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https://github.com/davidemms/OrthoFinder

Software Python application for comparative genomics analysis. Finds orthogroups and orthologs, infers rooted gene trees for all orthogroups and identifies all of gene duplcation events in those gene trees, infers rooted species tree for species being analysed and maps gene duplication events from gene trees to branches in species tree, improves orthogroup inference accuracy. Runs set of protein sequence files, one per species, in FASTA format.

Proper citation: OrthoFinder (RRID:SCR_017118) Copy   

•   Source: SciCrunch Registry

http://hpc-bioinformatics.cineca.it/stress_mice/

Sapienza University of Rome and Cineca consortium portal. Used for analyzing published RNAseq transcriptomes obtained from brain of mice exposed to different kinds of stress protocols, to generate database of stress related differentially expressed genes and to identify factors contributing to vulnerability or resistance to stress. Allows to query database of RNAseq data.

Proper citation: Stress Mice Portal (RRID:SCR_017572) Copy   

•   Source: SciCrunch Registry

http://www.khri.med.umich.edu/research/lesperance_lab/low_freq.php

This web site lists the disease causing mutations and polymorphisms found in the Wolfram syndrome (WFS1) gene. Sponsors: This resource is supported by the University of Michigan at Ann Arbor.

Proper citation: Human Genetics Laboratory: WFS1 Gene Mutation and Polymorphism Database (RRID:SCR_001113) Copy   

•   Source: SciCrunch Registry

http://toppcluster.cchmc.org/

A tool for performing multi-cluster gene functional enrichment analyses on large scale data (microarray experiments with many time-points, cell-types, tissue-types, etc.). It facilitates co-analysis of multiple gene lists and yields as output a rich functional map showing the shared and list-specific functional features. The output can be visualized in tabular, heatmap or network formats using built-in options as well as third-party software. It uses the hypergeometric test to obtain functional enrichment achieved via the gene list enrichment analysis option available in ToppGene.

Proper citation: ToppCluster (RRID:SCR_001503) Copy   

•   Source: SciCrunch Registry

http://ww2.sanbi.ac.za/Dbases.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The STACKdb is knowledgebase generated by processing EST and mRNA sequences obtained from GenBank through a pipeline consisting of masking, clustering, alignment and variation analysis steps. The STACK project aims to generate a comprehensive representation of the sequence of each of the expressed genes in the human genome by extensive processing of gene fragments to make accurate alignments, highlight diversity and provide a carefully joined set of consensus sequences for each gene. The STACK project is comprised of the STACKdb human gene index, a database of virtual human transcripts, as well as stackPACK, the tools used to create the database. STACKdb is organized into 15 tissue-based categories and one disease category. STACK is a tool for detection and visualization of expressed transcript variation in the context of developmental and pathological states. The data system organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index

Proper citation: Sequence Tag Alignment and Consensus Knowledgebase Database (RRID:SCR_002156) Copy   

•   Source: SciCrunch Registry

http://www.projects.roslin.ac.uk/sheepmap/front.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The project aims to apply genome mapping research to sheep, utilizing previous research in sheep (in other countries) and in other species (in the UK and abroad) to the benefit of the UK sheep industry. The project itself uses existing breeding structures, knowledge of the sheep genome and experimental resources. It has three main aims: i) To use the Suffolk, Texel and Charollais Sire Referencing Schemes to detect and verify quantitative trait loci (QTLs) for growth and carcass composition traits ii) To investigate candidate genes and/or chromosomal regions for associations with production traits. iii) To investigate approaches for optimizing future genotyping strategies within the sire referencing schemes for practical and cost effective application of marker-assisted selection By using commercial breeding populations for the research, immediate application of beneficial results is possible. Potential benefits include increased genetic progress through marker assisted selection which utilizes the genotype information, correction of possible parentage errors (ultimately leading to additional genetic progress) and opportunities for using marker information for product certification. The project will benefit the UK sheep industry by the use of Marker Assisted Selection (MAS) utilizing QTL or gene variants identified in the project. Additional benefits may arise from parentage verification and correction of errors e.g. misallocation of lamb to ewe. In the longer term, opportunities may exist to use markers for quality control, tracing products to their source. The major advantage of the design of this project is that the results are immediately applicable to the breeding schemes within which the QTLs and/or genes are detected. The time lag in the application of the results that is often seen with experimental populations is minimized. The project requires close involvement with the Sire Reference Schemes, in return for their assistance the results have immediate benefit to animals within these groups.

