RNA-Seq is a technique that allows transcriptome studies (see also Transcriptomics technologies) based on next-generation sequencing technologies. This technique is largely dependent on bioinformatics tools developed to support the different steps of the process. Here are listed some of the principal tools commonly employed and links to some important web resources.
Design Design is a fundamental step of a particular RNA-Seq experiment. Some important questions like sequencing depth/coverage or how many biological or technical replicates must be carefully considered. Design review.
PROPER: PROspective Power Evaluation for RNAseq. RNAtor: an Android Application to calculate optimal parameters for popular tools and kits available for DNA sequencing projects. Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. ssizeRNA Sample Size Calculation for RNA-Seq Experimental Design.
Quality control, trimming, error correction and pre-processing of data Quality assessment of raw data is the first step of the bioinformatics pipeline of RNA-Seq. It is often necessary to filter data, removing low quality sequences or bases (trimming), adapters, contaminations, overrepresented sequences or correcting errors to assure a coherent final result.
Quality control AfterQC - Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data. bam-lorenz-coverage A tool that can generate Lorenz plots and Coverage plots, or export these statistics to text files, directly from BAM file(s). dupRadar An R package which provides functions for plotting and analyzing the duplication rates dependent on the expression levels. FastQC is a quality control tool for high-throughput sequence data (Babraham Institute) and is developed in Java. Import of data is possible from FastQ files, BAM or SAM format. This tool provides an overview to inform about problematic areas, summary graphs and tables to rapid assessment of data. Results are presented in HTML permanent reports. FastQC can be run as a stand-alone application or it can be integrated into a larger pipeline solution. fastqp Simple FASTQ quality assessment using Python. Kraken: A set of tools for quality control and analysis of high-throughput sequence data. HTSeq The Python script htseq-qa takes a file with sequencing reads (either raw or aligned reads) and produces a PDF file with useful plots to assess the technical quality of a run. mRIN - Assessing mRNA integrity directly from RNA-Seq data. MultiQC - Aggregate and visualise results from numerous tools (FastQC, HTSeq, RSeQC, Tophat, STAR, others..) across all samples into a single report. NGSQC: cross-platform quality analysis pipeline for deep sequencing data. NGS QC Toolkit A toolkit for the quality control (QC) of next generation sequencing (NGS) data. The toolkit comprises user-friendly stand alone tools for quality control of the sequence data generated using Illumina and Roche 454 platforms with detailed results in the form of tables and graphs, and filtering of high-quality sequence data. It also includes few other tools, which are helpful in NGS data quality control and analysis. PRINSEQ is a tool that generates summary statistics of sequence and quality data and that is used to filter, reformat and trim next-generation sequence data. It is particular designed for 454/Roche data, but can also be used for other types of sequence. QC-Chain is a package of quality control tools for next generation sequencing (NGS) data, consisting of both raw reads quality evaluation and de novo contamination screening, which could identify all possible contamination sequences. QC3 a quality control tool designed for DNA sequencing data for raw data, alignment, and variant calling. qrqc Quickly scans reads and gathers statistics on base and quality frequencies, read length, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. RNA-SeQC is a tool with application in experiment design, process optimization and quality control before computational analysis. Essentially, provides three types of quality control: read counts (such as duplicate reads, mapped reads and mapped unique reads, rRNA reads, transcript-annotated reads, strand specificity), coverage (like mean coverage, mean coefficient of variation, 5'/3' coverage, gaps in coverage, GC bias) and expression correlation (the tool provides RPKM-based estimation of expression levels). RNA-SeQC is implemented in Java and is not required installation, however can be run using the GenePattern web interface. The input could be one or more BAM files. HTML reports are generated as output. RSeQC analyzes diverse aspects of RNA-Seq experiments: sequence quality, sequencing depth, strand specificity, GC bias, read distribution over the genome structure and coverage uniformity. The input can be SAM, BAM, FASTA, BED files or Chromosome size file (two-column, plain text file). Visualization can be performed by genome browsers like UCSC, IGB and IGV. However, R scripts can also be used for visualization. SAMStat identifies problems and reports several statistics at different phases of the process. This tool evaluates unmapped, poorly and accurately mapped sequences independently to infer possible causes of poor mapping. SolexaQA calculates sequence quality statistics and creates visual representations of data quality for second-generation sequencing data. Originally developed for the Illumina system (historically known as "Solexa"), SolexaQA now also supports Ion Torrent and 454 data. Trim galore is a wrapper script to automate quality and adapter trimming as well as quality control, with some added functionality to remove biased methylation positions for RRBS sequence files (for directional, non-directional (or paired-end) sequencing).
