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http://text0.mib.man.ac.uk/software/mldic/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9, 2022. System that retrieves relevant UniProt IDs from BioThesaurus entries using a soft string matching algorithm.

Proper citation: Smart Dictionary Lookup (RRID:SCR_000568) Copy   

•   Source: SciCrunch Registry

http://genome.igib.res.in/tbvar/

Database of the variome of Mycobacterium tuberculosis (Mtb) comprising of over 29,000 single nucleotide variations created from re-analyzed data sets corresponding to over 400 isolates of Mtb. Using a systematic computational pipeline, potential functional variants and drug-resistance associated variants have been annotated. The database has an option to annotate variants from clinical re-sequencing of Mtb.

Proper citation: tbvar (RRID:SCR_001178) Copy   

•   Source: SciCrunch Registry

http://mirna.imbb.forth.gr/SSCprofiler.html

Tool which can be used to identify novel miRNA gene candidates in the human genome.

Proper citation: SSCprofiler (RRID:SCR_001282) Copy   

•   Source: SciCrunch Registry

http://hipipe.ncgm.sinica.edu.tw/

Tool that provides high performance NGS (next-generation sequencing) data analysis pipelines so that researchers with minimum IT or bioinformatics knowledge can perform common analyses on NGS data. 3 TB of storage space is reserved for each task.

Proper citation: HiPipe (RRID:SCR_001215) Copy   

•   Source: SciCrunch Registry

https://omictools.com/casvm-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented December 12, 2016. CASVM is a web server for predicting caspase cleavage sites on protein sequences based on the SVM algorithms. It can also be used for predicting non-canonical caspase substrate cleavage sites. The server has a relational database containing experimentally verified caspase substrates retrievable using accession IDs, keywords or sequence similarity.

Proper citation: CASVM (RRID:SCR_001121) Copy   

•   Source: SciCrunch Registry

https://wiki.nci.nih.gov/display/cageneindex/Cancer+Gene+Index+End+User+Documentation

THIS RESOURCE IS NO LONGER IN SERVICE, documented on November 17, 2016. A database of genes that have been experimentally associated with human cancer diseases and/or pharmacological compounds, the evidence of these associations, and relevant annotations on the data.

Proper citation: Cancer Gene Index (RRID:SCR_001117) Copy   

•   Source: SciCrunch Registry

http://grouthbio.com/Home_Page.html

A comprehensive listing of companies and organizations involved with genomics and molecular biology. There are lists for genome software and services, genome institutions and supplies, and general institutions.

Proper citation: Geoffs Bio-Directories (RRID:SCR_001016) Copy   

•   Source: SciCrunch Registry

http://aclame.ulb.ac.be/

A database dedicated to the collection and classification of mobile genetic elements (MGEs) from various sources, comprising all known phage genomes, plasmids and transposons. In addition to provide information on the full genomes and genetic entities, it aims at building a comprehensive classification of the functional modules of MGE's at the protein, gene, and higher levels. Prophinder, a tool dedicated to the detection of prophages in sequenced bacterial genomes, is available on ACLAME.

Proper citation: A Classification of Mobile genetic Elements (RRID:SCR_001694) Copy   

•   Source: SciCrunch Registry

http://www.cisred.org/

Database for conserved sequence motifs identified by genome scale motif discovery, similarity, clustering, co-occurrence and coexpression calculations. Sequence inputs include low-coverage genome sequence data and ENCODE data. The database offers information on atomic motifs, motif groups and patterns. In promoter-based cisRED databases, sequence search regions for motif discovery extend from 1.5 Kb upstream to 200b downstream of a transcription start site, net of most types of repeats and of coding exons. Many transcription factor binding sites are located in such regions. For each target gene's search region, a base set of probabilistic ab initio discovery tools is used, in parallel, to find over-represented atomic motifs. Discovery methods use comparative genomics with over 40 vertebrate input genomes. In ChIP-seq-based cisRED databases, sequence search regions for motif discovery correspond to significant peaks that represent genome-wide sites of protein-DNA binding. Because such peaks occur in a wide range of genic and intergenic locations, ChIP-seq and promoter-based databases are complementary. Currently, motif discovery for ChIP-seq data uses scan-based approaches that make more explicit use of sets of sequences known to be functional transcription factor binding sites, and that consider a wide range of levels of conservation. For the human STAT1 ChIP-seq database search regions in the target species (human) was selected +/- 300 bp around the ChIP-seq peak maximum. Repeats and coding regions were masked. Multiple sequence alignment were used to assemble orthologous input sequences from other species.

