{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["29(20)"],"submitter":["Behr J"],"pubmed_abstract":["<h4>Motivation</h4>High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction.<h4>Results</h4>We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed "],"journal":["Bioinformatics (Oxford, England)"],"pagination":["2529-38"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3789545"],"repository":["biostudies-literature"],"pubmed_title":["MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples."],"pmcid":["PMC3789545"],"pubmed_authors":["Drewe P","Sreedharan VT","Behr J","Kahles A","Ratsch G","Zhong Y"],"additional_accession":[]},"is_claimable":false,"name":"MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples.","description":"<h4>Motivation</h4>High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction.<h4>Results</h4>We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed ","dates":{"release":"2013-01-01T00:00:00Z","publication":"2013 Oct","modification":"2025-04-29T11:06:55.23Z","creation":"2019-03-27T01:16:47Z"},"accession":"S-EPMC3789545","cross_references":{"pubmed":["23980025"],"doi":["10.1093/bioinformatics/btt442"]}}