Improving the quality Improvement of the RNA-Seq quality, correcting the bias is a complex subject. Each RNA-Seq protocol introduces specific type of bias, each step of the process (such as the sequencing technology used) is susceptible to generate some sort of noise or type of error. Furthermore, even the species under investigation and the biological context of the samples are able to influence the results and introduce some kind of bias. Many sources of bias were already reported – GC content and PCR enrichment, rRNA depletion, errors produced during sequencing, priming of reverse transcription caused by random hexamers. Different tools were developed to attempt to solve each of the detected errors.
Trimming and adapters removal AlienTrimmer implements a very fast approach (based on k-mers) to trim low-quality base pairs and clip technical (alien) oligonucleotides from single- or paired-end sequencing reads in plain or gzip-compressed FASTQ files (for more details, see AlienTrimmer). BBDuk multithreaded tool to trim adapters and filter or mask contaminants based on kmer-matching, allowing a hamming- or edit-distance, as well as degenerate bases. Also performs optimal quality-trimming and filtering, format conversion, contaminant concentration reporting, gc-filtering, length-filtering, entropy-filtering, chastity-filtering, and generates text histograms for most operations. Interconverts between fastq, fasta, sam, scarf, interleaved and 2-file paired, gzipped, bzipped, ASCII-33 and ASCII-64. Keeps pairs together. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies. clean_reads cleans NGS (Sanger, 454, Illumina and solid) reads. It can trim bad quality regions, adaptors, vectors, and regular expressions. It also filters out the reads that do not meet a minimum quality criteria based on the sequence length and the mean quality. condetri is a method for content dependent read trimming for Illumina data using quality scores of each base individually. It is independent from sequencing coverage and user interaction. The main focus of the implementation is on usability and to incorporate read trimming in next-generation sequencing data processing and analysis pipelines. It can process single-end and paired-end sequencing data of arbitrary length. cutadapt removes adapter sequences from next-generation sequencing data (Illumina, SOLiD and 454). It is used especially when the read length of the sequencing machine is longer than the sequenced molecule, like the microRNA case. Deconseq Detect and remove contaminations from sequence data. Erne-Filter is a short string alignment package whose goal is to provide an all-inclusive set of tools to handle short (NGS-like) reads. ERNE comprises ERNE-FILTER (read trimming and continamination filtering), ERNE-MAP (core alignment tool/algorithm), ERNE-BS5 (bisulfite treated reads aligner), and ERNE-PMAP/ERNE-PBS5 (distributed versions of the aligners). FastqMcf Fastq-mcf attempts to: Detect & remove sequencing adapters and primers; Detect limited skewing at the ends of reads and clip; Detect poor quality at the ends of reads and clip; Detect Ns, and remove from ends; Remove reads with CASAVA 'Y' flag (purity filtering); Discard sequences that are too short after all of the above; Keep multiple mate-reads in sync while doing all of the above. fastp is a tool designed to provide all-in-one preprocessing for FastQ files. This tool is developed in C++ with multithreading supported. fastq-trim is a tool written in C. It can be extended to support additional alignment algorithms. The default algorithm is similar to that used by cutadapt, and the results produced are nearly identical. FASTX-Toolkit is a set of command line tools to manipulate reads in files FASTA or FASTQ format. These commands