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Cancer Res. 2017 Nov 1;77(21):e43-e46. doi: 10.1158/0008-5472.CAN-17-0331.

An Accessible Proteogenomics Informatics Resource for Cancer Researchers.

Author information

1
Department of Biochemistry, Vanderbilt University, Nashville, Tennessee.
2
Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota.
3
Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota.
4
Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, Minnesota.
5
Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.
6
Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
7
KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.
8
Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway.
9
VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
10
Department of Biochemistry, Ghent University, Ghent, Belgium.
11
Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.
12
Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany.
13
Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany.
14
Comparative Genomics Centre and Department of Molecular and Cell Biology, James Cook University, Queensland, Australia.
15
Department of Biology, Johns Hopkins University, Baltimore, Maryland.
16
Department of Biological Chemistry, Center for Epigenetics and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland.
17
Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota. tgriffin@umn.edu.

Abstract

Proteogenomics has emerged as a valuable approach in cancer research, which integrates genomic and transcriptomic data with mass spectrometry-based proteomics data to directly identify expressed, variant protein sequences that may have functional roles in cancer. This approach is computationally intensive, requiring integration of disparate software tools into sophisticated workflows, challenging its adoption by nonexpert, bench scientists. To address this need, we have developed an extensible, Galaxy-based resource aimed at providing more researchers access to, and training in, proteogenomic informatics. Our resource brings together software from several leading research groups to address two foundational aspects of proteogenomics: (i) generation of customized, annotated protein sequence databases from RNA-Seq data; and (ii) accurate matching of tandem mass spectrometry data to putative variants, followed by filtering to confirm their novelty. Directions for accessing software tools and workflows, along with instructional documentation, can be found at z.umn.edu/canresgithub. Cancer Res; 77(21); e43-46. ©2017 AACR.

PMID:
29092937
PMCID:
PMC5675041
DOI:
10.1158/0008-5472.CAN-17-0331
[Indexed for MEDLINE]
Free PMC Article

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