MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery
Open accessAbstract
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.
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Several RNA-seq mappers are optimized for specific tasks: identifying SNPs or indels (GSNAP, PALMapper, MapSplice), detecting non-canonical splice junctions (STAR, MapSplice), ultra-fast mapping (GEM), or mapping long reads (STAR).
“Several other mappers exist that are optimized to identify SNPs or indels (GSNAP [202], PALMapper [203] MapSplice [204]), detect non-canonical splice junctions (STAR [15], MapSplice [204]), achieve ultra-fast mapping (GEM [205]) or map long-reads (STAR [15]).”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [204]
High inter-site false positive DEG rates were observed across multiple analysis pipelines (WHAM, Mapsplice, Novoalign, Cufflinks, HTSeq), independent of pipeline or alignment method.
“When we examined the inter-site DEG false positive rates for several other analysis pipelines (WHAM34, Mapsplice35, Novoalign36, Cufflinks23, 37, 38 and HTSeq39) we found similarly high false positive rates, regardless of the analysis pipeline or read alignment methods used (Supplementary Fig. 1).”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [35]
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- Authors
- Wang, K., Singh, D., Zeng, Z., Coleman, S. J., Huang, Y., Savich, G. L., He, X., Mieczkowski, P., Grimm, S. A., Perou, C. M., MacLeod, J. N., Chiang, D. Y., Prins, J. F., & Liu, J.
- Affiliations
- University of Kentucky (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Liu, Jinze
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-08-28
- Volume
- 38
- Issue
- 18
- Pages
- e178-e178
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC2952873
- OpenAlex
- W2126144698
- Cited by
- 1,193
- References
- 37
- Citations by year
- 2026: 12 · 2025: 30 · 2024: 43 · 2023: 42 · 2022: 58 · 2021: 116 · 2020: 110 · 2019: 102 · 2018: 129 · 2017: 112 · 2016: 127 · 2015: 76 · 2014: 74 · 2013: 66 · 2012: 49
- FWCI
- 20.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UKnowledge (University of Kentucky) (submittedVersion, cc-by-nc)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Cancer Institute (CA143848, U24CA143848)
- National Center for Research Resources (P20RR016481)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq read mapping, splice junction detection, alternative splicing, splice site detection, non-canonical splicing, long-read alignment, breast cancer, molecular subtypes, TopHat
- MeSH terms
- Algorithms, Breast Neoplasms, Female, Humans, Software, Alternative Splicing, Sequence Analysis, RNA, Gene Expression Profiling, RNA Splice Sites
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