make possible preprocess the files before mapping with tools like Bowtie. Some of the tasks allowed are: conversion from FASTQ to FASTA format, information about statistics of quality, removing sequencing adapters, filtering and cutting sequences based on quality or conversion DNA/RNA. Flexbar performs removal of adapter sequences, trimming and filtering features. FreClu improves overall alignment accuracy performing sequencing-error correction by trimming short reads, based on a clustering methodology. htSeqTools is a Bioconductor package able to perform quality control, processing of data and visualization. htSeqTools makes possible visualize sample correlations, to remove over-amplification artifacts, to assess enrichment efficiency, to correct strand bias and visualize hits. NxTrim Adapter trimming and virtual library creation routine for Illumina Nextera Mate Pair libraries. PRINSEQ generates statistics of your sequence data for sequence length, GC content, quality scores, n-plicates, complexity, tag sequences, poly-A/T tails, odds ratios. Filter the data, reformat and trim sequences. Sabre A barcode demultiplexing and trimming tool for FastQ files. Scythe A 3'-end adapter contaminant trimmer. SEECER is a sequencing error correction algorithm for RNA-seq data sets. It takes the raw read sequences produced by a next generation sequencing platform like machines from Illumina or Roche. SEECER removes mismatch and indel errors from the raw reads and significantly improves downstream analysis of the data. Especially if the RNA-Seq data is used to produce a de novo transcriptome assembly, running SEECER can have tremendous impact on the quality of the assembly. Sickle A windowed adaptive trimming tool for FASTQ files using quality. SnoWhite is a pipeline designed to flexibly and aggressively clean sequence reads (gDNA or cDNA) prior to assembly. It takes in and returns fastq or fasta formatted sequence files. ShortRead is a package provided in the R (programming language) / BioConductor environments and allows input, manipulation, quality assessment and output of next-generation sequencing data. This tool makes possible manipulation of data, such as filter solutions to remove reads based on predefined criteria. ShortRead could be complemented with several Bioconductor packages to further analysis and visualization solutions (BioStrings, BSgenome, IRanges, and so on). SortMeRNA is a program tool for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data. The core algorithm is based on approximate seeds and allows for analyses of nucleotide sequences. The main application of SortMeRNA is filtering ribosomal RNA from metatranscriptomic data. TagCleaner The TagCleaner tool can be used to automatically detect and efficiently remove tag sequences (e.g. WTA tags) from genomic and metagenomic datasets. It is easily configurable and provides a user-friendly interface. Trimmomatic performs trimming for Illumina platforms and works with FASTQ reads (single or pair-ended). Some of the tasks executed are: cut adapters, cut bases in optional positions based on quality thresholds, cut reads to a specific length, converts quality scores to Phred-33/64.
Detection of chimeric reads Recent sequencing technologies normally require DNA samples to be amplified via polymerase chain reaction (PCR). Amplification often generates chimeric elements (specially from ribosomal origin) - sequences formed from two or more original sequences joined.
UCHIME is an algorithm for detecting chimeric sequences. ChimeraSlayer is a chimeric sequence detection utility, compatible with near-full length Sanger sequences and shorter 454-FLX sequences (~500 bp).
Error correction High-throughput sequencing errors characterization and their eventual correction.
