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http://www.ics.uci.edu/~dnazip/

DNA sequence compression using a reference genome.

Proper citation: DNAzip (RRID:SCR_009032) Copy   

•   Source: SciCrunch Registry

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

Bioinformatics analysis software tool for optimized ChIP-seq peak detection written in R.

Proper citation: peakrots (RRID:SCR_007453) 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

http://www.imperial.ac.uk/AP/faces/pages/read/Home.jsp?person=l.coin&_adf.ctrl-state=pekvgdj4t_3&_afrRedirect=4092914325174000

Software for identifying haplogroups from low coverage sequence data.

Proper citation: YHap (RRID:SCR_007951) 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://web1.sph.emory.edu/users/hwu30/polyaPeak.html

An R package for ranking ChIP-seq peaks with shape information.

Proper citation: polyaPeak (RRID:SCR_007687) Copy   

•   Source: SciCrunch Registry

http://www.ensembl.org/info/docs/tools/vep/index.html

Data analysis service to predict the functional consequences of known and unknown variants.

Proper citation: Variant Effect Predictor (RRID:SCR_007931) Copy   

•   Source: SciCrunch Registry

http://www.annoj.org/

A REST-based web application designed for visualizing deep sequencing data and other genome annotation data.

Proper citation: Anno-J (RRID:SCR_008192) Copy   

•   Source: SciCrunch Registry

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

Infers both the sample genotypes and error parameters that produced a metagenome data set.

Proper citation: DADA (RRID:SCR_008205) Copy   

•   Source: SciCrunch Registry

https://github.com/eturro/mmseq#mmseq-transcript-and-gene-level-expression-analysis-using-multi-mapping-rna-seq-reads

Software package that contains a collection of statistical tools for analysing RNA-seq expression data.

Proper citation: MMSEQ (RRID:SCR_008184) Copy   

•   Source: SciCrunch Registry

https://igor.sbgenomics.com/

A cloud platform for next-generation sequencing analysis.

Proper citation: Seven Bridges Genomics (RRID:SCR_008308) Copy   

•   Source: SciCrunch Registry

http://epicenter.immunbio.mpg.de/services/chromos/

Combines genetic and epigenetic data to facilitate SNP classification, prioritization and prediction of their functional effect.

Proper citation: ChroMoS (RRID:SCR_008320) Copy   

•   Source: SciCrunch Registry

http://www.iqtree.org

Software tool for phylogenomic inference.

Proper citation: IQ TREE (RRID:SCR_021163) Copy   

•   Source: SciCrunch Registry

https://qiime2.org/

Software tool as next generation microbiome bioinformatics platform that is extensible, free, open source, and community developed.Enables researchers to start analysis with raw DNA sequence data and finish with publication quality figures and statistical results. Used to analyze and interpret nucleic acid sequence data from fungal, viral, bacterial, and archaeal communities.

Proper citation: QIIME2 (RRID:SCR_021258) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/mlgt/index.html

Software for processing and analysis of high throughput (Roche 454) sequences generated from multiple loci and multiple biological samples. Sequences are assigned to their locus and sample of origin, aligned and trimmed. Where possible, genotypes are called and variants mapped to known alleles.

Proper citation: mlgt (RRID:SCR_001211) Copy   

•   Source: SciCrunch Registry

http://pyro.cme.msu.edu/

Software to simplify the processing of large rRNA sequence libraries (including single-strand and paired-end reads) obtained through high-throughput sequencing technology. Tools for assembly, quality filtering, taxonomy based analysis and taxonomy independent analysis tools, and tools to convert the data to formats suitable for common ecological and statistical packages are available. For extremely large datasets, command line tools are available.

Proper citation: RDPipeline (RRID:SCR_001192) Copy   

•   Source: SciCrunch Registry

http://www.plantagora.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 18,2025. A web-based plant genome assembly simulation platform whose resources include out of the box scripts for analyzing assembly data, an on-demand web graphing tool to model your experiment, and a downloadable database with metrics and parameters from over 3,000 simulated genome assemblies.

