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http://www.omicsoft.com/fusionmap/

An efficient fusion aligner which aligns reads spanning fusion junctions directly to the genome without prior knowledge of potential fusion regions. It detects and characterizes fusion junctions at base-pair resolution. FusionMap can be applied to detect fusion junctions in both single- and paired-end dataset from either gDNA-Seq or RNA-Seq studies. FusionMap runs under both Windows and Linux (requiring MONO) environments. Although it can run on 32 bit machine, it is recommended to run on 64-bit machine with 8GB RAM or more. If you have an ArrayStudio License, you can run the fusion detection easily through its GUI.

Proper citation: FusionMap (RRID:SCR_005242) Copy   

•   Source: SciCrunch Registry

http://www.cs.helsinki.fi/en/gsa/traph/

A software tool for transcript identification and quantification with RNA-Seq. The method has a two-fold advantage: on the one hand, it translates the problem as an established one in the field of network flows, which can be solved in polynomial time, with different existing solvers; on the other hand, it is general enough to encompass many of the previous proposals under the least sum of squares model.

Proper citation: Traph (RRID:SCR_005119) Copy   

•   Source: SciCrunch Registry

http://www.raetschlab.org/suppl/rquant

Software for quantitative detection of alternative transcripts with RNA-Seq data. The method, based on quadratic programming, estimates biases introduced by experimental settings and is thus a powerful tool to reveal and quantify novel (alternative) transcripts.

Proper citation: rQuant (RRID:SCR_005150) Copy   

•   Source: SciCrunch Registry

http://www.bsse.ethz.ch/cbg/software/shorah

A software package that allows for inference about the structure of a population from a set of short sequence reads as obtained from ultra-deep sequencing of a mixed sample. The package contains programs that support mapping of reads to a reference genome, correcting sequencing errors by locally clustering reads in small windows of the alignment, reconstructing a minimal set of global haplotypes that explain the reads, and estimating the frequencies of the inferred haplotypes.

Proper citation: ShoRAH (RRID:SCR_005211) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/scientific-community/science/projects/viral-genomics/v-phaser-2

A software tool to call variants in genetically heterogeneous populations from ultra-deep sequence data. It combines information regarding the covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. V-Phaser can reliably detect rare variants in diverse populations that occur at frequencies of <1%. V-Phaser 2 is a complete rewrite of the original V-Phaser. It contains a new model for length polymorphisms (indels) and incorporates paired end read information in its phasing model. The data access and probability computation sections of the code have also been highly optimized, resulting in substantial improvements in running time and memory usage.

Proper citation: V-Phaser 2 (RRID:SCR_005212) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/nsmapforrnaseq/

Software designed to identify and quantify isoforms from RNA-seq by incorporating a sparsity term into expression level estimation to enable isoform structure prediction and expression estimation simultaneously.

Proper citation: NSMAP (RRID:SCR_005213) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/cova/

A variant annotation and comparison tool for next-generation sequencing. It annotates the effects of variants on genes and compares those among multiple samples, which helps to pinpoint causal variation(s) relating to phenotype.

Proper citation: COVA (RRID:SCR_005175) Copy   

•   Source: SciCrunch Registry

http://bmda.cs.unibas.ch/HivHaploTyper/

Software for reconstructing haplotypes from next-generation sequencing data., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PredictHaplo (RRID:SCR_005207) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/qure/

A software program for viral quasispecies reconstruction, specifically developed to analyze long read (>100 bp) next-generation sequencing (NGS) data. The software performs alignments of sequence fragments against a reference genome, finds an optimal division of the genome into sliding windows based on coverage and diversity and attempts to reconstruct all the individual sequences of the viral quasispecies--along with their prevalence--using a heuristic algorithm, which matches multinomial distributions of distinct viral variants overlapping across the genome division. QuRe comes with a built-in Poisson error correction method and a post-reconstruction probabilistic clustering, both parameterized on given error rates in homopolymeric and non-homopolymeric regions.

Proper citation: QuRe (RRID:SCR_005209) Copy   

•   Source: SciCrunch Registry

http://bioinfo.mc.vanderbilt.edu/VirusFinder/

Software tool for efficient and accurate detection of viruses and their integration sites in host genomes through next generation sequencing data. Specifically, it detects virus infection, co-infection with multiple viruses, virus integration sites in host genomes, as well as mutations in the virus genomes. It also facilitates virus discovery by reporting novel contigs, long sequences assembled from short reads that map neither to the host genome nor to the genomes of known viruses. VirusFinder 2 works with both paired-end and single-end data, unlike the previous 1.x versions that accepted only paired-end reads. The types of NGS data that VirusFinder 2 can deal with include whole genome sequencing (WGS), whole transcriptome sequencing (RNA-Seq), targeted sequencing data such as whole exome sequencing (WES) and ultra-deep amplicon sequencing.

Proper citation: VirusFinder (RRID:SCR_005205) Copy   

•   Source: SciCrunch Registry

http://rnaexpress.org/

Software designed as a user friendly solution to extract and annotate biologically important transcripts from next generation RNA sequencing data.

