DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Summary: DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser.

Availability and implementation: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project ( http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages.

Contact: samuel.wieczorek@ 123456cea.fr , florence.combes@ 123456cea.fr , thomas.burger@ 123456cea.fr

Related collections

Most cited references 2

Record: found
Abstract: found
Article: not found

DAnTE: a statistical tool for quantitative analysis of -omics data.

Andrew D. A. C. Smith, Pat Camp, Wei Qian … (2008)

Data Analysis Tool Extension (DAnTE) is a statistical tool designed to address challenges associated with quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing scheme that can handle unbalanced data and random effects. The graphical user interface (GUI) is designed to be very intuitive and user friendly. DAnTE may be downloaded free of charge at http://omics.pnl.gov/software/. An example dataset with instructions on how to perform a series of analysis steps is available at http://omics.pnl.gov/software/

0 comments Cited 169 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

ImputeLCMD: A Collection of Methods for Left-Censored Missing Data Imputation

C Lazar (2015)

0 comments Cited 5 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Bioinformatics

Journal ID (iso-abbrev): Bioinformatics

Journal ID (publisher-id): bioinformatics

Title: Bioinformatics

Publisher: Oxford University Press

ISSN (Print): 1367-4803

ISSN (Electronic): 1367-4811

Publication date (Print): 01 January 2017

Publication date (Electronic): 06 September 2016

Publication date PMC-release: 06 September 2016

Volume: 33

Issue: 1

Pages: 135-136

Affiliations

[1 ]Université Grenoble Alpes, BIG-BGE, Grenoble, 38000, France

[2 ]CEA, BIG-BGE, Grenoble, 38000, France

[3 ]INSERM, BGE, Grenoble, 38000, France

[4 ]Computational Proteomics Unit, Cambridge, CB2 1GA, UK

[5 ]Cambridge Center for Proteomics, Cambridge, CB2 1GA, UK

[6 ]CNRS, BIG-BGE, Grenoble, 38000, France

Author notes

[* ]To whom correspondence should be addressed.

Associate editor: Janet Kelso

Article

Publisher ID: btw580

DOI: 10.1093/bioinformatics/btw580

PMC ID: 5408771

PubMed ID: 27605098

SO-VID: a71cf7e7-7556-40e7-a2a4-f5313c93d30b

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 14 December 2015

Date revision received : 5 July 2016

Date accepted : 2 September 2016

Page count

Pages: 2

Comments

Comment on this article

scite_

Cited by 130

See all cited by

Most referenced authors 388

See all reference authors