List of RNA-Seq bioinformatics tools

RNA-Seq[1][2][3] is a technique [4] that allows transcriptome studies 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.

To follow an integrated guide about RNA-seq analysis, please see - github rnaseq_tutorial, Next Generation Sequencing (NGS)/RNA, RNA-seqlopedia, Hands-On Tutorial [5] or RNA-Seq Workflow. Also, important links are SEQanswers,Omictools, RNA-SeqList, RNA-SeqBlog, Biostar, homolog.us and bioscholar.


Quality control, trimming, error correction and pre-processing of data

Quality assessment is the first step of the bioinformatics pipeline of RNA-Seq. Often, is 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

Improving the Quality

Improvement of the RNA-Seq quality, correcting the bias is a complex subject.[13][14] 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 specie 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,[15][16] rRNA depletion,[17] errors produced during sequencing,[18] priming of reverse transcription caused by random hexamers.[19]

Different tools were developed to attempt to solve each of the detected errors.

Trimming and adapters removal

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 together.

Errors Correction

High-throughput sequencing errors characterization and their eventual correction.[26]

Bias Correction

Other tasks/Pre-processing data

Further tasks performed before alignment, namely paired-read mergers.

Alignment Tools

After control assessment, the first step of RNA-Seq analysis involves alignment (RNA-Seq alignment) of the sequenced reads to a reference genome (if available) or to a transcriptome database. See also [39] and 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, despite the time spent tend to be more sensitive, generating more reads correctly aligned. See a comparative study of short aligners - comparative study.

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.[40]

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).

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). See also De novo Splice Aligners.

De novo Splice Aligners that also use annotation optionally
Other Spliced Aligners

Evaluation of Alignment tools

Normalization, Quantitative analysis and Differential Expression

General Tools

These tools perform normalization and calculate the abundance of each gene expressed in a sample.[46] RPKM, FPKM and TPMs are some of the units employed to quantification of expression (RPKM-FPKM-TPMs video). 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.[47][48]

Evaluation of quantification and differential expression

Multi-tool solutions

Workbench (analysis pipeline / integrated solutions)

Commercial Solutions

Open (free) Source Solutions

Alternative Splicing Analysis

General Tools

Intron Retention Analysis

Fusion genes/chimeras/translocation finders/structural variations

Genome arrangements result of diseases like cancer can produce aberrant genetic modifications like fusions or translocations. Identification of these modifications play important role in carcinogenesis studies.[57]

Copy Number Variation identification

Single Cell RNA-Seq

Single cell sequencing. Comparative analysis of single-cell RNA-sequencing methods.[59]

RNA-Seq simulators

These Simulators generate in silico reads and are useful tools to compare and test the efficiency of algorithms developed to handle RNA-Seq data. Moreover, some of them make possible to analyse and model RNA-Seq protocols.See also Genetic Simulation Resources and some discussion about simulation at Biostars.

Transcriptome assemblers

The transcriptome is the total population of RNAs expressed in one cell or group of cells, including non-coding and protein-coding RNAs. There are two types of approaches to assemble transcriptomes. Genome-guided methods use a reference genome (if possible a finished and high quality genome) as a template to align and assembling reads into transcripts. Genome-independent methods does not require a reference genome and are normally used when a genome is not available. In this case reads are assembled directly in transcripts. Some important comparative studies [66][67] were already published.

Genome-Guided assemblers

Genome-Independent (de novo) assemblers

Assembly evaluation tools

Co-expression networks

miRNA prediction and analysis

Visualization tools

Functional, Network & Pathway Analysis Tools

Further annotation tools for RNA-Seq data

RNA-Seq Databases

Single specie RNA-Seq databases

Webinars and Presentations

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