Proper citation: cisRED: cis-regulatory element (RRID:SCR_002098) Copy   

•   Source: SciCrunch Registry

http://spliceosomedb.ucsc.edu/

A database of proteins and RNAs that have been identified in various purified splicing complexes. Various names, orthologs and gene identifiers of spliceosome proteins have been cataloged to navigate the complex nomenclature of spliceosome proteins. Links to gene and protein records are also provided for the spliceosome components in other databases. To navigate spliceosome assembly dynamics, tools were created to compare the association of spliceosome proteins with complexes that form at specific stages of spliceosome assembly based on a compendium of mass spectrometry experiments that identified proteins in purified splicing complexes.

Proper citation: Spliceosome Database (RRID:SCR_002097) Copy   

•   Source: SciCrunch Registry

http://mpromdb.wistar.upenn.edu/

A curated database that strives to annotate gene promoters identified from ChIP-Seq experiment results. The long term goal of the database is to provide an integrated resource for mammalian gene transcriptional regulation and epigenetics. Users can search based on Enterz gene id/symbol, or by tissue/cell specific activity and filter results based on any combination of tissue/cell specificity, known/novel, CpG/NonCpG, and protein-coding/non-coding gene promoters. It is also integrated with GBrowse genome browser for visualiztion of ChIP-seq profiles and display the annotations.

Proper citation: MPromDb (RRID:SCR_002136) Copy   

•   Source: SciCrunch Registry

http://ptmcode.embl.de/

Database of known and predicted functional associations between protein posttranslational modifications (PTMs) within proteins. In its first release it contains 13 different PTM types. PTM types are abbreviated in a two letter code as: Ph (phosphorylation), NG (N-linked glycosylation), Ac (acetylation), OG (O-linked glycosylation), Ub (ubiquitination), Me (methylation), SM (SUMOylation), Hy (hydroxylation), Ca (carboxylation), Pa (palmitoylation), Su (sulfation), Ni (nitrosylation) and CG (C-linked glycosylation). These PTMs are present in 25,765 proteins of 8 different eukaryotes. The database is focused on the exploration of the global post-translational regulation of proteins, not only by describing the set of its modifications, but by identifying the functional associations among the PTMs present in the protein. To do that, they combine five different evidence channels based on a literature survey, the modified residue co-evolution, their structural proximity, their competition for the same residue and the location within PTM highly-enriched protein regions (hotspots) and show the functional associations within the context of the protein architecture.

Proper citation: PTMcode (RRID:SCR_002046) Copy   

•   Source: SciCrunch Registry

http://cbrc.kaust.edu.sa/tcof/

Database that facilitates the exploration of proteins involved in the regulation of transcription in humans by binding to regulatory DNA regions (transcription factors) and proteins involved in the regulation of transcription in humans by interacting with transcription factors and not binding to regulatory DNA regions (transcription co-factors).

Proper citation: TcoF (RRID:SCR_002158) Copy   

•   Source: SciCrunch Registry

http://bisc.soe.ucsc.edu/

A protein-protein interaction (PPI) database intending to bridge between the two communities most active in their characterization: structural biology and functional genomics researchers. It offers users access to binary subcomplexes, (i.e. physical direct interactions between proteins) that are either structurally characterized or modellable entries in the main functional genomics PPI databases BioGRID, IntAct and HPRD. Selected web services are available to further investigate the validity of postulated PPI by inspection of their hypothetical modelled interfaces., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BISC (RRID:SCR_002064) Copy   

•   Source: SciCrunch Registry

http://srv00.recas.ba.infn.it/ASPicDB/

A database to access reliable annotations of the alternative splicing pattern of human genes, obtained by ASPic algorithm (Castrignano et al. 2006), and to the functional annotation of predicted isoforms. Users may select and extract specific sets of data related to genes, transcripts and introns fulfilling a combination of user-defined criteria. Several tabular and graphical views of the results are presented, providing a comprehensive assessment of the functional implication of alternative splicing in the gene set under investigation. ASPicDB also includes information on tissue-specific splicing patterns of normal and cancer cells, based on available EST data and their library source annotation.

