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http://www.cmbi.ru.nl/phylopat

A database of phylogenetic patterns of evolution between 46 different species. PhyloPat uses the latest release of EnsMart (release 52), and their one-to-one, one-to-many and many-to-many orthologies. First, we stored all of the Ensembl IDs within the 46 species, and the orthologies between them. Second, we determined the evolutionary order of the studied species using the NCBI Taxonomy database. The phylogenetic tree of these species can be viewed here. Third, we used this phylogenetic tree as a starting point for building our phylogenetic lineages. For each gene in the first species (S. cerevisiae), we looked for orthologs in the other species. All orthologs were added to the phylogenetic lineage, and in the next round were checked for orthologs themselves, until no more orthologies were found for any of the genes. This process was repeated for all genes in all species that were not connected to any phylogenetic lineage yet. The complete phylogenetic lineage determination generated 329,998 phylogenetic lineages, consisting of 973,821 genes. These lineages can be queried here by phylogenetic patterns, MySQL regular expressions or simply a list of Ensembl/EMBL/EntrezGene/HGNC IDs. Output can be given in HTML, Excel or plain text format.

Proper citation: PhyloPat (RRID:SCR_007851) Copy   

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http://phylomedb.bioinfo.cipf.es

Database for phylomes, that is, complete collections of phylogenetic trees for all proteins encoded in a given genome. It aims at providing a repository of high-quality phylogenies and alignments for proteins encoded in model species. To derive a phylome, each protein encoded in a given genome is used as a seed to retrieve its homologs in other complete genomes. These sequences are aligned and processed to derive reliable phylogenies using several phylogenetic methods. Besides providing the evolutionary history of the gene families, phylomeDB includes phylogeny based predictions of orthology and paralogy relationships., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PhylomeDB (RRID:SCR_007850) Copy   

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http://www.partigenedb.org/

A publicly available database resource containing the assembled partial genomes for ~700 eukaryotic organisms. Partial genomes are generated from expressed sequence tag datasets containing more than 1000 sequences. PartiGeneDB allows users to view sets of genes and identify genes of interest in organisms for which a full genome is not currently available. PartiGeneDB is automatically updated to include new organism datasets as they are generated. PartiGeneDB provides four portals of entry into the database. It is hosted and supported by the Hospital for Sick Children, Toronto. In addition to providing a comprehensive resource facilitating comparative analyses, PartiGeneDB allows researchers to access the partial genomes of organisms that may not be available elsewhere. However, we recommend and encourage users interested in exploring datasets from a single organism in more depth, that you visit the specific web sites associated with the sequencing effort associated with that organism .

Proper citation: PartiGeneDB (RRID:SCR_007848) Copy   

•   Source: SciCrunch Registry

http://tdrtargets.org/

This database functions both as a website where researchers can look for information on their targets of interest; and as a tool for prioritization of targets in whole genomes. Using the database as a tool, researchers can quickly prioritize a genome of interest by performing any number of individual queries on a species of interest, then assigning numerical weights to each query (in the history page) to finally obtain a ranked list of genes by combining the weighted queries. This site is part of a WHO/TDR project seeking to exploit the availability of diverse datasets to facilitate the identification and prioritization of drug targets in pathogens causing neglected diseases.

Proper citation: TDR Targets Database (RRID:SCR_007963) Copy   

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http://bioafrica.mrc.ac.za/rnavirusdb/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 19, 2016. It is a database and web application describing the genome organization and providing analytical tools for the 938 known species of RNA virus. It can identify submitted nucleotide sequences, can place them into multiple whole-genome alignments (in species where more than one isolate has been fully sequenced) and contains translated genome sequences for all species. It has been created for two main purposes: to facilitate the comparative analysis of RNA viruses and to become a hub for other, more specialised virus Web sites.

Proper citation: RNA Virus Database (RRID:SCR_007899) Copy   

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http://statgen.ncsu.edu/asg/

Alternative splicing essentially increases the diversity of the transcriptome and has important implications for physiology, development and the genesis of diseases. This resource uses a different approach to investigate alternative splicing (instead of the conventional case-by case fashion) and integrates all transcripts derived from a gene into a single splicing graph. ASG is a database of splicing graphs for human genes, using transcript information from various major sources (Ensembl, RefSeq, STACK, TIGR and UniGene). Each transcript corresponds to a path in the graph, and alternative splicing is displayed by bifurcations. This representation preserves the relationships between different splicing variants and allows us to investigate systematically all possible putative transcripts. Web interface allows users to display the splicing graphs, to interactively assemble transcripts and to access their sequences as well as neighboring genomic regions. ASG also provide for each gene, an exhaustive pre-computed catalog of putative transcriptsin total more than 1.2 million sequences. It has found that ~65 of the investigated genes show evidence for alternative splicing, and in 5 of the cases, a single gene might produce over 100 transcripts.

