VeRNAl – Discovering Flexible Motifs in RNA Graphs

VeRNAl

:: DESCRIPTION

veRNAl is an algorithm for identifying fuzzy recurrent subgraphs in RNA 3D networks.

::DEVELOPER

Carlos Oliver

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

VeRNAl

:: MORE INFORMATION

Citation:

Oliver C, Mallet V, Philippopoulos P, Hamilton WL, Waldispühl J.
VeRNAl: A Tool for Mining Fuzzy Network Motifs in RNA.
Bioinformatics. 2021 Nov 15:btab768. doi: 10.1093/bioinformatics/btab768. Epub ahead of print. PMID: 34791045.

Haystack 0.5.5 – Epigenetic Variability and Transcription Factor Motifs Analysis Pipeline

Haystack 0.5.5

:: DESCRIPTION

Haystack is a suite of computational tools implemented in a Python 2.7 package called haystack_bio to study epigenetic variability, cross-cell-type plasticity of chromatin states and transcription factors (TFs) motifs providing mechanistic insights into chromatin structure, cellular identity and gene regulation.

::DEVELOPER

Pinello Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

Haystack

:: MORE INFORMATION

Citation

Bioinformatics, 34 (11), 1930-1933 2018 Jun 1
Haystack: Systematic Analysis of the Variation of Epigenetic States and Cell-Type Specific Regulatory Elements
Luca Pinello, Rick Farouni, Guo-Cheng Yuan

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