Discrover 1.6.0 – Discover Discriminative Sequence Motifs

Discrover 1.6.0

:: DESCRIPTION

Discrover is a motif discovery method to find binding sites of nucleic acid binding proteins.

::DEVELOPER

N. Rajewsky Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

Discrover

:: MORE INFORMATION

Citation

Maaskola J, Rajewsky N.
Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models.
Nucleic Acids Res. 2014 Dec 1;42(21):12995-3011. doi: 10.1093/nar/gku1083. Epub 2014 Nov 11. PMID: 25389269; PMCID: PMC4245949.

eCAMI – Simultaneous Classification and Motif Identification for enzyme/CAZyme annotation

eCAMI

:: DESCRIPTION

eCAMI is a Python package: (i) has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction.

::DEVELOPER

YIN LAB @ UNL & ZHANG LAB @ NKU

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

eCAMI

:: MORE INFORMATION

Citation

Xu J, Zhang H, Zheng J, Dovoedo P, Yin Y.
eCAMI: simultaneous classification and motif identification for enzyme annotation.
Bioinformatics. 2020 Apr 1;36(7):2068-2075. doi: 10.1093/bioinformatics/btz908. PMID: 31794006.

Cister – Find Motif Clusters in DNA Sequences

Cister

:: DESCRIPTION

Cister (Cis-element Cluster Finder) predicts regulatory regions in DNA sequences by searching for clusters of cis-elements.

::DEVELOPER

Zlab

:: SCREENSHOTS

Command Line

Web version:

:: REQUIREMENTS

  • Linux / SUN Solaris 8 / SGI/IRIX/ Alpha (Compaq Tru64 UNIX V5.0A)

:: DOWNLOAD

Cister

:: MORE INFORMATION

Citation

Frith, M. C., Hansen U. and Weng, Z.
Detection of cis-element clusters in higher eukaryotic DNA
Bioinformatics 2001 Oct;17(10):878-889.

Cluster-Buster 20100219 – Find Dense Clusters of Motifs in Nucleotide Sequences

Cluster-Buster 20100219

:: DESCRIPTION

Cluster-Buster is the third generation program for finding clusters of pre-specified motifs in nucleotide sequences. The main application is detection of sequences that regulate gene transcription, such as enhancers and silencers, but other types of biological regulation may be mediated by motif clusters too.

::DEVELOPER

Zlab

:: SCREENSHOTS

Command Line

Web version:

:: REQUIREMENTS

  • Windows with CygWin / Linux / Mac OsX

:: DOWNLOAD

Cluster-Buster

:: MORE INFORMATION

Citation:

Martin C Frith, Michael C Li, and Zhiping Weng (2003). Cluster-Buster: Finding dense clusters of motifs in DNA sequencesNucleic Acids Research, 31(13):3666-8.

 

ROVER 20050711 – Find Relatively Overrepresented Motifs in DNA Sequences

ROVER 20050711

:: DESCRIPTION

ROVER (Relative OVER-abundance of cis-elements) is a tool for determining if one or more of a group of transcription factors is likely to regulate a group of genes. It was designed for use with promoters from groups of genes that are suspected of being co-regulated, such as those from a microarray study. ROVER compares two groups of promoters (a suspected co-regulated group and a non-regulated group) by determining the relative over-abundance of likely binding sites for a particular Transcription Factor (TF) in one group versus the other. ROVER calculates the significance of any over-abundance of binding sites for each TF and reports a probability of its chance occurrence. This can be interpreted as the probability that a given TF regulates the group of genes in question. Likely binding sites are found by looking for high-scoring matches to a Position Specific Weight Matrix (PSSM), which represents known binding sites for a transcription factor. In addition to determining the significance of each TF, ROVER also provides the subset of sequences likely to be regulated by each TF and the specific significant binding sites.

::DEVELOPER

Zlab

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

  • Windows / Mac OsX / Linux
  • JAVA

:: DOWNLOAD

ROVER

:: MORE INFORMATION

Citation:

Haverty, PM., Hansen, U., Weng, Z. (2004) Computational Inference of Transcriptional Regulatory Networks from Expression Profiling and Transcription Factor Binding Site Identification. Nucleic Acids Research, Vol. 32, 179-188.