Proper citation: Alternate splicing gallery (RRID:SCR_008129) Copy   

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http://www.mgc.ac.cn/VFs/

An integrated and comprehensive database of virulence factors for bacterial pathogens (also including Chlamydia and Mycoplasma). VFDB is a platform for further study of comparative pathogenomics. Major features include tabular comparison of pathogenomic composition in terms of virulence, multiple alignments and statistic analysis of homologous virulence genes, and graphical comparison of pathogenomic organization of VFs. Category: Genomics Databases (non-vertebrate) Subcategory: Prokaryotic genome databases

Proper citation: VFDB - Virulence Factors of Bacterial Pathogens (RRID:SCR_007969) Copy   

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http://phenoscape.org/

Project to create a scalable infrastructure that enables linking phenotypes across different fields of biology by the semantic similarity of their descriptions.

Proper citation: Phenoscape (RRID:SCR_003799) Copy   

•   Source: SciCrunch Registry

http://swift.cmbi.ru.nl/gv/pdbfinder/

It is a very information rich protein structure database. Unfortunately, the PDB people are not very good at making their data available for search engines. There are several reasons why search engines often fail on the PDB: * The PDB has zillions of small administrative errors * The PDB-format is search-engine unfriendly * Many PDB files are incomplete The PDBFINDER project is a possible solution to these problems. The PDBFINDER holds for each PDB file a structured, search-engine-friendly-formatted entry that holds the data-items most likely needed for people search for certain types of PDB entries. The PDBFINDER is not useful to search in atomic coordinates; it is meant to ad searches in the administrative records of PDB files. Originally, the PDBFINDER was just for searching in PDB files. However, as all the time more people are using the PDBFINDER to aid modelling and database projects, they decided to also produce the so-called PDBFINDER2. The PDBFINDER2 also holds a lot of quality information about the PDB entries. Please only use the PDBFINDER2 if you really need that quality determination aspect because the PDBFINDER2 is five times bigger than the original PDBFINDER.

Proper citation: PDB Finder (RRID:SCR_008284) Copy   

•   Source: SciCrunch Registry

http://lemur.amu.edu.pl/share/php/mirnest/home.php

A database of animal, plant and virus microRNA data maintained at the University of Poznan. The database provides: * 9980 miRNA candiates from 420 animal and plant species predicted in Expressed Sequence Tags * predicted targets for plant candidates * RNA-seq reads mapped to candidates from 29 species * external data from 12 databases that includes sequences, polymorphism, expression and regulation. miRNEST 1.0, it contains miRNA from 563 animals, plants and viruses plant species.

Proper citation: miRNEST (RRID:SCR_008907) Copy   

•   Source: SciCrunch Registry

http://www.phylogeny.fr/

A free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences. Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences.

Proper citation: Phylogeny.fr (RRID:SCR_010266) Copy   

•   Source: SciCrunch Registry

http://hipathdb.kobic.re.kr/

hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. (2010.03.09) hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways.

Proper citation: hiPathDB - human integrated Pathway DB with facile visualization (RRID:SCR_008900) Copy   

•   Source: SciCrunch Registry

http://hippocampome.org

A curated knowledge base of the circuitry of the hippocampus of normal adult, or adolescent, rodents at the mesoscopic level of neuronal types. Knowledge concerning dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex is distilled from published evidence and is continuously updated as new information becomes available. Each reported neuronal property is documented with a pointer to, and excerpt from, relevant published evidence, such as citation quotes or illustrations. Please note: This is an alpha-testing site. The content is still being vetted for accuracy and has not yet undergone peer-review. As such, it may contain inaccuracies and should not (yet) be trusted as a scholarly resource. The content does not yet appear uniformly across all combinations of browsers and screen resolutions.

