MERCI – Motif EmeRging and with Classes Identification

MERCI

:: DESCRIPTION

The MERCI motif locator program is called from within MERCI to locate the motifs in the positive and negative sequence set. It can also be called directly to locate the found motifs in a third sequence set by typing

::DEVELOPER

the DTAI research group

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Linux/ Windows
  • Perl

:: DOWNLOAD

  MERCI

:: MORE INFORMATION

Citation

Bioinformatics. 2011 May 1;27(9):1231-8. doi: 10.1093/bioinformatics/btr110. Epub 2011 Mar 3.
Identifying discriminative classification-based motifs in biological sequences.
Vens C1, Rosso MN, Danchin EG.

Alien_hunter 1.7 – Interpolated Variable Order Motifs for Identification of Horizontally Acquired DNA

Alien_hunter 1.7

:: DESCRIPTION

Alien_hunter is an application for the prediction of putative Horizontal Gene Transfer (HGT) events with the implementation of Interpolated Variable Order Motifs (IVOMs).

::DEVELOPER

Alien_hunter team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Alien_hunter

:: MORE INFORMATION

Citation

Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.
Vernikos GS and Parkhill J
Bioinformatics (Oxford, England)2006;22;18;2196-203

miRA 1.2.0 – micro RNA Identification tool

miRA 1.2.0

:: DESCRIPTION

miRA is a new tool to identify miRNA precursors in plants, allowing for heterogeneous and complex precursor populations.

::DEVELOPER

Michael Huttner

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 miRA

:: MORE INFORMATION

Citation

miRA: adaptable novel miRNA identification in plants using small RNA sequencing data.
Evers M, Huttner M, Dueck A, Meister G, Engelmann JC.
BMC Bioinformatics. 2015 Nov 5;16:370. doi: 10.1186/s12859-015-0798-3.

LPIA v1 – Latent Pathway Identification Analysis

LPIA v1

:: DESCRIPTION

LPIA is a computational method for identifying biological pathways as putative sources of transcriptional dysregulation. It looks for statistically signficant evidence of dysregulation in a network of pathways constructed in a manner that explicitly links pathways through their common function in the cell.

::DEVELOPER

Eric D. Kolaczyk

:: SCREENSHOTS

n/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Perl

:: DOWNLOAD

 LPIA

:: MORE INFORMATION

Citation

Pham, L., Christadore, L., Schaus, S., and Kolaczyk, E.D. (2011).
Network-based prediction for sources of transcriptional dysregulation via latent pathway identification analysis.
Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1100891108.

DPUC 2.08 – Using Context to Improve Protein Domain Identification

DPUC 2.08

:: DESCRIPTION

DPUC (Domain Prediction Using Context) extends your Pfam predictions without loss of precision using domain context!

::DEVELOPER

The Ochoa Lab 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 DPUC

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2011 Mar 31;12:90. doi: 10.1186/1471-2105-12-90.
Using context to improve protein domain identification.
Ochoa A1, Llinás M, Singh M.

GibbsModule – Multispecies de novo Identification of Cis-regulatory Motifs and Modules

GibbsModule

:: DESCRIPTION

GibbsModule is a software for de novo detection of cis-regulatory motifs and modules in eukaryote genomes. GibbsModule models the coexpressed genes within one species as sharing a core cis-regulatory motif and each homologous gene group as sharing a homologous cis-regulatory module (CRM), characterized by a similar composition of motifs.

::DEVELOPER

Zhong Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 GibbsModule

:: MORE INFORMATION

Citation

Genome Res. 2008 Aug;18(8):1325-35. Epub 2008 May 15.
Cross-species de novo identification of cis-regulatory modules with GibbsModule: application to gene regulation in embryonic stem cells.
Xie D, Cai J, Chia NY, Ng HH, Zhong S.

EgoNet – Identification of human Disease Ego-network Modules

EgoNet

:: DESCRIPTION

EgoNet is implemented by Python and it is designed to detecting disease related subnetwork from a large biological network (PPI, metabolic network) combined with gene expression data.

::DEVELOPER

Tianwei Yu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Python
  • BioPython

:: DOWNLOAD

EgoNet

:: MORE INFORMATION

Citation

BMC Genomics. 2014 Apr 28;15:314. doi: 10.1186/1471-2164-15-314.
EgoNet: identification of human disease ego-network modules.
Yang R, Bai Y, Qin Z, Yu T

Superficial 1.2 – Identification of Potential Epitopes or Binding Sites

Superficial 1.2

:: DESCRIPTION

SUPERFICIAL (Surface scan) is a program that uses protein structures as input and generates library proposals consisting of linear and non-linear peptides. This process can be influenced by a graphical user interface at different stages, from the surface computation up to the definition of spatial regions. Superficial (Surface scan) scan the surface of proteins, find binding sites etc.

::DEVELOPER

Structural Bioinformatics Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

Superficial

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2005 Sep 9;6:223.
SUPERFICIAL–surface mapping of proteins via structure-based peptide library design.
Goede A, Jaeger IS, Preissner R.

IgC2N – Identification of Germline Changes in Copy Number

IgC2N

:: DESCRIPTION

IgC2N is a three step computational framework to discover and genotype germline CNVs

::DEVELOPER

Demichelis Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /Windows / MacOSX
  • R

:: DOWNLOAD

  IgC2N

:: MORE INFORMATION

Citation:

PLoS One. 2011 Mar 29;6(3):e17539. doi: 10.1371/journal.pone.0017539.
A computational framework discovers new copy number variants with functional importance.
Banerjee S1, Oldridge D, Poptsova M, Hussain WM, Chakravarty D, Demichelis F.

InsertionMapper 1.1 – Targeted Site Identification from Next-generation Sequencing data with 3-D Pooling

InsertionMapper 1.1

:: DESCRIPTION

InsertionMapper is a pipeline tool for the identification of targeted sequences from multidimensional high throughput sequencing data.

::DEVELOPER

Chunguang Du

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java

:: DOWNLOAD

 InsertionMapper

:: MORE INFORMATION

Citation

BMC Genomics. 2013 Oct 4;14:679. doi: 10.1186/1471-2164-14-679.
InsertionMapper: a pipeline tool for the identification of targeted sequences from multidimensional high throughput sequencing data.
Xiong W1, He L, Li Y, Dooner HK, Du C.