Tools   Select Another Resource Report Type

http://mitcr.milaboratory.com/

An open source software package aimed at extraction of information on repertoire of T-cell clones from Next Generation Sequencing (NGS) data. It is designed with the knowledge of the critical challenges arising in everyday processing of immunological data.

Proper citation: MiTCR (RRID:SCR_004989) Copy   

•   Source: SciCrunch Registry

http://www.cs.helsinki.fi/u/lmsalmel/mip-scaffolder/

A software program for scaffolding contigs produced by fragment assemblers using mate pair data such as those generated by ABI SOLiD or Illumina Genome Analyzer.

Proper citation: MIP Scaffolder (RRID:SCR_005072) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.toronto.edu/hapsembler/scarpa.html

A stand-alone scaffolding tool for NGS data. It can be used together with virtually any genome assembler and any NGS read mapper that supports SAM format. Other features include support for multiple libraries and an option to estimate insert size distributions from data.

Proper citation: Scarpa (RRID:SCR_005073) Copy   

•   Source: SciCrunch Registry

http://petrov.stanford.edu/cgi-bin/Tlex.html

Software package for fast and accurate discovery, annotation, re-annotation and population analysis of Transposable Elements using Next-Generation Sequencing data.

Proper citation: T-lex (RRID:SCR_005134) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/popoolationte/

A quick and simple pipeline for the analysis of transposable element (TE) insertions in (natural) populations using next generation sequencing. It calculates TE insertion frequencies for TEs that are present in the reference genome as well as for novel TE insertions. PoPoolation TE requires paired-end reads from a pooled population, a reference sequence and transposable element sequences (fasta-file).

Proper citation: PoPoolation TE (RRID:SCR_005131) Copy   

•   Source: SciCrunch Registry

http://cbil.upenn.edu/BEERS/

A simulation engine for generating RNA-Seq data that was designed to benchmark RNA-Seq alignment algorithms and also algorithms that aim to reconstruct different isoforms and alternate splicing from RNA-Seq data. By default BEERS simulates either mouse or human paired-end RNA-Seq data modeled on the illumina platform. It starts with a large number of gene models (approx 500K) taken from about ten different published annotation efforts, and then chooses a fixed number of these genes at random (30,000 by default). This avoids biasing for or against any particular set of annotations. BEERS then introduces substitutions, indels, alternate spice forms, sequencing errors, and intron signal. BEERS can also simulate strand specific reads. BEERS does not simulate quality scores. There are four configuration files required, these are available for human and mouse. BEERS can also be configured to use any set of gene models. Pre-built indexes for human refseq are given. Using these indexes will generate a much tamer set of transcripts. BEERS is written in perl.

Proper citation: BEERS (RRID:SCR_005090) Copy   

•   Source: SciCrunch Registry

http://woldlab.caltech.edu/rnaseq

Software for Mapping and Quantifying Mammalian Transcriptomes by RNA-Seq. Its functions are to (i) assign reads that map uniquely in the genome to their site of origin and, for reads that match equally well to several sites (''multireads''), assign them to their most likely site(s) of origin; (ii) detect splice-crossing reads and assign them to their gene of origin; (iii) organize reads that cluster together, but do not map to an already known exon, into candidate exons or parts of exons; and (iv) calculate the prevalence of transcripts from each known or newly proposed RNA, based on normalized counts of unique reads, spliced reads and multireads. The new candidate RNA regions produced can be thought of as ESTs, and, like ESTs, some are provisionally appended to existing gene models if they meet several additional criteria. Remaining unassigned candidate transcribed regions (labeled RNAFAR features) can then be used in conjunction with other confirming data to develop new or revised gene models.

Proper citation: ERANGE (RRID:SCR_005240) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/cga/rna-seqc

Java software which computes a series of quality control metrics for RNA-seq data and can compare sequencing quality across different samples or experiments to evaluate different experimental parameters. The input can be one or more BAM files, and the output consists of HTML reports and tab delimited files of metrics data.

Proper citation: RNA-SeQC (RRID:SCR_005120) Copy   

•   Source: SciCrunch Registry

http://flux.sammeth.net/capacitor.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023. Software that aims at modeling RNA-Seq experiments in silico: sequencing reads are produced from a reference genome according annotated transcripts. The simulation pipeline models different steps as modules, each with a minimal set of parameters that can be estimated by experimental parameters. The first step is-in fact-a transcriptome simulator. Subsequently, common sources of systematic bias in the abundance and distribution of produced reads are simulated by in silico library preparation and sequencing.