Acacia Error-corrector for pyrosequenced amplicon reads. AllPathsLG error correction. AmpliconNoise AmpliconNoise is a collection of programs for the removal of noise from 454 sequenced PCR amplicons. It involves two steps the removal of noise from the sequencing itself and the removal of PCR point errors. This project also includes the Perseus algorithm for chimera removal. BayesHammer. Bayesian clustering for error correction. This algorithm is based on Hamming graphs and Bayesian subclustering. While BAYES HAMMER was designed for single-cell sequencing, it also improves on existing error correction tools for bulk sequencing data. Bless A bloom filter-based error correction solution for high-throughput sequencing reads. Blue Blue is a short-read error-correction tool based on k-mer consensus and context. BFC A sequencing error corrector designed for Illumina short reads. It uses a non-greedy algorithm with a speed comparable to implementations based on greedy methods. Denoiser Denoiser is designed to address issues of noise in pyrosequencing data. Denoiser is a heuristic variant of PyroNoise. Developers of denoiser report a good agreement with PyroNoise on several test datasets. Echo A reference-free short-read error correction algorithm. Lighter. A sequencing error correction without counting. LSC LSC uses short Illumina reads to corrected errors in long reads. Karect Karect: accurate correction of substitution, insertion and deletion errors for next-generation sequencing data. NoDe NoDe: an error-correction algorithm for pyrosequencing amplicon reads. PyroTagger PyroTagger: A fast, accurate pipeline for analysis of rRNA amplicon pyrosequence data. Quake is a tool to correct substitution sequencing errors in experiments with deep coverage for Illumina sequencing reads. QuorUM: An Error Corrector for Illumina Reads. Rcorrector. Error correction for Illumina RNA-seq reads. Reptile is a software developed in C++ for correcting sequencing errors in short reads from next-gen sequencing platforms. Seecer SEquencing Error CorrEction for Rna reads. SGA SOAPdenovo UNOISE
Bias correction Alpine Modeling and correcting fragment sequence bias for RNA-seq. cqn is a normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. EDASeq is a Bioconductor package to perform GC-Content Normalization for RNA-Seq Data. GeneScissors A comprehensive approach to detecting and correcting spurious transcriptome inference due to RNAseq reads misalignment. Peer is a collection of Bayesian approaches to infer hidden determinants and their effects from gene expression profiles using factor analysis methods. Applications of PEER have: a) detected batch effects and experimental confounders, b) increased the number of expression QTL findings by threefold, c) allowed inference of intermediate cellular traits, such as transcription factor or pathway activations. RUV is a R package that implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. svaSurrogate Variable Analysis. svaseq removing batch effects and other unwanted noise from sequencing data. SysCall is a classifier tool to identification and correction of systematic error in high-throughput sequence data.
Other tasks/pre-processing data Further tasks performed before alignment, namely paired-read mergers.
AuPairWise A Method to Estimate RNA-Seq Replicability through Co-expression. BamHash is a checksum based method to ensure that the read pairs in FASTQ files match exactly the read pairs stored in BAM files, regardless of the ordering of reads. BamHash can be used to verify the integrity of the files stored and discover any discrepancies. Thus, BamHash can be used to determine if it is safe to delete the FASTQ files storing raw sequencing reads after alignment, without the loss of data. BBMerge Merges paired reads based on overlap to create longer reads, and an insert-size histogram. Fast, multithreaded, and yields extremely few false positives. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies. Distributed with BBMap. Biopieces are a collection of bioinformatics tools that can be pieced together in a very easy and flexible manner to perform both simple and complex tasks. The Biopieces work on a data stream in such a way that the data stream can be passed through several different Biopieces, each performing one specific task: modifying or adding records to the data stream, creating plots, or uploading data to databases and web services. COPE COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly. DeconRNASeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. FastQ Screen screens FASTQ format sequences to a set of databases to confirm that the sequences contain what is expected (such as species content, adapters, vectors, etc.). FLASH is a read pre-processing tool. FLASH combines paired-end reads which overlap and converts them to single long reads. IDCheck ORNA and ORNA Q/K A tool for reducing redundancy in RNA-seq data which reduces the computational resource requirements of an assembler PANDASeq.is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence. PEAR PEAR: Illumina Paired-End reAd mergeR. qRNASeq script The qRNAseq tool can be used to accurately eliminate PCR duplicates from RNA-Seq data if Molecular Indexes™ or other stochastic labels have been used during library prep. SHERA a SHortread Error-Reducing Aligner. XORRO Rapid Paired-End Read Overlapper. DecontaMiner detects contamination in RNA-Seq data.
