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https://amoebadb.org/amoeba/

Integrated genomic and functional genomic database for Entamoeba and Acanthamoeba parasites. Contains genomes of three Entamoeba species and microarray expression data for E. histolytica. Integrates whole genome sequence and annotation and includes experimental data and environmental isolate sequences provided by community researchers.

Proper citation: AmoebaDB (RRID:SCR_017592) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/science/tools/seqtools

Software for sequence alignments that displays multiple match sequences aligned against a single genomic reference sequence. It can be used for manipulation, display and annotation of genomic data, to check the quality of an alignment, to find missing/misaligned sequence, and to identify splice sites and polyA sites.

Proper citation: Blixem (RRID:SCR_015994) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.mdc-berlin.de/pigx/

Software application as a collection of genomic pipelines used for raw fastq read data of bisulfite experiments, RNAseq samples, single cell dropseq analysis, reads from ChIPseq experiments, analysis of sequence mutations in CRISPR-CAS9 targeted amplicon sequencing data.

Proper citation: PiGx (RRID:SCR_016476) Copy   

•   Source: SciCrunch Registry

https://software.broadinstitute.org/gatk/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 18th,2023. Software package for genome analysis. Used for analysis of next generation genomic data in cancer.

Proper citation: IndelGenotyper (RRID:SCR_016663) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/Web/Search/entrezfs.html

Web portal for global query cross database search and retrieval system that provides access to all databases simultaneously with a single query string and user interface. Retrieves nucleotide and protein sequence data, gene centered and genomic mapping information, 3D structures, and references. Covers databases including protein sequence data from PIR-International, PRF, Swiss-Prot, and PDB and nucleotide sequence data from GenBank that includes information from EMBL and DDBJ.

Proper citation: Entrez (RRID:SCR_016640) Copy   

•   Source: SciCrunch Registry

https://doi.org/10.1016/j.genrep.2019.100414

Eutherian comparative genomic analysis protocol as one framework of eutherian gene data set revisions. Protocol integrated gene annotations, phylogenetic analysis and protein molecular evolution analysis with 3 new tests including test of reliability of public eutherian genomic sequences using genomic sequence redundancies, test of contiguity of public eutherian genomic sequences using multiple pairwise genomic sequence alignments and test of protein molecular evolution using relative synonymous codon usage statistics. Public eutherian reference genomic sequence data sets.

Proper citation: Eutherian comparative genomic analysis protocol (RRID:SCR_014401) Copy   

•   Source: SciCrunch Registry

http://gfpweb.aecom.yu.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Database of expression patterns of C. elegans promoter::GFP constructs. A text description of the observed pattern is provided, indicating the stage(s) and tissue(s) in which GFP is expressed. Also available for some strains are the corresponding 2D and 3D images. Investigators may browse the entire list, search by gene name, tissue, stage, and pattern. Search results may be downloaded in .csv and .txt formats. All of the strains in the expression pattern database are displayed in the browse page. The records are organized by gene; information such as locus name, genomic location (WormBase), the presence of images and videos, and the actual expression pattern are shown in a tabular format.

Proper citation: Expression Patterns for C. elegans promoter GFP fusions (RRID:SCR_001619) Copy   

•   Source: SciCrunch Registry

http://zfin.org

Model organism database that serves as central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. Data represented are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations.Serves as primary community database resource for laboratory use of zebrafish. Developed and supports integrated zebrafish genetic, genomic, developmental and physiological information and link this information extensively to corresponding data in other model organism and human databases.

Proper citation: Zebrafish Information Network (ZFIN) (RRID:SCR_002560) Copy   

•   Source: SciCrunch Registry

http://www.ifti.org/ootfd/

ooTFD (object-oriented Transcription Factors Database) is a successor to TFD, the original Transcription Factors Database. This database is aimed at capturing information regarding the polypeptide interactions which comprise and define the properties of transcription factors. ooTFD contains information about transcription factor binding sites, as well as composite relationships within transcription factors, which frequently occur as multisubunit proteins that form a complex interface to cellular processes outside the transcription machinery through protein-protein interactions. ooTFD contains information represented in TFD but also allows the representation of containment, composite, and interaction relationships between transcription factor polypeptides. It is designed to represent information about all transcription factors, both eukaryotic and prokaryotic, basal as well as regulatory factors, and multiprotein complexes as well as monomers.

