%0 Journal Article %1 Li:2010:Bioinformatics:20022975 %A Li, B %A Ruotti, V %A Stewart, R M %A Thomson, J A %A Dewey, C N %D 2010 %J Bioinformatics %K SHOULDREAD fulltext rna-seq rsem %N 4 %P 493-500 %R 10.1093/bioinformatics/btp692 %T RNA-Seq gene expression estimation with read mapping uncertainty %U https://www.ncbi.nlm.nih.gov/pubmed?cmd=Search&doptcmdl=Citation&defaultField=Title%20Word&term=Li%5Bauthor%5D%20AND%20RNA-Seq%20gene%20expression%20estimation%20with%20read%20mapping%20uncertainty %V 26 %X RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically.We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20-25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed.

@article{Li:2010:Bioinformatics:20022975, abstract = {RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically.We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20-25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed.}, added-at = {2017-07-23T22:12:47.000+0200}, author = {Li, B and Ruotti, V and Stewart, R M and Thomson, J A and Dewey, C N}, biburl = {https://www.bibsonomy.org/bibtex/2b83d933bad6cb63cacff706b54b3f3cb/marcsaric}, description = {RNA-Seq gene expression estimation with read mapping uncertainty. - PubMed - NCBI}, doi = {10.1093/bioinformatics/btp692}, interhash = {ddf660ca09a5784ccd6039a1dd3d6473}, intrahash = {b83d933bad6cb63cacff706b54b3f3cb}, journal = {Bioinformatics}, keywords = {SHOULDREAD fulltext rna-seq rsem}, month = feb, number = 4, pages = {493-500}, pmid = {20022975}, timestamp = {2017-07-23T22:12:47.000+0200}, title = {RNA-Seq gene expression estimation with read mapping uncertainty}, url = {https://www.ncbi.nlm.nih.gov/pubmed?cmd=Search&doptcmdl=Citation&defaultField=Title%20Word&term=Li%5Bauthor%5D%20AND%20RNA-Seq%20gene%20expression%20estimation%20with%20read%20mapping%20uncertainty}, volume = 26, year = 2010 }