BDVAL 1.2 – Biomarker Discovery in High-throughput datasets

BDVAL 1.2

:: DESCRIPTION

BDVAL ( Biomarker Discovery and VALidation ) is an open source project for biomarker discovery in high-throughput datasets.

::DEVELOPER

Campagne Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  •  Java

:: DOWNLOAD

 BDVAL

:: MORE INFORMATION

Citation:

Dorff KC, Chambwe N, Srdanovic M, Campagne F.
BDVal: reproducible large-scale predictive model development and validation in high-throughput datasets.
Bioinformatics. 2010 Oct 1;26(19):2472-3. Epub 2010 Aug 11

SFDR – Stratified False Discovery Rate control

SFDR

:: DESCRIPTION

SFDR (Stratified False Discovery Rate control) is a program to compute FDR q-values of genome-wide SNP association analysis within each stratum. Especialy the SFDR program is taylored to directly use genome-wide linkage scan results to assign strata. Weighted FDR q-values can be also computed as an alternative to SFDR. To understand the details of FDR, SFDR and WFDR control methods

::DEVELOPER

Lei Sun

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows / MacOsX
  • Perl
:: DOWNLOAD

 SFDR

:: MORE INFORMATION

Citation

Sun, L., Craiu, R.V., Paterson, A.D. & Bull, S.B.
Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies.
Genet. Epidemiol. 30, 519-530 (2006)

MotifBooster – Transcriptional Regulatory Motif Modeling and Discovery

MotifBooster

:: DESCRIPTION

MotifBooster is a software of modeling TF–DNA binding. Different from the widely used weight matrix model, which predicts TF–DNA binding based on a linear combination of position-specific contributions, our approach builds a TF binding classifier by combining a set of weight matrix based classifiers, thus yielding a non-linear binding decision rule.

::DEVELOPER

Pengyu Hong

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 MotifBooster

:: MORE INFORMATION

Citation

Hong, P., X. S., Zhou, Q., Lu, X., Liu, J. S., Wong, W. H.
A Boosting Approach for Motif Modeling Using ChIP-chip Data“.
Bioinformatics.

Composition Profiler 1.1 – Discovery of Amino Acid Composition Differences

Composition Profiler 1.1

:: DESCRIPTION

Composition Profiler is a web-based tool that automates detection of enrichment or depletion patterns of individual amino acids or groups of amino acids classified by several physico-chemical and structural properties.

Composition Profiler Online

:: SCREENSHOTS

N/A

::DEVELOPER

Vladimir Vacic and Stefano Lonardi (University of California, Riverside), and Vladimir N. Uversky and A. Keith Dunker (Indiana University School of Medicine, Indianapolis).

:: REQUIREMENTS

:: DOWNLOAD

 Composition Profiler

:: MORE INFORMATION

Citation:

Vacic V., Uversky V.N., Dunker A.K., and Lonardi S.
Composition Profiler: A tool for discovery and visualization of amino acid composition differences“.
BMC Bioinformatics. 8:211. (2007

FoldMiner 200312 – Structural Similarity Searches and Motif Discovery

FoldMiner 200312

:: DESCRIPTION

FoldMiner performs structural similarity searches and rapid, unsupervised structural motif discovery. Motifs are used to improve the sensitivity and specificity of the search.

::DEVELOPER

 The Brutlag Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Complier

:: DOWNLOAD

 FoldMiner

:: MORE INFORMATION

Citation:

Jessica Shapiro and Douglas L. Brutlag (2004).
FoldMiner: Structural Motif Discovery Using an Improved Superposition Algorithm.
Protein Science 13(1) 278-294.