Proper citation: object-oriented Transcription Factors Database (RRID:SCR_002435) Copy   

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http://romi.bu.edu/elisa/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. ELISA is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function neighborhoods. The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). It introduces a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind.

Proper citation: Evolutionary Lineage Inferred from Structural Analysis (RRID:SCR_002343) Copy   

•   Source: SciCrunch Registry

http://www.allgenes.org/

DoTS (Database Of Transcribed Sequences) is a human and mouse transcript index created from all publicly available transcript sequences. The input sequences are clustered and assembled to form the DoTS Consensus Transcripts that comprise the index. These transcripts are assigned stable identifiers of the form DT.123456 (and are often referred to as dots). The transcripts are in turn clustered to form putative DoTS Genes. These are assigned stable identifiers of the form DG.1234356. As of September 1, 2004, the DoTS annotation team has manually annotated 43,164 human and 78,054 mouse DoTS Transcripts (DTs), corresponding to 3,939 human and 7,752 mouse DoTS Genes (DGs). Use the manually annotated gene query to see the DoTS Transcripts that have been manually annotated. The focus of the DoTS project is integrating the various types of data (e.g., EST sequences, genomic sequence, expression data, functional annotation) in a structured manner which facilitates sophisticated queries that are otherwise not easy to perform. DoTS is built on the GUS Platform which includes a relational database that uses controlled vocabularies and ontologies to ensure that biologically meaningful queries can be posed in a uniform fashion. An easy way to start using the site is to search for DoTS Transcripts using an existing cDNA or mRNA sequence. Click on the BLAST tab at the top of the page and enter your sequence in the form provided. All the transcripts with significant sequence similarity to your query sequence will be displayed. Or use one of the provided queries to retrieve transcripts using a number of criteria. These queries are listed on the query page, which can also be reached by clicking on the tab marked query at the top of the page. Finally, the boolean query page allows these queries to be combined in a variety of ways. Sponsors: Funding provided by -NIH grant RO1-HG-01539-03 -DOE grant DE-FG02-00ER62893

Proper citation: Database of Transcribed Sequences (RRID:SCR_002334) Copy   

•   Source: SciCrunch Registry

http://wpicr.wpic.pitt.edu/WPICCompGen/hclust/hclust.htm

Software application that is a simple clustering method that can be used to rapidly identify a set of tag SNP's based upon genotype data (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: HCLUST (RRID:SCR_009154) Copy   