Proper citation: UK Sheep Genome Mapping Project (RRID:SCR_002272) Copy   

•   Source: SciCrunch Registry

http://www.nactem.ac.uk/facta/

Text mining tool to discover associations between biomedical concepts from MEDLINE articles. Use the service from your browser or via a Web Service. The whole MEDLINE corpus containing more than 20 million articles is indexed with an efficient text search engine, and it allows you to navigate such associations and their textual evidence in a highly interactive manner - the system accepts arbitrary query terms and displays relevant concepts immediately. A broad range of important biomedical concepts are covered by the combination of a machine learning-based term recognizer and large-scale dictionaries for genes, proteins, diseases, and chemical compounds. There is also a FACTA+ visualization service that can be found here: http://www.nactem.ac.uk/facta-visualizer/

Proper citation: FACTA+. (RRID:SCR_001767) Copy   

•   Source: SciCrunch Registry

http://soybase.org

Professionally curated repository for genetics, genomics and related data resources for soybean that contains the most current genetic, physical and genomic sequence maps integrated with qualitative and quantitative traits. SoyBase includes annotated Williams 82 genomic sequence and associated data mining tools. The genetic and sequence views of the soybean chromosomes and the extensive data on traits and phenotypes are extensively interlinked. This allows entry to the database using almost any kind of available information, such as genetic map symbols, soybean gene names or phenotypic traits. The repository maintains controlled vocabularies for soybean growth, development, and traits that are linked to more general plant ontologies. Contributions to SoyBase or the Breeder''s Toolbox are welcome.

Proper citation: SoyBase (RRID:SCR_005096) Copy   

•   Source: SciCrunch Registry

http://snpeffect.vib.be/

A database for phenotyping human single nucleotide polymorphisms (SNPs)that primarily focuses on the molecular characterization and annotation of disease and polymorphism variants in the human proteome. They provide a detailed variant analysis using their tools such as: * TANGO to predict aggregation prone regions * WALTZ to predict amylogenic regions * LIMBO to predict hsp70 chaperone binding sites * FoldX to analyse the effect on structure stability Further, SNPeffect holds per-variant annotations on functional sites, structural features and post-translational modification. The meta-analysis tool enables scientists to carry out a large scale mining of SNPeffect data and visualize the results in a graph. It is now possible to submit custom single protein variants for a detailed phenotypic analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SNPeffect (RRID:SCR_005091) Copy   

•   Source: SciCrunch Registry

http://www.GeneWeaver.org

Freely accessible phenotype-centered database with integrated analysis and visualization tools. It combines diverse data sets from multiple species and experiment types, and allows data sharing across collaborative groups or to public users. It was conceived of as a tool for the integration of biological functions based on the molecular processes that subserved them. From these data, an empirically derived ontology may one day be inferred. Users have found the system valuable for a wide range of applications in the arena of functional genomic data integration.

Proper citation: Gene Weaver (RRID:SCR_003009) Copy   

•   Source: SciCrunch Registry

http://tools.dice-database.org/GOnet/)

Web tool for interactive Gene Ontology analysis of any biological data sources resulting in gene or protein lists.

Proper citation: GOnet (RRID:SCR_018977) Copy   

•   Source: SciCrunch Registry

http://rats.pub

Web service that conducts comprehensive literature mining to identify roles of genes in addiction. Searches PubMed to find abstracts containing genes of interest and list of curated addiction related keywords.

Proper citation: RatsPub (RRID:SCR_018905) Copy   

•   Source: SciCrunch Registry

http://www.scienceexchange.com/facilities/university-of-utah

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 15,2024. Labs and facilities of the University of Utah, which include: Microarray and Genomic Analysis Core Facility, Flow Cytometry Core Facility, Mutation Generation and Detection Facility, and the Transgenic and Gene Targeting Core.

Proper citation: University of Utah Labs and Facilities (RRID:SCR_001042) Copy   

•   Source: SciCrunch Registry

http://archive.ics.uci.edu/ml/datasets/EEG+Database

Data set from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9-msec epoch) for 1 second. There were two groups of subjects: alcoholic and control. Each subject was exposed to either a single stimulus (S1) or to two stimuli (S1 and S2) which were pictures of objects chosen from the 1980 Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition where S1 was identical to S2 or in a non-matched condition where S1 differed from S2. There were 122 subjects and each subject completed 120 trials where different stimuli were shown. The electrode positions were located at standard sites (Standard Electrode Position Nomenclature, American Electroencephalographic Association 1990). Zhang et al. (1995) describes in detail the data collection process. There are three versions of the EEG data set. * The Small Data Set (smni97_eeg_data.tar.gz) contains data for the 2 subjects, alcoholic a_co2a0000364 and control c_co2c0000337. For each of the 3 matching paradigms, c_1 (one presentation only), c_m (match to previous presentation) and c_n (no-match to previous presentation), 10 runs are shown. * The Large Data Set (SMNI_CMI_TRAIN.tar.gz and SMNI_CMI_TEST.tar.gz) contains data for 10 alcoholic and 10 control subjects, with 10 runs per subject per paradigm. The test data used the same 10 alcoholic and 10 control subjects as with the training data, but with 10 out-of-sample runs per subject per paradigm. * The Full Data Set contains all 120 trials for 122 subjects. The entire set of data is about 700 MBytes.

