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http://www.pierroton.inra.fr/genetics/labo/Software/Famoz/index.html

Software application that uses likelihood calculation and simulation to perform parentage studies with codominant, dominant, cytoplasmic markers or combinations of the different types (entry from Genetic Analysis Software)

Proper citation: FAMOZ (RRID:SCR_007477) Copy   

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http://www.mds.qmw.ac.uk/statgen/dcurtis/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5th,2023. Software application that uses Monte Carlo method for assessing significance of a case-control association study with multi-allelic marker. (entry from Genetic Analysis Software).

Proper citation: CLUMP (RRID:SCR_007476) Copy   

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http://www.thomaskoenig.ch/Lester/ibaspm.htm

The aim of this work is to present a toolbox for structure segmentation of structural MRI images. All programs were developed in MATLAB based on a widely used fMRI, MRI software package, SPM99, SPM2, SPM5 (Wellcome Department of Cognitive Neurology, London, UK). Other previous works have developed a similar strategy for obtaining the segmentation of individual MRI image into different anatomical structures using a standardized Atlas. Have to be mentioned the one introduced by Montreal Neurological Institute (MNI) that merges the information coming from ANIMAL (algorithm that deforms one image (nonlinear registration) to match previously labelled) and INSECT (Cerebral Tissue Classification) programs for obtaining a suitable gross cortical structure segmentation (Collins et al, 1999). Here both, nonlinear registration and gray matter segmentation processes have been performed through SPM99, SPM2, SPM5 subroutines. Three principal elements for the labeling process are used: gray matter segmentation, normalization transform matrix (that maps voxels from individual space to standardized one) and MaxPro MNI Atlas. All three are combined to yield a good performance in segmenting gross cortical structures. The programs here can be used in general for any standardized Atlas and any MRI image modality. System Requirements: 1. The IBASPM graphical user interface (GUI) runs only under MATLAB 7.0 or higher. The non-graphical version runs under MATLAB 6.5 or higher. 2. Statistical Parametrical Mapping Software SPM2, SPM5 Main Functions: * Atlasing: Main function ( This file contains spm_select script from SPM5 toolbox and uigetdir script from MATLAB 7.0 ). * Auto_Labeling : Computes individual atlas. * Create_SPAMs : Constructs Statistical Probability Anatomy Maps (SPAMs). * Create_MaxProb : Creates Maximum Probability Atlas (MaxPro) using the SPAMs previously computed. * All_Brain_Vol : Computes whole brain volume masking the brain using the segmentation files (if the segmentation files does not exist it segments). * Struct_Vol : Computes the volume for different structures based on individual Atlas previously obtained by the atlasing process. * Vols_Stats : Computes mean and standard deviation for each structure in a group of individual atlases.

Proper citation: IBASPM: Individual Brain Atlases using Statistical Parametric Mapping Software (RRID:SCR_007110) Copy   

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http://liweilab.genetics.ac.cn/tm/

Web-based tool used to mine human protein-protein interactions (PPIs) from PubMed abstracts based on their co-occurrences and interaction words, followed by evidencs in human PPI databases and shared terms in GO database.

Proper citation: Human Protein-Protein Interaction Mining Tool (RRID:SCR_008040) Copy   

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http://www.birncommunity.org/tools-catalog/gradient-non-linearity-distortion-correction/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 16,2023. Software tool to correct gradient non-linearity distortions in MR structural images. This correction improves test-retest reproducibility crucial for multi-site studies.

Proper citation: Gradient Non-linearity Distortion Correction (RRID:SCR_007350) Copy   

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http://www.bcgsc.ca/platform/bioinfo/software/alea

A computational software toolbox for allele-specific (AS) epigenomics analysis. It incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. It provides a customizable pipeline of command line tools for AS analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. ALEA takes advantage of the available genomic resources for human (The 1000 Genomes Project Consortium) and mouse (The Mouse Genome Project) to reconstruct diploid in-silico genomes for human or hybrid mice under study. Then, for each accompanying ChIP-seq or RNA-seq dataset, it generates two Wiggle track format (WIG) files from short reads aligned differentially to each haplotype.