Proper citation: Hippocampome.org (RRID:SCR_009023) Copy   

•   Source: SciCrunch Registry

http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=lncBase/index

Database that hosts elaborated information for both predicted and experimentally verified, miRNA-lncRNA interactions. The database consists of two distinct modules. The Experimental Module contains detailed information for more than 5,000 interactions, between 2,958 lncRNAs and 120 miRNAs, ranging from miRNA and lncRNA related facts to information specific to their interaction, the experimental validation methodologies and their outcomes. The Prediction Module, which is based on the latest version of DIANA-microT target prediction algorithm (DIANA-microT-CDS), contains detailed information for more than 10 million interactions, between 56,097 lncRNAs and 3,078 miRNAs, ranging from miRNA and lncRNA related details to specific information regarding their interaction sites, graphical representation of their binding and the predicted score. This module exhibits a unique feature for searching the database. Users are able to add genomic locations to their queries thus browsing every miRNA-lncRNA interaction that has at least one MRE located inside the queried locus.

Proper citation: DIANA-LncBase (RRID:SCR_010840) Copy   

•   Source: SciCrunch Registry

http://www.rosaceae.org/

GDR is a curated and integrated web-based relational database. GDR contains comprehensive data of the genetically anchored peach physical map, annotated EST databases of apple, peach, almond, cherry, rose, raspberry and strawberry, Rosaceae maps and markers and all publicly available Rosaceae sequences. Annotations of ESTs include contig assembly, putative function, simple sequence repeats, ORFs, Gene Ontology and anchored position to the peach physical map where applicable. Our integrated map viewer provides graphical interface to the genetic, transcriptome and physical mapping information. We continue to add Rosaceae map data to CMap, a web-based tool that allows users to view comparisons of genetic and physical maps. ESTs, BACs and markers can be queried by various categories and the search result sites are linked to the integrated map viewer or to the WebFPC physical map sites. In addition to browsing and querying the database, users can compare their sequences with the annotated GDR sequences via a dedicated sequence similarity server running either the BLAST or FASTA algorithm, search their sequences for microsatellites using the SSR server or assemble their ESTs using the CAP3 Server.

Proper citation: Genome Database for Rosaceae (RRID:SCR_012756) Copy   

•   Source: SciCrunch Registry

http://www.cleanex.isb-sib.ch/

CleanEx is a database which provides access to public gene expression data via unique approved gene symbols and which represents heterogeneous expression data produced by different technologies in a way that facilitates joint analysis and cross-dataset comparisons. To achieve this goal, each single gene expression experiment is regularly mapped on a permanent target identifier consisting of a physical description of the targeted RNA. There is one entry per gene. To have a complete view of the transcript and its product, we also link each entry to the corresponding protein. We further provide the genomic position of the transcription start site from EPD, when available. Otherwise we give the annotated start site position in Ensembl.

Proper citation: CleanEx (RRID:SCR_012911) Copy   

•   Source: SciCrunch Registry

http://mbgd.genome.ad.jp/

MBGD is a database for comparative analysis of completely sequenced microbial genomes, the number of which is now growing rapidly. The aim of MBGD is to facilitate comparative genomics from various points of view such as ortholog identification, paralog clustering, motif analysis and gene order comparison. The heart of MBGD function is to create orthologous or homologous gene cluster table. For this purpose, similarities between all genes are precomputed and stored into the database, in addition to the annotations of genes such as function categories that were assigned by the original authors and motifs that were found in the translated sequence. Using these homology data, MBGD dynamically creates orthologous gene cluster table. Users can change a set of organisms or cutoff parameters to create their own orthologous grouping. Based on this cluster table, users can further analyze multiple genomes from various points of view with the functions such as global map comparison, local map comparison, multiple sequence alignment and phylogenetic tree construction.

Proper citation: MBGD - Microbial Genome Database (RRID:SCR_012824) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/iontree.html

Software package that provides utility functions to manage and analyse MS2/MS3 fragmentation data from ion trap mass spectrometry. It was designed for high throughput metabolomics data with many biological samples and a large numer of ion trees collected. Tests have been done with data from low-resolution mass spectrometry but could be readily extended to precursor ion based fragmentation data from high resoultion mass spectrometry.

Proper citation: iontree (RRID:SCR_002813) Copy   

•   Source: SciCrunch Registry

http://home.gwu.edu/~wpeng/Software.htm

A clustering software package for identification of enriched domains from histone modification ChIP-Seq data.

Proper citation: SICER (RRID:SCR_010843) Copy   

•   Source: SciCrunch Registry