Your top 3 RNA-seq read alignment tools


RNA-sequencing (RNA-seq) is currently the leading technology for transcriptome analysis. RNA-seq has a wide range of applications, from the study of alternative gene splicingpost-transcriptional modifications, to comparison of relative gene expression between different biological samples.

To help you perform your RNA-seq experiments in the best conditions, we are continuing our series of surveys by asking the OMICtools community to choose their favorite analysis tools step by step.

Mapping reads to reference genome

 After a first step of quality control (previous blog post here), the next step in the analysis of your RNA-seq experiment is alignment of reads to a reference genome or a transcriptome database.

There are two types of aligners: Splice-unaware and splice-aware. Splice-unaware aligners are able to align continuous reads to a genome of reference, but are not aware of exon/intron junctions. Therefore, in RNA-sequencing, there use is limited to the analysis of expression of known genes, or alignment to transcriptome. On the other hand, splice-aware aligners map reads over exon/intro junctions and are therefore used for discovering new splice forms, along with the analysis of gene expression levels.

With that in mind, we asked OMICtools members to vote for their favorite reads alignment tools (among splice-aware and splice unaware aligners). Here are the results of the survey.

Your number 1 reads aligner: STAR

Though it did not appear in the original survey, you were a lot to mention this tool so we thought it deserved the top spot!

Spliced Transcripts Alignment to a Reference (STAR) is a standalone software that uses sequential maximum mappable seed search followed by seed clustering and stitching to align RNA-seq reads. It is able to detect canonical junctions, non-canonical splices, and chimeric transcripts.

One of the main advantages of STAR are its high speed, accuracy, and efficiency (Engström et al.).

Schematic representation of the Maximum Mappable Prefix search in the STAR algorithm for detecting (a) splice junctions, (b) mis- matches and (c) tails.

STAR is implemented as a standalone C++ code and is freely available at

Your second favorite tool: Tophat

We were 54% to choose Tophat as your favorite RNA-seq aligner.

TopHat aligns RNA-seq reads to mammalian-sized genomes by first using the short read aligner Bowtie, and then by mapping to a reference genome to discover RNA splice sites de novo.

The TopHat pipeline. RNA-Seq reads are mapped against the whole reference genome, and those reads that do not map are set aside.

TopHat has been widely used in RNA-seq protocols and is often paired with the software Cufflinks for a full analysis of sequencing data (Trapnell et al.). Initially launched in 2009, Tophat got updated to Tophat2 in 2013, and has now been progressively replaced with HISAT.

Bronze medal for HISAT

We finish our podium with HISAT, chosen by 30% of voters.

HISAT (and its newer version HISAT2) is the next generation of spliced aligner from the same group that have developed TopHat.

HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-manzini (Fm) index, employing two types of indexes for alignment: a whole-genome Fm index to anchor each alignment and numerous local Fm indexes for very rapid extensions of these alignments.

HISAT most interesting features include its high speed and its low memory requirement.

Alignment speed of spliced alignment software for 20 million simulated 100-bp reads.

HISAT is open-source software freely available at software/hisat/.


Pär G Engström et al. (2013). Systematic evaluation of spliced alignment programs for rnA-seq data. Nature Methods.

Cole Trapnell et al. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics.

Alexander Dobin et al. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics.

Daehwan Kim et al. (2015). HISAT: a fast spliced aligner with low memory requirements. Nature Methods.

Link splice-isoform expression to cancer metabolism with GEMsplice


Metabolic models rely on genes and proteins expression to estimate or predict a metabolic cell phenotype. In the case of cancer, it is now admitted that metabolism dysregulations play a crucial role in cancer onset and proliferation. However, most metabolic models only rely on gene expression, and do not account for splice-isoform expression and/or alteration.

To solve this gap, Claudio Angione developed GEMsplice, a desktop application that allows to link splice-isoform gene expression data to cancer metabolism. Here, he describes the features and benefits of GEMsplice.

Solving the gap in cancer metabolism models

Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict the cellular phenotype. A genome-scale analysis of cancer metabolism captures many effects that cannot be identified using standard transcriptomic analysis.

