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http://www.bioconductor.org/

Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.

Proper citation: Bioconductor (RRID:SCR_006442) Copy   

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http://www.cgat.org/~andreas/documentation/cgat/cgat.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 3, 2023. A collection of tools for the computational genomicist written in the python language to assist in the analysis of genome scale data from a range of standard file formats. The toolkit enables filtering, comparison, conversion, summarization and annotation of genomic intervals, gene sets and sequences. The tools can both be run from the Unix command line and installed into visual workflow builders, such as Galaxy. Please note that the tools are part of a larger code base also including genomics and NGS pipelines. Everyone who uses parts of the CGAT code collection is encouraged to contribute. Contributions can take many forms: bugreports, bugfixes, new scripts and pipelines, documentation, tests, etc. All contributions are welcome.

Proper citation: Computational Genomics Analysis Tools (RRID:SCR_006390) Copy   

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http://www.gaworkshop.org/

The Genetic Analysis Workshops (GAWs) are a collaborative effort among genetic epidemiologists to evaluate and compare statistical genetic methods. For each GAW, topics are chosen that are relevant to current analytical problems in genetic epidemiology, and sets of real or computer-simulated data are distributed to investigators worldwide. Results of analyses are discussed and compared at meetings held in even-numbered years. The GAWs began in 1982 were initially motivated by the development and publication of several new algorithms for statistical genetic analysis, as well as by reports in the literature in which different investigators, using different methods of analysis, had reached contradictory conclusions. The impetus was initially to determine the numerical accuracy of the algorithms, to examine the robustness of the methodologies to violations of assumptions, and finally, to compare the range of conclusions that could be drawn from a single set of data. The Workshops have evolved to include consideration of problems related to analyses of specific complex traits, but the focus has always been on analytical methods. The Workshops provide an opportunity for participants to interact in addressing methodological issues, to test novel methods on the same well-characterized data sets, to compare results and interpretations, and to discuss current problems in genetic analysis. The Workshop discussions are a forum for investigators who are evolving new methods of analysis as well as for those who wish to gain further experience with existing methods. The success of the Workshops is due at least in part to the focus on specific problems and data sets, the informality of sessions, and the requirement that everyone who attends must have made a contribution. Topics are chosen and a small group of organizers is selected by the GAW Advisory Committee. Data sets are assembled, and six or seven months before each GAW, a memo is sent to individuals on the GAW mailing list announcing the availability of the GAW data. Included with the memo is a short description of the data sets and a form for requesting data. The form contains a statement to be signed by any investigator requesting the data, acknowledging that the data are confidential and agreeing not to use them for any purpose other than the Genetic Analysis Workshop without written permission from the data provider(s). Data are distributed by the ftp or CD-ROM or, most recently, on the web, together with a more complete written description of the data sets. Investigators who wish to participate in GAW submit written contributions approximately 6-8 weeks before the Workshop. The GAW Advisory Committee reviews contributions for relevance to the GAW topics. Contributions are assembled and distributed to all participants approximately two weeks before the Workshop. Participation in the GAWs is limited to investigators who (1) submit results of their analyses for presentation at the Workshop, or (2) are data providers, invited speakers or discussants, or Workshop organizers. GAWs are held just before the meetings of the American Society of Human Genetics or the International Genetic Epidemiology Society, at a meeting site nearby. We choose a location that will encourage interaction among participants and permit an intense period of concentrated work. The proceedings of each GAW are published. Proceedings from GAW16 were published in part by Genetic Epidemiology 33(Suppl 1), S1-S110 (2009) and in part by Biomed Central (BMC Proceedings, Volume 3, Supplement 7, 2009). Sponsors: GAW is funded by the Southwest Foundation for Biomedical Research.

Proper citation: Genetic Analysis Workshop (RRID:SCR_008350) Copy   

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http://www.clcbio.com/products/clc-main-workbench/

A suite of software for DNA, RNA and protein sequence data analysis. The software allows for the analysis and visualization of Sanger sequencing data as well as gene expression analysis, molecular cloning, primer design, phylogenetic analyses, and sequence data management.

