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http://statgen.ncsu.edu/zaykin/htr.html

Software application for haplotype association mapping using unrelated individuals; fixed and sliding window analysis; overall tests and tests for individual haplotype effects (entry from Genetic Analysis Software)

Proper citation: HTR (RRID:SCR_009241) Copy   

•   Source: SciCrunch Registry

http://lbm.ab.a.u-tokyo.ac.jp/software.html

Software programs that allow a user to get results on segregation ratio, linkage test, recombination value, grouping of markers, ordering of markers by metric multidimensional scaling, drawing map and graphical genotype. ALso QTL analysis by interval mapping and ANOVA are possible. (entry from Genetic Analysis Software)

Proper citation: MAPL (RRID:SCR_009278) Copy   

•   Source: SciCrunch Registry

http://www.nslij-genetics.org/soft/mapdraw.v2.2.xls

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. Software application that draws genetic linkage maps on PC same as what MAPMAKER does on Mac. (entry from Genetic Analysis Software)

Proper citation: MAPDRAW (RRID:SCR_009276) Copy   

•   Source: SciCrunch Registry

http://www.genes.org.uk/software/midas

Software application for analysis and visualisation of interallelic disequilibrium between multiallelic markers (entry from Genetic Analysis Software)

Proper citation: MIDAS (RRID:SCR_009274) Copy   

•   Source: SciCrunch Registry

http://www.biometris.wur.nl/uk/Software/MapChart/

Software application that produces charts of genetic linkage and QTL data. The charts are composed of a sequence of vertical bars representing the linkage groups or chromosomes. On these bars the positions of loci are indicated, and next to the bars QTL intervals and QTL graphs can be shown. MapChart reads the linkage information (i.e. the locus and QTL names and their positions) from text files. Many options to adapt the charts to different purposes. Can produce graphic files (enhanced windows metafile format) which can be enhanced with other MS-Windows software. (entry from Genetic Analysis Software)

Proper citation: MAPCHART (RRID:SCR_009273) Copy   

•   Source: SciCrunch Registry

http://polymorphism.ucsd.edu/cgi-bin/PRL/mama/mama.cgi

Software application (entry from Genetic Analysis Software)

Proper citation: MAMA (RRID:SCR_009270) Copy   

•   Source: SciCrunch Registry

http://www.atgc.org/XLinkage/MadMapper/

Suite of Python scripts for quality control of genetic markers, group analysis and inference of linear order of markers on linkage groups. MadMapper_RECBIT analyses raw marker scores for recombinant inbred lines. MadMapper_RECBIT generates pairwise distance scores for all markers, clusters based on pairwise distances, identifies genetic bins, assigns new markers to known linkage groups, validates allele calls, and assigns quality classes to each marker based on several criteria and cutoff values. MadMapper_XDELTA utilizes new algorithm, Minimum Entropy Approach and Best-Fit Extension, to infer linear order of markers. MadMapper_XDELTA analyzes two-dimensional matrices of all pairwise scores and finds best map that has minimal total sum of differences between adjacent cells (map with lowest entropy). MadMapper is freely available at http://www.atgc.org/XLinkage/MadMapper/ (entry from Genetic Analysis Software)

Proper citation: MADMAPPER (RRID:SCR_009267) Copy   

•   Source: SciCrunch Registry

http://cogent.iop.kcl.ac.uk/MaGIC.cogx

Software program to generate targeted marker sets for genome-wide association studies.

Proper citation: Marker And Gene Interpolation and Correlation (RRID:SCR_009268) 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. A software suite designed to more powerfully leverage clinical-covariates such as age, bmi, smoking status, and gender when conducting case-control association studies. Including these covariates in standard regression models is not only suboptimal, but can in many instances reduce power. LTSOFT employs a liability threshold model approach that takes advantage of known epidemiological results to better model the covariates'' relationship to the phenotype of interest (entry from Genetic Analysis Software)

Proper citation: LTSOFT (RRID:SCR_009266) Copy   

•   Source: SciCrunch Registry

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

Software application that is part of the LINKAGE auxiliary program (entry from Genetic Analysis Software)

Proper citation: LRP (RRID:SCR_009263) Copy   

•   Source: SciCrunch Registry

http://statgen.iop.kcl.ac.uk/lpop/

Software application that detects population stratification in samples of unrelated individuals for whom a number of unlinked genotypes have been measured. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: L-POP (RRID:SCR_009262) Copy   

