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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
http://metagenomics.atc.tcs.com/binning/SOrt-ITEMS/
Sequence orthology based software for improved taxonomic estimation of metagenomic sequences.
Proper citation: SOrt-ITEMS (RRID:SCR_004716) Copy
A multiple-sample, technology-aware SNP and indel caller.
Proper citation: UnifiedGenotyper (RRID:SCR_004710) Copy
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0075146
An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations.
Proper citation: NuChart (RRID:SCR_004703) Copy
http://matsen.fhcrc.org/pplacer/
Software that places query sequences on a fixed reference phylogenetic tree to maximize phylogenetic likelihood or posterior probability according to a reference alignment. Pplacer is designed to be fast, to give useful information about uncertainty, and to offer advanced visualization and downstream analysis.
Proper citation: Pplacer (RRID:SCR_004737) Copy
Standalone software programs that can be used to calculate how well tetranucleotide usage patterns in DNA sequences correlate. Such correlations can provide valuable hints on the relatedness of DNA sequences.
Proper citation: TETRA (RRID:SCR_004573) Copy
https://sites.google.com/a/lbl.gov/biopig/
Software providing a framework for genomic data analysis using Apache Pig and Hadoop.
Proper citation: BioPig (RRID:SCR_004636) Copy
https://github.com/lmrodriguezr/nonpareil
Estimate average coverage and create Nonpareil curves for metagenomic datasets.
Proper citation: Nonpareil (RRID:SCR_004629) Copy
http://omics.informatics.indiana.edu/AbundanceBin/
An abundance-based software tool for binning metagenomic sequences, such that the reads classified in a bin belong to species of identical or very similar abundances. AbundanceBin also gives estimations of species abundances and their genome sizes -two important characteristic parameters for a microbial community.
Proper citation: AbundanceBin (RRID:SCR_004648) Copy
http://compbio.cs.sfu.ca/software-variation-hunter
A software tool for discovery of structural variation in one or more individuals simultaneously using high throughput technologies.
Proper citation: VariationHunter (RRID:SCR_004865) Copy
http://www.cbcb.umd.edu/software/phymm/
Software for Phylogenetic Classification of Metagenomic Data with Interpolated Markov Models to taxonomically classify DNA sequences and accurately classify reads as short as 100 bp. PhymmBL, the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.
Proper citation: Phymm and PhymmBL (RRID:SCR_004751) Copy
https://code.google.com/p/destruct/
A software tool for identifying structural variation in tumour genomes from whole genome illumina sequencing.
Proper citation: deStruct (RRID:SCR_004747) Copy
http://bioinformatics.rutgers.edu/Software/SLiQ/
Software for simple linear inequalities based Mate-Pair reads filtering and scaffolding. A set of simple linear inequalities (SLIQ) derived from the geometry of contigs on the line that can be used to predict the relative positions and orientations of contigs from individual mate pair reads and thus produce a contig digraph. The SLIQ inequalities can also filter out unreliable mate pairs and can be used as a pre-processing step for any scaffolding algorithm. This tool filters mate pairs and then produces a Directed Contig Graph (contig diGraph). Also provided is a Naive scaffolder that can then produce scaffolds out of the contig diGraph.
Proper citation: SLIQ (RRID:SCR_005003) Copy
http://cortexassembler.sourceforge.net/index_cortex_var.html
A tool for genome assembly and variation analysis from sequence data. You can use it to discover and genotype variants on single or multiple haploid or diploid samples. If you have multiple samples, you can use Cortex to look specifically for variants that distinguish one set of samples (eg phenotype=X, cases, parents, tumour) from another set of samples (eg phenotype=Y, controls, child, normal). cortex_var features * Variant discovery by de novo assembly - no reference genome required * Supports multicoloured de Bruijn graphs - have multiple samples loaded into the same graph in different colours, and find variants that distinguish them. * Capable of calling SNPs, indels, inversions, complex variants, small haplotypes * Extremely accurate variant calling - see our paper for base-pair-resolution validation of entire alleles (rather than just breakpoints) of SNPs, indels and complex variants by comparison with fully sequenced (and finished) fosmids - a level of validation beyond that demanded of any other variant caller we are aware of - currently cortex_var is the most accurate variant caller for indels and complex variants. * Capable of aligning a reference genome to a graph and using that to call variants * Support for comparing cases/controls or phenotyped strains * Typical memory use: 1 high coverage human in under 80Gb of RAM, 1000 yeasts in under 64Gb RAM, 10 humans in under 256 Gb RAM
Proper citation: cortex var (RRID:SCR_005081) Copy
http://www.physics.rutgers.edu/~anirvans/SOPRA/
Software tool to exploit the mate pair/paired-end information for assembly of short reads from high throughput sequencing platforms, e.g. Illumina and SOLiD.
Proper citation: SOPRA (RRID:SCR_005035) Copy
http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/sspacev12/
A stand-alone software program for scaffolding pre-assembled contigs using paired-read data. Main features are: a short runtime, multiple library input of paired-end and/or mate pair datasets and possible contig extension with unmapped sequence reads.
Proper citation: SSPACE (RRID:SCR_005056) Copy
http://meringlab.org/software/hpc-clust/
A set of tools designed to cluster large numbers (>1 million) of pre-aligned nucleotide sequences. It performs the clustering of sequences using the Hierarchical Clustering Algorithm (HCA). There are currently three different cluster metrics implemented: single-linkage, complete-linkage, and average-linkage. In addition, there are currently four sequence distance functions implemented, these are: identity (gap-gap counting as match), nogap (gap-gap being ignored), nogap-single (like nogap, but consecutive gap-nogap''s count as a single mismatch), tamura (distance is calculated with the knowledge that transitions are more likely than transversions). One advantage that HCA has over other algorithms is that instead of producing only the clustering at a given threshold, it produces the set of merges occuring at each threshold. With this approach, the clusters can afterwards very quickly be reported for every arbitrary threshold with little extra computation. This approach also allows the plotting of the variation of number of clusters with clustering threshold without requiring the clustering to be run for each threshold independently. Another feature of the way HPC-CLUST is implemented is that the single-, complete-, and average-linkage clusterings can be computed in a single run with little overhead.
Proper citation: HPC-CLUST (RRID:SCR_005052) Copy
http://plaza.ufl.edu/xywang/Mpick.htm
A modularity-based clustering software for Operational Taxonomic Unit (OTU) picking of 16S rRNA sequences. The algorithm does not require a predetermined cut-off level, and our simulation studies suggest that it is superior to existing methods that require specified distance or variance levels to define OTUs.
Proper citation: M-pick (RRID:SCR_004995) Copy
http://plaza.ufl.edu/sunyijun/ES-Tree.htm
Software for hierarchical Clustering Analysis of Millions of 16S rRNA Pyrosequences in Quasi-linear Time.
Proper citation: ESPRIT-Tree (RRID:SCR_005045) Copy
http://cran.r-project.org/web/packages/MBCluster.Seq/index.html
Software to cluster genes based on Poisson or Negative-Binomial model for RNA-Seq or other digital gene expression (DGE) data.
Proper citation: MBCluster.Seq (RRID:SCR_005079) Copy
http://www.biomedcentral.com/1471-2105/13/189
An algorithm to use optical map information directly within the de Bruijn graph framework to help produce an accurate assembly of a genome that is consistent with the optical map information provided. AGORA takes as input two data structures: OpMap ? an ordered list of fragment sizes representing the optical map; and Edges ? a list of de Bruijn graph edges with their corresponding sequences.
Proper citation: AGORA (RRID:SCR_005070) Copy
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