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• W2024954384 abstract "The advent of inexpensive RNA-seq technologies and other deep sequencing technologies for RNA has the promise to radically improve genomic annotation, providing information on transcribed regions and splicing events in a variety of cellular conditions. Using MS-based proteogenomics, many of these events can be confirmed directly at the protein level. However, the integration of large amounts of redundant RNA-seq data and mass spectrometry data poses a challenging problem. Our paper addresses this by construction of a compact database that contains all useful information expressed in RNA-seq reads. Applying our method to cumulative C. elegans data reduced 496.2 GB of aligned RNA-seq SAM files to 410 MB of splice graph database written in FASTA format. This corresponds to 1000× compression of data size, without loss of sensitivity. We performed a proteogenomics study using the custom data set, using a completely automated pipeline, and identified a total of 4044 novel events, including 215 novel genes, 808 novel exons, 12 alternative splicings, 618 gene-boundary corrections, 245 exon-boundary changes, 938 frame shifts, 1166 reverse strands, and 42 translated UTRs. Our results highlight the usefulness of transcript + proteomic integration for improved genome annotations." @default.
• W2024954384 created "2016-06-24" @default.
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• W2024954384 date "2013-07-17" @default.
• W2024954384 modified "2023-09-23" @default.
• W2024954384 title "Proteogenomic Database Construction Driven from Large Scale RNA-seq Data" @default.
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• W2024954384 doi "https://doi.org/10.1021/pr400294c" @default.
• W2024954384 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4034692" @default.
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