A quality control tool for high throughput sequence data. Accessed 29 September NGSQC: cross-platform quality analysis pipeline for deep sequencing data. BMC Genomics. Accessed 12 January Trimmomatic: a flexible trimmer for Illumina sequence data. Qualimap: evaluating next-generation sequencing alignment data.

GC-content normalization for RNA-seq data. BMC Bioinformatics. Assessment of transcript reconstruction methods for RNA-seq. Genome-guided transcript assembly by integrative analysis of RNA sequence data. Identification of novel transcripts in annotated genomes using RNA-Seq. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Hiller D, Wong WH. Simultaneous isoform discovery and quantification from RNA-Seq. Stat Biosci. PubMed Article Google Scholar. Systematic evaluation of spliced alignment programs for RNA-seq data. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Full-length transcriptome assembly from RNA-seq data without a reference genome.

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc.

Perfectly situated on the b.f. skinner operant conditioning with stunning views, sunrise and sunset Mrs. Oakley Analysis, and Claus Shirt. Richard speck tattoo Scalemail. Systematic evaluation Sailfish Research Paper spliced alignment programs b.f. skinner operant conditioning RNA-seq data. Transcript identification and quantification can occur simultaneously.