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http://sourceforge.net/projects/nailsystemsbiology/

A set of software tools to simplify the range of computational activities involved in regulatory network inference. It is technology-independent and includes an interface layer to allow easy integration of components into other applications. It is implemented in MATLAB and is available for all researchers to use.

Proper citation: NAIL (RRID:SCR_012134) Copy   

•   Source: SciCrunch Registry

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

A fast and scalable local network alignment software tool for the identification of functionally conserved modules in multiple networks. LocalAli outperforms all existing algorithms in terms of coverage, consistency and scalability, meanwhile retains a high precision in the identification of functionally coherent subnetworks.

Proper citation: LocalAli (RRID:SCR_012147) Copy   

•   Source: SciCrunch Registry

https://github.com/steinmann/peakzilla

An algorithm to identify transcription factor binding sites from ChIP-seq data.

Proper citation: Peakzilla (RRID:SCR_007471) Copy   

•   Source: SciCrunch Registry

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

A collection of programs for the removal of noise from 454 sequenced PCR amplicons. This project also includes the Perseus algorithm for chimera removal.

Proper citation: AmpliconNoise (RRID:SCR_007814) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/bigpre/

A quality assessment software package for next-genomics sequencing data.

Proper citation: BIGpre (RRID:SCR_006781) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/2.12/bioc/html/seqbias.html

Software package that implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence.

Proper citation: seqbias (RRID:SCR_006832) Copy   

•   Source: SciCrunch Registry

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

Comprehensive differentially methylated regions (DMR) analysis based on bimodal normal distribution model and weighted cost function for regional methylation analysis optimization.

Proper citation: eDMR (RRID:SCR_006960) Copy   

•   Source: SciCrunch Registry

http://bioinfo.au.tsinghua.edu.cn/software/NURD/

An algorithm to inference isoform expression., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NURD (RRID:SCR_010988) Copy   

•   Source: SciCrunch Registry

http://sammeth.net/confluence/display/FLUX/Home

Software to recontruct abundances of known transcript forms from RNAseq data. The algorithm works by distributing the reads mapping to a given exonic region (or splice junction) among the transcripts including the exon (or splice junction). The input is the annotation of a reference transcriptome and reads from RNAseq technologies aligned to the genome. From the reference annotation, splicing graphs are produced and reads are mapped to corresponding edges in these graphs according to the position where they align in the genomic sequence. The resulting graph with edges labelled by the number of reads can be interpreted as a flow network where each transcript representing a transportation path from its start to its end and consequently each edge a possibly shared segment of transportation along which a certain number of reads per nucleotide -- i.e., a flux -- is observed. Given a density function of reads along a transcript, the expected participation of each transcript in an edge under consideration can be estimated. The basic idea is to cast back from these latter participations and the observed number of reads - allowing for a certain amount of noise - to the original transcript abundancies. To do so, a linear constraint is formalized for each edge, and an optimal solution for the complete set of constraints is found by a standard linear program solver.

Proper citation: FLUX CAPACITOR (RRID:SCR_006651) Copy   

•   Source: SciCrunch Registry

http://www.niehs.nih.gov/research/resources/software/biostatistics/eagleview/

An information-rich viewer for next-generation genome assembles with data integration capability. EagleView can display a dozen different types of information including base qualities, machine specific trace signals, and genome feature annotations. It provides an easy way for inspecting visually the quality of a genome assembly and validating polymorphism candidate sites (e.g., SNPs) reported by polymorphism discovery tools. It can also facilitate data interpretation and hypothesis generation. EagleView is a multi-platform application developed with C++ and is available for all three major platforms: Windows, Linux, and Mac OS.

Proper citation: EagleView (RRID:SCR_006859) Copy   

•   Source: SciCrunch Registry

http://research.cs.wisc.edu/wham/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. High-throughput sequence alignment tool that aligns short DNA sequences (reads) to the whole human genome at a rate of over 1500 million 60bps reads per hour, which is one to two orders of magnitudes faster than the leading state-of-the-art techniques. Feature list for the current version (v 0.1.5) of WHAM: * Supports paired-end reads * Supports up to 5 errores * Supports alignments with gaps * Supports quality scores for filtering invalid alignments, and sorting valid alignments * finds ALL valid alignments * Supports multi-threading * Supports rich reporting modes * Supports SAM format output

Proper citation: WHAM (RRID:SCR_005497) Copy   

•   Source: SciCrunch Registry

http://www-personal.umich.edu/~jianghui/seqmap/

A software tool for mapping large amount of oligonucleotide to the genome. It is designed for finding all the places in a genome where an oligonucleotide could potentially come from. SeqMap can efficiently map as many as dozens of millions of short sequences to a genome of several billions of nucleotides. While doing the mapping, several mutations as well as insertions / deletions of the nucleotide bases in the sequences can be tolerated and furthermore detected. Various input and output formats are supported, as well as many command line options for tuning almost every steps in the mapping process. A typical mapping can be done in a few hours on an ordinary PC.

