sgp2 1.1 – Predict Genes by comparing Anonymous Genomic Sequences from two different Species

sgp2 1.1

:: DESCRIPTION

sgp2 is a program to predict genes by comparing anonymous genomic sequences from two different species. It combines tblastx, a sequence similarity search program, with geneid, an “ab initio” gene prediction program. In “assymetric” mode, genes are predicted in one sequence from one species (the target sequence), using a set of sequences (maybe only one) from the other species (the reference set). Essentially, geneid is used to predict all potential exons along the target sequence. Scores of exons are computed as log-likelihood ratios, function of the splice sites defining the exon, the coding bias in composition of the exon sequence as measured by a Markov Model of order five, and of the optimal alignment at the amino acid level between the target exon sequence and the counterpart homologous sequence in the reference set. From the set of predicted exons, the gene structure is assembled (eventually multiple genes in both strands) maximizing the sum of the scores of the assembled exons.

::DEVELOPER

RODERIC GUIGO LAB

 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 sgp2

:: MORE INFORMATION

Citation

G. Parra, P. Agarwal, J.F. Abril, T. Wiehe, J.W. Fickett and R. Guigó.
Comparative gene prediction in human and mouse.”
Genome Research 13(1):108-117 (2003)

geneid 1.4.4 – Predict Genes in Anonymous Genomic Sequences

geneid 1.4.4

:: DESCRIPTION

geneid is a program to predict genes in anonymous genomic sequences designed with a hierarchical structure. In the first step, splice sites, start and stop codons are predicted and scored along the sequence using Position Weight Arrays (PWAs). In the second step, exons are built from the sites. Exons are scored as the sum of the scores of the defining sites, plus the the log-likelihood ratio of a Markov Model for coding DNA. Finally, from the set of predicted exons, the gene structure is assembled, maximizing the sum of the scores of the assembled exons. geneid offers some type of support to integrate predictions from multiple source via external gff files and the redefinition of the general gene structure or model is also feasible. The accuracy of geneid compares favorably to that of other existing tools, but geneid is likely more efficient in terms of speed and memory usage.

geneid Online

:: DEVELOPER

geneid Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 geneid

:: MORE INFORMATION

Citation:

E. Blanco, G. Parra and R. Guigó,
Using geneid to Identify Genes.”,
Curr Protoc Bioinformatics. 2007 Jun;Chapter 4:Unit 4.3.

MGEnrichment – Microglia Gene List Enrichment Calculator

MGEnrichment

:: DESCRIPTION

MGEnrichment is a web application developed both to disseminate to the community our curated database of microglia-relevant gene lists, and to allow non-programming scientists to easily conduct statistical enrichment analysis on their gene expression data.

::DEVELOPER

Ciernia Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web server

:: DOWNLOAD

MGEnrichment

:: MORE INFORMATION

Citation

Jao J, Ciernia AV.
MGEnrichment: A web application for microglia gene list enrichment analysis.
PLoS Comput Biol. 2021 Nov 17;17(11):e1009160. doi: 10.1371/journal.pcbi.1009160. PMID: 34788279; PMCID: PMC8598070.

Varclus – Detection of Positive Selection in Genes and Genomes through Variation Clusters

Varclus

:: DESCRIPTION

Varclus is a perl utility to identify clusters of amino acid or nucleotide changes in a sequence that are too tightly spaced to have occurred by chance alone.

::DEVELOPER

Andreas Wagner Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Perl

:: DOWNLOAD

 Varclus

:: MORE INFORMATION

Citation:

Wagner, A. (2007)
Rapid detection of positive selection in genes and genomes through variation clusters.
Genetics 176: 2451–2463

CoRe v1.0.2 – Identifying Core-fitness Genes in Genome-wide Pooled CRISPR-Cas9 Screens

CoRe v1.0.2

:: DESCRIPTION

CoRe is an R package implementing existing and novel methods for the identification of core-fitness genes (at two different level of stringency) from joint analyses of multiple CRISPR-Cas9 screens.

::DEVELOPER

CoRe team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

CoRe

:: MORE INFORMATION

Citation

Vinceti A, Karakoc E, Pacini C, Perron U, De Lucia RR, Garnett MJ, Iorio F.
CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens.
BMC Genomics. 2021 Nov 17;22(1):828. doi: 10.1186/s12864-021-08129-5. PMID: 34789150.

MaxTiC – Ranking Nodes in a Phylogeny using inferred Horizontal Gene Transfers

MaxTiC

:: DESCRIPTION

MaxTiC (Maximum Time Consistency) is a software which takes as input a species tree and a series of (possibly inconsistent) time constraints between its internal nodes, weighted by confidence scores.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MaxTiC

:: MORE INFORMATION

Citation

MaxTiC: Fast ranking of a phylogenetic tree by Maximum Time Consistency with lateral gene transfers
Cédric ChauveAkbar RafieyAdrián A. DavínCeline ScornavaccaPhilippe VeberBastien BoussauGergely J. SzöllősiVincent DaubinEric Tannier

PhySca – SCJ small parsimony problem for weighted Gene Adjacencies

PhySca

:: DESCRIPTION

PhySca samples solutions to the Single-Cut-and-Join (SCJ) small parsimony problem for weighted gene adjacencies.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

PhySca

:: MORE INFORMATION

Citation

Luhmann N, Lafond M, Thevenin A, Ouangraoua A, Wittler R, Chauve C.
The SCJ Small Parsimony Problem for Weighted Gene Adjacencies.
IEEE/ACM Trans Comput Biol Bioinform. 2019 Jul-Aug;16(4):1364-1373. doi: 10.1109/TCBB.2017.2661761. Epub 2017 Jan 31. PMID: 28166504.

ANGST – ANalyzer of Gene and Species Trees

ANGST

:: DESCRIPTION

AnGST performs a phylogenetic reconciliation between a given species and gene tree, positing the best scoring set of HGT, DUP, and LOS events to explain all topological incongruities between the two trees. In order to protect against phylogenetic noise, we have designed AnGST to be capable of incorporating information from dozens of bootstrap trees simultaneously. In cases of variations among bootstrap subtrees, the subtree that best accords with the reference tree is adopted.

::DEVELOPER

The Alm lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • Python

:: DOWNLOAD

 ANGST

:: MORE INFORMATION

Citation

LA David & EJ Alm.
Rapid evolutionary innovation during an Archaean Genetic Expansion.”
Nature, 2010. doi:10.1038/nature09649.

GeneSelector 1.0 – Find Small subset of Genes for Classification of Expression data

GeneSelector 1.0

:: DESCRIPTION

GeneSelector finds a small subset of genes for classification of expression data.

::DEVELOPER

Ari Frank. @Laboratory of Computational Biology , Technion

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 GeneSelector

:: MORE INFORMATION

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.