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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

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

http://archive.gersteinlab.org/proj/rnaseq/IQSeq/

Software for integrated Isoform Quanti?cation Analysis based on A Partial Sampling Framework.

Proper citation: IQSeq (RRID:SCR_005238) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/expands/

Software that characterizes coexisting subpopulations (SPs) in a tumor using copy number and allele frequencies derived from exome- or whole genome sequencing input data. The model amplifies the statistical power to detect coexisting genotypes, by fully exploiting run-specific tradeoffs between depth of coverage and breadth of coverage. ExPANdS predicts the number of clonal expansions, the size of the resulting SPs in the tumor bulk, the mutations specific to each SP and tumor purity. The main function runExPANdS provides the complete functionality needed to predict coexisting SPs from single nucleotide variations (SNVs) and associated copy numbers. The robustness of the subpopulation predictions by ExPANdS increases with the number of mutations provided. It is recommended that at least 200 mutations are used as an input to obtain stable results.

Proper citation: ExPANdS (RRID:SCR_005199) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.toronto.edu/ireckon/

An algorithm for the simultaneous isoform reconstruction and abundance estimation. In addition to modelling novel isoforms, multi-mapped reads and read duplicates, this method takes into account the possible presence of unspliced pre-mRNA and intron retention. iReckon only requires a set of transcription start and end sites, but can use known full isoforms to improve sensitivity. Starting from the set of nearly all possible isoforms, iReckon uses a regularized EM algorithm to determine those actually present in the sequenced sample, together with their abundances. iReckon is multi-threaded to increase efficiency in all its time consuming steps.

Proper citation: iReckon (RRID:SCR_005232) Copy   

•   Source: SciCrunch Registry

http://gmt.genome.wustl.edu/somatic-sniper/current/

Software program to identify single nucleotide positions that are different between tumor and normal (or, in theory, any two bam files). It takes a tumor bam and a normal bam and compares the two to determine the differences. It outputs a file in a format very similar to Samtools consensus format. It uses the genotype likelihood model of MAQ (as implemented in Samtools) and then calculates the probability that the tumor and normal genotypes are different. This probability is reported as a somatic score. The somatic score is the Phred-scaled probability (between 0 to 255) that the Tumor and Normal genotypes are not different where 0 means there is no probability that the genotypes are different and 255 means there is a probability of 1 ? 10(255/-10) that the genotypes are different between tumor and normal. This is consistent with how the SAM format reports such probabilities. It is currently available as source code via github or as a Debian APT package.

Proper citation: SomaticSniper (RRID:SCR_005108) Copy   

•   Source: SciCrunch Registry

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

A stand-alone executable software with user-friendly graphical interface implemented in Python/C++ for rare variant association studies. It is designed as a unified simulation framework to provide an unbiased and easy manner to evaluate association methods, including novel methods, under a broad range of choice of biological contexts. It consists of three modules, variant data simulator, genotype/phenotype generator and association method evaluator. SimRare generates variant data for gene regions using forward-time simulation which incorporates realistic population demographic and evolutionary scenarios. For phenotype data it is capable of generating both case-control and quantitative traits. The phenotypic effects of variants can be detrimental, protective or non-causal. SimRare has a graphical user interface which allows for easy entry of genetic and phenotypic parameters. Simulated data can be written into external files in a standard format. For novel association method implemented in R it can be imported into SimRare, which has been equipped built in functions to evaluate performance of new method and visually compare it with currently available ones in an unbiased manner.

Proper citation: SimRare (RRID:SCR_005226) Copy   

•   Source: SciCrunch Registry