•   Source: SciCrunch Registry

http://mitores.ba.itb.cnr.it

MitoRes, is a comprehensive and reliable resource for massive extraction of sequences and sub-sequences of nuclear genes and encoded products targeting mitochondria in metazoa. It has been developed for supporting high-throughput in-silico analyses aimed to studies of functional genomics related to mitochondrial biogenesis, metabolism and to their pathological dysfunctions. It integrates information from the most accredited world-wide databases to bring together gene, transcript and encoded protein sequences associated to annotations on species name and taxonomic classification, gene name, functional product, organelle localization, protein tissue specificity, Enzyme Classification (EC), Gene Ontology (GO) classification and links to other related public databases. The section Cluster, has been dedicated to the collection of data on protein clustering of the entire catalogue of MitoRes protein sequences based on all versus all global pair-wise alignments for assessing putative intra- and inter-species functional relationships. The current version of MitoRes is based on the UniProt release 4 and contains 64 different metazoan species. The incredible explosion of knowledge production in Biology in the past two decades has created a critical need for bioinformatic instruments able to manage data and facilitate their retrieval and analysis. Hundreds of biological databases have been produced and the integration of biological data from these different resources is very important when we want to focus our efforts towards the study of a particular layer of biological knowledge. MitoRes is a completely rebuilt edition of MitoNuc database, which has been extensively modified to deal successfully with the challenges of the post genomic era. Its goal is to represent a comprehensive and reliable resource supporting high-quality in-silico analyses aimed to the functional characterization of gene, transcript and amino acid sequences, encoded by the nuclear genome and involved in mitochondrial biogenesis, metabolism and pathological dysfunctions in metazoa. The central features of MitoRes are: # an integrated catalogue of protein, transcript and gene sequences and sub-sequences # a Web-based application composed of a wide spectrum of search/retrieval facilities # a sequence export manager allowing massive extraction of bio-sequences (genes, introns, exons, gene flanking regions, transcripts, UTRs, CDS, proteins and signal peptides) in FASTA, EMBL and GenBank formats. It is an interconnected knowledge management system based on a MySQL relational database, which ensures data consistency and integrity, and on a Web Graphical User Interface (GUI), built in Seagull PHP Framework, offering a wide range of search and sequence extraction facilities. The database is compiled extracting and integrating information from public resources and data generated by the MitoRes team. The MitoRes database consists of comprehensive sequence entries whose core data are protein, transcript and gene sequences and taxonomic information describing the biological source of the protein. Additional information include: bio-sequences structure and location, biological function of protein product and dynamic links to both, external public databases used as data resources and public databases reporting complementary information. The core entity of the MitoRes database is represented by the protein so that each MitoRes entry is generated for each protein reported in the UniProt database as a nuclear encoded protein involved in mitochondrial biogenesis and function. Sponsors: MitoRes has been supported by Ministero Universit e Ricerca Scientifica, Italy (PRIN, Programma Biotecnologie legge 95/95-MURST 5, Proiect MURST Cluster C03/2000, CEGBA). Currently it is supported by operating grants from the Ministero dellIstruzione, dellUniversit e della Ricerca (MIUR), Italy (PNR 2001-2003 (FIRB art.8) D.M. 199, Strategic Program: Post-genome, grant 31-063933 and Project n.2, Cluster C03 L. 488/929).

Proper citation: MitoRes (RRID:SCR_008208) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/parasites/parasite-genome.html

This website contains information about the genomic sequence of parasites. It also contains multiple search engines to search six frame translations of parasite nucleotide databases for motifs, parasite protein databases for motifs, and parasite protein databases for keywords and text terms. * Guide to Internet Access to Parasite Genome Information * Guide to web-based analysis tools * Parasite Genome BLAST Server: Search a range of parasite specific nucleotide sequence databases with your own sequence. * Parasite Proteome Keyword Search Facility: Search parasite protein databases for keywords and text terms * Parasite Proteome Motif Search Facility: Search parasite protein databases for motifs * Parasite Six Frame Translation Motif Search Facility: Search six frame translations of parasite nucleotide databases for motifs * Genome computing resources: A list of ftp and gopher sites where genome computing applications and other resources can be found.

Proper citation: Parasite genome databases and genome research resources (RRID:SCR_008150) Copy   

•   Source: SciCrunch Registry

http://ccm.ucdavis.edu/

The University of California Davis Center for Comparative Medicine (CCM) is a cooperative, interdisciplinary research and teaching center that is co-sponsored by the School of Medicine and the School of Veterinary Medicine. CCM Faculty members have academic appointments in one or both Schools. The CCM Research Mission is to investigate the pathogenesis of human and animal disease, using animal models or naturally occurring animal diseases. Areas of emphasis include host-agent interactions during infectious disease, intervention and prevention strategies for infectious diseases, cancer, and mouse biology. CCM faculty contribute a broad range of expertise to these areas, including the disciplines of immunology, genomics, pathology, biochemistry, physiology, microbiology, molecular virology, and informatics. Through its robust and interdisciplinary research programs, the CCM provides a rich academic environment for teaching at the professional, graduate, and post-graduate levels within the School of Medicine and School of Veterinary Medicine. Opportunities are available for professional students from both schools to gain research experience. PhD candidates can pursue training opportunities in the CCMs faculty-sponsored research laboratories, with support from a number of training grants. This diverse research environment is intended to attract and train high-quality candidates to the disciplines of comparative medicine, independent and collaborative research, and mouse biology. Sponsors: CCM is supported by UC Davis.