Proper citation: ALEA (RRID:SCR_006417) Copy   

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http://darwin.uvigo.es/software/treescan.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. Software application that is intended to provide p-values for the hypothesis of association between evolutionary clades and continuous traits, using haplotype trees. (entry from Genetic Analysis Software)

Proper citation: TREESCAN (RRID:SCR_007108) Copy   

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http://pngu.mgh.harvard.edu/purcell/whap/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 24, 2015. This package is no longer supported. The majority of the functionality for conditional haplotype tests in population-based samples has been implemented in PLINK, with a better interface and more robust, faster computation: please use that from now on. Software tool to perform haplotype-based association analysis, for quantitative and qualitative traits, in population and family samples, using single nucleotide polymorphism or multiallelic marker data. What whap can do: * Analyze quantitative and qualitative traits * Handle unrelated individuals and/or parent-offspring trio data * Perform a regression-based haplotype association test for SNP data * Perform a secondary test based on pairwise haplotype similarity * Phase genotype data using a standard E-M approach, and handle ambiguity in E-M inferred haplotypes * Include covariates and moderator variables * Flexibly constrain effects across haplotypes to tested nested models * Perform a robust within-family test when parental genotypes are present * Analyze multiallelic markers (new) * Use dominant or recessive (new) genetic models (new)

Proper citation: Whap (RRID:SCR_007103) Copy   

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http://amazonia.montp.inserm.fr/

A web interface and associated tools for easy query of public human transcriptome data by keyword, through thematic pages with list annotations. Amazonia provides a thematic entry to public transcriptomes: users may for instance query a gene on a Stem Cells page, where they will see the expression of their favorite gene across selected microarray experiments related to stem cell biology. This selection of samples can be customized at will among the 6331 samples currently present in the database. Every transcriptome study results in the identification of lists of genes relevant to a given biological condition. In order to include this valuable information in any new query in the Amazonia database, they indicate for each gene in which lists it is included. This is a straightforward and efficient way to synthesize hundreds of microarray publications., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: AmaZonia: Explore the Jungle of Microarrays Results (RRID:SCR_008405) Copy   

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http://www.ebi.ac.uk/asd/altextron/indexhtml

THIS RESOURCE IS NO LONGER IN SERVICE. A computer generated high quality dataset of human transcript-confirmed constitutive and alternative exons and introns. The alternative events have been delineated and annotated with various characterizations. AltExtron is the prototype database for the production version AltSplice. AltExtron is more geared towards investigating various aspects of the methodologies used, and focuses in general on the biology behind alternative splicing. The complete data used in this work is available for downloading in several flat files, containing human genes, introns, exons, isoform events, human-mouse comparisons, and additional information on GC-AG introns. Two versions of AltExtron data are available - one as prototype (for human) and another as latest build (for human, drosophila, mouse, and others) based on EMBL/GenBank (Feb 2003).

Proper citation: AltExtron Database (RRID:SCR_008404) Copy   

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http://www.gavialliance.org/

Although Haemophilus influenza type b (Hib) diseases and Hepatitis B (Hep B) infections are preventable with one combined life-saving vaccine, both continue to pose risks to the worlds most vulnerable populations, leading to life-long disabilities or even death. Vaccinating against Hib and Hepatitis B represents an essential step towards reaching Millennium Development Goal 4. The Hib bacterium causes meningitis and pneumonia and is considered the third vaccine-preventable cause of death in children aged under five. It is estimated that there are three million cases of serious Hib infection annually, of which 400,000 result in childhood death. The majority of survivors suffer paralysis, deafness, mental retardation and learning disabilities. Babies and young children are most at risk from Hep B, a viral disease, which attacks the liver and can cause both acute and chronic disease. This can lead to chronic liver disease and puts victims at high risk of death from cirrhosis of the liver and liver cancer in later life. More than two billion people are infected by Hep B worldwide of whom 360 million suffer from chronic Hep B infection; the latter is highly prevalent in all countries that GAVI supports. Children are most vulnerable to infection with 90 percent of infants infected in the first months of their lives developing chronic Hep B infection. Infections in the developing world are mostly from mother to child, or from child to child, mainly through cuts, bites, scrapes and scratches. Vaccinating against Hib and Hep B represents an essential step towards reaching Millennium Development Goal 4, which is to reduce the under-five mortality rate by two thirds by 2015. GAVI uses two mechanisms that draw heavily on private-sector thinking to help overcome historic limitations to development funding for immunisation. These mechanisms are the AMC and the IFFIm. The former reflects the need to meet disproportionately high costs in the early stages of implementing aid programmes; the latter developing countries'' need for sustainable predictable funding., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GAVI (RRID:SCR_008528) Copy   

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http://www.lamhdi.org/

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Proper citation: LAMHDI: The Initiative to Link Animal Models to Human DIsease (RRID:SCR_008643) Copy   

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http://bioinf.uab.es/aggrescan/

Web-based tool for identifying hot spots of aggregation in polypeptides. Aggrescan uses an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies.

