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http://taylor0.biology.ucla.edu/structureHarvester/

Web based program for collating results generated by program STRUCTURE. Provides assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of number of genetic groups that best fit data. Reformats data for use in downstream programs, such as CLUMPP.It is complement for using software Structure in genetics population. Website and program for visualizing STRUCTURE output and implementing Evanno method., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Structure Harvester (RRID:SCR_017636) Copy   

•   Source: SciCrunch Registry

http://chemrich.fiehnlab.ucdavis.edu/

Software tool for chemical similarity enrichment analysis of metabolomics datasets. Used in studies to uncover biological mechanisms in organisms under genetic or environmental stress in system biology manner or finding risk factors for chronic diseases in exposome wise association studies using blood specimens. Allows users to realize pathway analysis.

Proper citation: ChemRICH (RRID:SCR_017609) Copy   

•   Source: SciCrunch Registry

http://www.semel.ucla.edu/creativity/

The purpose of this center is to study the molecular, cellular, systems and cognitive mechanisms that result in cognitive enhancements and explain unusual levels of performance in gifted individuals, including extraordinary creativity. Additionally, by understating the mechanisms responsible for enhancements in performance we may be better suited to intervene and reverse disease states that result in cognitive deficits. One of the key topics addressed by the Center is the biological basis of cognitive enhancements, a topic that can be studied in human subjects and animal models. In the past much of the focus in the brain sciences has been on the study of brain mechanisms that degrade cognitive performance (for example, on mutations or other lesions that cause cognitive deficits). The Tennenbaum Center for the Biology of Creativity at UCLA enables an interdisciplinary team of leading scientists to advance knowledge about the biological bases of creativity. Starting with a pilot project program, a series of investigations was launched, spanning disciplines from basic molecular biology to cognitive neuroscience. Because the concept of creativity is multifaceted, initial efforts targeted refinement of the component processes necessary to generate novel, useful cognitive products. The identified core cognitive processes: 1.) Novelty Generation the ability to flexibly and adaptively generate products that are unique; 2.) Working Memory and Declarative Memory the ability to maintain, and then use relevant information to guide goal-directed performance, along with the capacity to store and retrieve this information; and 3.) Response Inhibition the ability to suppress habitual plans and substitute alternate actions in line with changing problem-solving demands. To study the basic mechanisms underlying these complex brain functions we use translational strategies. Starting from foundational studies in basic neuroscience, we forged an interdisciplinary strategy that permits the most advanced techniques for genetic manipulation and basic neurobiological research to be applied in close collaboration with human studies that converge on the same core cognitive processes. Our integrated research program aims to reveal the genetic architecture and fundamental brain mechanisms underlying creative cognition. The work holds enormous promise for both enhancing healthy cognitive performance and designing new treatments for diverse cognitive disorders. Sponsors: The Tennenbaum Center for the Biology of Creativity was inspired by the vision and generosity of Michael Tennenbaum.

Proper citation: Tennenbaum Center for the Biology of Creativity (RRID:SCR_000668) Copy   

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http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) Copy   

•   Source: SciCrunch Registry

http://soap.genomics.org.cn/

Software package that provides full solution to next generation sequencing data analysis consisting of an alignment tool (SOAPaligner/soap2), a re-sequencing consensus sequence builder (SOAPsnp), an indel finder ( SOAPindel ), a structural variation scanner ( SOAPsv ), a de novo short reads assembler ( SOAPdenovo ), and a GPU-accelerated alignment tool for aligning short reads with a reference sequence. (SOAP3/GPU)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SOAP (RRID:SCR_000689) Copy   

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http://krunch.med.yale.edu/haplo/

Software application that estimates frequencies of multi-site haplotypes using the EM algorithm (entry from Genetic Analysis Software)

Proper citation: HAPLO 2 (RRID:SCR_009059) Copy   

•   Source: SciCrunch Registry

https://dsgweb.wustl.edu/aldi/software/manuals/unphased/Unphased_manual.pdf

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016. A suite of programs for association analysis of multilocus haplotypes from unphased genotype data. These include TDTPHASE for case-parent trios, COCAPHASE for case/control data, QTPHASE for quantitative traits in unrelateds, PDTPHASE for general pedigrees, and QPDTPHASE for quantitative traits in general pedigrees. Features include global and individual haplotype tests, main effects and conditional tests, grouping of rare haplotypes, pairwise comparisons of haplotype risk, flexible permutation procedures and calculation of LD measures.

