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http://www.imtech.res.in/raghava/bhairpred/

Bhairpred server is based on machine learning technique SVM using single sequence information, evolutionary profile, predicted and observed secondary structure (as obtained using Psipred and DSSP), predicted and observed accessibility values (as obtainned from Netasa and DSSP). The methods were trained and tested on dataset of 2880 proteins and their performance was evaluated on dataset of 534 proteins used by Thornton (PNAS, 2002). Best prediction results were obtained with hybrid approach that combined prediction results from evolutionary profile, predicted secondary structure and accessibility.

Proper citation: SVM based method for predicting beta hairpin structures in proteins (RRID:SCR_008349) Copy   

•   Source: SciCrunch Registry

http://bioinfo2.ugr.es/IsoF/isofinder.html

Isofinder is an algorithm running on the web able to predict isochores at the sequence level. Isochores are long genome segments homogeneous in G+C. The algorithm works by moving a sliding pointer from left to right along the DNA sequence and computing the mean G+C values to the left and to the right of the pointer at each point. Additionally, the program checks whether this significance exceeds a probability threshold. If so, the sequence is cut at this point into two subsequences; otherwise, the sequence remains undivided. The procedure continues recursively for each of the two resulting subsequences created by each cut. This leads to the decomposition of a chromosome sequence into long homogeneous genome regions (LHGRs) with well-defined mean G+C contents, each significantly different from the G+C contents of the adjacent LHGRs. Most LHGRs can be identified with Bernardi''s isochores, given their correlation with biological features such as gene density, SINE and LINE (short, long interspersed repetitive elements) densities, recombination rate or single nucleotide polymorphism variability. The resulting isochore maps are available at http://bioinfo2.ugr.es/isochores/, and also at the UCSC Genome Browser (http://genome.cse.ucsc.edu/). Sponsors: Isofinder is funded by Universidad de Granada, Spain.

Proper citation: Isofinder: Isochore Computational Prediction (RRID:SCR_008342) 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.medinfopoli.polimi.it/GFINDer/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 16, 2019. Multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyze the obtained classifications, aiding in better interpreting microarray experiment results.

Proper citation: GFINDer: Genome Function INtegrated Discoverer (RRID:SCR_008868) Copy   

•   Source: SciCrunch Registry

http://www-personal.umich.edu/~jianghui/rseq/

A software toolkit for RNA sequence data analysis. It contains programs that cover several aspects of RNA-Seq data analysis such as read quality assessment, reference sequence generation, sequence mapping, and gene and isoform expressions estimations.

Proper citation: rSeq (RRID:SCR_000562) Copy   

•   Source: SciCrunch Registry

http://www.glycosciences.de/tools/GlycoFragments/

Service that calculates and displays the main fragments (Band C-, Z- and Y-, A- and X-ions) of oligosaccharides that should occur in MS-spectra. The extended ASCII nomenclature as recommended by IUPAC is used to input the sequence of complex oligosaccharides. However, some additional input rules have to be fulfilled. In case only the topology and composition of the oligosaccharide is known, a simpler way to input carbohydrate sequences is possible. Since the hydroxyl groups of synthetic carbohydrates are often the are protected they have included a way to indicate if sugar residue are persubstituted. Please have a look at the examples of valid input structures.

Proper citation: GlycoFragment (RRID:SCR_001573) Copy   

•   Source: SciCrunch Registry

https://services.healthtech.dtu.dk/services/DictyOGlyc-1.1/

Server that produces neural network predictions for GlcNAc O-glycosylation sites in Dictyostelium discoideum proteins.

Proper citation: DictyOGlyc (RRID:SCR_001600) 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://www.thearkdb.org/

The ArkDB database system aims to provide a comprehensive public repository for genome mapping data from farmed and other animal Species. The system also aims to provide a route in to genomic and other sequence from the initial viewpoint of linkage mapping, RH mapping, physical mapping or - possibly more importantly - QTL mapping data. Sponsors: ArkDB is funded by Biotechnology and Biological Sciences Research Council (BBSRC), UK. Cat, Chicken, Cow, Deer, Duck, Horse, Pig, Quail, Salmon, Sea Bass, Sheep, Turkey, QLT map, Linkage map, RH map, Farm animal, Genome map, Sequence, Mapping

Proper citation: ArkDB - Genomes For The Rest of Us (RRID:SCR_001838) Copy   

•   Source: SciCrunch Registry

https://cell-innovation.nig.ac.jp/GNP/index_e.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Integrated database of experiment data generated by participating research institutes and public databases relating to: 1) transcription starting position of human genes in the human genome, 2) conjunction to control region on transcriptional factors and the human genome 3) protein-protein interaction with a central focus on transcription factors organized for use in genome level research. Gene Search is the function to search the integrated database by using keywords and public IDs. The search results can be visualized by: * Genome Explorer : provides annotation of landmarks (genes, transcription start sites, etc.) aligned in accordance with their genome locations. * PPI Network : provides a graphical view of protein-protein interaction (PPI) network from the experimental data generated under the project and the public datasets. * Expression Profile : clusters genes by expression pattern and display the result with heatmap. The function provides genes which have relation of coregulation and anti-coregulation. * Comparison Viewer : This function gives the view to compare the genomic regions between human and mouse homologous genes. The viewer shows the distribution of transcription start sites (TSS) as the way of separable by tissues or time points with other landmarks on genome region. * Gene Stock : This is the function to save the gene list that you are interested until the session is closed.

