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http://eyegene.ophthy.med.umich.edu/madeline/

Software tool designed for preparing, visualizing, and exploring human pedigree data used in genetic linkage studies. It converts pedigree and marker data into formats required by popular linkage analysis packages, provides powerful ways to query pedigree data sets, and produces Postscript pedigree drawings that are useful for rapid data review.

Proper citation: MADELINE (RRID:SCR_001979) Copy   

•   Source: SciCrunch Registry

http://weatherby.genetics.utah.edu/cgi-bin/Phevor/PhevorWeb.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. Tool that integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. It works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant prioritization tools. It does so using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single exome and family trio-based diagnostic analyses, the most commonly occurring clinical scenarios, and ones for which existing personal-genomes diagnostic tools are most inaccurate and underpowered. Phevor not only improves diagnostic accuracy for individuals presenting with established disease phenotypes, but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases, or known disease-causing alleles.

Proper citation: Phevor (RRID:SCR_002273) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/ieb/research/acembly/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented August 29, 2016. AceView offers an integrated view of the human, nematode and Arabidopsis genes reconstructed by co-alignment of all publicly available mRNAs and ESTs on the genome sequence. Our goals are to offer a reliable up-to-date resource on the genes and their functions and to stimulate further validating experiments at the bench. AceView provides a curated, comprehensive and non-redundant sequence representation of all public mRNA sequences (mRNAs from GenBank or RefSeq, and single pass cDNA sequences from dbEST and Trace). These experimental cDNA sequences are first co-aligned on the genome then clustered into a minimal number of alternative transcript variants and grouped into genes. Using exhaustively and with high quality standards the available cDNA sequences evidences the beauty and complexity of mammals' transcriptome, and the relative simplicity of the nematode and plant transcriptomes. Genes are classified according to their inferred coding potential; many presumably non-coding genes are discovered. Genes are named by Entrez Gene names when available, else by AceView gene names, stable from release to release. Alternative features (promoters, introns and exons, polyadenylation signals) and coding potential, including motifs, domains, and homologies are annotated in depth; tissues where expression has been observed are listed in order of representation; diseases, phenotypes, pathways, functions, localization or interactions are annotated by mining selected sources, in particular PubMed, GAD and Entrez Gene, and also by performing manual annotation, especially in the worm. In this way, both the anatomy and physiology of the experimentally cDNA supported human, mouse and nematode genes are thoroughly annotated. Our goals are to offer an up-to-date resource on the genes, in the hope to stimulate further experiments at the bench, or to help medical research. AceView can be queried by meaningful words or groups of words as well as by most standard identifiers, such as gene names, Entrez Gene ID, UniGene ID, GenBank accessions.

Proper citation: AceView (RRID:SCR_002277) Copy   

•   Source: SciCrunch Registry

http://www.dogmap.ch/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. An international collaboration between 46 labs from 20 different countries towards a low resolution canine marker map under the auspices of the International Society for Animal Genetics (ISAG). The map under development should achieve a resolution of about 20 cM and some of the markers should be mapped physically. The participants have agreed to use microsatellites as markers on a common panel of reference families which will provide the backbone of the marker map. It is foreseen to also include type I markers in the mapping effort and to produce cosmid derived microsatellites for physical mapping. For this purpose part of the effort focuses on the standardization of the canine karyotype. Special attention is payed to hereditary diseases where efforts are under way to establish resource families either by collecting families or by specific breeding. A point of emphasis of the DogMap project is the setting up of an internationally accessible database for handling the mapping data. The structure of the DogMap collaboration includes a managing committee and scientific advisers. The managing committee is responsible for the overall coordination of the activities within the collaboration, for the dissemination of relevant information to all of the participants and for the representation of DogMap outside the collaboration., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DogMap (RRID:SCR_002332) Copy   