Proper citation: University of California Davis Center for Comparative Medicine (RRID:SCR_008294) Copy   

•   Source: SciCrunch Registry

http://www.biobankcentral.org/resource/wwibb.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 27, 2013. Web-based portal to connect all the constituencies in the global biobank community. The project seeks to increase the transparency and accessibility of the scientific research process by connecting researchers with an additional source of funding - microinvestments received from the broader online community. In exchange for these public investments, researchers will maintain research logs detailing the play-by-play progress made in their project, as well as publishing all of their data in a public database under a science commons license. These research projects, in turn, will serve to continually update a research-based neuroscience-based human brain & body curriculum. Biobanks are the meeting point of two major information trends in biomedical research: the generation of huge amounts of genomic and other laboratory data, and the electronic capture and integration of patient clinical records. They are comprised of large numbers of human biospecimens supplemented with clinical data. Biobanks when implemented effectively can harness the power of both genomic and clinical data and serve as a critical bridge between basic and applied research, linking laboratory to patient and getting to cures faster. As science and technology leaders work to address the many challenges facing U.S. biobanks logistical, technical, ethical, financial, intellectual property, and IT BioBank Central will serve as an accurate and timely source of knowledge and news about biorepositories and their role in research and drug development. The Web site also provides a working group venue, patient and public education programs, and a forum for international collaboration and harmonization of best practices.

Proper citation: BioBank Central (RRID:SCR_008645) Copy   

•   Source: SciCrunch Registry

http://genes2mind.org

genes2mind is a tool for rapid exploratory analysis of psychotropic drug-induced gene expression in the brain. We present here an open resource containing comparison of effects of various classes of psychotropic drugs on transcriptional alterations of ~20,000 genes in the mouse brain (C57BL/6J). Data stored in the database include raw gene expression values as well as results of drug comparison. * Genomic Signature Identification section allows for the identification of drug-specific genomic signatures. * Genomic Signature Analysis section allows for further inspection and visualization of the signatures using multidimensional data analysis (PCA), co-expression analysis and heatmaps. * Single Gene Inspection allows for brief review of expression of specific candidate genes using barplots.

Proper citation: genes2mind (RRID:SCR_008872) Copy   

•   Source: SciCrunch Registry

http://www.allelix.net

THIS RESOURCE IS NO LONGER IN SERVICE, documented on September 23, 2013. Software application / data analysis service where one can enter the alleles of commonly used STR by clicking the mouse. The algorithm calculates the paternity index and the Essen-Moeller probability of kinship for the deficiency- and the trio case. Everybody can use the network-software online after registering. The usage on the internet is free. Academic users can ask me to unlock an option to display the details (formulas/frequencies etc.) and to have an export-funktion to MS Word. The program is in German and (non-professional) English. An expansion to other languages is easy, if somebody helps us with the translation. For those who are interested to have the software running on their own intranet (for database security reasons) an individual agreement can be found. (entry from Genetic Analysis Software) (German version is: http://www.allelix.de)

Proper citation: ALLELIX (RRID:SCR_009115) Copy   

•   Source: SciCrunch Registry

http://wpicr.wpic.pitt.edu/WPICCompGen/bars.htm

Software application that is a statistical method that bridges the gap between single-locus and haplotype-based tests of association. It is based on the non-parametric regression techniques embodied by Bayesian Adaptive Regression Splines. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BARS (RRID:SCR_009123) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/GRO

Ontology that is a conceptual model for the domain of gene regulation. It covers processes that are linked to the regulation of gene expression as well as physical entities that are involved in these processes (such as genes and transcription factors) in terms of ontology classes and semantic relations between classes. GRO is intended to represent common knowledge about gene regulation in a formal way rather than representing extremely fine-grained classes as can be found in ontologies such as the Gene Ontology (GO) (created for data base annotation purposes) and various relevant databases. The main purpose of the ontology is to support NLP applications. It has a particular focus on the relations between processes and the molecules (participants) involved. The basic structure of the GRO is a direct acyclic graph (DAG) with ontology classes as nodes and is-a relations between classes as edges. The taxonomic backbone is further enriched by several semantic relation types (part-of, from-species, participates-in with the two sub-relations agent-of and patient-of).

Proper citation: Gene Regulation Ontology (RRID:SCR_010590) Copy   

•   Source: SciCrunch Registry