Alignment tools After quality control, the first step of RNA-Seq analysis involves alignment of the sequenced reads to a reference genome (if available) or to a transcriptome database. See also List of sequence alignment software.
Short (unspliced) aligners Short aligners are able to align continuous reads (not containing gaps result of splicing) to a genome of reference. Basically, there are two types: 1) based on the Burrows–Wheeler transform method such as Bowtie and BWA, and 2) based on Seed-extend methods, Needleman–Wunsch or Smith–Waterman algorithms. The first group (Bowtie and BWA) is many times faster, however some tools of the second group tend to be more sensitive, generating more correctly aligned reads.
BFAST aligns short reads to reference sequences and presents particular sensitivity towards errors, SNPs, insertions and deletions. BFAST works with the Smith–Waterman algorithm. Bowtie is a short aligner using an algorithm based on the Burrows–Wheeler transform and the FM-index. Bowtie tolerates a small number of mismatches. Bowtie2 Bowtie 2 is a tool for aligning sequencing reads to long reference sequences. It is particularly recommended for aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM-index. Bowtie 2 supports gapped, local, and paired-end alignment modes. Burrows–Wheeler Aligner (BWA) BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads. Short Oligonucleotide Analysis Package (SOAP) GNUMAP performs alignment using a probabilistic Needleman–Wunsch algorithm. This tool is able to handle alignment in repetitive regions of a genome without losing information. The output of the program was developed to make possible easy visualization using available software. Maq first aligns reads to reference sequences and after performs a consensus stage. On the first stage performs only ungapped alignment and tolerates up to 3 mismatches. Mosaik Mosaik is able to align reads containing short gaps using Smith–Waterman algorithm, ideal to overcome SNPs, insertions and deletions. NovoAlign (commercial) is a short aligner to the Illumina platform based on Needleman–Wunsch algorithm. It is able to deal with bisulphite data. Output in SAM format. PerM is a software package which was designed to perform highly efficient genome scale alignments for hundreds of millions of short reads produced by the ABI SOLiD and Illumina sequencing platforms. PerM is capable of providing full sensitivity for alignments within 4 mismatches for 50bp SOLID reads and 9 mismatches for 100bp Illumina reads. RazerS SEAL uses a MapReduce model to produce distributed computing on clusters of computers. Seal uses BWA to perform alignment and Picard MarkDuplicates to detection and duplicate read removal. segemehl SeqMap SHRiMP employs two techniques to align short reads. Firstly, the q-gram filtering technique based on multiple seeds identifies candidate regions. Secondly, these regions are investigated in detail using Smith–Waterman algorithm. SMALT Stampy combines the sensitivity of hash tables and the speed of BWA. Stampy is prepared to alignment of reads containing sequence variation like insertions and deletions. It is able to deal with reads up to 4500 bases and presents the output in SAM format. Subread is a read aligner. It uses the seed-and-vote mapping paradigm to determine the mapping location of the read by using its largest mappable region. It automatically decides whether the read should be globally mapped or locally mapped. For RNA-seq data, Subread should be used for the purpose of expression analysis. Subread can also be used to map DNA-seq reads. ZOOM (commercial) is a short aligner of the Illumina/Solexa 1G platform. ZOOM uses extended spaced seeds methodology building hash tables for the reads, and tolerates mismatches and insertions and deletions. WHAM WHAM is a high-throughput sequence alignment tool developed at University of Wisconsin-Madison. It aligns short DNA sequences (reads) to the whole human genome at a rate of over 1500 million 60bit/s reads per hour, which is one to two orders of magnitudes faster than the leading state-of-the-art techniques.
Spliced aligners Many reads span exon-exon junctions and can not be aligned directly by Short aligners, thus specific aligners were necessary - Spliced aligners. Some Spliced aligners employ Short aligners to align firstly unspliced/continuous reads (exon-first approach), and after follow a different strategy to align the rest containing spliced regions - normally the reads are split into smaller segments and mapped independently. See also.
