Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer
Not Available
Zhang, Hui↗
Engineering topics
Bafna, Vineet
Publications and source records attributed to Bafna, Vineet.
Proteogenomic strategies for identification of aberrant cancer peptides using large-scale Next Generation Sequencing data
Cancer is driven by the acquisition of somatic DNA lesions. Distinguishing the early driver mutations from subsequent passenger mutations is key to molecular sub-typing of cancers, and the discovery of novel biomarkers. The availability of genomics technologies (mainly wholegenome and exome sequencing, and transcript sampling via RNA-seq, collectively referred to as NGS) have fueled recent studies on somatic mutation discovery. However, the vision is challenged by the complexity, redundancy, and errors in genomic data, and the difficulty of investigating the proteome using only genomic approaches. Recently, combination of proteomic and genomic technologies are increasingly employed. However, the complexity and redundancy of NGS data remains a challenge for proteogenomics, and various trade-offs must be made to allow for the searches to take place. This paperprovides a discussion of two such trade-offs, relating to large database search, and FDR calculations, and their implication to cancer proteogenomics. Moreover, it extends and develops the idea of a unified genomic variant database that can be searched by any mass spectrometry sample. A total of 879 BAM files downloaded from TCGA repository were used to create a 4.34 GB unified FASTA database which contained 2,787,062 novel splice junctions, 38,464 deletions, 1105 insertions, and 182,302 substitutions. Proteomic data from a single ovarian carcinoma sample (439,858 spectra) was searched against the database. By applying the most conservative FDR measure, we have identified 524 novel peptides and 65,578 known peptides at 1% FDR threshold. The novel peptides include interesting examples of doubly mutated peptides, frame-shifts, and non-sample-recruited mutations, which emphasize the strength of our approach.
59 BASIC BIOLOGICAL SCIENCES↗
Protein Identification Using Top-Down
In the last two years, due to advances in protein separation and mass spectrometry, top-down mass spectrometry moved from analyzing single proteins to analyzing complex samples and identifying hundreds and even thousands of proteins. However, computational tools for database search of top-down spectra against protein databases are still in infancy. We describe MS-Align+, a fast algorithm for top-down protein identification based on spectral alignment that enables searches for unexpected post-translational modifications (PTMs). We also propose a method for evaluating statistical significance of top-down protein identifications and further benchmark MS-Align+ along with PIITA, ProSightPTM and SEQUEST, which were previously used for top-down MS/MS database searches. We demonstrate that MS-Align+ and PIITA significantly increase the number of identified proteins as compared to ProSightPTM and SEQUEST.
Environmental Molecular Sciences Laboratory↗