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http://seqanswers.com/wiki/

Wiki forum providing an extensive catalogue of manually categorized analysis tools, technologies and information about service providers, maintained by the members of the SEQanswers community. * Minimum Information about a high-throughput Sequencing Experiment * Software Hub: The place to add to, edit or browse the software database on SEQwiki. * Service Providers: Browse or edit the list of NGS service facilities. * How-to Hub: Mini reviews for the most used tools broken down by common tasks. * Developers Hub: The place to discuss the development of the SEQwiki site and its associated data. See also publishing SEQ*. * Publications: Publication about SEQwiki and selected citations.

Proper citation: SEQanswers Wiki (RRID:SCR_004810) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/sra

Repository of raw sequencing data from next generation of sequencing platforms including including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, Complete Genomics, and Pacific Biosciences SMRT. In addition to raw sequence data, SRA now stores alignment information in form of read placements on reference sequence. Data submissions are welcome. Archive of high throughput sequencing data,part of international partnership of archives (INSDC) at NCBI, European Bioinformatics Institute and DNA Database of Japan. Data submitted to any of this three organizations are shared among them.

Proper citation: NCBI Sequence Read Archive (SRA) (RRID:SCR_004891) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/ega/

Web service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects. The repository allows you to explore datasets from numerous genotype experiments, supplied by a range of data providers. The EGA''s role is to provide secure access to the data that otherwise could not be distributed to the research community. The EGA contains exclusive data collected from individuals whose consent agreements authorize data release only for specific research use or to bona fide researchers. Strict protocols govern how information is managed, stored and distributed by the EGA project. As an example, only members of the EGA team are allowed to process data in a secure computing facility. Once processed, all data are encrypted for dissemination and the encryption keys are delivered offline. The EGA also supports data access only for the consortium members prior to publication.

Proper citation: European Genome phenome Archive (RRID:SCR_004944) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://github.com/AlexeyG/GRASS

A generic algorithm for scaffolding next-generation sequencing assemblies.

Proper citation: GRASS (RRID:SCR_005071) Copy   

•   Source: SciCrunch Registry

http://www.bioinf.boku.ac.at/pub/MapAl/

A software tool for RNA-Seq expression profiling that builds on the established programs Bowtie and Cufflinks. Allowing an incorporation of ''gene models'' already at the alignment stage almost doubles the number of transcripts that can be measured reliably.

Proper citation: MapAl (RRID:SCR_004938) Copy   

•   Source: SciCrunch Registry

http://www.arb-silva.de/aligner/

Service to align and optionally taxonomically classify your rRNA gene sequences. The results can be combined with any other sequences aligned by SINA or taken from the SILVA databases by concatenation of FASTA files or using the ARB MERGE tool. Note: Submission is currently limited to at most 1000 sequences of at most 6000 bases each. If your requirements exceed this limitation, get Opens internal link in current windowSINA for local installation.

Proper citation: SINA (RRID:SCR_005067) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/apps/mediawiki/amos/index.php?title=Bambus2

Software for scaffolding to address some of the challenges encountered when analyzing metagenomes. Scaffolding represents the task of ordering and orienting contigs by incorporating additional information about their relative placement along the genome. While most other scaffolders are closely tied to a specific assembly program, Bambus accepts the output from most current assemblers and provides the user with great flexibility in choosing the scaffolding parameters. In particular, Bambus is able to accept contig linking data other than specified by mate-pairs. Such sources of information include alignment to a reference genome (Bambus can directly use the output of MUMmer), physical mapping data, or information about gene synteny.

Proper citation: Bambus (RRID:SCR_005068) Copy   

•   Source: SciCrunch Registry

http://www.comp.hkbu.edu.hk/~chxw/software/G-BLASTN.html

A GPU-accelerated nucleotide alignment tool based on the widely used NCBI-BLAST. It can produce exactly the same results as NCBI-BLAST, and it also has very similar user commands. It also supports a pipeline mode, which can fully utilize the GPU and CPU resources when handling a batch of medium to large sized queries.

Proper citation: G-BLASTN (RRID:SCR_005062) Copy   

•   Source: SciCrunch Registry

http://clams.jgi-psf.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 2nd, 2023. Sequence composition based classifier for metagenomic sequences. It works by capturing signatures of each sequence based on the sequence composition. Each sequence is modeled as a walk in a de Bruijn graph with underlying Markov chain properties. ClaMS captures stationary parameters of the underlying Markov chain as well as structural parameters of the underlying de Bruijn graph to form this signature. In practice, for each sequence to binned, such a signature is computed and matched to similar signatures computed for the training sets. The best match that also qualifies the normalized distance cut-off wins. In the case that the best match does not qualify this cut-off, the sequence remains un-binned.

Proper citation: Classifier for Metagenomic Sequences (RRID:SCR_004929) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/jingyijli/SLIDE.zip

Software package that takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms that are most likely to present in an RNA-Seq sample. It is based on a linear model with a design matrix that models the sampling probability of RNA-Seq reads from different mRNA isoforms. To tackle the model unidentifiability issue, SLIDE uses a modified Lasso procedure for parameter estimation. Compared with deterministic isoform assembly algorithms (e.g., Cufflinks), SLIDE considers the stochastic aspects of RNA-Seq reads in exons from different isoforms and thus has increased power in detecting more novel isoforms. Another advantage of SLIDE is its flexibility of incorporating other transcriptomic data such as RACE, CAGE, and EST into its model to further increase isoform discovery accuracy. SLIDE can also work downstream of other RNA-Seq assembly algorithms to integrate newly discovered genes and exons. Besides isoform discovery, SLIDE sequentially uses the same linear model to estimate the abundance of discovered isoforms.

Proper citation: SLIDE (RRID:SCR_005137) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/viralfusionseq/

A versatile high-throughput sequencing (HTS) tool for discovering viral integration events and reconstruct fusion transcripts at single-base resolution. It combines soft-clipping information, read-pair analysis, and targeted de novo assembly to discover and annotate viral-human fusion events. A simple yet effective empirical statistical model is used to evaluate the quality of fusion breakpoints. Minimal user defined parameters are required.

Proper citation: VFS (RRID:SCR_005138) Copy   

•   Source: SciCrunch Registry