Proper citation: Flux Simulator (RRID:SCR_005088) Copy   

•   Source: SciCrunch Registry

http://www.jobs.ac.uk/

International job board for careers in academic, research, science and related professions in the UK, Europe, Australasia, Africa, America and Asia & Middle East. Launched by the University of Warwick, they have grown to become the top recruitment site in their sector, attracting the most qualified and talented people from the UK, Europe and across the world. Users may subscribe to Jobs by Email for vacancies in universities, colleges, research institutions, commercial and public sector, schools and charities. You may upload your CV to give yourself an advantage by making your CV visible to top employers now!

Proper citation: jobs.ac.uk (RRID:SCR_005154) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/methylkit/

An R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: methylKit (RRID:SCR_005177) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/gesnd/

A software package and a pipeline for identifying causal mutations for rare congenital diseases by next-generation sequencing. Features * one-stop solution for identifying causal mutations of rare genetic diseases * detect wide-spctrum variants, including medium and large sized indels, and tandem repeats * annotate and filter variants * prioritize candidate variants

Proper citation: GESND (RRID:SCR_005179) Copy   

•   Source: SciCrunch Registry

http://www.well.ox.ac.uk/~kgaulton/chaos.shtml

A Perl-based system for annotation of variants identified in high-throughput sequencing experiments. Functionality includes annotation of variants with information relating to population genetics, known transcripts, positional records, and sequence motif-based prediction. In addition, annotated variants can be summarized and extracted to facilitate downstream analysis. There is also basic support for gene-based biological annotation, and eventually will include tools for variant and genotype analysis and visualization.

Proper citation: CHAoS (RRID:SCR_005174) Copy   

•   Source: SciCrunch Registry

http://anntools.sourceforge.net/

Software tool for annotating single nucleotide substitutions (SNP/SNV), small insertions/deletions (indels), and copy number variations (CNV) calls generated from sequencing and microarray data. Only human genome build 37/hg19 can be annotated at this time.

Proper citation: AnnTools (RRID:SCR_005170) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/software/pathseq/

A computational tool for the identification and analysis of microbial sequences in high-throughput human sequencing data that is designed to work with large numbers of sequencing reads in a scalable manner. This process is composed of a subtractive phase in which input reads are subtracted by alignment to human reference sequences, and an analytic phase in which the remaining reads are aligned to microbial reference sequences (viral, fungal, bacterial, archaeal) and de novo assembled. PathSeq is currently available in a cloud computing environment via Amazon Web Services The typical approach one would take to pathogen discovery with PathSeq: RNA or DNA is extracted from the tissue of interest and sequencing libraries are constructed to be run on the next-generation DNA sequencing platform of choice. The resulting sequence data is run through the PathSeq pipeline in a cloud computing environment. PathSeq reports potential microbes in the sequence data as well as the complete set of reads that could not be identified as human or microbial sequences.

Proper citation: PathSeq (RRID:SCR_005203) Copy   

•   Source: SciCrunch Registry

http://cbrc.kaust.edu.sa/readscan/

A highly scalable parallel software program to identify non-host sequences (of potential pathogen origin) and estimate their genome relative abundance in high-throughput sequence datasets.

Proper citation: READSCAN (RRID:SCR_005204) Copy   

•   Source: SciCrunch Registry

http://odin.mdacc.tmc.edu/~xsu1/VirusSeq.html

An algorithmic software tool for detecting known viruses and their integration sites using next-generation sequencing of human cancer tissue. VirusSeq takes FASTQ files (paired-end reads) as input.

Proper citation: VirusSeq (RRID:SCR_005206) Copy   

•   Source: SciCrunch Registry

http://smithlab.usc.edu/methpipe/

A computational pipeline for analyzing bisulfite sequencing data.

Proper citation: MethPipe (RRID:SCR_005168) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/hivcd/

Informatics software tool to identify patient sequences that are too similar to happen by chance alone. Highly similar sequences are likely to occur from contamination or other situations like geographic linkage.

Proper citation: HIVCD (RRID:SCR_005201) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/asoovir/

A set of Ruby modules to annotate consequence terms, defined by the Sequence Ontology, of variants (SNP/SNVs, INDELs, SVs, CNAs) using Ensembl gene sets. Prior to annotation of variants an Ensembl gene set and reference coding sequences are loaded into memory from a database file, which can be downloaded or generated by the user from reference files. This allows rapid annotation of variants, making it suitable for annotation of whole genome scale calls. Annotation is performed on a transcript level basis, identifying associated sequence ontology terms for affected and nearby transcripts. Default output can be obtained on a gene basis, summarising the consequences for each gene affected, or on a transcript level basis. Output information is also readily customisable using user-generated scripts.

Proper citation: ASOoViR (RRID:SCR_005161) Copy   

•   Source: SciCrunch Registry