fuzzyClustering – K-partite Graph Clustering algorithm that allows for Overlapping (Fuzzy) Clusters

fuzzyClustering

:: DESCRIPTION

fuzzyClustering is a fast and efficient k-partite graph clustering algorithm that allows for overlapping (fuzzy) clusters. It is based on multiplicative update rules commonly used in non-negative matrix factorization.

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • MatLab

:: DOWNLOAD

 fuzzyClustering

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Oct 20;11:522. doi: 10.1186/1471-2105-11-522.
Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs.
Hartsperger ML, Blöchl F, Stümpflen V, Theis FJ.

scHiCluster v0.1.1 – Single-cell Clustering algorithm for Hi-C Contact Matrices

scHiCluster v0.1.1

:: DESCRIPTION

scHiCluster is a comprehensive python package for single-cell chromosome contact data analysis. It includes the identification of cell types (clusters), loop calling in cell types, and domain and compartment calling in single cells.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

scHiCluster

:: MORE INFORMATION

Citation:

Zhou J, Ma J, Chen Y, Cheng C, Bao B, Peng J, Sejnowski TJ, Dixon JR, Ecker JR.
Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation.
Proc Natl Acad Sci U S A. 2019 Jul 9;116(28):14011-14018. doi: 10.1073/pnas.1901423116. Epub 2019 Jun 24. PMID: 31235599; PMCID: PMC6628819.

NOFOLD 1.0.1 – RNA Structure Clustering without Folding or Alignment

NOFOLD 1.0.1

:: DESCRIPTION

NoFold is an approach for characterizing and clustering RNA secondary structures without computational folding or alignment. It works by mapping each RNA sequence of interest to a structural feature space, where each coordinate within the space corresponds to the probabilistic similarity of the sequence to an empirically defined structure model (e.g. Rfam family covariance models).

::DEVELOPER

the Kim Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
  • R

:: DOWNLOAD

NOFOLD

:: MORE INFORMATION

Citation

Middleton SA, Kim J.
NoFold: RNA structure clustering without folding or alignment.
RNA. 2014 Nov;20(11):1671-83. doi: 10.1261/rna.041913.113. Epub 2014 Sep 18. PMID: 25234928; PMCID: PMC4201820.

boostKCP – Boosting k-means Clustering for the Pearson correlation distance

boostKCP

:: DESCRIPTION

boostKCP is a simple but powerful heuristic method for accelerating k-means clustering of large-scale data in life science.

::DEVELOPER

Morishita Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++

:: DOWNLOAD

boostKCP

:: MORE INFORMATION

Citation

Ichikawa K, Morishita S.
A Simple but Powerful Heuristic Method for Accelerating k-Means Clustering of Large-Scale Data in Life Science.
IEEE/ACM Trans Comput Biol Bioinform. 2014 Jul-Aug;11(4):681-92. doi: 10.1109/TCBB.2014.2306200. PMID: 26356339.

Gclust 3.5.5z3 – Genome-wide Clustering

Gclust 3.5.5z3

:: DESCRIPTION

Gclust software was developed to make clusters of protein sequences from all predicted protein sequences in a selected set of genomes.

::DEVELOPER

Sato Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Perl
  • C++ Compiler

:: DOWNLOAD

  Gclust

:: MORE INFORMATION

Citation

Bioinformatics. 2009 Mar 1;25(5):599-605. doi: 10.1093/bioinformatics/btp047.
Gclust: trans-kingdom classification of proteins using automatic individual threshold setting.
Sato N.

FreClu – Efficient Frequency-based De novo Short Read Clustering

FreClu

:: DESCRIPTION

FreClu: Efficient Frequency-based De novo Short Read Clustering — de novo clustering

::DEVELOPER

Morishita Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / Mac OsX
  • Java 

:: DOWNLOAD

 FreClu

:: MORE INFORMATION

Citation

Wei Qu, Shin-ichi Hashimoto and Shinichi Morishita
Efficient frequency-based de novo short read clustering for error trimming in next-generation sequencing.
Genome Res. 2009. 19:1309-1315

 

CSS-Palm 4.0 – Palmitoylation Site Prediction with a Clustering and Scoring Strategy

CSS-Palm 4.0

:: DESCRIPTION

CSS-Palm is a computer program for palmitoylation site prediction, Clustering and Scoring Strategy for Palmitoylation Sites Prediction.The program’s prediction performance is encouraging with highly positive Jack-Knife validation results (sensitivity 82.16% and specificity 83.17% for cut-off score 2.6).

::DEVELOPER

The CUCKOO Workgroup

:: SCREENSHOTS

:: REQUIREMENTS

  • WIndows / Linux / MacOsX
  • Java

:: DOWNLOAD

 CSS-Palm

:: MORE INFORMATION

Citation

CSS-Palm 2.0: an updated software for palmitoylation sites prediction
Jian Ren, Longping Wen, Xinjiao Gao, Changjiang Jin, Yu Xue and Xuebiao Yao.
Protein Engineering, Design and Selection.2008 21(11):639-644

MCRL v1.01 – Metagenomic Clustering by Reference Library

MCRL v1.01

:: DESCRIPTION

MCLR is a data mining tool that can be used to probe a metagenome for homologs of a pre-defined reference library.

::DEVELOPER

MCRL team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • Matlab

:: DOWNLOAD

MCLR

:: MORE INFORMATION

Citation

Tadmor AD, Phillips R.
MCRL: using a reference library to compress a metagenome into a nonredundant list of sequences, considering viruses as a case study.
Bioinformatics. 2021 Oct 12:btab703. doi: 10.1093/bioinformatics/btab703. Epub ahead of print. PMID: 34636854.

EnsembleClust 1.0 – Fast and Accurate Clustering of noncoding RNAs

EnsembleClust 1.0

:: DESCRIPTION

EnsembleClust enables fast and accurate clustering of ncRNAs.

::DEVELOPER

Sakakibara Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Vienna RNA package

:: DOWNLOAD

  EnsembleClust 

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S48. doi: 10.1186/1471-2105-12-S1-S48.
Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures.
Saito Y1, Sato K, Sakakibara Y.

CONCOCT 1.1.0 – Clustering cONtigs with COverage and ComposiTion

CONCOCT 1.1.0

:: DESCRIPTION

CONCOCT is a program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads.

::DEVELOPER

the Science for Life Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

CONCOCT

:: MORE INFORMATION

Citation

Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, Lahti L, Loman NJ, Andersson AF, Quince C.
Binning metagenomic contigs by coverage and composition.
Nat Methods. 2014 Nov;11(11):1144-6. doi: 10.1038/nmeth.3103. Epub 2014 Sep 14. PMID: 25218180.

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