Proper citation: Aggrescan: The Hot Spot Finder (RRID:SCR_008403) Copy   

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https://www.i2b2.org/NLP/DataSets/Main.php

The data for the smoking challenge consisted exclusively of discharge summaries from Partners HealthCare which were preprocessed and converted into XML format, and separated into training and test sets. I2B2 is a data warehouse containing clinical data on over 150k patients, including outpatient DX, lab results, medications, and inpatient procedures. ETL processes authored to pull data from EMR and finance systems Institutional review boards of Partners HealthCare approved the challenge and the data preparation process. The data were annotated by pulmonologists and classified patients into Past Smokers, Current Smokers, Smokers, Non-smokers, and unknown. Second-hand smokers were considered non-smokers. Other institutions involved include Massachusetts Institute of Technology, and the State University of New York at Albany. i2b2 is a passionate advocate for the potential of existing clinical information to yield insights that can directly impact healthcare improvement. In our many use cases (Driving Biology Projects) it has become increasingly obvious that the value locked in unstructured text is essential to the success of our mission. In order to enhance the ability of natural language processing (NLP) tools to prise increasingly fine grained information from clinical records, i2b2 has previously provided sets of fully deidentified notes from the Research Patient Data Repository at Partners HealthCare for a series of NLP Challenges organized by Dr. Ozlem Uzuner. We are pleased to now make those notes available to the community for general research purposes. At this time we are releasing the notes (~1,000) from the first i2b2 Challenge as i2b2 NLP Research Data Set #1. A similar set of notes from the Second i2b2 Challenge will be released on the one year anniversary of that Challenge (November, 2010).

Proper citation: Smoking NLP Challenge Data (RRID:SCR_008644) Copy   

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http://www.ebi.ac.uk/msd-srv/ssm/

Secondary Structure Matching (SSM) is an interactive service for comparing protein structures in 3D. SSM compares to other protein matching services, see results here. It is used as a structure search engine in PISA service (Protein Interfaces, Surfaces and Assemblies). It queries may be launched from any web site, see instructions here and it is based on the CCP4 Coordinate Library, found here. The service provides for: -pairwise comparison and 3D alignment of protein structures -multiple comparison and 3D alignment of protein structures -examination of a protein structure for similarity with the whole PDB or SCOP archives -best Ca-alignment of compared structures -download and visualization of best-superposed structures using Rasmol (Unix/Linux platforms), Rastop (MS Windows machines) and Jmol (platform-independent server-side java viewer) -linking the results to other services - PDBe Motif, OCA, SCOP, GeneCensus, FSSP, 3Dee, CATH, PDBSum, SWISS-PROT and ProtoMap. Sponsors: The project is funded by the Collaborative Computational Project Number 4 in Protein Crystallography of the Biotechnology and Biological Sciences Research Council

Proper citation: Secondary Structure Matching (RRID:SCR_008365) Copy   

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http://www.diabetesgenes.org

Our genes and lifestyle factors, such as calorie rich diets and a lack of exercise, contribute to the development of Type 2 diabetes. But what we dont know is the exact nature of the genetic risk and how this interacts with lifestyle factors to cause diabetes. The Diabetes UK Warren 2 Group was formed in 1992 to investigate the genetic basis of Type 2 diabetes. The Group, comprising of researchers from six UK diabetes research centres, began by recruiting families into the study enriched for Type 2 diabetes ie having two or more siblings with the condition. With over 2000 individuals from 843 families in the collection, it is now being used to search for the genes that make people susceptible to Type 2 diabetes. When identifying susceptibility genes it is important to know how the gene affects normal metabolism in relatives without diabetes. In 1999. the Warren 2 Extension study recruited first degree relatives (siblings and children) of the original Warren 2 families, who did not have diabetes, in the knowledge that they would also be enriched with the same susceptibility genes. This study recruited 811 relatives without diabetes (586 offspring and 225 siblings) all of whom have undergone detailed metabolic assessment. A similar study was undertaken in Oxford called the Diabetes In Families (DIF) Study. In 2001, the Warren 2 Trios and Duos Study recruited 500 families from around the country consisting of an individual with Type 2 diabetes and both their parents (trios) or an individual with diabetes, one of their parents and at least 2 siblings (duos). In addition to this, a further 1500 individuals with diabetes were recruited as part of the Warren 2 Cases study. This website is run by the Diabetes Research department and the Centre for Molecular Genetics at the Peninsula Medical School and Royal Devon and Exeter Hospital, Exeter, UK.