Aligners based on known splice junctions (annotation-guided aligners) In this case the detection of splice junctions is based on data available in databases about known junctions. This type of tools cannot identify new splice junctions. Some of this data comes from other expression methods like expressed sequence tags (EST).
Erange is a tool to alignment and data quantification to mammalian transcriptomes. IsoformEx MapAL OSA RNA-MATE is a computational pipeline for alignment of data from Applied Biosystems SOLID system. Provides the possibility of quality control and trimming of reads. The genome alignments are performed using mapreads and the splice junctions are identified based on a library of known exon-junction sequences. This tool allows visualization of alignments and tag counting. RUM performs alignment based on a pipeline, being able to manipulate reads with splice junctions, using Bowtie and Blat. The flowchart starts doing alignment against a genome and a transcriptome database executed by Bowtie. The next step is to perform alignment of unmapped sequences to the genome of reference using BLAT. In the final step all alignments are merged to get the final alignment. The input files can be in FASTA or FASTQ format. The output is presented in RUM and SAM format. RNASEQR. SAMMate SpliceSeq X-Mate
De novo splice aligners De novo Splice aligners allow the detection of new Splice junctions without need to previous annotated information (some of these tools present annotation as a suplementar option).
ABMapper BBMap Uses short kmers to align reads directly to the genome (spanning introns to find novel isoforms) or transcriptome. Highly tolerant of substitution errors and indels, and very fast. Supports output of all SAM tags needed by Cufflinks. No limit to genome size or number of splices per read. Supports Illumina, 454, Sanger, Ion Torrent, PacBio, and Oxford Nanopore reads, paired or single-ended. Does not use any splice-site-finding heuristics optimized for a single taxonomic branch, but rather finds optimally-scoring multi-affine-transform global alignments, and thus is ideal for studying new organisms with no annotation and unknown splice motifs. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies. ContextMap was developed to overcome some limitations of other mapping approaches, such as resolution of ambiguities. The central idea of this tool is to consider reads in gene expression context, improving this way alignment accuracy. ContextMap can be used as a stand-alone program and supported by mappers producing a SAM file in the output (e.g.: TopHat or MapSplice). In stand-alone mode aligns reads to a genome, to a transcriptome database or both. CRAC propose a novel way of analyzing reads that integrates genomic locations and local coverage, and detect candidate mutations, indels, splice or fusion junctions in each single read. Importantly, CRAC improves its predictive performance when supplied with e.g. 200 nt reads and should fit future needs of read analyses. GSNAP GMAP A Genomic Mapping and Alignment Program for mRNA and EST Sequences. HISAT is a spliced alignment program for mapping RNA-seq reads. In addition to one global FM-index that represents a whole genome, HISAT uses a large set of small FM-indexes that collectively cover the whole genome (each index represents a genomic region of ~64,000 bp and ~48,000 indexes are needed to cover the human genome). These small indexes (called local indexes) combined with several alignment strategies enable effective alignment of RNA-seq reads, in particular, reads spanning multiple exons. HISAT2 is an alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs [Sirén et al. 2014], we designed and implemented a graph FM-index (GFM), an original approach and its first implementation to the best of our knowledge. In addition to using one global GFM index that represents a population of human genomes, HISAT2 uses a large set of small GFM indexes that collectively cover the whole genome (each index representing a genomic region of 56 Kbp, with 55,000 indexes needed to cover the human population). These small indexes (called local indexes), combined with several alignment strategies, enable rapid and accurate alignment of sequencing reads. This new indexing scheme is called a Hierarchical Graph FM index (HGFM). HMMSplicer can identify canonical and non-canonical splice junctions in short-reads. Firstly, unspliced reads are removed with Bowtie. After that, the remaining reads are one at a time divided in half, then each part is seeded against a genome and the exon borders are determined based on the Hidden Markov Model. A quality score is assigned to each junction, useful to detect false positive rates. MapSplice PALMapper Pass aligns gapped, ungapped reads and also bisulfite sequencing data. It includes the possibility to filter data before alignment (remotion of adapters). Pass uses Needleman–Wunsch and Smith–Waterman algorithms, and performs alignment in 3 stages: scanning positions of seed sequences in the genome, testing the contiguous regions and finally refining the alignment. PASSion PASTA QPALMA predicts splice junctions supported on machine learning algorithms. In this case the training set is a set of spliced reads with quality information and already known alignments. RASER: reads aligner for SNPs and editing sites of RNA. SeqSaw SoapSplice A tool for genome-wide ab initio detection of splice junction sites from RNA-Seq, a method using new generation sequencing technologies to sequence the messenger RNA. SpliceMap SplitSeek SuperSplat was developed to find all type of splice junctions. The algorithm splits each read in all possible two-chunk combinations in an iterative way, and alignment is tried to each chunck. Output in "Supersplat" format.