However, although metabolic models have been successfully integrated with transcriptomic data to provide a mechanistic link between genotype and phenotype in cancer, there is no method for integration of splice isoform expression levels into such models. As a result, transcriptomic data in metabolic models can only be integrated at the gene level. Expression data at the splice-isoform level is currently neglected or simply averaged within the same gene to approximate the expression at the gene level.

This issue has been outlined in a number of recent reviews, and recently acknowledged by the scientific community as one of the main issues of metabolic modelling. In fact, the incorporation of splice isoforms is needed to understand complex diseases like cancer, where alternative splicing plays a crucial role.

GEMsplice features

GEMsplice is the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. It is validated by generating cancer-versus-normal predictions on metabolic pathways and by comparing them with available literature on pathways affected by breast cancer.

GEMsplice uses gene expression and transcript level information to incorporate them into the model (Figure 1). As a result, it exploits the full potential of next-generation sequencing in the context of genome-scale metabolic reconstructions. A set of phenotype-specific RNA-Seq transcript expression levels in a variety of breast cancer types and stages from the Cancer RNA-Seq Nexus dataset (Li et al., 2016), including data from TCGA, GEO and SRA, are then mapped onto the model using constraint-based modeling. Cancer-specific metabolic models are finally generated and investigated using multilevel linear programming, leading to phenotype prediction for different types of breast cancer (Figure 1).

Figure 1: GEMsplice incorporates RNA-Seq data into genome-scale metabolic models at the splice-isoform level.

GEMsplice is freely available for academic use on Github.

With respect to state-of-the-art methods, GEMsplice will enable for the first time computational analyses of metabolism at transcript level with splice-isoform resolution.


Claudio Angione. (2018). Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism. Bioinformatics.

Map functional networks of ncRNAs with circlncRNAnet


Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) lack protein-coding potential but have nonetheless emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation.

To fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest, Dr. Bertrand Chin-MingTan and his team have implemented an integrative bioinformatics approach to examine in silico the functional networks of non-coding RNAs. Here, they present their web server tool “circlncRNAnet” and discuss its main features.

In-depth analyses of non-coding RNA biology

The main purpose for implementing this web server is to provide biologists with a user-friendly, “one-stop” web tool to study from a network perspective the biology of lncRNAs or circRNAs of interest.

Despite their lack of protein-coding potential, lncRNAs and circRNAs have emerged as key determinant in gene regulation, acting to fine-tune transcriptional and signaling output. Given the widespread regulatory roles and target spectrum of non-coding RNAs, complete understanding of their biological relevance depends on integrative analyses of systems data at multiple levels. However, only a handful of available databases have been reported in this field, and they are limited in the scope of reference data and analytic modules. Through an integrated and streamlined design, circlncRNAnet is aimed to broaden the understanding of ncRNA candidates by testing in silico several hypotheses of ncRNA-based functions on the basis of large-scale RNA-seq data.

Overall design and analytic workflow of circlncRNAnet.

Main functionalities 

  • This web server is implemented with several features representing advances in the bioinformatics of ncRNAs:
    circlncRNAnet is designed with the flexibility of accepting private or public data. To further support efficient analyses and presentation, we have extensively curated public data into reference annotations for the circlncRNAnet workflow.
  • Multi-layer modules and algorithms then provide outputs on expression profiles, co-expression networks & pathways, and molecular interactomes (i.e. microRNAs, RNA-binding proteins, and transcription factors), which are dynamically and interactively displayed according to user-defined criteria.
  • Users may apply circlncRNAnet to obtain, in real time, multiple lines of functionally relevant information on the circRNAs/lncRNAs of their interest. The overall workflow takes only a few minutes, as compared to hours of manual efforts of independent database searches and analyses.
Schematic showing example outputs of circlncRNAnet analyses of lncRNA-based networks in colorectal cancer. After dataset upload, the server executes differential expression and expression correlation analyses. The web server allows the user to select query genes and correlation criteria. From Shao-Min Wu et al.

CiclncRNAnet is freely available at:

Reference :

(Wu et al., 2017). circlncRNAnet: An integrated web-based resource for mapping functional networks of long or circular forms of non-coding RNAs. Gigascience.