Proper citation: CLC Main Workbench (RRID:SCR_000354) Copy   

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http://www.biochem.mpg.de/en/rd/baumeister/research/ContentCEM/Software_development

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A software platform for low dose electron tomography (ET) for all processing steps: acquisition, alignment, reconstruction, and analysis. Requires: Matlab R2008a and Image Processing Toolbox (V6.1)

Proper citation: Acquisition and Analysis for Electron Tomography (RRID:SCR_000192) Copy   

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http://bmsr.usc.edu/software/targetgene/

MATLAB tool to effectively identify potential therapeutic targets and drugs in cancer using genetic network-based approaches. It can rapidly extract genetic interactions from a precompiled database stored as a MATLAB MAT-file without the need to interrogate remote SQL databases. Millions of interactions involving thousands of candidate genes can be mapped to the genetic network within minutes. While TARGETgene is currently based on the gene network reported in (Wu et al.,Bioinformatics 26:807-813, 2010), it can be easily extended to allow the optional use of other developed gene networks. The simple graphical user interface also enables rapid, intuitive mapping and analysis of therapeutic targets at the systems level. By mapping predictions to drug-target information, TARGETgene may be used as an initial drug screening tool that identifies compounds for further evaluation. In addition, TARGETgene is expected to be applicable to identify potential therapeutic targets for any type or subtype of cancers, even those rare cancers that are not genetically recognized. Identification of Potential Therapeutic Targets * Prioritize potential therapeutic targets from thousands of candidate genes generated from high-throughput experiments using network-based metrics * Validate predictions (prioritization) using user-defined benchmark genes and curated cancer genes * Explore biologic information of selected targets through external databases (e.g., NCBI Entrez Gene) and gene function enrichment analysis Initial Drug Screening * Identify for further evaluation existing drugs and compounds that may act on the potential therapeutic targets identified by TARGETgene * Explore general information on identified drugs of interest through several external links Operating System: Windows XP / Vista / 7

Proper citation: TARGETgene (RRID:SCR_001392) Copy   

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http://www.preger.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Sample collection of oocytes obtained from various sized antral follicles, and embryos obtained through a variety of different protocols. The PREGER makes it possible to undertake quantitative gene-expression studies in rhesus monkey oocytes and embryos through simple and cost-effective hybridization-based methods.

Proper citation: Primate Embryo Gene Expression Resource (RRID:SCR_002765) Copy   

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http://www.fmri.wfubmc.edu/cms/software

Research group based in the Department of Radiology of Wake Forest University School of Medicine devoted to the application of novel image analysis methods to research studies. The ANSIR lab also maintains a fully-automated functional and structural image processing pipeline supporting the image storage and analysis needs of a variety of scientists and imaging studies at Wake Forest. Software packages and toolkits are currently available for download from the ANSIR Laboratory, including: WFU Biological Parametric Mapping Toolbox, WFU_PickAtlas, and Adaptive Staircase Procedure for E-Prime.

Proper citation: Advanced Neuroscience Imaging Research Laboratory Software Packages (RRID:SCR_002926) Copy   

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http://www.altanalyze.org/

Software application for microarry, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), it will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, it provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichement and more). AltAnalyze can be run through an intuitive graphical user interface or command-line and requires no advanced knowledge of bioinformatics programs or scripting. Alternative regulated exons can be subsequently visualized in the context of proteins, domains and microRNA binding sites with the Cytoscape Plugin DomainGraph.

Proper citation: AltAnalyze - Alternative Splicing Analysis Tool (RRID:SCR_002951) Copy   

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http://imaging.indyrad.iupui.edu/projects/SPHARM/

A matlab-based 3D shape modeling and analysis toolkit, and is designed to aid statistical shape analysis for identifying morphometric changes in 3D structures of interest related to different conditions. SPHARM-MAT is implemented based on a powerful 3D Fourier surface representation method called SPHARM, which creates parametric surface models using spherical harmonics.

Proper citation: SPHARM-MAT (RRID:SCR_002545) Copy   

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http://nipy.org/nipype/

A package for writing fMRI analysis pipelines and interfacing with external analysis packages (SPM, FSL, AFNI). Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.

