NEMO v0.1 – Neighborhood based multi-omics Clustering

NEMO v0.1

:: DESCRIPTION

NEMO is a multi-omic clustering algorithm. NEMO supports data in which some omics were measured for only a subset of the samples.

::DEVELOPER

Ron Shamir’s lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/ MacOsX
  • R
  • SNFtool

:: DOWNLOAD

NEMO

:: MORE INFORMATION

Citation

Bioinformatics. 2019 Sep 15;35(18):3348-3356. doi: 10.1093/bioinformatics/btz058.
NEMO: cancer subtyping by integration of partial multi-omic data.
Rappoport N, Shamir R.

geneRxCluster 1.22.0 – Detect Differential Clustering of Genomic Sites

geneRxCluster 1.22.0

:: DESCRIPTION

geneRxCluster detects differential clustering of genomic sites such as gene therapy integrations.

::DEVELOPER

Charles Berry <ccberry at ucsd.edu>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 geneRxCluster

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jun 1;30(11):1493-500. doi: 10.1093/bioinformatics/btu035. Epub 2014 Jan 30.
Comparing DNA integration site clusters with scan statistics.
Berry CC, Ocwieja KE, Malani N, Bushman FD.

ClustEval 1.6 – Integrative Clustering Evaluation Framework

ClustEval 1.6

:: DESCRIPTION

ClustEval is a free and extendable opensource platform for objective performance comparison of arbitrary Clustering Methods on different datasets.

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • JRE

:: DOWNLOAD

 ClustEval

:: MORE INFORMATION

Citation

Guiding biomedical clustering with ClustEval.
Wiwie C, Baumbach J, Röttger R.
Nat Protoc. 2018 Jun;13(6):1429-1444. doi: 10.1038/nprot.2018.038.

Comparing the performance of biomedical clustering methods.
Wiwie C, Baumbach J, Röttger R.
Nat Methods. 2015 Nov;12(11):1033-8. doi: 10.1038/nmeth.3583.

TiCoNE 2.0.0 – Cluster multi-patient-sample Time-series data

TiCoNE 2.0.0

:: DESCRIPTION

TiCoNE (Time Course Network Enricher) is a tool for the combined analysis of time series expression data together with biological networks.It will find time patterns emerging in the expression data and check for network modules enriched with genes of similar expression behavior over time.

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Java
  • Cytoscape

:: DOWNLOAD

TiCoNE

:: MORE INFORMATION

Citation

Syst Med (New Rochelle). 2019 Mar 28;2(1):1-9. doi: 10.1089/sysm.2018.0013.
Time-Resolved Systems Medicine Reveals Viral Infection-Modulating Host Targets.
Wiwie C, Kuznetsova I, Mostafa A, Rauch A, Haakonsson A, Barrio-Hernandez I, Blagoev B, Mandrup S, Schmidt HHHW, Pleschka S, Röttger R, Baumbach J

BiCluE – Weighted Bi-cluster Editing of Biomedical Data

BiCluE

:: DESCRIPTION

BiCluE is a bi-clustering software for solving the weighted/unweighted bi-cluster editing problem.

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Java

:: DOWNLOAD

 BiCluE

:: MORE INFORMATION

Citation

BiCluE – Exact and heuristic algorithms for weighted bi-cluster editing of biomedical data.
Sun P, Guo J, Baumbach J.
BMC Proc. 2013 Dec 20;7(Suppl 7):S9. doi: 10.1186/1753-6561-7-S7-S9. Epub 2013 Dec 20.

Bi-Force v2 – Large-scale Bicluster Editing and its application to Gene Expression data Biclustering

Bi-Force v2

:: DESCRIPTION

Bi-Force is a novel way of modeling the problem as combinatorial optimization problem on graphs: Weighted Bi-Cluster Editing. It is a very flexible model that can handle arbitrary kinds of multi-condition data sets (not limited to gene expression).

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Java

:: DOWNLOAD

 Bi-Force

:: MORE INFORMATION

Citation

Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering.
Sun P, Speicher NK, Röttger R, Guo J, Baumbach J.
Nucleic Acids Res. 2014 May;42(9):e78. doi: 10.1093/nar/gku201.

TransClust – A Feature-rich Clustering tool for Biomedical Data sets

TransClust

:: DESCRIPTION

TransClust is a comprehensive clustering tool that incorporates the hidden transitive nature occuring e.g. within biomedical data sets. It is based on Weighted Transitive Graph Projection problem.

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Java

:: DOWNLOAD

  TransClust

:: MORE INFORMATION

Citation

Partitioning biological data with transitivity clustering.
Wittkop T, Emig D, Lange S, Rahmann S, Albrecht M, Morris JH, Böcker S, Stoye J, Baumbach J.
Nat Methods. 2010 Jun;7(6):419-20. doi: 10.1038/nmeth0610-419.

CABRA – Cluster & Annotate Blast Results Algorithm

CABRA

:: DESCRIPTION

CABRA is a web tool , which enables a rapid BLAST search in a variety of updated reference proteomes, and provides a new way to functionally evaluate the results by the subsequent clustering of the hits and annotation of the clusters.

::DEVELOPER

Computational Biology and Data Mining (CBDM) Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Web Server

:: DOWNLOAD

CABRA

:: MORE INFORMATION

Citation

BMC Res Notes. 2016 Apr 30;9:253. doi: 10.1186/s13104-016-2062-y.
CABRA: Cluster and Annotate Blast Results Algorithm.
Mier P, Andrade-Navarro MA.

FastaHerder2 – Clustering for Analysis of Protein Similarity

FastaHerder2

:: DESCRIPTION

FastaHerder2 can cluster any protein database, putting together very similar protein sequences based on near-full-length similarity and/or high threshold of sequence identity.

::DEVELOPER

Computational Biology and Data Mining (CBDM) Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J Comput Biol. 2016 Apr;23(4):270-8. doi: 10.1089/cmb.2015.0191. Epub 2016 Feb 1.
FastaHerder2: Four Ways to Research Protein Function and Evolution with Clustering and Clustered Databases.
Mier P, Andrade-Navarro MA.

HAC 1.2.1 – Hierarchical Agglomerative Clustering for a large-scale Network data

HAC 1.2.1

:: DESCRIPTION

HAC is developed for fast clustering of heterogeneous interaction networks.

::DEVELOPER

Joel Bader lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux

:: DOWNLOAD

 HAC

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S44. doi: 10.1186/1471-2105-12-S1-S44.
Resolving the structure of interactomes with hierarchical agglomerative clustering.
Park Y, Bader JS.