Proper citation: RNA-eXpress (RRID:SCR_005167) Copy   

•   Source: SciCrunch Registry

http://unoseq.sourceforge.net/

A Java library to analyze next generation sequencing data and especially perform expression profiling in organisms where no well-annotated reference genome exists.

Proper citation: UnoSeq (RRID:SCR_005116) Copy   

•   Source: SciCrunch Registry

http://gds.nih.gov/

NIH established expectations for sharing data obtained through NIH-funded genome-wide association studies (GWAS) with the implementation of the GWAS Policy. Information and resources related to the GWAS Policy can be found on this website.

Proper citation: Genomic Datasharing (RRID:SCR_005233) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.wistar.upenn.edu/isoformex

Software that estimates transcript expression levels and gene expression levels from mRNA-Seq data. Technically speaking, IsoformEx parses bowtie alignment files in a project directory (e.g. ~yourid/isoformex/xxx, where xxx is the project name) and generates two files: (1) xxx/xxx_transcript_1.txt: expression levels of all transcripts, (2) xxx/xxx_gene_1.txt: expression levels of all genes.

Proper citation: IsoformEx (RRID:SCR_005235) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/aldex/

RNA-seq tool that uses the Dirichlet distribution and a transformation to identify genes that exhibit small within-condition and large between-condition variance.

Proper citation: aldex (RRID:SCR_005110) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/cga/absolute

Software to estimate purity / ploidy, and from that compute absolute copy-number and mutation multiplicities. When DNA is extracted from an admixed population of cancer and normal cells, the information on absolute copy number per cancer cell is lost in the mixing. The purpose of ABSOLUTE is to re-extract these data from the mixed DNA population. This process begins by generation of segmented copy number data, which is input to the ABSOLUTE algorithm together with pre-computed models of recurrent cancer karyotypes and, optionally, allelic fraction values for somatic point mutations. The output of ABSOLUTE then provides re-extracted information on the absolute cellular copy number of local DNA segments and, for point mutations, the number of mutated alleles.

Proper citation: ABSOLUTE (RRID:SCR_005198) Copy   

•   Source: SciCrunch Registry

http://variant.bioinfo.cipf.es/

Analysis tool that can report the functional properties of any variant in all the human, mouse or rat genes (and soon new model organisms will be added) and the corresponding neighborhoods. Also other non-coding extra-genic regions, such as miRNAs are included in the analysis. It not only reports the obvious functional effects in the coding regions but also analyzes noncoding SNVs situated both within the gene and in the neighborhood that could affect different regulatory motifs, splicing signals, and other structural elements. These include: Jaspar regulatory motifs, miRNA targets, splice sites, exonic splicing silencers, calculations of selective pressures on the particular polymorphic positions, etc. Software analysis pipelines used in the analysis of NGS data are highly modular, heterogeneous, and rapidly evolving. VARIANT can easily be incorporated into a NGS resequencing pipeline either as a CLI or invoked a webservice. It inputs data directly from the most widely used programs for SNV detection., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VARIANT (RRID:SCR_005194) Copy   

•   Source: SciCrunch Registry

https://www.rostlab.org/services/snpdbe/

A database to fill the annotation gap left by the high cost of experimental testing for functional significance of protein variants. It joins related bits of knowledge, currently distributed throughout various databases, into a consistent, easily accessible, and updatable resource. It currently covers over 155,000 protein sequences which come from more than 2,600 organisms. Overall more than one million single amino acid substitutions (SAASs) are referenced consisting of natural variants, SAASs from mutagenesis experiments and sequencing conflicts. SNPdbe offers the following pieces of information (if available) on each SAAS: * Experimentally derived functional and structural impact * Predicted functional effect * Associated disease * Average heterozygosity * Experimental evidence of the nsSNP * Evolutionary conservation of wildtype and mutant amino acid * Link-outs to external databases A convenient webinterface to query SAASs on the following levels is offered: * Protein and gene identifiers and keywords * Disease keywords * Protein sequence on different sequence identity thresholds * Variant identifier (dbSNP rs, SwissVar, PMD) or specific mutant like XposY and specified sequence They offer the possibility to submit protein sequences along with experimentally substantiated mutations in order to predict their functional effect and inclusion into our database.

Proper citation: SNPdbe (RRID:SCR_005190) Copy   

•   Source: SciCrunch Registry

http://skylign.org/

A tool for creating logos representing both sequence alignments and profile hidden Markov models. The interactive logos enable scrolling, zooming, and inspection of underlying values. Skylign can avoid sampling bias in sequence alignments by down-weighting redundant sequences and by combining observed counts with informed priors. It also simplifies the representation of gap parameters, and can optionally scale letter heights based on alternate calculations of the conservation of a position.

Proper citation: Skylign (RRID:SCR_001176) Copy   

•   Source: SciCrunch Registry

https://rdrr.io/bioc/sRAP/

Software package that provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data.

Proper citation: sRAP (RRID:SCR_001297) Copy   

•   Source: SciCrunch Registry