GFICLEE 1.0 – Gene Function Inferred by Common Loss Evolutionary Events

GFICLEE 1.0

:: DESCRIPTION

GFICLEE is a program for predicting the new members in biological pathway or biological complex. It is based on the common loss events in gene evolution.

::DEVELOPER

Yang Fang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / WIndows / MacOS
Java

:: DOWNLOAD

GFICLEE

:: MORE INFORMATION

Citation

Fang Y, Li M, Li X, Yang Y.
GFICLEE: ultrafast tree-based phylogenetic profile method inferring gene function at the genomic-wide level.
BMC Genomics. 2021 Oct 29;22(1):774. doi: 10.1186/s12864-021-08070-7. PMID: 34715785; PMCID: PMC8557005.

OncodriveROLE – Classifying cancer driver genes into Loss of Function and Activating roles.

OncodriveROLE

:: DESCRIPTION

OncodriveROLE is a machine-learning based approach to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development.

::DEVELOPER

The Biomedical Genomics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
R package

:: DOWNLOAD

OncodriveROLE

:: MORE INFORMATION

Citation

Schroeder MP, Rubio-Perez C, Tamborero D, Gonzalez-Perez A, Lopez-Bigas N.
OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action.
Bioinformatics. 2014 Sep 1;30(17):i549-55. doi: 10.1093/bioinformatics/btu467. PMID: 25161246; PMCID: PMC4147920.

GLOOME 201305 – Gain Loss Mapping Engine

GLOOME 201305

:: DESCRIPTION

The main purpose of GLOOME server is to accurately infers branch specific and site specific gain and loss events.

:: DEVELOPER

Edmond J. Safra Center for Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

GLOOME

:: MORE INFORMATION

Citation:

Genome Biol Evol. 2011;3:1265-75. doi: 10.1093/gbe/evr101. Epub 2011 Oct 4.
Inference of gain and loss events from phyletic patterns using stochastic mapping and maximum parsimony–a simulation study.
Cohen O1, Pupko T.

GLOOME: gain loss mapping engine.
Cohen O, Ashkenazy H, Belinky F, Huchon D, Pupko T.
Bioinformatics. 2010 Nov 15;26(22):2914-5. doi: 10.1093/bioinformatics/btq549.

GISTIC 2.0.23 – Detect Regions of Significant Copy-number Gains and Losses

GISTIC 2.0.23

:: DESCRIPTION

GISTIC (Genomic Identification of Significant Targets in Cancer) facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

::DEVELOPER

The Cancer Genome Analysis (CGA) group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

GISTIC

:: MORE INFORMATION

Citation

Genome Biol. 2011;12(4):R41. Epub 2011 Apr 28.
GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers.
Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G.

DupLoCut – Duplication Loss Phylogeny by Cutting Planes

DupLoCut

:: DESCRIPTION

DupLoCut computes ancestral gene orders, given a phylogenetic tree and gene orders assigned to the leaves of the tree. It attempts to find the most parsimony assignment of gene orders under the duplication-loss evolutionary model.

::DEVELOPER

The Center for Computational Biology at Johns Hopkins University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Mac OsX

:: DOWNLOAD

DupLoCut

:: MORE INFORMATION

Citation

J Comput Biol. 2013 Sep;20(9):643-59. doi: 10.1089/cmb.2013.0057.
The duplication-loss small phylogeny problem: from cherries to trees.
Andreotti S1, Reinert K, Canzar S.

CLImAT 1.2.2 – Detection of Copy Number Alteration and Loss of Heterozygosity

CLImAT 1.2.2

:: DESCRIPTION

CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors) is a bioinformatic tool for identification of genome-wide aberrations from tumor samples using whole-genome sequencing data.

::DEVELOPER

HI_Lab @ USTC

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/MacOsX/Linux
Matlab

:: DOWNLOAD

CLImAT

:: MORE INFORMATION

Citation:

CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data.
Yu Z, Liu Y, Shen Y, Wang M, Li A.
Bioinformatics. 2014 Sep 15;30(18):2576-83. doi: 10.1093/bioinformatics/btu346.

DynaDup 2.3.2 – Inferring Optimal Species Trees under Gene Duplication and Loss

DynaDup 2.3.2

:: DESCRIPTION

DynaDup is a Dynamic Programing based Java application for building species trees from gene trees minimizing gene duplication and gene duplication and loss.

::DEVELOPER

The Warnow Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux /Windows/MacOsX
Java

:: DOWNLOAD

DynaDup

:: MORE INFORMATION

Citation

Pac Symp Biocomput. 2013:250-61.
Inferring optimal species trees under gene duplication and loss.
Bayzid MS1, Mirarab S, Warnow T.

hapLOH 14 – Analysis of loss of Heterozygosity in Tumor Genomes

hapLOH 1.4

:: DESCRIPTION

hapLOH profiles and characterizes tumor genomes using data from SNP microarrays. It is designed to be effective in the presence of high levels of germline contamination.

::DEVELOPER

paul scheet lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/MacOsX
Perl
Python

:: DOWNLOAD

hapLOH

:: MORE INFORMATION

Citation

Vattathil, Selina, and Paul Scheet.
“Haplotype-based profiling of subtle allelic imbalance with SNP arrays.”
Genome research 23.1 (2013): 152-158.

ExomeCNV 1.4 – Exome Sequencing-based Copy-number Variation and Loss of Heterozygosity Detection

ExomeCNV 1.4

:: DESCRIPTION

ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads.

::DEVELOPER

Nelsonlab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
R package
Perl
Python
Samtool
GATK

:: DOWNLOAD

ExomeCNV

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Oct 1;27(19):2648-54. doi: 10.1093/bioinformatics/btr462. Epub 2011 Aug 9.
Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV.
Sathirapongsasuti JF, Lee H, Horst BA, Brunner G, Cochran AJ, Binder S, Quackenbush J, Nelson SF.