•   Source: SciCrunch Registry

http://www.stat.washington.edu/thompson/Genepi/Loki.shtml

Software program for analyses a quantitative trait observed on large pedigrees using Markov chain Monte Carlo multipoint linkage and segregation analysis. The trait may be determined by multiple loci. (entry from Genetic Analysis Software)

Proper citation: LOKI (RRID:SCR_009260) Copy   

•   Source: SciCrunch Registry

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://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   

•   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   

•   Source: SciCrunch Registry

http://www.seattle.eric.research.va.gov/VETR/Home.asp

The Vietnam Era Twin (VET) Registry is a closed cohort composed of approximately 7,000 middle-aged male-male twin pairs both of whom served in the military during the time of the Vietnam conflict (1964-1975). The Registry is a United States Department of Veterans Affairs (VA) resource that was originally constructed from military records; the Registry has been in existence for almost 20 years. It is one of the largest national twin registries in the US and currently has members living in all 50 states. Initially formed to address questions about the long-term health effects of service in Vietnam, the Registry has evolved into a resource for genetic epidemiological studies of mental and physical health conditions. Several waves of mail and telephone surveys have collected a wealth of health-related information on Registry twins, referred to as members. In addition to twins, selected adult offspring of twins and the mothers of those offspring are also VET Registry members. More recent data collection efforts have focused on specific sets of twin pairs and have conducted detailed clinical or laboratory testing. Selected Vietnam Era Registry Research Studies: * Veteran Health Study * VETSA 2: A Longitudinal Study of Cognitive Aging * Alcoholism Course thought Midlife: A Twin Family Study and Offspring of Twins: G, E and GxE Risk for Alcoholism * GE: Offspring of Twins with Substance Use Disorder * Mechanisms Linking Depression to Cardiovascular Risk (Twins Heart Study 2) * Post-traumatic Stress Disorder and Cardiovascular Disease * Biological Markers for Post-traumatic Stress Disorder (T3) * Memory and the Hippocampus in Vietnam-era Twins with PTSD (Time 3)

Proper citation: Vietnam Era Twin Registry (RRID:SCR_008807) Copy   

•   Source: SciCrunch Registry

http://sandberg.cmb.ki.se/media/data/rnaseq/rpkmforgenes.py

Python script which calculates gene expression for RNA-Sequencing data. It analyzes files in formats such as BED, BAM, and SAM to output data about RNA.

Proper citation: rpkmforgenes.py (RRID:SCR_014938) Copy   

•   Source: SciCrunch Registry

https://www.beast2.org/

Software package for advanced Bayesian evolutionary analysis by sampling trees. Used for phylogenetics, population genetics and phylodynamics. Program for Bayesian phylogenetic analysis of molecular sequences. Estimates rooted, time measured phylogenies using strict or relaxed molecular clock models. Framework can be extended by third parties. Comprised of standalone programs including BEAUti, BEAST, MASTER, RBS, SNAPP, MultiTypeTree, BDSKY, LogAnalyser, LogCombiner, TreeAnnotator, DensiTree and package manager.

Proper citation: BEAST2 (RRID:SCR_017307) Copy   

•   Source: SciCrunch Registry

https://depmap.org/portal/

Portal for identifying genetic and pharmacologic dependencies and biomarkers that predicts them by providing access to datasets, visualizations, and analysis tools that are being used by Cancer Dependency Map Project at Broad Institute. Project to systematically identify genes and small molecule dependencies and to determine markers that predict sensitivity. All data generated by DepMap Project are available to public under CC BY 4.0 license on quarterly basis and pre-publication.

Proper citation: Cancer Dependency Map Portal (RRID:SCR_017655) Copy   

•   Source: SciCrunch Registry

https://deepblue.mpi-inf.mpg.de/

Central data access hub for large collections of epigenomic data. It organizes data from different sources using controlled vocabularies and ontologies. Data Server for storing, organizing, searching, and retrieving genomic and epigenomic data, handling associated metadata, and to perform different types of analysis.

Proper citation: Deep Blue Epigenomic Data Server (RRID:SCR_017490) Copy   

•   Source: SciCrunch Registry