↓ Skip to main content

PGTools: A Software Suite for Proteogenomic Data Analysis and Visualization

Overview of attention for article published in Journal of Proteome Research, April 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
9 X users
googleplus
1 Google+ user

Citations

dimensions_citation
52 Dimensions

Readers on

mendeley
86 Mendeley
citeulike
5 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
PGTools: A Software Suite for Proteogenomic Data Analysis and Visualization
Published in
Journal of Proteome Research, April 2015
DOI 10.1021/acs.jproteome.5b00029
Pubmed ID
Authors

Shivashankar H. Nagaraj, Nicola Waddell, Anil K. Madugundu, Scott Wood, Alun Jones, Ramya A. Mandyam, Katia Nones, John V. Pearson, Sean M. Grimmond

Abstract

We describe PGTools, an open source software suite for analysis and visualization of proteogenomic data. PGTools is comprised of 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 datasets 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 dataset 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Netherlands 1 1%
Turkey 1 1%
France 1 1%
Unknown 80 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 23%
Student > Ph. D. Student 19 22%
Student > Master 13 15%
Other 6 7%
Professor 5 6%
Other 13 15%
Unknown 10 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 28 33%
Agricultural and Biological Sciences 22 26%
Computer Science 6 7%
Chemistry 5 6%
Medicine and Dentistry 4 5%
Other 9 10%
Unknown 12 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 October 2015.
All research outputs
#2,541,447
of 22,794,367 outputs
Outputs from Journal of Proteome Research
#594
of 6,023 outputs
Outputs of similar age
#34,231
of 264,853 outputs
Outputs of similar age from Journal of Proteome Research
#8
of 96 outputs
Altmetric has tracked 22,794,367 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,023 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,853 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.