Proper citation: Diabetes Genes (RRID:SCR_008639) Copy   

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http://www.kccmr.org/

This colony provides a national resource of rhesus monkeys and their tissues to carry out research benefiting the scientific community. The RMBRR maintains a colony of monkeys that have been derived to be specific pathogen free for members of both the herpes and retrovirus families. Over its history, the RMBRR has developed specialized management techniques, housing facilities and highly trained staff to avail these purposefully bred laboratory models, which are 93% genetically identical to humans, to researchers worldwide. Historically, this animal model has been instrumental in research involving blood classification, polio vaccine development, and drug safety and efficacy while currently they are the preferred model for studying the mechanisms of immunodeficiency diseases. Their susceptibility to Simian Immunodeficiency Virus and their homology to the human major histocompatibility complex (MHC) Class I, II and TCR genes make them valuable in HIV research. They are currently the models of choice for HIV/AIDS vaccine development and study. Other areas of research include atherosclerosis, myocarditis, alcoholism, diabetes, cancer and aging. The overall objectives of this resource are to improve the resources available at the RMBRR and to conduct resource-relevant research that improves both the health of the rhesus colony and its usefulness for studies of human disease. The Resource and Management Core is responsible for providing animal resources, tissues/biological fluids, cell lines, expert advice and research support to NIH extramural and intramural programs, other federal agencies and to private sponsors. The Resource-Related Research Core conducts research to improve the health of the animals maintained with special emphasis on studies that will enhance the usefulness of the rhesus as a model for studies of human disease.

Proper citation: Rhesus Monkey Breeding and Research (RRID:SCR_008357) Copy   

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http://pbil.univ-lyon1.fr/databases/homolens.php

Database of homologous genes from Ensembl organisms, structured under ACNUC sequence database management system. It allows to select sets of homologous genes among species, and to visualize multiple alignments and phylogenetic trees. It is possible to search for orthologous genes in a wide range of taxons. HOMOLENS is particularly useful for comparative sequence analysis, phylogeny and molecular evolution studies. More generally, HOMOLENS gives an overall view of what is known about a peculiar gene family. Note that HOMOLENS is split into two databases on this server: HOMOLENS contains the protein sequences while HOMOLENSDNA contains the nucleotide sequences. Protein sequences of HOMOLENS have been generated by translating the CDS of HOMOLENSDNA and using associated cross-references to generate the annotations.

Proper citation: Homologous Sequences in Ensembl Animal Genomes (RRID:SCR_008356) Copy   

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http://www.familybuilder.com

Family members connect with our Family Tree application. We have over 178,385,793 relatives added and growing! Familybuilder, the top family destination on social networks, today announced that it has been chosen by AlwaysOn as one of the AlwaysOn East Top 100 winners. Inclusion in the AlwaysOn East 100 signifies leadership amongst its peers and game-changing approaches and technologies that are likely to disrupt existing markets and entrenched players. Familybuilder was specially selected by the AlwaysOn editorial team and industry experts spanning the globe based on a set of five criteria: innovation, market potential, commercialization, stakeholder value, and media buzz. Familybuilder and the AlwaysOn East Top 100 companies will be honored at AlwaysOn''s Venture Summit East event on June 21st, 2010, at Harvard Business School in Boston, MCA. This two-day executive event features CEO presentations and high-level debates that highlight the significant economic, political, and technology trends impacting the global growth investor on the East Coast. Now more than ever, staying abreast of market conditions and innovations is crucial to financial success. After examining the companies that are on the AOE100 list, it''s obvious that innovation is not only alive and well on the East Coast, it''s accelerating in economic power and scope. says Tony Perkins, founder and editor of AlwaysOn. The companies certainly represent some of the highest-growth opportunities in the private company marketplace. The AlwaysOn East 100 winners were selected from among hundreds of other technology companies nominated by investors, bankers, journalists, and industry insiders. The AlwaysOn editorial team conducted a rigorous three-month selection process to finalize the 2010 list. Familybuilder is the first company to offer applications for communicating and staying in touch with family members on social networks including Facebook, MySpace, and others. The Company, with over 26 million users and over 160 million family members added, is quickly becoming one of the most used family services online. The Company''s flagship application on Facebook, Family Tree, is one of the top 15 non-gaming applications on the platform. With its substantial base of customers, Familybuilder is now expanding into new lines of businesses including a newsletter http://familybuilder.com/newsletter as well as subscription functionality. About AlwaysOn AlwaysOn is the leading business media brand networking the Global Silicon Valley. AlwaysOn helped ignite the social media revolution in early 2003 when it launched the AlwaysOn network. In 2004, it became the first media brand to socially network its online readers and event attendees. AlwaysOn''s preeminent executive event series includes the Summit at Stanford, OnMedia, OnHollywood, Venture Summit Mid-Atlantic, OnDemand, Venture Summit Silicon Valley, Venture Summit East, GoingGreen Silicon Valley, GoingGreen East, and GoingGreen Europe. The AlwaysOn network and live event series continue to lead the industry by empowering its readers, event participants, sponsors, and advertisers like no other media brand.

Proper citation: Familybuilder (RRID:SCR_008513) Copy   

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http://gcat.davidson.edu/rakarnik/kyte-doolittle.htm

Kyte-Doolittle hydropathy plots give you information about the possible structure of a protein. A hydropathy plot can indicate potential transmembrane or surface regions in proteins i did not find a parent or sponsor

Proper citation: Kyte Doolittle Hydropathy Plots (RRID:SCR_008358) Copy   

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