Tag: Proteomic data

myProMS 3.9.3 – Management, Validation and Interpretation of MS-based Proteomic data

myProMS 3.9.3

:: DESCRIPTION

myProMS is a comprehensive bioinformatics environment (database and web server) for management of Mass Spectrometry (MS) protein identification data generated by database-search engines such as Mascot or Sequest. Multiple functionalities are available to mine, validate and interpret the data from both MS and biological point of views. In particular, biological interpretation of the results is facilitated through the use of sophisticated data comparison modules, annotation enrichments and links to external resources. myProMS was designed to optimize data access and sharing during collaboration between users with complementary expertises; typically MS experts and biologists.

::DEVELOPER

Institut Curie, Bioinformatics Core Facility

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  myProMS

:: MORE INFORMATION

Citation

Poullet P, Carpentier S, Barillot E.
myProMS, a web server for management and validation of mass spectrometry-based proteomic data.
Proteomics. 2007, 7 (15):2553-6.

GoMiner Build454- Resource for Biological Interpretation of Genomic and Proteomic data

GoMiner Build454

:: DESCRIPTION

GoMiner organizes and allows the visualization of large sets of genes based on Gene Ontology classifications.GoMiner is a tool for biological interpretation of ‘omic’ data – including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question

::DEVELOPER

GoMiner Team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • Java

:: DOWNLOAD

 GoMiner

:: MORE INFORMATION

Citation:

Barry R Zeeberg et al.
GoMiner: a resource for biological interpretation of genomic and proteomic data
Genome Biology 2003, 4:R28

PatternLab for proteomics V – tool for analyzing Shotgun Proteomic data

PatternLab for proteomics V

:: DESCRIPTION

PatternLab for proteomics is a one-stop shop computational environment for analyzing shotgun proteomic data. Its modules provide means to pinpoint proteins/peptides that are differentially expressed and those that are unique to a state. It can also cluster the ones that share similar expression profiles in time-course experiments, as well as help in interpreting results according to Gene Ontology.

::DEVELOPER

Laboratory for Computational and Strucutural Proteomics – Fiocruz

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 PatternLab for proteomics

:: MORE INFORMATION

Citation

Paulo C. Carvalho, John R. Yates III, Valmir C. Barbosa
Analyzing Shotgun Proteomic Data with PatternLab for Proteomics
Current Protocols in Bioinformatics Unit 13.13 DOI: 10.1002/0471250953.bi1313s30

CaBLASTP 1.0.3 – Performs BLAST on Compressed Proteomic data

CaBLASTP 1.0.3

:: DESCRIPTION

CaBLASTP is a suite of homology search tools, powered by compressively-accelerated protein BLAST, which are significantly faster than and comparably accurate to all known state-of- the-art tools including HHblits, DELTA-BLAST, and PSI-BLAST. Further, our tools are implemented in a manner that allows direct substitution into existing analysis pipelines.

::DEVELOPER

Berger Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  MacOsX
  • BLAST+

:: DOWNLOAD

 CaBLASTP

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Jul 1;29(13):i283-90. doi: 10.1093/bioinformatics/btt214.
Compressive genomics for protein databases.
Daniels NM, Gallant A, Peng J, Cowen LJ, Baym M, Berger B.

ProClassify 1.4.2 – Proteomic data Classification

ProClassify 1.4.2

:: DESCRIPTION

 ProClassify is a tool for proteomic data classification. It was intended originally for high-resolution mass-spectrometry data classification, but it can be of use for datasets of a completely different nature as well.

::DEVELOPER

Genomics & Bioinformatics Graz, Graz University of Technology

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 ProClassify 

:: MORE INFORMATION

Citation

Yu J.S.,Ongarello S., Fiedler R., Chen X.W., Toffolo G., Cobelli C., Trajanoski Z.
Ovarian Cancer Identification Based on Dimensionality Reduction for High-Througput Mass Spectrometry Data.
Bioinformatics 2005;21:2200-2209