 

Clover 20120216 – Find Overrepresented Motifs in DNA Sequences

Clover 20120216

:: DESCRIPTION

Clover (Cis-eLement OVERrepresentation) is a program for identifying functional sites in DNA sequences. If you give it a set of DNA sequences that share a common function, it will compare them to a library of sequence motifs (e.g. transcription factor binding patterns), and identify which if any of the motifs are statistically overrepresented in the sequence set.

::DEVELOPER

Zlab

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

  • Windows / Mac OsX / Linux / SUN Solaris 8 / SGI/IRIX

:: DOWNLOAD

Clover

:: MORE INFORMATION

Citation:

Martin C Frith, Yutao Fu, Liqun Yu, Jiang-Fan Chen, Ulla Hansen, Zhiping Weng (2004). Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Research 32(4):1372-81.

 

DiNAMO 1.0 – Discriminative DNA IUPAC motif discovery tool

DiNAMO 1.0

:: DESCRIPTION

The DiNAMO software implements an exhaustive algorithm to detect over-represented IUPAC motifs in a set of DNA sequences. It has two modes: scanning mode, where all windows are parsed, or fixed-position mode, where only motifs occurring at a specific position in the sequences are taken into account.

::DEVELOPER

Bonsai Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX

:: DOWNLOAD

DiNAMO

:: MORE INFORMATION

Citation:

Saad C, Noé L, Richard H, Leclerc J, Buisine MP, Touzet H, Figeac M.
DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.
BMC Bioinformatics. 2018 Jun 11;19(1):223. doi: 10.1186/s12859-018-2215-1. PMID: 29890948; PMCID: PMC5996464.

COMET – Cluster Of Motifs E-value Tool

COMET

:: DESCRIPTION

COMET (Cluster Of Motifs E-value Tool) finds statistically significant clusters of motifs in a DNA sequence. The motifs are represented using 4 x L matrices, which record the frequencies of the nucleotides A, C, G, and T at each position in the motif.

::DEVELOPER

Zlab

:: SCREENSHOTS

Command Line

Web version:

:: REQUIREMENTS

  • Linux / SUN Solaris 8 / SGI/IRIX/ Mac OS X / Alpha (Compaq Tru64 UNIX V5.0A)

:: DOWNLOAD

COMET

:: MORE INFORMATION

Citation:

Frith MC, Spouge JL, Hansen U, Weng Z
Statistical significance of clusters of motifs represented by position specific scoring matrices in nucleotide sequences.
Nucleic Acids Res 2002 Jul 15;30(14):3214-24

 

MEDEA – Motif Enrichment of Differential Elements of Accessibility

MEDEA

:: DESCRIPTION

MEDEA identifies lineage-specifying transcription factors (TFs) from chromatin accessibility assays.

::DEVELOPER

The Bulyk Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MEDEA

:: MORE INFORMATION

Citation

Mariani L, Weinand K, Gisselbrecht SS, Bulyk ML.
MEDEA: analysis of transcription factor binding motifs in accessible chromatin.
Genome Res. 2020 May;30(5):736-748. doi: 10.1101/gr.260877.120. Epub 2020 May 18. PMID: 32424069; PMCID: PMC7263192.

AhoPro – Seach for Overrepresentation of Given motifs in DNA sequences, P-value calculation

AhoPro

:: DESCRIPTION

The project AhoPro was created to seach for overrepresentation of given motifs in DNA sequences and to search for motif cooccurrence. This could discover the synergy of transcription factors (TF), which usually takes place in regulatory modules of genes.

::DEVELOPER

Boeva lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WIndows

:: DOWNLOAD

 AhoPro

:: MORE INFORMATION

Citation:

Algorithms Mol Biol. 2007 Oct 10;2:13.
Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules.
Boeva V, Clément J, Régnier M, Roytberg MA, Makeev VJ.