ALTREE 1.3.1 – Phylogeny-based Analysis

ALTREE 1.3.1

:: DESCRIPTION

ALTREE performs these two phylogeny-based analysis: (1) it tests the association between a candidate gene and a disease; (2) it pinpoints markers (SNPs) that are putative disease susceptibility loci

::DEVELOPER

Claire Bardel-Danjean

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 ALTREE

:: MORE INFORMATION

Citation:

Bardel et al,
On the use of haplotype phylogeny to detect disease susceptibility loci
BMC Genet 6:24, 2005

BiasViz 2 – Protein Amino Acid Bias Analysis Applet

BiasViz 2

:: DESCRIPTION

BiasViz can be used to look at the composition of amino acids within a sliding window given a multiple sequence alignment. The window size and amino acid(s) of interest can be modified and the results are displayed in real time, allowing the user to tweak both of these parameters to their liking. The values can also be downloaded as a CSV file which allows further visualization in a separate program such as Microsoft Excel.

::DEVELOPER

Matthew R. Huska

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 BiasViz

:: MORE INFORMATION

Citation

BiasViz: visualization of amino acid biased regions in protein alignments.
Huska MR, Buschmann H, Andrade-Navarro MA.
Bioinformatics. 2007 Nov 15;23(22):3093-4. Epub 2007 Oct 6.

JOY 5.0 – Protein Sequence-Structure Representation and Analysis

JOY 5.0

:: DESCRIPTION

JOY is a program to annotate protein sequence alignments with three-dimensional (3D) structural features. It was developed to display 3D structural information in a sequence alignment and help understand the conservation of amino acids in their specific local environments. For instance, it has been recognised that a sidechain hydrogen-bonded to a main-chain amide plays an important role in stabilizing the 3D structure and is generally well conserved during evolution. Such a residue is shown in a bold-face letter in the formatted alignments. Another example is the importance of solvent inaccessible residues which are shown in UPPER-CASE letters.

::DEVELOPER

the Mizuguchi Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 JOY

:: MORE INFORMATION

Citation

Mizuguchi, K., Deane, C.M., Blundell, T.L., Johnson,M.S. and Overington, J.P. (1998)
JOY: protein sequence-structure representation and analysis.
Bioinformatics 14:617-623.

BRB-ArrayTools 4.6.2 – Visualization & Analysis of DNA Microarray Gene Expression Data

BRB-ArrayTools 4.6.2

:: DESCRIPTION

BRB-ArrayTools is an integrated software package for the analysis of DNA microarray data.

BRB-ArrayTools contains utilities for processing expression data from multiple experiments, visualization of data, multidimensional scaling, clustering of genes and samples, and classification and prediction of samples. BRB-ArrayTools features drill-down linkage to NCBI databases using clone, GenBank, or UniGene identifiers, and drill-down linkage to the NetAffx database using Probeset ids.

::DEVELOPER

the Biometric Research Branch of the Division of Cancer Treatment & Diagnosis of the National Cancer Institute

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

BRB-ArrayTools

:: MORE INFORMATION

Citation

Analysis of gene expression data using BRB-ArrayTools.
Simon R, Lam A, Li MC, Ngan M, Menenzes S, Zhao Y.
Cancer Inform. 2007 Feb 4;3:11-7.

Pyteomics 4.4.2 – Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics

Pyteomics 4.4.2

:: DESCRIPTION

Pyteomics is a collection of lightweight and handy tools for Python that help to handle various sorts of proteomics data. Pyteomics provides a growing set of modules to facilitate the most common tasks in proteomics data analysis

::DEVELOPER

Pyteomics team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Python

:: DOWNLOAD

 Pyteomics

:: MORE INFORMATION

Citation

Goloborodko, A.A.; Levitsky, L.I.; Ivanov, M.V.; and Gorshkov, M.V. (2013)
Pyteomics – a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics”,
Journal of The American Society for Mass Spectrometry, 24(2), 301–304. DOI: 10.1007/s13361-012-0516-6

BPLA kernel 1.1 – Structural RNA Analysis

BPLA kernel 1.1

:: DESCRIPTION

BPLA kernel (Base-pairing profile local alignment) is a powerful method that evaluates the similarity between a pair of RNAs taking into account secondary structure information. Based on the accurate similarity calculated by BPLA kernel, you can perform a broad range of structural RNA analysis, including family prediction, hierarchical clustering, and remote homology search. The method is applicable not only to classical genomic screens, but also to unannotated noncoding transcripts produced by next-generation sequencing technologies (RNA-seq).

