Référentiel des outils installés sur la plateforme Migale
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.
Documentation : http://genome.jouy.inra.fr/doc/genome/divers/i-adhore-3.0.01/
Usage : #i-adhore
igv (version 2.3.67 - 2016-01-11)
The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.
Documentation : http://www.broadinstitute.org/software/igv/Version2.0Guide
Remarque : To cite your use of IGV in your publication:James T. Robinson, Helga Thorvaldsdóttir, Wendy Winckler, Mitchell Guttman, Eric S. Lander, Gad Getz, Jill P. Mesirov. Integrative Genomics Viewer. Nature Biotechnology 29, 24–26 (2011)
Usage : #igv
IM-TORNADO (version 126.96.36.199 - 2016-02-22)
Illumina paired-end sequencing, which produces two separate reads for eachDNA fragment, has become the platform of choice for 16S rDNA hypervariabletag sequencing. However, when the two reads do not overlap, existingcomputational pipelines analyze data from read separately and underutilizethe information contained in the paired-end reads. IM-TORNADO is a tool forprocessing non-overlapping reads while retaining maximal information content.
Download : http://sourceforge.net/projects/imtornado
Remarque : If you use IM-TORNADO for your project, please cite the following manuscript:Jeraldo P, Kalari K, Chen X, Bhavsar J, Mangalam A, White B, et al. IM-TORNADO: A Tool for Comparison of 16S Reads from Paired-End Libraries. PLOS ONE 9 (12):e114804. Available from: http://dx.plos.org/10.1371/journal.pone.0114804
Insyght (version - 2014-01-01)
Insyght is genomic visualisation tool that combines a symbolic and a proportional view of the genes, syntenies and genomic regions. Another of Insyght's feature is synchronized navigation and zooming across multiple species.
JAGS (version 3.4.0 - 2013-12-17)
JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: To have a cross-platform engine for the BUGS language To be extensible, allowing users to write their own functions, distributions and samplers. To be a plaftorm for experimentation with ideas in Bayesian modelling
Download : http://mcmc-jags.sourceforge.net/
Documentation : http://genome.jouy.inra.fr/doc/genome/calcul_numerique/
Usage : #jags
jalview (version 2.9.0 - 2016-03-10)
Jalview is a multiple alignment editor
Download : http://www.jalview.org/download.html
Documentation : http://www.jalview.org/help.html
Usage : #jalview
jellyfish (version 1.1.3 - 2011-12-21)
JELLYFISH is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. JELLYFISH can count k-mers using an order of magnitude less memory and an order of magnitude faster than other k-mer counting packages by using an efficient encoding of a hash table and by exploiting the "compare-and-swap" CPU instruction to increase parallelism.JELLYFISH is a command-line program that reads FASTA and multi-FASTA files containing DNA sequences. It outputs its k-mer counts in an binary format, which can be translated into a human-readable text format using the "jellyfish dump" command. See the documentation below for more details.
Documentation : http://genome.jouy.inra.fr/doc/genome/NGS/jellyfish-1.1.3/
Remarque : If you use JELLYFISH in your research, please cite: Guillaume Marcais and Carl Kingsford, A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics (2011) 27(6): 764-770 (first published online January 7, 2011) doi:10.1093/bioinformatics/btr011
Usage : #jellyfish
Julia (version 0.5.0 - 2017-02-08)
Julia is a high-level, high-performance dynamic programming language fortechnical computing, with syntax that is familiar to users of other technicalcomputing environments.It is a very performant programming language somehow similar to R, Matlab orPython, but with performances approaching those of C/Fortran.
Download : http://julialang.org/
Documentation : http://docs.julialang.org/en/stable/#manual
Usage : #julia
kaiju (version 1.5.0 - 2017-05-14)
Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.
Remarque : CitationMenzel P., Ng K.L., Krogh A. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257
Usage : #kaiju -t nodes.dmp -f kaiju_db.fmi -i reads.fastq [-j reads2.fastq]
kraken (version 0.10.5 - 2015-11-25)
raken is a system for assigning taxonomic labels to short DNA sequences, usually obtained through metagenomic studies. Previous attempts to accomplish this task have often used sequence alignment or machine learning techniques that were quite slow, leading to the development of less sensitive but much faster abundance estimation programs. Kraken aims to achieve high sensitivity and high speed by utilizing exact alignments of k-mers and a novel classification algorithm.
Documentation : http://ccb.jhu.edu/software/kraken/MANUAL.html
Remarque : If you use Kraken in your research, please cite our paper; the citationis available on the Kraken website.
Usage : #kraken [options]