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https://cran.r-project.org/package=pROC

Software R tools for visualizing, smoothing and comparing receiver operating characteristic. Partial area under curve AUC can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

Proper citation: proc (RRID:SCR_024286) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/package=psyphy

Software R package useful in analyzing data from psychophysical experiments.Includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice data to be used with binomial family in glm and self-Start functions for estimating gamma values for CRT screen calibrations.

Proper citation: psyphy (RRID:SCR_024289) Copy   

•   Source: SciCrunch Registry

https://bioruby.org/

Software tools and libraries for bioinformatics and molecular biology, for the Ruby programming language. BioRuby has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO.

Proper citation: ruby-bio (RRID:SCR_024322) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/rpact/index.html

Software R package for design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints.

Proper citation: rpact (RRID:SCR_024300) Copy   

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https://cran.r-project.org/web/packages/shazam/index.html

Software R package provides computational framework for analyzing mutations in immunoglobulin sequences. Immunoglobulin Somatic Hypermutation Analysis.

Proper citation: shazam (RRID:SCR_024301) Copy   

•   Source: SciCrunch Registry

https://github.com/rrwick/Unicycler

Software assembly pipeline for bacterial genomes. Used for resolving bacterial genome assemblies from short and long sequencing reads. Can assemble Illumina only read sets where it functions as SPAdes-optimiser. Can assembly long read only sets for PacBio or Nanopore where it runs miniasm+Racon pipeline.

Proper citation: Unicycler (RRID:SCR_024380) Copy   

•   Source: SciCrunch Registry

http://tab2mage.sourceforge.net/

Software package written and supported by ArrayExpress curation team, which aims to ease the process of submitting large microarray experiment datasets to our public repository database.

Proper citation: tab2mage (RRID:SCR_024359) Copy   

•   Source: SciCrunch Registry

http://phospho.elm.eu.org/

Database of experimentally verified phosphorylation sites in eukaryotic proteins. Entries are manually curated with links to literature references, information about structure, interaction partners and sub-cellular compartment tissues, and sequences from the UniProt database.

Proper citation: Phospho.ELM (RRID:SCR_001109) 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://www.coryneregnet.de

Reference database and analysis platform for corynebacterial transcription factors and gene regulatory networks. It generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure.

Proper citation: CoryneRegNet (RRID:SCR_002255) Copy   

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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://www.ebi.ac.uk/astd/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Alternative Splicing and Transcript Diversity (ASTD) database project is creating a database of alternative splice events and transcripts of genes from human, mouse and rat. Full length transcripts are generated with the aim of understanding the mechanism of alternative splicing on a genome-wide scale. The current release of the human genome consists of: 16715 genes, 14101 have more than one splice isoform, with an average of 5.6 splice patterns per gene. 10831 transcripts are annotated as full length with a transcription start site and a poly(A). The current release of the mouse genome consists of: 16491 genes, 13028 have more than one splice isoform, with an average of 4 splice patterns per gene. 6011 transcripts are annotated as full length with a transcription start site and a poly(A). The current release of the rat genome consists of: 10424 genes, 6344 have more than one splice isoform, with an average of 2.6 splice patterns per gene. 1250 transcripts are annotated as full length with a transcription start site and a poly(A). Sponsors: The ASTD project at EBI is supported by a grant from the EC: Eurasnet Network of Excellence (LSHG-CT-2005-518238). It was also supported by the ASD grant from the EC (QLRT-CT-2001-02062) until November 2005 and the ATD grant from the EC (LSHG-CT-2003-503329) until May 2007.

Proper citation: The Alternatve Splicing Database (RRID:SCR_001883) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/chebi/beta/

Collection of chemical compounds and other small molecular entities that incorporates an ontological classification of chemical compounds of biological relevance, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms.

