Detection of splice junctions from paired-end RNA-seq data by SpliceMap

Abstract

Alternative splicing is a prevalent post-transcriptional process, which is not only important to normal cellular function but is also involved in human diseases. The newly developed second generation sequencing technique provides high-throughput data (RNA-seq data) to study alternative splicing events in different types of cells. Here, we present a computational method, SpliceMap, to detect splice junctions from RNA-seq data. This method does not depend on any existing annotation of gene structures and is capable of finding novel splice junctions with high sensitivity and specificity. It can handle long reads (50–100 nt) and can exploit paired-read information to improve mapping accuracy. Several parameters are included in the output to indicate the reliability of the predicted junction and help filter out false predictions. We applied SpliceMap to analyze 23 million paired 50-nt reads from human brain tissue. The results show at this depth of sequencing, RNA-seq can support reliable detection of splice junctions except for those that are present at very low level. Compared to current methods, SpliceMap can achieve 12% higher sensitivity without sacrificing specificity.

An atlas of over 90,000 conserved noncoding sequences provides insight into crucifer regulatory regions

“…The parameters were refined relative to an 800,000-base sequence generated from concatenated fourfold-degenerate sites within which a CNS FDR of <1% was required. Independent verifications based on evolutionary signatures of coding sequences using RNAcode 93 , the absence of splice sites 88 and a uniform density of stop codons suggested that very few CNSs correspond with unannotated protein-coding exons.…”

Next-generation sequencing in the clinic: Promises and challenges

“…Conventional mapping programs such as ELAND, Bowtie [121] and MAQ [123] that need to allocate reads to contiguous sequences are inappropriate for spliced alignment. New algorithms have been developed to map splice-crossing reads, some of which utilize previously known splice events (e.g., ERANGE [129]), while others (e.g., GSNAP [130], MapSplice [131], RUM [132], SpliceMap [133], TopHat [134]) do not rely upon prior knowledge. In particular, some algorithms have been specifically designed for the identification of gene fusions, including deFuse [157], FusionSeq [158], ShortFuse [159] and TopHat-Fusion [160].…”

Next Generation Sequencing in Cancer Research and Clinical Application

“…One is to align reads to the reference transcriptome using standard DNA-seq reads aligner. The alternative is to map reads to the reference genome allowing for the identification of novel splice junctions using a RNA-seq specific aligner, such as TopHat [61], MapSplice [62], SpliceMap [63], GSNAP [64], and STAR [65]. Having aligned reads, expression values are quantified by aggregating reads into counts and differential expression analysis is performed based on counts (DEseq [66],edgeR [67]) or FPKM/RPKM values (CuffLinks [68,69]).…”