RNASEQR-a streamlined and accurate RNA-seq sequence analysis program

Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. RNASEQR was developed to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient.  RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs).

RNASEQR takes advantage of annotated transcripts and genomic reference sequences to obtain high quality mapping/alignment results. To evaluate the performance of RNASEQR, we compared the results to those from other widely used RNA-seq tools, including ERANGE, MapSplice, SpliceMap  and TopHat , with a simulated dataset derived from the Consensus CDS (CCDS) project  and two experimental data sets generated from a patient with glioblastoma multiforme (GBM). RNASEQR significantly improves the mapping results, especially on transcripts containing smaller exons, which results in more accurate assessment of gene-expression profiles and better transcript structures.

RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers.

RNASEQR was written in Python 2.7, and shall run on most platforms as long as a Python interpreter (v2.7+) is installed. RNASEQR was developed on the 64-bit Cent OS Linux and shall run on both the 64- and the 32-bit Linux systems. RNASEQR has been tested on Bowtie v.0.12.7, BLAT v.34, Python 2.7, and shall theoretically work with newer versions.

RNASEQR can be downloaded from here.


Chen LY, Wei KC, Huang AC, Wang K, Huang CY, Yi D, Tang CY, Galas DJ, Hood LE. RNASEQR–a streamlined and accurate RNA-seq sequence analysis program. Nucleic Acids Res. 2012 Mar;40(6):e42. Epub 2011 Dec 22.

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