Proper citation: Nipype (RRID:SCR_002502) Copy   

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http://www.kcl.ac.uk/ioppn/depts/neuroimaging/research/imaginganalysis/Software/PIPR.aspx

Software toolbox designed to provide machine learning methods for pre-processed imaging data allowing for two (or more) class classification in the context of drug development. The Toolbox includes implementations of Gaussian Process Classification, Support Vector Machines, Ordinal Regression and Sparse Multinomial Logistic Regression for fMRI, Structural and ASL imaging data.

Proper citation: Pharmacological Imaging and Pattern Recognition toolbox (RRID:SCR_003874) Copy   

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http://www.primervfx.com/#welcome

PrimerParadise is an online PCR primer database for genomics studies. The database contains predesigned PCR primers for amplification of exons, genes and SNPs of almost all sequenced genomes. Primers can be used for genome-wide projects (resequencing, mutation analysis, SNP detection etc). The primers for eukaryotic genomes have been tested with e-PCR to make sure that no alternative products will be generated. Also, all eukaryotic primers have been filtered to exclude primers that bind excessively throughout the genome. Genes are amplified as amplicons. Amplicons are defined as only one genes exons containing maximaly 3000 bp long dna segments. If gene is longer than 3000 bp then it is split into the segments at length 3000 bp. So for example gene at length 5000 bp is split into two segment and for both segments there were designed a separate primerpair. If genes exons length is over 3000 bp then it is split into amplicons as well. Every SNP has one primerpair. In addition of considering repetitive sequences and mono-dinucleotide repeats, we avoid designing primers to genome regions which contain other SNPs. -There are two ways to search for primers: you can use features IDs ( for SNP primers Reference ID, for gene/exon primers different IDs (Ensembl gene IDs, HUGO IDs for human genes, LocusLink IDs, RefSeq IDs, MIM IDs, NCBI gene names, SWISSPROT IDs for bacterial genes, VEGA gene IDs for human and mouse, Sanger S.pombe systematic gene names and common gene names, S.cerevisiae GeneBanks Locus, AccNo, GI IDs and common gene names) -you can use genome regions (chromosome coordinates, chromosome bands if exists) -Currently we provide 3 primers collections: proPCR for prokaryotic organisms genes primers -euPCR for eukaryotic organisms genes/exons primers -snpPCR for eukaryotic organisms SNP primers Sponsors: PrimerStudio is funded by the University of Tartu.

Proper citation: PrimerStudio (RRID:SCR_008232) Copy   

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http://locustdb.genomics.org.cn/

The migratory locust (Locusta migratoria) is an orthopteran pest and a representative member of hemimetabolous insects. Its transcriptomic data provide invaluable information for molecular entomology study of the insect and pave a way for comparative studies of other medically, agronomically, and ecologically relevant insects. This first transcriptomic database of the locust (LocustDB) has been developed, building necessary infrastructures to integrate, organize, and retrieve data that are either currently available or to be acquired in the future. It currently hosts 45,474 high quality EST sequences from the locust, which were assembled into 12,161 unigenes. This database contains original sequence data, including homologous/orthologous sequences, functional annotations, pathway analysis, and codon usage, based on conserved orthologous groups (COG), gene ontology (GO), protein domain (InterPro), and functional pathways (KEGG). It also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. LocustDB also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. It starts with the first transcriptome information for an orthopteran and hemimetabolous insect and will be extended to provide a framework for incorporation of in-coming genomic data of relevant insect groups and a workbench for cross-species comparative studies.

Proper citation: Migratory Locust EST Database (RRID:SCR_008201) Copy   

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https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 29,2025. Electroencephalogram (EEG) data recorded from invasive and scalp electrodes. The EEG database contains invasive EEG recordings of 21 patients suffering from medically intractable focal epilepsy. The data were recorded during an invasive pre-surgical epilepsy monitoring at the Epilepsy Center of the University Hospital of Freiburg, Germany. In eleven patients, the epileptic focus was located in neocortical brain structures, in eight patients in the hippocampus, and in two patients in both. In order to obtain a high signal-to-noise ratio, fewer artifacts, and to record directly from focal areas, intracranial grid-, strip-, and depth-electrodes were utilized. The EEG data were acquired using a Neurofile NT digital video EEG system with 128 channels, 256 Hz sampling rate, and a 16 bit analogue-to-digital converter. Notch or band pass filters have not been applied. For each of the patients, there are datasets called ictal and interictal, the former containing files with epileptic seizures and at least 50 min pre-ictal data. the latter containing approximately 24 hours of EEG-recordings without seizure activity. At least 24 h of continuous interictal recordings are available for 13 patients. For the remaining patients interictal invasive EEG data consisting of less than 24 h were joined together, to end up with at least 24 h per patient. An interdisciplinary project between: * Epilepsy Center, University Hospital Freiburg * Bernstein Center for Computational Neuroscience (BCCN), Freiburg * Freiburg Center for Data Analysis and Modeling (FDM).