Proper citation: UNPHASED (RRID:SCR_009056) Copy   

•   Source: SciCrunch Registry

http://dceg.cancer.gov/tools/design/power

Windows-based program for computation of sample size and power for binary outcome studies (case-control and cohort studies) based on a logistic-like regression model with one covariate or two covariates (e.g., gene-exposure interactions). (entry from Genetic Analysis Software)

Proper citation: POWER (RRID:SCR_009057) Copy   

•   Source: SciCrunch Registry

http://galton.uchicago.edu/~mcpeek/software/dhsmap/

Software application for fine-mapping of qualitative traits by linkage disequilibrium. Given a set of marker haplotypes or genotypes from affected individuals, haplotypes or genotypes from appropriately selected controls, and a genetic map of the markers at which both sets of individuals are typed, DHSMAP estimates the location of the trait-associated variant by maximum likelihood or maximum quasi-likelihood. (entry from Genetic Analysis Software)

Proper citation: DHSMAP (RRID:SCR_009160) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/resources/software/amelia/

Software application that employs allele matching to analyse the effects of rare variants within a specific locus. There is increasing evidence that rare variants play a role in some complex traits, but their analysis is not straightforward. Locus-based tests become necessary due to low power in rare variant single-point association analyses. In addition, variant quality scores are available for sequencing data, but are rarely taken into account. To enable this analysis, AMELIA has been developed as an allele-matching approach that is robust to the presence of both directions of effect for variants within the locus analysed. (entry from Genetic Analysis Software)

Proper citation: AMELIA (RRID:SCR_009119) Copy   

•   Source: SciCrunch Registry

http://www.decode.com/software/

Software application that is a faster version of GENEHUNTER and Allegro 1.2 (several degrees of increase of speed, can handle bigger families, up to 50 bits) (entry from Genetic Analysis Software)

Proper citation: ALLEGRO (RRID:SCR_009116) Copy   

•   Source: SciCrunch Registry

http://www.imbs.uni-luebeck.de/pub/sibsim/index.html

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016. Software application that simulates either genotype and/or quantitative phenotype data in family structures in a modern, easy to use and highly scalable way. It is based on XML, completely written in C++ and published under the GNU General Public License.

Proper citation: SIBSIM (RRID:SCR_009113) Copy   

•   Source: SciCrunch Registry

https://github.com/gaow/genetic-analysis-software/blob/master/pages/IGG.md

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016.

Proper citation: IGG (RRID:SCR_009114) Copy   

•   Source: SciCrunch Registry

http://ibi.zju.edu.cn/software/qtlnetwork

Software package for mapping and visualizing the genetic architecture underlying complex traits for experimental populations derived from a cross between two inbred lines. (entry from Genetic Analysis Software)

Proper citation: QTLNETWORK (RRID:SCR_009078) Copy   

•   Source: SciCrunch Registry

http://pritch.bsd.uchicago.edu/treeld.html

Free software tool for mapping complex trait loci. TreeLD performs a multipoint LD-analysis by inferring the ancestry of a genomic region and analyzing this ancestry for signals of disease mutations. The generated likelihoods can be used to test for the presence of a disease locus and to fine-map its location, providing a point estimate and a credible region. Furthermore, the package provides a novel way of visualizing the association signal in a sample. TreeLD is designed for high-density SNP haplotypes and can be applied to case-control data, TDT trio data and quantitative trait data. (entry from Genetic Analysis Software)

Proper citation: TREELD (RRID:SCR_009111) Copy   

•   Source: SciCrunch Registry

http://www.hsph.harvard.edu/faculty/alkes-price/software/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 16,2023. Software application using a method for combining SNP association and admixture association statistics to increase power in GWAS in admixed populations. (entry from Genetic Analysis Software)

Proper citation: MIXSCORE (RRID:SCR_009076) Copy   

•   Source: SciCrunch Registry

http://www.twin-research.ac.uk/WebPowQ/PowQ.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. A user-friendly, graphical package for power evaluation and enhancement planning through variance component linkage analysis in a multipoint framework. (entry from Genetic Analysis Software)

Proper citation: POWQ (RRID:SCR_009077) Copy   

•   Source: SciCrunch Registry

http://pritchardlab.stanford.edu/software.html

Software application that is a companion program to STRUCTURE that is a structured association method, for use in association mapping, enabling valid case-control studies even in the presence of population structure. (entry from Genetic Analysis Software)

Proper citation: STRAT (RRID:SCR_009110) Copy   

•   Source: SciCrunch Registry

http://www.cs.helsinki.fi/u/prastas/haplovisual/

Software application (entry from Genetic Analysis Software)

Proper citation: HAPLOVISUAL (RRID:SCR_009073) Copy   

•   Source: SciCrunch Registry

http://www.uni-bonn.de/~umt70e/becker.html

Software application for haplotype association analysis of unphased genotype data. It can be used both for population data (case-control) and nuclear family data. The program is optimized for haplotype frequency estimation with the EM-algorithm for many markers. FAMHAP provides a method which searches for potential genotyping errors and several tests for haplotype-based association analysis. Particular emphasis is on Monte-Carlo simulations, which are necessary in the context of haplotype association, where asymptotic theory often fails, and in the context of multiple testing problems. (entry from Genetic Analysis Software)

Proper citation: FAMHAP (RRID:SCR_009070) Copy   

•   Source: SciCrunch Registry