De novo splice aligners that also use annotation optionally MapNext OLego STAR is a tool that employs "sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure", detects canonical, non-canonical splices junctions and chimeric-fusion sequences. It is already adapted to align long reads (third-generation sequencing technologies) and can reach speeds of 45 million paired reads per hour per processor. Subjunc is a specialized version of Subread. It uses all mappable regions in an RNA-seq read to discover exons and exon-exon junctions. It uses the donor/receptor signals to find the exact splicing locations. Subjunc yields full alignments for every RNA-seq read including exon-spanning reads, in addition to the discovered exon-exon junctions. Subjunc should be used for the purpose of junction detection and genomic variation detection in RNA-seq data. TopHat is prepared to find de novo junctions. TopHat aligns reads in two steps. Firstly, unspliced reads are aligned with Bowtie. After, the aligned reads are assembled with Maq resulting islands of sequences. Secondly, the splice junctions are determined based on the initially unmapped reads and the possible canonical donor and acceptor sites within the island sequences.
Other spliced aligners G.Mo.R-Se is a method that uses RNA-Seq reads to build de novo gene models.
Evaluation of alignment tools AlignerBoost is a generalized software toolkit for boosting Next-Gen sequencing mapping precision using a Bayesian-based mapping quality framework. CADBURE Bioinformatics tool for evaluating aligner performance on your RNA-Seq dataset. QualiMap: Evaluating next generation sequencing alignment data. RNAseqEVAL A collection of tools for evaluating RNA seq mapping. Teaser: Individualized benchmarking and optimization of read mapping results for NGS data.
Normalization, quantitative analysis and differential expression
General tools These tools perform normalization and calculate the abundance of each gene expressed in a sample. RPKM, FPKM and TPMs are some of the units employed to quantification of expression. Some software are also designed to study the variability of genetic expression between samples (differential expression). Quantitative and differential studies are largely determined by the quality of reads alignment and accuracy of isoforms reconstruction. Several studies are available comparing differential expression methods.
