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BreakTrans: uncovering the genomic architecture of gene fusions.

ABSTRACT: Producing gene fusions through genomic structural rearrangements is a major mechanism for tumor evolution. Therefore, accurately detecting gene fusions and the originating rearrangements is of great importance for personalized cancer diagnosis and targeted therapy. We present a tool, BreakTrans, that systematically maps predicted gene fusions to structural rearrangements. Thus, BreakTrans not only validates both types of predictions, but also provides mechanistic interpretations. BreakTrans effectively validates known fusions and discovers novel events in a breast cancer cell line. Applying BreakTrans to 43 breast cancer samples in The Cancer Genome Atlas identifies 90 genomically validated gene fusions. BreakTrans is available at http://bioinformatics.mdanderson.org/main/BreakTrans.

SUBMITTER: Chen K

PROVIDER: S-EPMC4054677 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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BreakTrans: uncovering the genomic architecture of gene fusions.

Chen Ken K Navin Nicholas E NE Wang Yong Y Schmidt Heather K HK Wallis John W JW Niu Beifang B Fan Xian X Zhao Hao H McLellan Michael D MD Hoadley Katherine A KA Mardis Elaine R ER Ley Timothy J TJ Perou Charles M CM Wilson Richard K RK Ding Li L

Genome biology 20130823 8

Producing gene fusions through genomic structural rearrangements is a major mechanism for tumor evolution. Therefore, accurately detecting gene fusions and the originating rearrangements is of great importance for personalized cancer diagnosis and targeted therapy. We present a tool, BreakTrans, that systematically maps predicted gene fusions to structural rearrangements. Thus, BreakTrans not only validates both types of predictions, but also provides mechanistic interpretations. BreakTrans effe ...[more]

PMID: 23972288

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Project description:BackgroundEscherichia coli a model organism provides information for annotation of other genomes. Our analysis of its genome has shown that proteins encoded by fused genes need special attention. Such composite (multimodular) proteins consist of two or more components (modules) encoding distinct functions. Multimodular proteins have been found to complicate both annotation and generation of sequence similar groups. Previous work overstated the number of multimodular proteins in E. coli. This work corrects the identification of modules by including sequence information from proteins in 50 sequenced microbial genomes.ResultsMultimodular E. coli K-12 proteins were identified from sequence similarities between their component modules and non-fused proteins in 50 genomes and from the literature. We found 109 multimodular proteins in E. coli containing either two or three modules. Most modules had standalone sequence relatives in other genomes. The separated modules together with all the single (un-fused) proteins constitute the sum of all unimodular proteins of E. coli. Pairwise sequence relationships among all E. coli unimodular proteins generated 490 sequence similar, paralogous groups. Groups ranged in size from 92 to 2 members and had varying degrees of relatedness among their members. Some E. coli enzyme groups were compared to homologs in other bacterial genomes.ConclusionThe deleterious effects of multimodular proteins on annotation and on the formation of groups of paralogs are emphasized. To improve annotation results, all multimodular proteins in an organism should be detected and when known each function should be connected with its location in the sequence of the protein. When transferring functions by sequence similarity, alignment locations must be noted, particularly when alignments cover only part of the sequences, in order to enable transfer of the correct function. Separating multimodular proteins into module units makes it possible to generate protein groups related by both sequence and function, avoiding mixing of unrelated sequences. Organisms differ in sizes of groups of sequence-related proteins. A sample comparison of orthologs to selected E. coli paralogous groups correlates with known physiological and taxonomic relationships between the organisms.

Dataset Information

BreakTrans: uncovering the genomic architecture of gene fusions.

Publications

BreakTrans: uncovering the genomic architecture of gene fusions.

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