MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery

By Kai Wang, Darshan Singh, Z. Zeng, S. J. Coleman, Yan Huang, G. Savich, Xiaping He, P. Mieczkowski, Sara A. Grimm, Charles M. Perou and 4 more. Nucleic Acids Research, 2010

1,200 citations recorded by the source corpus.

The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. We introduce a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. MapSplice can be applied to both short (<75 bp) and long reads (≥75 bp). MapSplice is not dependent on splice site features or intron length, consequently it can detect novel canonical as well as non-canonical splices. MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy. We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data. Experimental studies also support the accuracy of the algorithm. Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alternative splicing on a global level as well as the differences between molecular subtypes of breast cancer. These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions. Software download URL: http://www.netlab.uky.edu/p/bioinfo/MapSplice.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bb1223a03c8419afdea1c081ac5a4f5672e79f79

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In Molecular Biology

Citations in the corpus

Computational methods for transcriptome annotation and quantification using RNA-seq (2011, methodology): “Exon-first 47–49 methods such as MapSplice 49 , SpliceMap 47 and TopHat 48 use a two-step process.”
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions (2013, methodology): “MapSplice [8] uses a two-step approach similar to TopHat2.”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “…Mayakonda and Koeffler, 2016 https://bioconductor.org/packages/release/bioc/ html/maftools.html MapSplice Wang et al., 2010 http://www.netlab.uky.edu/p/bioinfo/MapSplice/ MARINa (MAster Regulator INference Algorithm), ssMARINa Lefebvre et al., 2010 Aytes…”
The bioinformatics toolbox for circRNA discovery and analysis (2020, methodology): “Another example is RAISE [80], which can also measure circRNA abundance and predict circRNA structure, with the help of MapSplice, find_circ, ACFS and circRNA_finder.”
Circular RNAs are abundant, conserved, and associated with ALU repeats. (2013, methodology): “Upon confirmation of enrichment of this rare circular species, we next performed high-throughput sequencing of such samples on an Illumina HiSeq yielding ∼300 million 100-bp reads per sample, which were aligned to the human genome using a de novo splice mapping algorithm, MapSplice (Wang et al. 2010”
Circular RNAs: diversity of form and function (2014, background): “Additionally, circRNAs have been recently reported in plants (Arabidopsis thaliana), yeast (Schizosaccharomyces pombe and Saccharomyces cerevisiae), and protists (Plasmodium falciparum and Dictyostelium discoideum) (Wang et al. 2014).”