ABSSeq a new RNA-Seq analysis method based on modelling absolute expression differences. ALDEx2 is a tool for comparative analysis of high-throughput sequencing data. ALDEx2 uses compositional data analysis and can be applied to RNAseq, 16S rRNA gene sequencing, metagenomic sequencing, and selective growth experiments. Alexa-Seq is a pipeline that makes possible to perform gene expression analysis, transcript specific expression analysis, exon junction expression and quantitative alternative analysis. Allows wide alternative expression visualization, statistics and graphs. ARH-seq – identification of differential splicing in RNA-seq data. ASC Ballgown BaySeq is a Bioconductor package to identify differential expression using next-generation sequencing data, via empirical Bayesian methods. There is an option of using the "snow" package for parallelisation of computer data processing, recommended when dealing with large data sets. GMNB is a Bayesian method to temporal gene differential expression analysis across different phenotypes or treatment conditions that naturally handles the heterogeneity of sequencing depth in different samples, removing the need for ad-hoc normalization. BBSeq BitSeq (Bayesian Inference of Transcripts from Sequencing Data) is an application for inferring expression levels of individual transcripts from sequencing (RNA-Seq) data and estimating differential expression (DE) between conditions. CEDER Accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq. CPTRA The CPTRA package is for analyzing transcriptome sequencing data from different sequencing platforms. It combines advantages of 454, Illumina GAII, or other platforms and can perform sequence tag alignment and annotation, expression quantification tasks. casper is a Bioconductor package to quantify expression at the isoform level. It combines using informative data summaries, flexible estimation of experimental biases and statistical precision considerations which (reportedly) provide substantial reductions in estimation error. Cufflinks/Cuffdiff is appropriate to measure global de novo transcript isoform expression. It performs assembly of transcripts, estimation of abundances and determines differential expression (Cuffdiff) and regulation in RNA-Seq samples. DESeq is a Bioconductor package to perform differential gene expression analysis based on negative binomial distribution. DEGSeq Derfinder Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach. DEvis is a powerful, integrated solution for the analysis of differential expression data. Using DESeq2 as a framework, DEvis provides a wide variety of tools for data manipulation, visualization, and project management. DEXSeq is Bioconductor package that finds differential differential exon usage based on RNA-Seq exon counts between samples. DEXSeq employs negative binomial distribution, provides options to visualization and exploration of the results. DEXUS is a Bioconductor package that identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. In contrast to other methods, DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. DGEclust is a Python package for clustering expression data from RNA-seq, CAGE and other NGS assays using a Hierarchical Dirichlet Process Mixture Model. The estimated cluster configurations can be post-processed in order to identify differentially expressed genes and for generating gene- and sample-wise dendrograms and heatmaps. DiffSplice is a method for differential expression detection and visualization, not dependent on gene annotations. This method is supported on identification of alternative splicing modules (ASMs) that diverge in the different isoforms. A non-parametric test is applied to each ASM to identify significant differential transcription with a measured false discovery rate. DROP The detection of RNA Outliers Pipeline (DROP) is an integrative workflow to detect aberrant expression, aberrant splicing, and mono-allelic expression from raw sequencing files. EBSeq is a Bioconductor package for identifying genes and isoforms differentially expressed (DE) across two or more biological conditions in an RNA-seq experiment. It also can be used to identify DE contigs after performing de novo transcriptome assembly. While performing DE analysis on isoforms or contigs, different isoform/contig groups have varying estimation uncertainties. EBSeq models the varying uncertainties using an empirical Bayes model with different priors. EdgeR is a R package for analysis of differential expression of data from DNA sequencing methods, like RNA-Seq, SAGE or ChIP-Seq data. edgeR employs statistical methods supported on negative binomial distribution as a model for count variability. EdgeRun an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test. EQP The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data. ESAT The End Sequence Analysis Toolkit (ESAT) is specially designed to be applied for quantification of annotation of specialized RNA-Seq gene libraries that target the 5' or 3' ends of transcripts. eXpress performance includes transcript-level RNA-Seq quantification, allele-specific and haplotype analysis and can estimate transcript abundances of the multiple isoforms present in a gene. Although could be coupled directly with aligners (like Bowtie), eXpress can also be used with de novo assemblers and thus is not needed a reference genome to perform alignment. It runs on Linux, Mac and Windows. ERANGE performs alignment, normalization and quantification of expressed genes. FASDA (Fast And Simple Differential Analysis) is a differential analysis tool written in C. It takes SAM/BAM/CRAM or kallisto abundance files directly as input, and produces fold-changes and exact P-values for up to 5 replicates, near-exact P-values for up to 12 replicates, and Mann-Whitney (Wilcoxon rank-sum) P-values for 8 or more replicates. featureCounts an efficient general-purpose read quantifier. FDM FineSplice Enhanced splice junction detection and estimation from RNA-Seq data. GFOLD Generalized fold change for ranking differentially expressed genes from RNA-seq data. globalSeq Global test for counts: testing for association between RNA-Seq and high-dimensional data. GPSeq This is a software tool to analyze RNA-seq data to estimate