Rainbow v2.0.4 – Clustering and Assembling Short Reads, especially for RAD

Rainbow v2.0.4

:: DESCRIPTION

Rainbow package consists of several programs used for RAD-seq related clustering and de novo assembly.

::DEVELOPER

Rainbow team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler

:: DOWNLOAD

 Rainbow

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Nov 1;28(21):2732-7. doi: 10.1093/bioinformatics/bts482.
Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads.
Chong Z, Ruan J, Wu CI.

Epiclomal – Clustering of sparse DNA Methylation data

Epiclomal

:: DESCRIPTION

Epiclomal package is software for clustering of sparse DNA methylation data

::DEVELOPER

Shah Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Python

:: DOWNLOAD

Epiclomal

:: MORE INFORMATION

Citation

Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data,
Camila P. E. de Souza, Mirela Andronescu, Tehmina Masud, Farhia Kabeer, Justina Biele, Emma Laks, Daniel Lai, Jazmine Brimhall, Beixi Wang, Edmund Su, Tony Hui, Qi Cao, Marcus Wong, Michelle Moksa, Richard A. Moore, Martin Hirst, Samuel Aparicio, Sohrab P. Shah,
doi: https://doi.org/10.1101/414482

clusterScore 0.12 – Clustering of Cavbase Scores and other proximity matrices

clusterScore 0.12

:: DESCRIPTION

clusterScore is a software to explore the important parameters of a clustering procedure, which will allow an accurate classification of proteins. It has been successfully applied on two diverse and challenging data sets. The predicted number of clusters, as suggested by clusterScore and the subsequent clustering of proteins are in agreement with the EC and Merops classifications. Furthermore, putative cross-reactivity mapped between calpain-1 and cysteine cathepsins on structural level has so far only been described based on ligand data.

::DEVELOPER

Group of Prof. Dr. G. Klebe

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

clusterScore 

:: MORE INFORMATION

Citation

J Chem Inf Model. 2013 Aug 26;53(8):2082-92. doi: 10.1021/ci300550a.
Cavities tell more than sequences: exploring functional relationships of proteases via binding pockets.
Glinca S, Klebe G.

kClust – Fast and Sensitive Clustering of large Protein Sequence Databases

kClust

:: DESCRIPTION

kClust is a fast and sensitive clustering method for the clustering of protein sequences. It is able to cluster large protein databases down to 20-30% sequence identity. kClust generates a clustering where each cluster is represented by its longest sequence (representative sequence).

::DEVELOPER

Söding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

kClust

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013 Aug 15;14:248. doi: 10.1186/1471-2105-14-248.
kClust: fast and sensitive clustering of large protein sequence databases.
Hauser M, Mayer CE, Söding J.

MMseqs2 R10 – ultra Fast and Sensitive Protein Search and Clustering suite

MMseqs2 R10

:: DESCRIPTION

MMseqs2 (Many-against-Many sequence searching) is a software suite for very fast protein sequence searches and clustering of huge protein sequence data sets.

::DEVELOPER

Söding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler
:: DOWNLOAD

 MMseqs2

:: MORE INFORMATION

Citation:

MMseqs software suite for fast and deep clustering and searching of large protein sequence sets.
Hauser M, Steinegger M, Söding J.
Bioinformatics. 2016 Jan 6. pii: btw006.

Steinegger M and Soeding J.
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.
Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).

GraphCrunch 2.1.1 – Network Modeling, Alignment and Clustering

GraphCrunch 2.1.1

:: DESCRIPTION

GraphCrunch is a  software tool for network analysis, modeling and alignment. It automates tasks of finding the best fitting model for the network data, pairwise comparisons of networks, alignment of two networks using GRAAL algorithm (a better network alignment algorithm, MI-GRAAL, is also available for download), and provides capabilities of clustering network nodes based on their topological surrounding in the network.

::DEVELOPER

Nataša Pržulj, Imperial College

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • QT

:: DOWNLOAD

 GraphCrunch

:: MORE INFORMATION

Citation

GraphCrunch 2: Software tool for network modeling, alignment and clustering.
Kuchaiev O, Stevanović A, Hayes W, Pržulj N.
BMC Bioinformatics. 2011 Jan 19;12:24. doi: 10.1186/1471-2105-12-24.

LRAcluster 1.0 – Low Rank Approximation based Multi-omics Data Clustering

LRAcluster 1.0

:: DESCRIPTION

LRAcluster is a new method to discover molecular subtypes by detecting the low-dimensional intrinsic space of high-dimensional cancer multi-omics data.

::DEVELOPER

Bioinformatics & Intelligent Information Processing Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

 LRAcluster

:: MORE INFORMATION

Citation

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.
Wu D, Wang D, Zhang MQ, Gu J.
BMC Genomics. 2015 Dec 1;16(1):1022. doi: 10.1186/s12864-015-2223-8.

CREAM 1.1.1 – Clustering of Genomic Regions Analysis Method

CREAM 1.1.1

:: DESCRIPTION

CREAM (Clustering of Genomic Regions Analysis Method) provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). ‘CREAM’ uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. ‘CREAM’ considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.

::DEVELOPER

Princess Margaret Bioinformatics and Computational Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

 CREAM

:: MORE INFORMATION

 

USEARCH 8.1.1861 / UCLUST / UBLAST – Sequence Search & Clustering

USEARCH 8.1.1861 / UCLUST / UBLAST

:: DESCRIPTION

UBLAST and USEARCH are algorithms designed to enable high-throughput, sensitive search of very large sequence databases.

UBLAST searches for local alignments, USEARCH for global alignments. UBLAST and USEARCH are orders of magnitude faster than BLAST for some applications.

UCLUST is a clustering algorithm that uses USEARCH as a subroutine to achieve exceptional high speed and sensitivity.

::DEVELOPER

Robert Edgar

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX

:: DOWNLOAD

USEARCH

:: MORE INFORMATION

Citation

Bioinformatics. 2015 Jul 2. pii: btv401.
Error filtering, pair assembly, and error correction for next-generation sequencing reads.
Edgar RC, Flyvbjerg H

Edgar, R.C. (2010),
Search and clustering orders of magnitude faster than BLAST,
Bioinformatics.doi: 10.1093/bioinformatics/btq461

HML – Tool to perform Hierarchical Maximum Likelihood (HML) Clustering

HML

:: DESCRIPTION

HML is an algorithm which uses distribution and centroid information to cluster a sample and was applied to biological data.

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

  HML

:: MORE INFORMATION

Citation

IEEE Trans Biomed Eng. 2016 Mar 24.
Hierarchical Maximum Likelihood Clustering Approach.
Sharma A, Boroevich K, Shigemizu D, Kamatani Y, Kubo M, Tsunoda T.