::DEVELOPER

Sakakibara Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 BPLA kernel

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Oct 15;11 Suppl 7:S3. doi: 10.1186/1471-2105-11-S7-S3.
Robust and accurate prediction of noncoding RNAs from aligned sequences.
Saito Y1, Sato K, Sakakibara Y.

EBSeq 1.33.0 / EBSeq-HMM 1.26.0 – RNA-Seq Differential Expression Analysis

EBSeq 1.33.0 / EBSeq-HMM 1.26.0

:: DESCRIPTION

R/EBSeq is an R package for identifying genes and isoforms differentially expressed (DE) across two or more biological conditions in an RNA-seq experiment.

EBSeq-HMM is a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments.

::DEVELOPER

Kendziorski Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • R package
  • BioConductor

:: DOWNLOAD

 R/EBSeq , EBSeq-HMM

:: MORE INFORMATION

Citation

EBSeq-HMM: A Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments.
Leng N, Li Y, Mcintosh BE, Nguyen BK, Duffin B, Tian S, Thomson JA, Dewey C, Stewart R, Kendziorski C.
Bioinformatics. 2015 Apr 5. pii: btv193.

Leng, N., J.A. Dawson, J.A. Thomson, V. Ruotti, A.I. Rissman, B.M.G. Smits, J.D. Haag, M.N. Gould, R.M. Stewart, and C. Kendziorski.
EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments,
Bioinformatics. 2013 Apr 15;29(8):1035-43.

Ontologizer 2.1 – Analysis and Visualization of High-Throughput Biological Data Using Gene Ontology

Ontologizer 2.1

:: DESCRIPTION

Ontologizer is a Java webstart application for GO term enrichment analysis that provides browsing and graph visualization capabilities. The Ontologizer allows users to analyze data with the standard Fisher exact test and also the parent-child method and topology methods.

::DEVELOPER

The Computational Biology @ Charité Berlin

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Java

:: DOWNLOAD

  Ontologizer

:: MORE INFORMATION

Citation

Bioinformatics. 2008 Jul 15;24(14):1650-1. Epub 2008 May 29.
Ontologizer 2.0–a multifunctional tool for GO term enrichment analysis and data exploration.
Bauer S, Grossmann S, Vingron M, Robinson PN.

MetaPath 0.83 – Comparative Analysis of Metabolic Pathways in Metagenomics

MetaPath 0.83

:: DESCRIPTION

MetaPath can identify differentially abundant pathways in metagenomic data-sets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge.

::DEVELOPER

MetaPath  team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

MetaPath

:: MORE INFORMATION

Citation:

Liu B, Pop M:
Identifying Differentially Abundant Metabolic Pathways in Metagenomic Datasets.
Lect Notes Comput Sci 2010, 6053: 101-112

RnBeads 2.10.0 – Comprehensive DNA Methylation Analysis

RnBeads 2.10.0

:: DESCRIPTION

RnBeads is the first software tool that implements a comprehensive workflow for analyzing DNA methylation data in the context of large cohort studies. Its functionality comprises data normalization, quality control, probe and sample filtering, handing of batch effects, global DNA methylation analysis, detection of differentially methylated regions and interpretation by statistical enrichment analysis.

::DEVELOPER

Max-Planck-Institut Informatik

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 RnBeads

:: MORE INFORMATION

Citation

Comprehensive analysis of DNA methylation data with RnBeads.
Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C.
Nat Methods. 2014 Sep 28. doi: 10.1038/nmeth.3115.