Proper citation: ASPicDB (RRID:SCR_002102) Copy   

•   Source: SciCrunch Registry

https://jeonslab.snu.ac.kr/publication/trip-database-2-0-a-manually-curated-information-hub-for-accessing-trp-channel-interaction-network/

A manually curated database of protein-protein interactions (PPIs) for mammalian transient receptor potential (TRP) channels. The detailed summary of PPI data, fits into 4 categories: screening, validation, characterization, and functional consequence. These categorizations give answers for four basic questions about PPIs: how to identify PPIs (screening); how to confirm PPIs (validation); what are biochemical properties of PPIs (characterization); what are biological meaning of PPIs (functional consequence). Users can find in-depth information specified in the literature on relevant analytical methods, gene constructs, and cell/tissue types. The database has a user-friendly interface with several helpful features, including a search engine, an interaction map, and a function for cross-referencing useful external databases.

Proper citation: TRIP Database (RRID:SCR_002058) Copy   

•   Source: SciCrunch Registry

http://netbio.bgu.ac.il/tissuenet/

Database of human tissue protein-protein interactions (PPIs) that associates each interaction with human tissues that express both pair mates. This was achieved by integrating current data of experimentally detected PPIs with extensive data of gene and protein expression across 16 main human tissues. Users can query TissueNet using a protein and retrieve its PPI partners per tissue, or using a PPI and retrieve the tissues expressing both pair mates. The graphical representation of the output highlights tissue-specific and tissue-wide PPIs. Thus, TissueNet provides a unique platform for assessing the roles of human proteins and their interactions across tissues.

Proper citation: TissueNet - The Database of Human Tissue Protein-Protein Interactions (RRID:SCR_002052) Copy   

•   Source: SciCrunch Registry

http://srv00.recas.ba.infn.it/SpliceAidF/search.php

A database of human splicing factors and their RNA - binding sites. For each splicing factor (SF) the database reports its functional domains and its protein and chemical interactors. Furthermore, experimentally validated RNA-SF interactions are collected, including relevant information on the RNA binding sites such as the genes where these sites lie, their genomic coordinates, the splicing effects, experimental procedures, as well as the corresponding bibliographic references. Information from experiments showing no RNA-SF binding is also collected, at least in the assayed conditions. SpliceAid-F contains 4227 interactions, 2622 RNA binding sites and 1170 no-binding sites, including information on binding and no-binding specificity in different cellular contexts. SpliceAid-F can provide significant information to explain an observed splicing pattern as well as the effect of mutations in functional regulatory elements.

Proper citation: SpliceAid-F (RRID:SCR_002082) Copy   

•   Source: SciCrunch Registry

http://rsnp.psych.ac.cn/

Database for curated regulatory single nucleotide polymorphisms (SNPs) to assist researchers in selecting candidate SNPs for further genetic studies (especially for QTL studies), identifying causal variants of certain phenotypes, and exploring in-depth molecular mechanisms. It is characterized by several unique features: (i) To improve reliability, all SNPs in rSNPBase are annotated with reference to experimentally supported regulatory elements. (ii) rSNPBase focuses on rSNPs involved in a wide range of regulation types, including proximal and distal transcriptional regulation and post-transcriptional regulation, and identifies their potentially regulated genes. (iii) Linkage disequilibrium (LD) correlations between SNPs were analysed so that the regulatory feature is annotated to SNP-set rather than a single SNP. (iv) rSNPBase provides the spatio-temporal labels and experimental eQTL labels for SNPs.

Proper citation: rSNPBase (RRID:SCR_001947) Copy   

•   Source: SciCrunch Registry

http://genome.ufl.edu/mapper/mapper-run?init=rs

Data analysis service to identify single nucleotide polymorphisms (SNPs) that may have an effect on the presence of one or more transcription factor binding sites (TFBSs). The input to regulatory SNPs (rSNPs) can consist of one or more genes, a genomic region, or a user-provided sequence. SNPs may be downloaded from dbSNP, entered manually by the user, or obtained by comparing two sequences. The result is a list of TFBSs whose score changes significantly as a consequence of the allele substitution caused by the SNP.

Proper citation: rSNPs MAPPER (RRID:SCR_001945) Copy   

•   Source: SciCrunch Registry