Detection and verification of 2.3 million cancer mutations in NCI60 cancer cell lines with a cloud search engine

Prakash, Amol; Majumder, Swetaketu; Ahmad, Shadab; Varkey, Manu; Anish, T.A.; Jenkins, Conor; Rigby, Megan; Orsburn, Benjamin C.

doi:10.1016/j.jprot.2019.103488

Cited by 9 publications

(9 citation statements)

References 29 publications

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“…While proteomic data analysis may be performed using FASTA data directly obtained from sequencers, few supercomputers or Cloud based proteomic search engines currently exist to make these analyses truly feasible due to their expense and lack of widespread access. Nearly all processing for proteomics is performed on desktop computers using reduced, annotated, often manually reviewed, and often targeted theoretical protein FASTA databases (reviewed in Bolt 8,9 ). The release of the NCBI annotations essentially permits, for the first time, both shotgun and top-down analysis of proteomics data from Cannabis plants.…”

Section: Resultsmentioning

confidence: 99%

The First Publicly Available Annotated Genome for Cannabis plants

Jenkins

Orsburn

2019

Preprint

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Recently we have seen a relaxation of the historic restrictions on the use and subsequent research on the Cannabis plants in North America. We have recently described a pipeline for the creation of annotated protein databases using a combination of genomic and proteomic data and the application of this method toward the analysis of the proteomes of Cannabis plants. In parallel with our work, we approached the National Center for Biotechnology Information (NCBI) to align and annotate publicly deposited genomic files for these plants.

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Section: Resultsmentioning

confidence: 99%

The First Publicly Available Annotated Genome for Cannabis plants

Jenkins

Orsburn

2019

Preprint

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“…A challenge in evaluating peptide and protein counts, as an objective metric for overall method performance, is in the number of variables that can be altered in the data processing pipelines that can affect these results. For example, utilizing a larger potential database to compare shotgun proteomics data to invariably increases the number of peptide identifications [ 3 ]. Increasing the search space further, to evaluate an increasing number of biologically likely post-translational modifications, will have a similar effect [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Orsburn

2021

Proteomes

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Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

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“…While desktop PC architecture may have been suitable for thorough interrogation of LCMS data generated at 1 Hz, the only way to process today's data is through a series of compromises to limit the overall search space [12]. We have recently described the use of a scalable cloud computing workflow using the modern search engine Bolt for the reanalysis of proteomic data [6,13]. While other solutions for proteomic analysis through cloud computing exist today, these largely require the skills of a dedicated bioinformatician to execute successfully [14].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Bolt can be used to simultaneously search for hundreds of common human post-translational modifications (PTMs) by pulling more computational space from the cloud. These searches are either impractical or simply impossible to complete using desktop computers and/or traditional search engines and limit the biological relevance of proteomic data analysis [13]. In 2020, OptysTech was awarded an NCI contract through program 75N91020C00011 to evaluate the post-translational modifications present and currently unidentified in the existing CPTAC data.…”

Section: Introductionmentioning

confidence: 99%

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

Prakash¹,

Taylor

Varkey³

et al. 2021

Cancers

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The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

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Detection and verification of 2.3 million cancer mutations in NCI60 cancer cell lines with a cloud search engine

Cited by 9 publications

References 29 publications

The First Publicly Available Annotated Genome for Cannabis plants

The First Publicly Available Annotated Genome for Cannabis plants

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

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