RAP – Rank Aggregation-based data Fusion for Gene Prioritization

RAP

:: DESCRIPTION

RAP is a rank aggregation-based data fusion approach for gene prioritization in plants. It can be used to perform the gene prioritization in Arabidopsis thaliana and 28 non-plant species.

::DEVELOPER

Ma Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

RAP

:: MORE INFORMATION

Citation

Zhai J, Tang Y, Yuan H, Wang L, Shang H, Ma C.
A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function.
Front Plant Sci. 2016 Dec 15;7:1914. doi: 10.3389/fpls.2016.01914. PMID: 28018423; PMCID: PMC5156684.

TAGOPSIN 1.3 – Collating Taxa-specific Gene and Protein Functional and Structural Information

TAGOPSIN 1.3

:: DESCRIPTION

TAGOPSIN (TAxonomy, Gene, Ontology, Protein, Structure INtegrated) retrieves select data from NCBI Taxonomy, NCBI Nucleotide, UniProtKB, Gene Ontology, Pfam, EBI SIFTS and RCSB PDB, and assembles them in the database management system PostgreSQL. TAGOPSIN is an organism-centred data warehousing tool that works with prokaryotic and eukaryotic organisms as well as viruses.

::DEVELOPER

TAGOPSIN team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Java
  • PostgreSQL

:: DOWNLOAD

TAGOPSIN

:: MORE INFORMATION

Citation

Bundhoo E, Ghoorah AW, Jaufeerally-Fakim Y.
TAGOPSIN: collating taxa-specific gene and protein functional and structural information.
BMC Bioinformatics. 2021 Oct 23;22(1):517. doi: 10.1186/s12859-021-04429-5. PMID: 34688246; PMCID: PMC8541804.

DigSee v2.01 – Disease Gene Search Engine with Evidence Sentence

DigSee v2.01

:: DESCRIPTION

DigSee is a text mining search engine to provide evidence sentences describing that “genes” are involved in the development of “disease” through “biological events”. Biological events such as gene expression, regulation, phosphorylation, localization, and protein catabolism play important roles in the development of diseases. Understanding the association between diseases and genes can be enhanced with the identification of involved biological events in this association. With input of (disease, genes, events), users can obtain Medline abstracts with highlighted evidence sentences.

::DEVELOPER

Data Mining & Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Kim J, So S, Lee HJ, Park JC, Kim JJ, Lee H.
DigSee: Disease gene search engine with evidence sentences (version cancer).
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W510-7. doi: 10.1093/nar/gkt531. Epub 2013 Jun 12. PMID: 23761452; PMCID: PMC3692119.

GenAPI v1.0 – Gene Absence Presence Identification tool

GenAPI v1.0

:: DESCRIPTION

GenAPI is a program for gene presence absence table generation for series of closely related bacterial genomes from annotated GFF files.

::DEVELOPER

Migle Gabrielaite

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

GenAPI

:: MORE INFORMATION

Citation

Gabrielaite M, Marvig RL.
GenAPI: a tool for gene absence-presence identification in fragmented bacterial genome sequences.
BMC Bioinformatics. 2020 Jul 20;21(1):320. doi: 10.1186/s12859-020-03657-5. PMID: 32690023; PMCID: PMC7372895.

PCAdapt 201405 – Detect Genes Targetted by Selection

PCAdapt 201405

:: DESCRIPTION

PCAdapt is a software to detect footprints of local adaptation in population genetics data set.

::DEVELOPER

Nicolas Duforet-Frebourg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX
  • C Compiler

:: DOWNLOAD

 PCAdapt

:: MORE INFORMATION

Citation

Mol Biol Evol. 2014 Jun 3. pii: msu182. [Epub ahead of print]
Genome scans for detecting footprints of local adaptation using a Bayesian factor model.
Duforet-Frebourg N1, Bazin E2, Blum MG3.

2020plus v1.2.3 – Ratiometric Prediction of Cancer driver Genes

2020plus v1.2.3

:: DESCRIPTION

20/20+ classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests.

::DEVELOPER

Karchin Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

2020plus

:: MORE INFORMATION

Citation

Tokheim CJ, Papadopoulos N, Kinzler KW, Vogelstein B, Karchin R.
Evaluating the evaluation of cancer driver genes.
Proc Natl Acad Sci U S A. 2016 Dec 13;113(50):14330-14335. doi: 10.1073/pnas.1616440113. Epub 2016 Nov 22. PMID: 27911828; PMCID: PMC5167163.

GeneCOST – Identifying Disease causing Genes

GeneCOST

:: DESCRIPTION

GeneCOST is a novel scoring based method to evaluate every gene for its disease association.

::DEVELOPER

Advanced Genomics and Bioinformatic Research Group, İGBAM

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

GeneCOST

:: MORE INFORMATION

Citation

Ozer B, Sağıroğlu M, Demirci H.
GeneCOST: a novel scoring-based prioritization framework for identifying disease causing genes.
Bioinformatics. 2015 Nov 15;31(22):3715-7. doi: 10.1093/bioinformatics/btv424. Epub 2015 Jul 21. PMID: 26203168.

SSCprofiler – miRNA Gene Prediction

SSCprofiler

:: DESCRIPTION

SSCprofiler (Sequence, Structure and Conservation) is a new computational tool utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors.

::DEVELOPER

Ioannis Iliopoulos’ Bioinformatics & Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2009 Jun;37(10):3276-87. doi: 10.1093/nar/gkp120. Epub 2009 Mar 25.
Prediction of novel microRNA genes in cancer-associated genomic regions–a combined computational and experimental approach.
Oulas A, Boutla A, Gkirtzou K, Reczko M, Kalantidis K, Poirazi P.

GenRev 1.0.1 – Explore Functional Relevance of Genes in Molecular Networks

GenRev 1.0.1

:: DESCRIPTION

GenRev is a Python software package that identifies subnetworks for a set of genes from a large network. Three algorithms are implemented in GenRev, the Klein-Ravi algorithm for node weighted Steiner tree problem, the limited k-walk algorithm and a heuristic local search algorithm. Each algorithm is implemented in an independent module. Another analysis module is also developed for graph clustering and gene ranking.

::DEVELOPER

Bioinformatics and Systems Medicine Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 GenRev

:: MORE INFORMATION

Citation

Zheng S, Zhao Z (2012)
GenRev: exploring functional relevance of genes in molecular networks.
Genomics 99(3):183-188.

GS-LAGE – Gene-specific Analysis of Microarray Global Data

GS-LAGE

:: DESCRIPTION

GS-LAGE (Gene Specific Large-scale Analysis of Gene Expression) is a global analysis strategy for large-scale microarray datasets integrated from public databases such as NCBI GEO.

::DEVELOPER

Lab of Bioinformatics and Molecular Design

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Jul 15;26(14):1723-30. doi: 10.1093/bioinformatics/btq279.
Global analysis of microarray data reveals intrinsic properties in gene expression and tissue selectivity.
Kim C1, Choi J, Park H, Park Y, Park J, Park T, Cho K, Yang Y, Yoon S.