•   Source: SciCrunch Registry

http://fullmal.hgc.jp/index_ajax.html

FULL-malaria is a database for a full-length-enriched cDNA library from the human malaria parasite Plasmodium falciparum. Because of its medical importance, this organism is the first target for genome sequencing of a eukaryotic pathogen; the sequences of two of its 14 chromosomes have already been determined. However, for the full exploitation of this rapidly accumulating information, correct identification of the genes and study of their expression are essential. Using the oligo-capping method, this database has produced a full-length-enriched cDNA library from erythrocytic stage parasites and performed one-pass reading. The database consists of nucleotide sequences of 2490 random clones that include 390 (16%) known malaria genes according to BLASTN analysis of the nr-nt database in GenBank; these represent 98 genes, and the clones for 48 of these genes contain the complete protein-coding sequence (49%). On the other hand, comparisons with the complete chromosome 2 sequence revealed that 35 of 210 predicted genes are expressed, and in addition led to detection of three new gene candidates that were not previously known. In total, 19 of these 38 clones (50%) were full-length. From these observations, it is expected that the database contains approximately 1000 genes, including 500 full-length clones. It should be an invaluable resource for the development of vaccines and novel drugs. Full-malaria has been updated in at least three points. (i) 8934 sequences generated from the addition of new libraries added so that the database collection of 11,424 full-length cDNAs covers 1375 (25%) of the estimated number of the entire 5409 parasite genes. (ii) All of its full-length cDNAs and GenBank EST sequences were mapped to genomic sequences together with publicly available annotated genes and other predictions. This precisely determined the gene structures and positions of the transcriptional start sites, which are indispensable for the identification of the promoter regions. (iii) A total of 4257 cDNA sequences were newly generated from murine malaria parasites, Plasmodium yoelii yoelii. The genome/cDNA sequences were compared at both nucleotide and amino acid levels, with those of P.falciparum, and the sequence alignment for each gene is presented graphically. This part of the database serves as a versatile platform to elucidate the function(s) of malaria genes by a comparative genomic approach. It should also be noted that all of the cDNAs represented in this database are supported by physical cDNA clones, which are publicly and freely available, and should serve as indispensable resources to explore functional analyses of malaria genomes. Sponsors: This database has been constructed and maintained by a Grant-in-Aid for Publication of Scientific Research Results from the Japan Society for the Promotion of Science (JSPS). This work was also supported by a Special Coordination Funds for Promoting Science and Technology from the Science and Technology Agency of Japan (STA) and a Grant-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Science, Sports and Culture of Japan.

Proper citation: Full-Malaria: Malaria Full-Length cDNA Database (RRID:SCR_002348) Copy   

•   Source: SciCrunch Registry

http://dunham.gs.washington.edu/protocols.shtml

A portal for Maitreya Dunham's lab, which works on the genomic analysis of experimental evolution in yeast using microarrays and the chemostat. Research interests of the lab include experimental evolution of genetic networks in yeast, aneuploidy and copy number variation, comparative genomics, technology development and human genetics in yeast.

Proper citation: Maitreya Dunham's Lab (RRID:SCR_000784) Copy   

•   Source: SciCrunch Registry

http://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

•   Source: SciCrunch Registry

http://www.fged.org/

Society that develop standards for biological research data quality, annotation and exchange. They facilitate the creation and use of software tools that build on these standards and allow researchers to annotate and share their data easily. They promote scientific discovery that is driven by genome wide and other biological research data integration and meta-analysis. Historically, FGED began with a focus on microarrays and gene expression data. However, the scope of FGED now includes data generated using any technology when applied to genome-scale studies of gene expression, binding, modification and other related applications.

Proper citation: FGED (RRID:SCR_001897) Copy   

•   Source: SciCrunch Registry

http://galton.uchicago.edu/~junzhang/LAPSTRUCT.html

Software application to describe population structure using biomarker data ( typically SNPs, CNVs etc.) available in a population sample. The main features different from PCA are: (1) geometrically motivated and graphic model based; (2)robustness of outliers. (entry from Genetic Analysis Software)

Proper citation: LAPSTRUCT (RRID:SCR_007550) Copy   

•   Source: SciCrunch Registry

http://www.pedigree-draw.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 12,2024. Software application for pedigree drawing (entry from Genetic Analysis Software)

