S-MART, A Software Toolbox to Aid RNA-seq Data Analysis

High-throughput sequencing is now routinely performed in many experiments. But the analysis of the millions of sequences generated, is often beyond the expertise of the wet labs who have no personnel specializing in bioinformatics. Whereas several tools are now available to map high-throughput sequencing data on a genome, few of these can extract biological knowledge from the mapped reads. We have developed a toolbox called S-MART, which handles mapped RNA-Seq data. S-MART is an intuitive and lightweight tool which performs many of the tasks usually required for the analysis of mapped RNA-Seq reads. S-MART does not require any computer science background and thus can be used by all of the biologist community through a graphical interface. S-MART can run on any personal computer, yielding results within an hour even for Gb of data for most queries. S-MART may perform the entire analysis of the mapped reads, without any need for other ad hoc scripts. With this tool, biologists can easily perform most of the analyses on their computer for their RNA-Seq data, from the mapped data to the discovery of important loci.

[1]  N. Rajewsky,et al.  Discovering microRNAs from deep sequencing data using miRDeep , 2008, Nature Biotechnology.

[2]  S. Lewis,et al.  The generic genome browser: a building block for a model organism system database. , 2002, Genome research.

[3]  Manolis Kellis,et al.  Discrete Small RNA-Generating Loci as Master Regulators of Transposon Activity in Drosophila , 2007, Cell.

[4]  Tom H. Pringle,et al.  The human genome browser at UCSC. , 2002, Genome research.

[5]  Siu-Ming Yiu,et al.  SOAP2: an improved ultrafast tool for short read alignment , 2009, Bioinform..

[6]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[7]  Sandrine Dudoit,et al.  Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.

[8]  R. Durbin,et al.  Mapping Quality Scores Mapping Short Dna Sequencing Reads and Calling Variants Using P

, 2022 .

[9]  M. Gerstein,et al.  The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing , 2008, Science.

[10]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[11]  Richard Durbin,et al.  Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..

[12]  A. Oshlack,et al.  Transcript length bias in RNA-seq data confounds systems biology , 2009, Biology Direct.

[13]  Daniel J. Blankenberg,et al.  A framework for collaborative analysis of ENCODE data: making large-scale analyses biologist-friendly. , 2007, Genome research.