Proper citation: EEG Database (RRID:SCR_001581) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/genomes

The EBI genomes pages give access to a large number of complete genomes including bacteria, archaea, viruses, phages, plasmids, viroids and eukaryotes. Methods using whole genome shotgun data are used to gain a large amount of genome coverage for an organism. WGS data for a growing number of organisms are being submitted to DDBJ/EMBL/GenBank. Genome entries have been listed in their appropriate category which may be browsed using the website navigation tool bar on the left. While organelles are all listed in a separate category, any from Eukaryota with chromosome entries are also listed in the Eukaryota page. Within each page, entries are grouped and sorted at the species level with links to the taxonomy page for that species separating each group. Within each species, entries whose source organism has been categorized further are grouped and numbered accordingly. Links are made to: * taxonomy * complete EMBL flatfile * CON files * lists of CON segments * Project * Proteomes pages * FASTA file of Proteins * list of Proteins

Proper citation: EBI Genomes (RRID:SCR_002426) Copy   

•   Source: SciCrunch Registry

http://bpg.utoledo.edu/~afedorov/lab/eid.html

Data sets of protein-coding intron-containing genes that contain gene information from humans, mice, rats, and other eukaryotes, as well as genes from species whose genomes have not been completely sequenced. This is a comprehensive and convenient dataset of sequences for computational biologists who study exon-intron gene structures and pre-mRNA splicing. The database is derived from GenBank release 112, and it contains protein-coding genes that harbor introns, along with extensive descriptions of each gene and its DNA and protein sequences, as well as splice motif information. They have created subdatabases of genes whose intron positions have been experimentally determined. The collection also contains data on untranslated regions of gene sequences and intron-less genes. For species with entirely sequenced genomes, species-specific databases have been generated. A novel Mammalian Orthologous Intron Database (MOID) has been introduced which includes the full set of introns that come from orthologous genes that have the same positions relative to the reading frames.

Proper citation: EID: Exon-Intron Database (RRID:SCR_002469) Copy   

•   Source: SciCrunch Registry

http://www.sci.unisannio.it/docenti/rampone/

Data set of Homo Sapiens Exons, Introns and Splice regions extracted from GenBank Rel.123 with an aim of giving standardized material to train and to assess the prediction accuracy of computational approaches for gene identification and characterization. From the complete GenBank (Primate Sequences Division) Rel.123 (162,557 entries), entries of Human Nuclear DNA including Complete CDS and more than one Exon have been selected, and 4523 exons and 3802 introns have been extracted from these entries. Details about extracted exons and introns are reported (Locus, number, Start and End position in the entry, sequence, length, G+C content, presence of not AGCT data (nucleotide scan check)). Statistics are also reported (overall nucleotides, average G+C content, nucleotide scan check results, number of not GT starting / AG ending introns, minimum / maximum / average length, length standard deviation). 3799+3799 donor and acceptor sites, as windows of 140 nucleotides around each splice site have been extracted. After discarding sequences not including canonical GTAG junctions (65+74), including insufficient data (not enough material for a 140 nucleotide window) (686+589), including not AGCT bases (29+30), and redundant (218+226) there are 2796+ 2880 windows. Finally, there are 271,937 + 332,296 windows of false splice sites, selected by searching canonical GTAG pairs in not splicing positions. The false sites in a range of +/- 60 from a true splice site are marked as proximal.

Proper citation: HS3D - Homo Sapiens Splice Sites Dataset (RRID:SCR_002939) Copy   

•   Source: SciCrunch Registry

http://qnl.bu.edu/SLDB

Curated lists of genes associated to speech / language phenotypes and structural or functional abnormalities observed in patient populations. Entrez ID gene information, as well as gene expression profiles from the Allen Brain Atlas are available. You can also download expression data for a given gene in JSON or XML format.

Proper citation: Speech Language Disorders Database (RRID:SCR_003655) Copy   

•   Source: SciCrunch Registry

https://www.ngvbcc.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. Archiving services, insertional site analysis, pharmacology and toxicology resources, and reagent repository for academic investigators and others conducting gene therapy research. Databases and educational resources are open to everyone. Other services are limited to gene therapy investigators working in academic or other non-profit organizations. Stores reserve or back-up clinical grade vector and master cell banks. Maintains samples from any gene therapy related Pharmacology or Toxicology study that has been submitted to FDA by U.S. academic investigator that require storage under Good Laboratory Practices. For certain gene therapy clinical trials, FDA has required post-trial monitoring of patients, evaluating clinical samples for evidence of clonal expansion of cells. To help academic investigators comply with this FDA recommendation, the NGVB offers assistance with clonal analysis using LAM-PCR and LM-PCR technology.

Proper citation: National Gene Vector Biorepository (RRID:SCR_004760) Copy   

•   Source: SciCrunch Registry

http://linux1.softberry.com/spldb/SpliceDB.html

Database of canonical and non-canonical mammalian splice sites. The information about verified splice site sequences for canonical and non-canonical sites is presented with the supporting evidence. Weight matrices were built for the major splice groups, which can be incorporated into gene prediction programs.

Proper citation: SpliceDB (RRID:SCR_006262) Copy   

•   Source: SciCrunch Registry