Proper citation: Pedigree-Draw (RRID:SCR_008302) Copy   

•   Source: SciCrunch Registry

http://bnmc.caltech.edu/

The Beckman Institute BNMC brings together researchers from many disciplines at Caltech to address problems in the mechanistic modeling of coupled genomic, intercellular and intracellular processes. It represents an attempt to encourage closer interaction and collaboration between groups in Biology, Control and Dynamical Systems, and the Center for Advanced Computing Research. The focus of BNMC is biochemical phenomena occurring within and between cells, in particular the mechanistic modeling of molecular networks of all kinds (e.g., transcriptional, regulatory, metabolic, signal transduction, mechanical, etc.) with and without spatial variation and intercellular communication. BNMC is formed as a coordinated effort aimed at (1) applying existing capabilities to collaboratively solve biological modeling problems that arise in answering scientific questions in Caltech laboratories, (2) exploring a diversity of novel approaches in order to achieve fundamental advances necessary to address the classes of modeling problems biologists want to solve, and (3) organizing projects to better share human experience as well as common infrastructure to avoid duplication and maximize solution interoperability.

Proper citation: Caltech, The Beckman Institute: The Biological Network Modeling Center (RRID:SCR_008060) Copy   

•   Source: SciCrunch Registry

https://bioinformatics.forsyth.org/

Core specializes in oral microbial genomics, taxonomy, phylogenetics and the next generation sequence (NGS) data analysis with both in house and cloud high performance computational resource. In addition to supporting funded bioinformatics projects, Bioinformatics Core will also provide computational support to Forsyth and other researchers for processing, analyzing, and interpreting biological data.

Proper citation: Forsyth Institute Bioinformatics Core Facility (RRID:SCR_009783) Copy   

•   Source: SciCrunch Registry

http://www.uoguelph.ca/~rdanzman/software/PROBMAX/

Software application for assigning unknown parentage in pedigree analysis from known genotypic pools of parents and progeny (entry from Genetic Analysis Software)

Proper citation: PROBMAX (RRID:SCR_009337) Copy   

•   Source: SciCrunch Registry

http://www.statgen.ucr.edu/software.html

Software application that is a user defined SAS procedure for mapping quantitative trait loci (QTL). Since this procedure is not a built-in SAS procedure, users need to obtain a copy of the executable file of PROC QTL and install the software in their personal computers before PROC QTL can be executed. Of course, users need a regular SAS license prior to the installation of PROC QTL. Once PROC QTL is installed, it can be called just like any other SAS procedures. Users will not notice the differences between this customized procedure and other built-in SAS procedures. (entry from Genetic Analysis Software)

Proper citation: PROC QTL (RRID:SCR_009338) Copy   

•   Source: SciCrunch Registry

http://phg.mc.vanderbilt.edu/content/powertrim

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11,2023. Software application that automate the decision to remove objects from a pedigree with a minimum loss information (entry from Genetic Analysis Software)

Proper citation: POWERTRIM (RRID:SCR_009333) Copy   

•   Source: SciCrunch Registry

http://ftp://statgen.ncsu.edu/pub/zaykin/

Software application performing a shuffling version of the exact conditional tests for different combinations of allelic and genotypic disequilibrium on haploid and diploid data, or their combination. (entry from Genetic Analysis Software)

Proper citation: MLD (RRID:SCR_009298) Copy   

•   Source: SciCrunch Registry

http://ftp://ftp.biomath.jussieu.fr/pub/mlbgh (not available)

Software application that is an extension of the GENEHUNTER program to perform sib-pair and sib-ship linkage analysis using the Maximum Likelihood Binomial (MLB) method. (entry from Genetic Analysis Software)

Proper citation: MLBGH (RRID:SCR_009297) Copy   

•   Source: SciCrunch Registry

http://www.uni-kiel.de/medinfo/mitarbeiter/krawczak/download/index.html

Software application (entry from Genetic Analysis Software)

Proper citation: MKGST (RRID:SCR_009295) Copy   

•   Source: SciCrunch Registry

http://www.uni-hohenheim.de/~frisch/software.html

Software application (entry from Genetic Analysis Software)

Proper citation: PLABSIM (RRID:SCR_009328) Copy   

•   Source: SciCrunch Registry

http://www.people.fas.harvard.edu/~junliu/plem/

Software application (entry from Genetic Analysis Software)

Proper citation: PL-EM (RRID:SCR_009329) Copy   

•   Source: SciCrunch Registry