Tag: Omics

BIODICA – Independent Component Analysis for Big Omics Data

BIODICA

:: DESCRIPTION

BIODICA is a user-friendly pipeline for high-performant computation of independent components for omics data, using stability analysis and computing the optimal number of the components from their stabilities, and performing analyses for interpreting the results of ICA application.

::DEVELOPER

Computational Systems Biology of Cancer group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Java

:: DOWNLOAD

BIODICA

:: MORE INFORMATION

Citation:

Seisenova A, Daniyarov A, Molkenov A, Sharip A, Zinovyev A, Kairov U.
Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.
Front Genet. 2021 Oct 21;12:683632. doi: 10.3389/fgene.2021.683632. PMID: 34795689; PMCID: PMC8594933.

NetGestalt / NetSAM 1.32.0 – Integrating Multidimensional Omics data over Biological Networks

NetGestalt / NetSAM 1.32.0

:: DESCRIPTION

NetGestalt is a data integration framework that allows simultaneous presentation of large-scale experimental and annotation data from various sources in the context of a biological network to facilitate data visualization, analysis, interpretation, and hypothesis generation.

NetSAM (Network Seriation and Modularization) is an R package that takes an edge-list representation of a network as an input and generates files that can be used as an input for the one-dimensional network visualization tool NetGestalt  or other network analysis.

::DEVELOPER

the Zhang Lab

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 NetSAM

:: MORE INFORMATION

Citation

Nat Methods. 2013 Jul;10(7):597-8. doi: 10.1038/nmeth.2517.
NetGestalt: integrating multidimensional omics data over biological networks.
Shi Z, Wang J, Zhang B.

DanteR 0.2 – Quantitative Analysis of -omics Data

DanteR 0.2

:: DESCRIPTION

DanteR is a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup.

::DEVELOPER

Biological MS Data and Software Distribution Center , Pacific Northwest National Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/ MacOsX
  • R

:: DOWNLOAD

 DanteR

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Sep 15;28(18):2404-6. doi: 10.1093/bioinformatics/bts449. Epub 2012 Jul 19.
DanteR: an extensible R-based tool for quantitative analysis of -omics data.
Taverner T1, Karpievitch YV, Polpitiya AD, Brown JN, Dabney AR, Anderson GA, Smith RD.

Normalyzer 1.1.1 – Normalization methods for Omics data sets

Normalyzer 1.1.1

:: DESCRIPTION

Normalyzer normalizes the uploaded data using twelve different well known normalization methods and compares the resulting data based on quantitative and qualitative parameters.

::DEVELOPER

Normalyzer team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux /MacOsX
  • R

:: DOWNLOAD

 Normalyzer

:: MORE INFORMATION

Citation:

J Proteome Res. 2014 Jun 6;13(6):3114-20. doi: 10.1021/pr401264n. Epub 2014 May 2.
Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets.
Chawade A1, Alexandersson E, Levander F.

validate – Omics Validation Calculator

validate

:: DESCRIPTION

validate is a key component of performing any genomics experiment for validation of significant features (genes, proteins, etc.). This software can be used to assess the statistical evidence for validation of a particular analysis/technology on the basis of a random sample of significant results.

::DEVELOPER

Jeffrey Leek

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • R package

:: DOWNLOAD

 validate

:: MORE INFORMATION

Citation

Leek JT, Rasgon JL, and Taub MA (2011)
“A statistical approach to selecting and confirming validation targets in -omics experiments”
BMC Bioinformatics 2012, 13:150

MT-HESS 0.3 – Simultaneous Association Detection in OMICS Datasets

MT-HESS 0.3

:: DESCRIPTION

MT-HESS is a Bayesain hierarchical model that analyses the association between a large set of predictors, e.g. SNPs (single nucleotide polymorphisms), and many responses, e.g. gene expression, in multiple tissues, cells or conditions.

::DEVELOPER

MT-HESS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/  MacOSX

:: DOWNLOAD

 MT-HESS

:: MORE INFORMATION

Citation

MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues.
Lewin A, Saadi H, Peters JE, Moreno-Moral A, Lee JC, Smith KG, Petretto E, Bottolo L, Richardson S.
Bioinformatics. 2015 Oct 26. pii: btv568

mixOmics 6.16.1 – Omics Data Integration Project

mixOmics 6.16.1

:: DESCRIPTION

mixOmics analyses highly dimensional data sets: regularized Canonical Correlation Analysis (‘rCCA’) and sparse Partial Least Squares variants (‘sPLS’) to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) where the p and q variables are measured on the same samples or individuals n.

::DEVELOPER

mixOmics team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

 mixOmics

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2012 Dec 6;13:325. doi: 10.1186/1471-2105-13-325.
A novel approach for biomarker selection and the integration of repeated measures experiments from two assays.
Liquet B1, Lê Cao KA, Hocini H, Thiébaut R.

BioMet Toolbox 2.0 – Genome-wide analysis of Metabolism and Omics data

BioMet Toolbox 2.0

:: DESCRIPTION

The BioMet ToolBox is a web-based resource for exploiting the capabilites of metabolic networks described in genome scale models using flux analysis and random sampling, powered by RAVEN, gene set analysis and basic microarray analysis using PIANO, thereby providing an integrated analysis to identify coregulated subnetwork structures within the metabolic network and also for identifying statistically significant gene sets enabling biological interpretation.

:: DEVELOPER

Systems & Synthetic Biology – SYSBIO, Chalmers University of Technology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data.
Garcia-Albornoz M, Thankaswamy-Kosalai S, Nilsson A, V?remo L, Nookaew I, Nielsen J.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W175-81. doi: 10.1093/nar/gku371.

RepExplore – Exploit Technical Replicate Variance in Omics Analysis

RepExplore

:: DESCRIPTION

RepExplore is a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets.

::DEVELOPER

Bioinformatics Core, Luxembourg Centre for Systems Biomedicine (LCSB)

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

RepExplore: Addressing technical replicate variance in proteomics and metabolomics data analysis.
Glaab E, Schneider R.
Bioinformatics. 2015 Feb 25. pii: btv127

GenePEN – Network Analysis of Omics Data

GenePEN

:: DESCRIPTION

GenePEN, an efficient machine learning approach to identify coordinated alterations of genes or proteins in biological networks using omics data, providing provably optimal results in few seconds

::DEVELOPER

Bioinformatics Core, Luxembourg Centre for Systems Biomedicine (LCSB)

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Windows/ Linux
  • MatLab

:: DOWNLOAD

 GenePEN

:: MORE INFORMATION

Citation

Stat Appl Genet Mol Biol. 2015 Feb 3. pii: /j/sagmb.ahead-of-print/sagmb-2014-0045/sagmb-2014-0045.xml. doi: 10.1515/sagmb-2014-0045. [Epub ahead of print]
GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net.
Vlassis N, Glaab E.