Proper citation: Electroencephalogram Database: Prediction of Epileptic Seizures (RRID:SCR_008032) Copy   

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http://www.schematikon.org/Nh3D.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. It is freely available as a reference dataset for the statistical analysis of sequence and structure features of proteins in the PDB. It is a dataset of structurally dissimilar proteins. This dataset has been compiled by selecting well resolved representatives from the Topology level of the CATH database which hierarchically classifies all protein structures. These have been been pruned to remove: i) domains that may contain homologous elements (by pairwise sequence comparison and structural superposition of aligned residues) ii) internal duplications (by repeat detection) iii) regions with high B-Factor The statistical analysis of protein structures requires datasets in which structural features can be considered independently distributed, i.e. not related through common ancestry, and that fulfill minimal requirements regarding the experimental quality of the structures it contains. However, non-redundant datasets based on sequence similarity invariably contain distantly related homologues. Here a reference dataset of non-homologous protein domains is provided, assuming that structural dissimilarity at the topology level is incompatible with recognizable common ancestry. It contains the best refined representatives of each Topology level, validates structural dissimilarity and removes internally duplicated fragments. The compilation of Nh3D is fully scripted. The current Nh3D list contains 570 domains with a total of 90780 residues. It covers more than 70% of folds at the Topology level of the CATH database and represents more than 90% of the structures in the PDB that have been classified by CATH. Even though all protein pairs are structurally dissimilar, some pairwise sequence identities after global alignment are greater than 30%. Nh3D is freely available as a reference dataset for the statistical analysis of sequence and structure features of proteins in the PDB.

Proper citation: Nh3D: A Reference Dataset of Structures of Non-homologous Proteins (RRID:SCR_008212) Copy   

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http://www.cs.tau.ac.il/~spike/

Database of curated human signaling pathways with an associated interactive software tool for analysis and dynamic visualization of pathways. Individual pathway maps can be viewed and downloaded; the entire database may be browsed, or launched via a map viewer tool that allows dynamic visualization of the database and save networks in XGMML format that can be viewed in all generic XGMML viewers. Map Topics * Cell cycle progress and check points * DNA damage response * Programmed cell death related processes * Stress-activated transcription factors * Mitogen-activated protein kinase pathways * Immune response signaling * HEarSpike: hearing related pathways

Proper citation: SPIKE (RRID:SCR_010466) Copy   

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http://www.informatics.jax.org/phenotypes.shtml

Enables comparative phenotype analysis, searches for human disease models, and hypothesis generation by providing access to spontaneous, induced, and genetically engineered mutations and their strain-specific phenotypes.

Proper citation: Phenotypes and Mutant Alleles (RRID:SCR_017523) Copy   

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https://www.sourcebioscience.com/products/life-sciences-research/clones/rnai-resources/c-elegans-rnai-collection-ahringer/

C. elegans RNAi feeding library distributed by Source BioScience Ltd. Designed for genome wide study of gene function in C. elegans through loss of function studies.

Proper citation: C. elegans RNAi Collection (Ahringer) (RRID:SCR_017064) Copy   

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https://ualr.edu/bioinformatics/midsouth-bioinformatics-center/

Core provides bioinformatics consulting, training, technical assistance, and access to computational infrastructure for faculty, students, and researchers in region with their bioscience computational needs. Offers private sessions, workshops and training on specialty topics. Computing resources including software, computing cluster, technical advice.

Proper citation: University of Arkansas at Little Rock MidSouth Bioinformatics Center Core Facility (RRID:SCR_017168) Copy   

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