Proper citation: Plantagora (RRID:SCR_001227) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.avadis-ngs.com

Software integrated platform that provides analysis, management and visualization tools for next-generation sequencing data. It supports workflows for RNA-Seq, DNA-Seq, ChIP-Seq and small RNA-Seq experiments. Avadis has a built-in Gene Ontology browser to view ontology hierarchies. There are common ontology paths for multiple genes. Platform has collection of data / text mining algorithms, data visualization libraries, workflow/application automation layers, and enterprise data organization functions. These functions are available as libraries that allow developers to rapidly build software prototypes, applications and off-the-shelf products. The collection of algorithms and visualizations in AVADIS grows as new applications using the platform are developed. Currently, the algorithms that AVADIS platform contains range from general purpose statistical mining and modelling algorithms, to text mining algorithms, to very application-specific algorithms for microarray / NGS data analysis, QSAR modelling and biological networks analysis. AVADIS has a collection of powerful mining algorithms like PCA, ANOVA, T-test, clustering, classification and regression methods. The range of visualizations includes most statistical and data modelling related graphing views, and very application-specific visualizations. Some of the statistical views include 2D/3D scatter plots, profile plots, heat maps, histograms and matrix plot; data modelling relevant views include dendrograms, cluster profiles, similarity images and SOM U-matrices. Application-specific views in AVADIS include pathway network views, genome browsers, chemical structure views and pipe-line views. Platform: Windows compatible, Mac OS X compatible, Linux compatible,

Proper citation: Avadis (RRID:SCR_000644) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/metavar/

Software package that enables detection of sequence variation between metagenomic samples.

Proper citation: MaryGold (RRID:SCR_000528) Copy   

•   Source: SciCrunch Registry

http://bioinfo-out.curie.fr/projects/maia/index.php

Software package for automatic processing of the one- and two- (typically, Cy3-green/Cy5-red) color images produced in cDNA, CGH (comparative genome hybridization) or protein microarray technologies. It incorporates the following modules: * The spot localization module (i) identifies the position of each spot on the array, so that the name of the spotted clone can be associated with the correspondent spot; and (ii) establishes the borders between the neighborhood spots letting one to perform further data processing procedures (i.e. to extract quantitative information) for each spot independently of the other neighborhood spots. Visually this results in the generation of a grid covering the image. The spot localization algorithm is fully automatic and robust with respect to deviations from perfect spot alignment and contamination. As an input, it requires only the common array design parameters: number of blocks and number of spots in the x and y directions of the array. * The spot quantification module for one-color images performs segmentation of the spots and estimates the averaged spot and local background intensities. The spot quantification module for two-color images estimates the ratio of the measured intensities in the two color channels at each spot reflecting differential gene (cDNA technology) or protein expression or a change in DNA copy number (CGH experiments) between the test and control samples for the corresponding gene. This module includes algorithms based on the linear regression and segmentation of the spots. A special procedure for detection and removal of the aberrant pixels has been developed to make ratio estimates more resistant to array contamination. It ensures more consistent ratio estimates obtained from different algorithms, and allows delivery of a single trustable ratio value. * The quality control module provides a value of spot quality reflecting the level of confidence in the obtained quantitative estimates at each spot. These quality values can be used either directly to flag out some spots with the quality lower than the user-defined threshold, or in the follow-up analysis as a weight controlling the contribution/influence of the obtained ratio estimates. The unique spot quality value for a spot is derived from a set of marginal quality parameters characterizing certain features of the spot. The contribution of each quality parameter in the overall quality is automatically evaluated based on the user visual classification of the spots, or using information available from the replicated spots, located at the same array or over a set of replicated arrays. * The image simulator allows the generation of a broad spectrum of microarray images with different types of contamination (like non-specific hybridization and dust) and noise. Since in simulation experiment the true values of the ratios are known exactly, it allows one to evaluate, to test and to compare different algorithms for microarray image processing objectively.

Proper citation: MAIA (Microarray Image Analysis) (RRID:SCR_002239) Copy   

•   Source: SciCrunch Registry