PGTools: A software suite for proteogenomic data analysis and visualization
Journal of Proteome Research, ISSN: 1535-3907, Vol: 14, Issue: 5, Page: 2255-2266
2015
- 51Citations
- 89Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations51
- Citation Indexes51
- 51
- CrossRef49
- Captures89
- Readers89
- 89
Article Description
We describe PGTools, an open source software suite for analysis and visualization of proteogenomic data. PGTools comprises applications, libraries, customized databases, and visualization tools for analysis of mass-spectrometry data using combined proteomic and genomic backgrounds. A single command is sufficient to search databases, calculate false discovery rates, group and annotate proteins, generate peptide databases from RNA-Seq transcripts, identify altered proteins associated with cancer, and visualize genome scale peptide data sets using sophisticated visualization tools. We experimentally confirm a subset of proteogenomic peptides in human PANC-1 cells and demonstrate the utility of PGTools using a colorectal cancer data set that led to the identification of 203 novel protein coding regions missed by conventional proteomic approaches. PGTools should be equally useful for individual proteogenomic investigations as well as international initiatives such as chromosome-centric Human Proteome Project (C-HPP). PGTools is available at http://qcmg.org/bioinformatics/PGTools.
Bibliographic Details
American Chemical Society (ACS)
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