Proper citation: SeqMap (RRID:SCR_005495) Copy   

•   Source: SciCrunch Registry

http://www.genome.umd.edu/jellyfish.html

A software tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. JELLYFISH can count k-mers quickly by using an efficient encoding of a hash table and by exploiting the compare-and-swap CPU instruction to increase parallelism. Jellyfish is a command-line program that reads FASTA and multi-FASTA files containing DNA sequences. It outputs its k-mer counts in an binary format, which can be translated into a human-readable text format using the jellyfish dump command., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Jellyfish (RRID:SCR_005491) Copy   

•   Source: SciCrunch Registry

http://mrfast.sourceforge.net/

Software designed to map short reads generated with the Illumina platform to reference genome assemblies; in a fast and memory-efficient mannerl. Currently Supported Features: * Output in SAM format * Indels up to 8 bp (4 bp deletions and 4 bp insertions) * Paired-end mapping ** Discordant option to generate mapping file ready for VariationHunter to detect structural variants. * One end anchored (OEA) map locations for novel sequence insertion detection with NovelSeq * Matepair library mapping (long inserts with RF orientation). Planned Features: * Multithreading

Proper citation: mrFAST (RRID:SCR_005487) Copy   

•   Source: SciCrunch Registry

http://dna.cs.byu.edu/gnumap/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 3rd,2023. A software program designed to accurately map sequence data obtained from next-generation sequencing machines (specifically that of Solexa/Illumina) back to a genome of any size. By using the posterior probability of mapping a given read to a specific genomic loation, we are able to account for repetitive reads by distributing them across several regions in the genome. In addition, the output of the program is created in such a way that it can be easily viewed through other free and readily- available programs. Several benchmark data sets were created with spiked-in duplicate regions, and GNUMAP was able to more accurately account for these duplicate regions.

Proper citation: GNUMAP (RRID:SCR_005482) Copy   

•   Source: SciCrunch Registry

http://samstat.sourceforge.net/

C software program for displaying sequence statistics for next generation sequencing. Works with large fasta, fastq and SAM/BAM files.

Proper citation: SAMStat (RRID:SCR_005432) Copy   

•   Source: SciCrunch Registry

http://dna.leeds.ac.uk/methylviewer/

A simple integrated software tool for handling MAP (methyltransferase accessibility protocol) and MAP-IT (MAP individual templates) footprinting projects. It can process sequence data (*.txt, *.ab1 and *.scf) derived from the use of up to four different DNA methyltransferases.

Proper citation: MethylViewer (RRID:SCR_005448) Copy   

•   Source: SciCrunch Registry

http://epigenome.usc.edu/publicationdata/bissnp2011/

A software package based on the Genome Analysis Toolkit (GATK) map-reduce framework for genotyping and accurate DNA methylation calling in bisulfite treated massively parallel sequencing (Bisulfite-seq, NOMe-seq, RRBS and any other bisulfite treated sequencing) with Illumina directional library protocol. It contains the following key features: * Call and summarize methylation of any cytosine context provided (CpG, CHH, CHG, GCH et.al.); * Work for single end and paired-end data; * Accurtae variant detection. Enable base quality recalibration and indel calling in bisulfite sequencing; * Based on Java map-reduce framework, allow multi-thread computing. Cross-platform; * Allow multiple output format, detailed VCF files, CpG haplotype reads file for mono-allelic methylation analysis, simplified bedGraph, wig and bed format for visualization in UCSC genome broswer and IGV browser. BisSNP uses bayesian inference with locus specific methylation probabilities and bisulfite conversion rate of different cytosine context(not only CpG, CHH, CHG in Bisulfite-seq, but also GCH et.al. in other bisulfite treated sequencing) to determine genotypes and methylation levels simultaneously., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bis-SNP (RRID:SCR_005439) Copy   

•   Source: SciCrunch Registry

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

A user-friendly software pipeline designed to map short reads in a MapReduce framework on a local Hadoop cluster. It is designed to be easily implemented by researchers who do not have expert knowledge of bioinformatics. As it does not have any dependencies, it provides full flexibility and control to the user. The user can use any version of a compatible mapper and any reference genome assembly. There is no need to maintain the mapper, reference or DistMap source code on each of the slaves (nodes) in the Hadoop cluster, making maintenance extremely easy.

Proper citation: DistMap (RRID:SCR_005473) Copy   

•   Source: SciCrunch Registry

http://www.well.ox.ac.uk/project-stampy

A software package for the mapping of short reads from illumina sequencing machines onto a reference genome. It''s recommended for most workflows, including those for genomic resequencing, RNA-Seq and Chip-seq. Stampy excels in the mapping of reads containing that contain sequence variation relative to the reference, in particular for those containing insertions or deletions. It can map reads from a highly divergent species to a reference genome for instance. Stampy achieves high sensitivity and speed by using a fast hashing algorithm and a detailed statistical model. Stampy has the following features: * Maps single, paired-end and mate pair Illumina reads to a reference genome * Fast: about 20 Gbase per hour in hybrid mode (using BWA) * Low memory footprint: 2.7 Gb shared memory for a 3Gbase genome * High sensitivity for indels and divergent reads, up to 10-15% * Low mapping bias for reads with SNPs * Well calibrated mapping quality scores * Input: Fastq and Fasta; gzipped or plain * Output: SAM, Maq''s map file * Optionally calculates per-base alignment posteriors * Optionally processes part of the input * Handles reads of up to 4500 bases

Proper citation: Stampy (RRID:SCR_005504) Copy   

•   Source: SciCrunch Registry