Référentiel des outils installés sur la plateforme Migale

La liste des packages R installés sur la plateforme Migale est disponible ici.

gatk (version - 2020-01-25)
The Genome Analysis Toolkit or GATK is a software package developed at the Broad Institute to analyze high-throughput sequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size.
Usage : #gatk --help

Gblocks (version 0.91b - 2006-07-19)
Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis Gblocks eliminates poorly aligned positions and divergent regions of an alignment of DNA or protein sequences.
Usage : #Gblocks

GCTA (version 1.26.0 - 2017-09-15)
GCTA (Genome-wide Complex Trait Analysis) was originally designed to estimate the proportion of phenotypic variance explained by genome- or chromosome-wide SNPs for complex traits (the GREML method), and has subsequently extended for many other analyses to better understand the genetic architecture of complex traits. GCTA currently supports the analyses as follows.
Usage : #gcta64

GEM (version 20121106-022124 - 2013-07-25)
The GEM library(Also home to: The GEM mapper, The GEM RNA mapper, The GEM mappability, and others).Next-generation sequencing platforms (Illumina/Solexa, ABI/SOLiD, etc.) call for powerful and very optimized tools to index/analyze huge genomes. The GEM library strives to be a true "next-generation" tool for handling any kind of sequence data, offering state-of-the-art algorithms and data structures specifically tailored to this demanding task. At the moment, efficient indexing and searching algorithms based on the Burrows-Wheeler transform (BWT) have been implemented.

get_homologues (version 03012018 - 2018-01-24)
GET_HOMOLOGUES: a versatile software package for pan-genome analysis
Usage : #get_homologues.pl -h

Git lfs (version 2.4.2 - 2018-06-11)
Git Large File Storage (LFS) replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server like GitHub.com or GitHub Enterprise.
Usage : #git-lfs []

HH-suite (version 2.0.16 - 2013-07-24)
The HH-suite is an open-source software package for highly sensitive sequence searching and sequence alignment. Its two most important programs are HHsearch and HHblits. Both are based on the pairwise comparison of pro file hidden Markov models (HMMs).

HISAT2 (version 2.0.4 - 2016-09-07)
HISAT is a fast and sensitive spliced alignment program for mapping RNA-seq reads. In addition to one global FM index that represents a whole genome, HISAT uses a large set of small FM indexes that collectively cover the whole genome (each index represents a genomic region of ~64,000 bp and ~48,000 indexes are needed to cover the human genome). These small indexes (called local indexes) combined with several alignment strategies enable effective alignment of RNA-seq reads, in particular, reads spanning multiple exons. The memory footprint of HISAT is relatively low (~4.3GB for the human genome). We have developed HISAT based on the Bowtie2 implementation to handle most of the operations on the FM index.
Usage : #hisat2 [options]* -x {-1 -2 | -U | --sra-acc } [-S ]

hmmer (version 3.2.1 - 2019-01-22)
HMMER: profile HMMs for protein sequence analysis Profile hidden Markov models (profile HMMs) can be used to do sensitive database searching using statistical descriptions of a sequence family's consensus.
Usage : #hmmsearch [options]

i-ADHoRe (version 3.0.01 - 2013-10-30)
This novel version of i-ADHoRe is designed to detect genomic homology in extremely large-scale data sets. Along with several under-the hood-improvements, resulting in a 30 fold reduction in runtime over previous versions, theimplementation of multithreading and MPI now enables i-ADHoRe to take advantage of a parallel computing platform. As the scale of the data sets increased, the need for a new alignment algorithm able to cope with dozens of genomicsegments became apparent. Therefore a new greedy graph based alignment algorithm has been implemented (described in Fostier et al., 2011), allowing analysis of even the largest data sets currently available.
Usage : #i-adhore


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by Dr. Radut