SCENIC 1.1.2 / pySCENIC 0.11.1- Single-cell Regulatory Network Inference and Clustering

SCENIC 1.1.2 / pySCENIC 0.11.1

:: DESCRIPTION

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

::DEVELOPER

aertslab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/ MacOsX
  • R / Python

:: DOWNLOAD

SCENIC / pySCENIC

:: MORE INFORMATION

Citation

Aibar S, et al.
SCENIC: single-cell regulatory network inference and clustering.
Nat Methods. 2017 Nov;14(11):1083-1086. doi: 10.1038/nmeth.4463. Epub 2017 Oct 9. PMID: 28991892; PMCID: PMC5937676.

Van de Sande B,et al.
A scalable SCENIC workflow for single-cell gene regulatory network analysis.
Nat Protoc. 2020 Jul;15(7):2247-2276. doi: 10.1038/s41596-020-0336-2. Epub 2020 Jun 19. PMID: 32561888.

NNN 1.01 – Nearest Neighbor Networks Clustering

NNN 1.01

:: DESCRIPTION

NNN (Nearest Neighbor Networks) is a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.

::DEVELOPER

NNN team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • java

:: DOWNLOAD

 NNN

:: MORE INFORMATION

Citation

Huttenhower, C., Flamholz, A., Landis, J., Sahi, S., Myers, C., Olszewski, K., Hibbs, M., Siemers, N., Troyanskaya, O., Coller, H.,
Nearest Neighbor Networks: Clustering Expression Data Based on Gene Neighborhoods“,
BMC Bioinformatics 8:250, 2007

CrunchClust V43 – Clustering software for 454 Sequence

CrunchClust V43

:: DESCRIPTION

Crunchclust is an efficient clustering algorithm that is capable of handling the most common Roche’s 454 sequencing error ( Homopolymers ). It uses Levenshtein distance for sequence comparison during clustering. It is also used successfully for the clustering of Illumina Miseq sequences.

::DEVELOPER

Martin Hartmann

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • C++ Compiler

:: DOWNLOAD

  CrunchClust 

:: MORE INFORMATION

Citation:

Hartmann M, Howes CG, VanInsberghe D, Yu H, Bachar D, Christen R, Nilsson RH, Hallam SJ, Mohn WW (2012).
Significant and persistent impact of timber Harvesting on soil microbial communities in Northern coniferous forests.
The ISME Journal 6: 2199-2218.

CLUSTOM-CLOUD 1.0.0 – CLUSTering 16S NGS Sequences by Overlap Minimization

CLUSTOM-CLOUD 1.0.0

:: DESCRIPTION

CLUSTOM-CLOUD that is categorized into hierarchical clustering approach is a program for clustering high-throughput 16S sequences with user-defined thresholds.

::DEVELOPER

CLUSTOM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 CLUSTOM

:: MORE INFORMATION

Citation

Oh J, Choi CH, Park MK, Kim BK, Hwang K, Lee SH, Hong SG, Nasir A, Cho WS, Kim KM.
CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment.
PLoS One. 2016 Mar 8;11(3):e0151064. doi: 10.1371/journal.pone.0151064. PMID: 26954507; PMCID: PMC4783016.

CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization.
Hwang K, Oh J, Kim TK, Kim BK, Yu DS, Hou BK, Caetano-Anollés G, Hong SG, Kim KM.
PLoS One. 2013 May 1;8(5):e62623. doi: 10.1371/journal.pone.0062623

Mfuzz 2.51.0 – Soft Clustering of Microarray data

Mfuzz 2.51.0

:: DESCRIPTION

Mfuzz implementing soft clustering tools for microarray data analysis.

::DEVELOPER

SysBioLab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R package
  • BioConductor
  • R-TclTk

:: DOWNLOAD

 Mfuzz

:: MORE INFORMATION

Citation

Mfuzz: a software package for soft clustering of microarray data.
Kumar L, E Futschik M.
Bioinformation. 2007 May 20;2(1):5-7.

QuantumClone 1.0.0.6 – Clustering Mutations using High Throughput Sequencing (HTS) Data

QuantumClone 1.0.0.6

:: DESCRIPTION

QuantumClone is a clonal reconstruction method for whole genome or whole exome sequencing data.

::DEVELOPER

Boeva lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX/ Windows
  • R

:: DOWNLOAD

QuantumClone 

:: MORE INFORMATION

Citation

Deveau P, et al.
QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction.
Bioinformatics. 2018 Jun 1;34(11):1808-1816. doi: 10.1093/bioinformatics/bty016. PMID: 29342233; PMCID: PMC5972665.

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.

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

 

TriCluster / MicroCluster – Microarray Gene Expression Clustering

TriCluster / MicroCluster

:: DESCRIPTION

Tricluster is the first tri-clustering algorithm for microarray expression clustering. It builds upon the new microCluster bi-clustering approach. Tricluster first mines all the bi-clusters across the gene-sample slices, and then it extends these into tri-clusters across time or space (depending on the third dimension). It can find both scaling and shifting patterns

MicroCluster is a deterministic biclustering algorithm that can mine arbitrarily positioned and overlapping clusters of gene expression data to find interesting patterns

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 TriCluster / MicroCluster

:: MORE INFORMATION

Citation

Lizhuang Zhao and Mohammed J. Zaki,
TriCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data.
In ACM SIGMOD Conference on Management of Data. Jun 2005.

Lizhuang Zhao and Mohammed J. Zaki,
MicroCluster: An Efficient Deterministic Biclustering Algorithm for Microarray Data.
IEEE Intelligent Systems, 20(6):40-49. Nov/Dec 2005

OLYMPUS – Hybrid Clustering method in Time Series Gene Expression

OLYMPUS

:: DESCRIPTION

OLYMPUS is an automated hybrid clustering method in the field of time series gene expression analysis.

::DEVELOPER

the Biosignal Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 OLYMPUS

:: MORE INFORMATION

Citation

OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection.
Dimitrakopoulou K, Vrahatis AG, Wilk E, Tsakalidis AK, Bezerianos A.
Comput Methods Programs Biomed. 2013 Sep;111(3):650-61. doi: 10.1016/j.cmpb.2013.05.025.