Proper citation: Genome Network Platform (RRID:SCR_001737) Copy   

•   Source: SciCrunch Registry

http://services.bio.ifi.lmu.de:1046/AutoPSIDB/

Searchable database for predicted protein sequences and structures. It has the ability to search through PDB ID, UniProt ID, and descriptive classifiers.

Proper citation: AutoPSI database of predicted SCOP classifications (RRID:SCR_001923) Copy   

•   Source: SciCrunch Registry

http://pepbank.mgh.harvard.edu/

A database of peptides based on sequence text mining and public peptide data sources. Only peptides that are 20 amino acids or shorter are stored. Only peptides with available sequences are stored. After submitting a query you can further refine the results using the new heat map retrieval tool to quickly find the entries that are most relevant to you. Text classification helps you find candidate peptides that are related to cancer, cardiovascular diseases, diabetes, apoptosis, angiogenesis and molecular imaging or peptides for which binding data exist.

Proper citation: PepBank Peptide Database (RRID:SCR_002086) Copy   

•   Source: SciCrunch Registry

http://www.cbil.upenn.edu/ParaDBs/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. These databases were constructed by extracting the organism specific ESTs from dbEST, removing polyA sequences from the ends and trimming 5' and 3' regions with greater than 25% N's in a 20 base pair window. These quality sequences were then aligned using the cap2 program and the consensus sequences thus generated put into a database that is available on the web. A number of parasitic organisms were chosen that have between 3000 and 15000 ESTs. The attempt here is to provide useful information and analyses to the scientific community without curating the results in any way. A total of 55192 ESTs, deposited into dbEST/GenBank, were included in the analyses. The resulting sequences have been clustered into nonredundant gene assemblies and deposited into a relational database that supports a variety of sequence and text searches. This database has been used to compare the gene assemblies using BLAST similarity comparisons to the public protein databases to identify putative genes. Of these new entries, approximately 15%-20% represent putative homologs with a conservative cutoff of p < 10(-9), thus identifying many conserved genes that are likely to share common functions with other well-studied organisms. Gene assemblies were also used to identify strain polymorphisms, examine stage-specific expression, and identify gene families. An interesting class of genes that are confined to members of this phylum and not shared by plants, animals, or fungi, was identified. These genes likely mediate the novel biological features of members of the Apicomplexa and hence offer great potential for biological investigation and as possible therapeutic targets.

Proper citation: Parasite Databases of Clustered ESTs (RRID:SCR_002262) Copy   

•   Source: SciCrunch Registry

http://www.transcriptionfactor.org/index.cgi?Home

Database of predicted transcription factors in completely sequenced genomes. The predicted transcription factors all contain assignments to sequence specific DNA-binding domain families. The predictions are based on domain assignments from the SUPERFAMILY and Pfam hidden Markov model libraries. Benchmarks of the transcription factor predictions show they are accurate and have wide coverage on a genomic scale. The DBD consists of predicted transcription factor repertoires for 930 completely sequenced genomes.

Proper citation: DBD: Transcription factor prediction database (RRID:SCR_002300) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/compneur-srv/LGICdb/

Database providing access to information about transmembrane proteins that exist under different conformations, with three primary subfamilies: the cys-loop superfamily, the ATP gated channels superfamily, and the glutamate activated cationic channels superfamily. Due to the lack of evolutionary relationship, these three superfamilies are treated separately. It currently contains 554 entries of ligand-activated ion channel subunits. In this database one may find: the nucleic and proteic sequences of the subunits. Multiple sequence alignments can be generated, and some phylogenetic studies of the superfamilies are provided. Additionally, the atomic coordinates of subunits, or portion of subunits, are provided when available. Redundancy is kept to a minimum, i.e. one entry per gene. Each entry in the database has been manually constructed and checked by a researcher of the field in order to reduce the inaccuracies to a minimum. NOTE: This database is not actively maintained anymore. People should not consider it as an up-to-date trustable resource. For any new work, they should consider using alternative sources, such as UniProt, Ensembl, Protein Databank etc.

Proper citation: Ligand-Gated Ion Channel Database (RRID:SCR_002418) 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   

•   Source: SciCrunch Registry

http://genome.imim.es/datasets/abs2005/index.html

Public database of known binding sites identified in promoters of orthologous vertebrate genes that have been manually curated from bibliography. We have annotated 650 experimental binding sites from 68 transcription factors and 100 orthologous target genes in human, mouse, rat or chicken genome sequences. Computational predictions and promoter alignment information are also provided for each entry. For each gene, TFBSs conserved in orthologous sequences from at least two different species must be available. Promoter sequences as well as the original GenBank or RefSeq entries are additionally supplied in case of future identification conflicts. The final TSS annotation has been refined using the database dbTSS. Up to this release, 500 bps upstream the annotated transcription start site (TSS) according to REFSEQ annotations have been always extracted to form the collection of promoter sequences from human, mouse, rat and chicken. For each regulatory site, the position, the motif and the sequence in which the site is present are available in a simple format. Cross-references to EntrezGene, PubMed and RefSeq are also provided for each annotation. Apart from the experimental promoter annotations, predictions by popular collections of weight matrices are also provided for each promoter sequence. In addition, global and local alignments and graphical dotplots are also available.

Proper citation: ABS: A Database of Annotated Regulatory Binding Sites From Orthologous Promoters (RRID:SCR_002276) Copy   

•   Source: SciCrunch Registry

http://www.drugbank.ca/

Bioinformatics and cheminformatics database that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.

Proper citation: DrugBank (RRID:SCR_002700) Copy   

•   Source: SciCrunch Registry

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