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Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer
by
Griffith, Obi L.
, Chapman, William C.
, Lin, Yiing
, Campbell, Katie M.
, Devarakonda, Siddhartha
, Chen, Timothy Hung-Po
, Griffith, Malachi
, Maher, Christopher A.
, Govindan, Ramaswamy
, Skidmore, Zachary L.
, Xia, Huiming
, Adkins, Douglas
, Richters, Megan
, Kunisaki, Jason
, Feng, Yang-Yang
, Freshour, Sharon L.
, Rozycki, Emily B.
, Uppaluri, Ravindra
, Arora, Vivek
, Cotto, Kelsy C.
, Dunn, Gavin P.
, Ramu, Avinash
, McMichael, Joshua F.
, Sankararaman, Sumithra
in
45/23
/ 45/91
/ 631/114/1314
/ 631/114/794
/ 631/1647/48
/ 631/337/1645/1946
/ 631/67/69
/ Alternative splicing
/ Alternative Splicing - genetics
/ Cancer
/ Computational efficiency
/ Computer applications
/ Exons
/ Flexibility
/ Freeware
/ Genome
/ Genomic analysis
/ Genomics
/ Genotypes
/ Humanities and Social Sciences
/ Humans
/ Isoforms
/ multidisciplinary
/ Mutation
/ Neoplasms - genetics
/ Nucleotide sequence
/ Open source software
/ p53 Protein
/ RNA Splicing - genetics
/ Science
/ Science (multidisciplinary)
/ Software
/ Software packages
/ Splice junctions
/ Splicing
/ Transcriptome - genetics
/ Transcriptomics
/ Tumors
2023
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Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer
by
Griffith, Obi L.
, Chapman, William C.
, Lin, Yiing
, Campbell, Katie M.
, Devarakonda, Siddhartha
, Chen, Timothy Hung-Po
, Griffith, Malachi
, Maher, Christopher A.
, Govindan, Ramaswamy
, Skidmore, Zachary L.
, Xia, Huiming
, Adkins, Douglas
, Richters, Megan
, Kunisaki, Jason
, Feng, Yang-Yang
, Freshour, Sharon L.
, Rozycki, Emily B.
, Uppaluri, Ravindra
, Arora, Vivek
, Cotto, Kelsy C.
, Dunn, Gavin P.
, Ramu, Avinash
, McMichael, Joshua F.
, Sankararaman, Sumithra
in
45/23
/ 45/91
/ 631/114/1314
/ 631/114/794
/ 631/1647/48
/ 631/337/1645/1946
/ 631/67/69
/ Alternative splicing
/ Alternative Splicing - genetics
/ Cancer
/ Computational efficiency
/ Computer applications
/ Exons
/ Flexibility
/ Freeware
/ Genome
/ Genomic analysis
/ Genomics
/ Genotypes
/ Humanities and Social Sciences
/ Humans
/ Isoforms
/ multidisciplinary
/ Mutation
/ Neoplasms - genetics
/ Nucleotide sequence
/ Open source software
/ p53 Protein
/ RNA Splicing - genetics
/ Science
/ Science (multidisciplinary)
/ Software
/ Software packages
/ Splice junctions
/ Splicing
/ Transcriptome - genetics
/ Transcriptomics
/ Tumors
2023
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Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer
by
Griffith, Obi L.
, Chapman, William C.
, Lin, Yiing
, Campbell, Katie M.
, Devarakonda, Siddhartha
, Chen, Timothy Hung-Po
, Griffith, Malachi
, Maher, Christopher A.
, Govindan, Ramaswamy
, Skidmore, Zachary L.
, Xia, Huiming
, Adkins, Douglas
, Richters, Megan
, Kunisaki, Jason
, Feng, Yang-Yang
, Freshour, Sharon L.
, Rozycki, Emily B.
, Uppaluri, Ravindra
, Arora, Vivek
, Cotto, Kelsy C.
, Dunn, Gavin P.
, Ramu, Avinash
, McMichael, Joshua F.
, Sankararaman, Sumithra
in
45/23
/ 45/91
/ 631/114/1314
/ 631/114/794
/ 631/1647/48
/ 631/337/1645/1946
/ 631/67/69
/ Alternative splicing
/ Alternative Splicing - genetics
/ Cancer
/ Computational efficiency
/ Computer applications
/ Exons
/ Flexibility
/ Freeware
/ Genome
/ Genomic analysis
/ Genomics
/ Genotypes
/ Humanities and Social Sciences
/ Humans
/ Isoforms
/ multidisciplinary
/ Mutation
/ Neoplasms - genetics
/ Nucleotide sequence
/ Open source software
/ p53 Protein
/ RNA Splicing - genetics
/ Science
/ Science (multidisciplinary)
/ Software
/ Software packages
/ Splice junctions
/ Splicing
/ Transcriptome - genetics
/ Transcriptomics
/ Tumors
2023
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Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer
Journal Article
Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer
2023
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Overview
Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (
www.regtools.org
), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as
TP53
,
CDKN2A
, and
B2M
, and other genes.
Analysing the regulatory consequences of mutations and splice variants at large scale in cancer requires efficient computational tools. Here, the authors develop RegTools, a software package that can identify splice-associated variants from large-scale genomics and transcriptomics data with efficiency and flexibility.
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