Category: Cluster Analysis

AncestralClust – Cluster Divergent Sequences

AncestralClust

:: DESCRIPTION

AncestralClust is a clustering program, which is developed for clustering divergent sequences.

::DEVELOPER

Lenore Pipes @ Nielsen Berkeley Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux

:: DOWNLOAD

AncestralClust

:: MORE INFORMATION

Citation

Pipes L, Nielsen R.
AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees.
Bioinformatics. 2021 Oct 20:btab723. doi: 10.1093/bioinformatics/btab723. Epub ahead of print. PMID: 34668516.

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.

Calib v0.3.6 – Clustering UMI-barcoded Sequencing data

Calib v0.3.6

:: DESCRIPTION

Calib clusters barcode tagged paired-end reads based on their barcode and sequence similarity.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • conda
  • Python

:: DOWNLOAD

Calib

:: MORE INFORMATION

Citation

Orabi B, Erhan E, McConeghy B, Volik SV, Le Bihan S, Bell R, Collins CC, Chauve C, Hach F.
Alignment-free clustering of UMI tagged DNA molecules.
Bioinformatics. 2019 Jun 1;35(11):1829-1836. doi: 10.1093/bioinformatics/bty888. PMID: 30351359.

PathoGiST v0.3.6 – Clustering Pathogen Isolates by combining multiple Genotyping Signals

PathoGiST v0.3.6

:: DESCRIPTION

PathOGiST is an algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • conda

:: DOWNLOAD

PathOGiST

:: MORE INFORMATION

Citation

Katebi M. et al. (2020)
PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals.
Algorithms for Computational Biology. AlCoB 2020. Lecture Notes in Computer Science, vol 12099. Springer, Cham. https://doi.org/10.1007/978-3-030-42266-0_9

McKmeans 0.42 – Multi-core algorithm for Clustering extremely large datasets

McKmeans 0.42

:: DESCRIPTION

McKmeans (multi-core parallel cluster algorithm) is highly efficient multi-core k-means algorithm for clustering extremely large datasets.

::DEVELOPER

Medical Systems Biology, University of Ulm

:: SCREENSHOTS

McKmeans

:: REQUIREMENTS

  • Linux/ Mac OsX/ Windows
  • Java/ R package

:: DOWNLOAD

 McKmeans

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Apr 6;11:169. doi: 10.1186/1471-2105-11-169.
A highly efficient multi-core algorithm for clustering extremely large datasets.
Kraus JM, Kestler HA.

TimesVector 1.5 – Analysis of Time Series Transcriptome data from multiple Phenotypes

TimesVector 1.5

:: DESCRIPTION

TimesVector is a triclustering tool for clustering time-series data that comprises multiple conditions, or phenotypes.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
  • R

:: DOWNLOAD

TimesVector

:: MORE INFORMATION

Citation

Jung I, Jo K, Kang H, Ahn H, Yu Y, Kim S.
TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.
Bioinformatics. 2017 Dec 1;33(23):3827-3835. doi: 10.1093/bioinformatics/btw780. PMID: 28096084.

DBC 6.11.13 / dbOTU3 – Distribution-based Clustering

DBC 6.11.13 / dbOTU3

:: DESCRIPTION

The DBC software is an alternative method for organizing sequence data into operational taxonomic units (OTUs) for next-generation sequencing technologies, such as Illumina. The focus of this method is to identify sequences that are genetically and ecologically similar to group them, while keeping ecologically distinct organisms apart, regardless of sequence identity.

dbOTU3 is a new implementation of dbOTU that is faster and more user-friendly.

::DEVELOPER

The Alm lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl
  • R

:: DOWNLOAD

DBC  / dbOTU3

:: MORE INFORMATION

Citation

Olesen SW, Duvallet C, Alm EJ.
dbOTU3: A new implementation of distribution-based OTU calling.
PLoS One. 2017 May 4;12(5):e0176335. doi: 10.1371/journal.pone.0176335. PMID: 28472072; PMCID: PMC5417438.

Sarah P. Preheim, Allison R. Perrotta, Antonio M. Martin-Platero, Anika Gupta and Eric J. Alm. 2013.
Distribution-based clustering: Using ecology to refine the operational taxonomic unit.
Appl. Environ. Microbiol. 2013, 79(21):659

BnpC – Bayesian non-parametric Clustering of Single-cell Mutation Profiles

BnpC

:: DESCRIPTION

BnpC is a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles.

::DEVELOPER

Computational Biology Group (CBG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux
  • Python

:: DOWNLOAD

BnpC

:: MORE INFORMATION

Citation:

Borgsmüller N, Bonet J, Marass F, Gonzalez-Perez A, Lopez-Bigas N, Beerenwinkel N.
BnpC: Bayesian non-parametric clustering of single-cell mutation profiles.
Bioinformatics. 2020 Dec 8;36(19):4854-4859. doi: 10.1093/bioinformatics/btaa599. PMID: 32592465; PMCID: PMC7750970.

IntNMF 1.2.0 – Integrative Clustering of Multiple Genomic Dataset

IntNMF 1.2.0

:: DESCRIPTION

IntNMF is an R package for Integrative clustering of multiple genomic datasets with Non-negative matrix factorization (NMF).

::DEVELOPER

Fridley Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • R

:: DOWNLOAD

IntNMF

:: MORE INFORMATION

Citation:

Chalise P, Fridley BL.
Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm.
PLoS One. 2017 May 1;12(5):e0176278. doi: 10.1371/journal.pone.0176278. PMID: 28459819; PMCID: PMC5411077.

NB.MClust 1.1.1 – Negative Binomial Model-Based Clustering

NB.MClust 1.1.1

:: DESCRIPTION

NB.Mclust is an R package for Negative binomial model-based clustering.

::DEVELOPER

Fridley Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • R

:: DOWNLOAD

NB.Mclust

:: MORE INFORMATION

Citation:

Li Q, Noel-MacDonnell JR, Koestler DC, Goode EL, Fridley BL.
Subject level clustering using a negative binomial model for small transcriptomic studies.
BMC Bioinformatics. 2018 Dec 12;19(1):474. doi: 10.1186/s12859-018-2556-9. PMID: 30541426; PMCID: PMC6292049.