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Seong Won Cha

Researcher at University of California, San Diego

Publications -  11
Citations -  1252

Seong Won Cha is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Proteogenomics & Genome. The author has an hindex of 7, co-authored 10 publications receiving 906 citations.

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Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

TL;DR: A view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC is provided.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

TL;DR: In this article, a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer was provided, such as how different copy-number alterna-tions in the Proteome, the proteins associated with chromosomal instability, the sets of signalingpathways that diverse genome rearrangements converge on, and the ones associated with short overall survival.
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Assembling the Community-Scale Discoverable Human Proteome.

TL;DR: The MassIVE Knowledge Base (MassIVE-KB), a community-wide, continuously updating knowledge base that aggregates proteomics mass spectrometry discoveries into an open reusable format with full provenance information for community scrutiny, is built.
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Proteogenomic database construction driven from large scale RNA-seq data.

TL;DR: This paper construction of a compact database that contains all useful information expressed in RNA-seq reads is presented, highlighting the usefulness of transcript + proteomic integration for improved genome annotations.
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Proteogenomic strategies for identification of aberrant cancer peptides using large‐scale next‐generation sequencing data

TL;DR: A discussion of applying different strategies relating to large database search, FDR (false discovery rate) ‐based error control, and their implication to cancer proteogenomics extends and develops the idea of a unified genomic variant database that can be searched by any MS sample.