Proper citation: CHEBI (RRID:SCR_002088) Copy   

•   Source: SciCrunch Registry

http://pgfe.umassmed.edu/ffs/

Database of Drosophila transcription factor DNA binding specificity using the bacterial one-hybrid method, DNase I or SELEX methods. The database provides community access to recognition motifs and position weight matrices for transcription factors (TFs), including many unpublished motifs. Search tools and flat file downloads are provided to retrieve binding site information (as sequences, matrices and sequence logos) for individual TFs, groups of TFs or for all TFs with characterized binding specificities. Linked analysis tools allow users to identify motifs within the database that share similarity to a query matrix or to view the distribution of occurrences of an individual motif throughout the Drosophila genome. This database and its associated tools provide computational and experimental biologists with resources to predict interactions between Drosophila TFs and target cis-regulatory sequences.

Proper citation: FlyFactorSurvey (RRID:SCR_002113) Copy   

•   Source: SciCrunch Registry

http://bionumbers.hms.harvard.edu/

Database of key numbers in molecular and cell biology--the quantitative properties of biological systems of interest to computational, systems and molecular cell biologists. Contents of the database range from cell sizes to metabolite concentrations, from reaction rates to generation times, from genome sizes to the number of mitochondria in a cell. Along with the numbers, you'll find the relevant references to the original literature, useful comments, and related numbers. While always of importance to biologists, having numbers in hand is becoming increasingly critical for experimenting, modeling, and analyzing biological systems. BioNumbers was motivated by an appreciation of how long it can take to find even the simplest number in the vast biological literature. All numbers are taken directly from a literature source and that reference is provided with the number. BioNumbers is designed to be highly searchable and queries can be performed by keywords or browsed by menus. BioNumbers is a collaborative community platform where registered users can add content and make comments on existing data. All new entries and commentary are curated to maintain high quality.

Proper citation: BioNumbers (RRID:SCR_002782) Copy   

•   Source: SciCrunch Registry

http://www.germonline.org/

Cross-species microarray expression database focusing on high-throughput expression data relevant for germline development, meiosis and gametogenesis as well as the mitotic cell cycle. The database contains a unique combination of information: 1) High-throughput expression data obtained with whole-genome high-density oligonucleotide microarrays (GeneChips). 2) Sample annotation (mouse over the sample name and click on it) using the Multiomics Information Management and Annotation System (MIMAS 3.0). 3) In vivo protein-DNA binding data and protein-protein interaction data (available for selected species). 4) Genome annotation information from Ensembl version 50. 5) Orthologs are identified using data from Ensembl and OMA and linked to each other via a section in the report pages. The portal provides access to the Saccharomyces Genomics Viewer (SGV) which facilitates online interpretation of complex data from experiments with high-density oligonucleotide tiling microarrays that cover the entire yeast genome. The database displays only expression data obtained with high-density oligonucleotide microarrays (GeneChips)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: GermOnline (RRID:SCR_002807) 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://cubic.bioc.columbia.edu/db/LOC3d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. LOC3d is a database of predicted subcellular localization for eukaryotic proteins of known 3-D structure taken from the Protein Databank. Subcellular localization is currently predicted using four different methods: predictNLS (nuclear localization signal), LOChom (using homology), LOCkey (using keywords) and LOC3d (neural network based prediction). The reported localization is based on the method which predicts localization of a given protein with the highest confidence. LOCtree is a novel system of support vector machines (SVMs) that predict the subcellular localization of proteins, and DNA-binding propensity for nuclear proteins, by incorporating a hierarchical ontology of localization classes modeled onto biological processing pathways. Biological similarities are incorporated from the description of cellular components provided by the gene ontology consortium (GO). GO definitions have been simplified and tailored to the problem of protein sorting. Technically the ontology has been implemented using a decision tree with SVMs as the nodes. LOCtree, was extremely successful at learning evolutionary similarities among subcellular localization classes and was significantly more accurate than other traditional networks at predicting subcellular localization. Whenever available, LOCtree also reports predictions based on the following: 1) Nuclear localization signals found by PredictNLS, 2) Localization inferred using Prosite motifs and Pfam domains found in the protein, and 3) SWISS-PROT keywords associated with a protein. Localization is inferred in the last two cases using the entropy-based LOCkey algorithm. Additional information can be found in the LOCtree manuscript and associated PredictNLS and LOCkey publications.

Proper citation: Database oDatabase of Predicted Subcellular Localization for Eukaryotic PDB Chainsf Predicted Subcellular Localization for Eukaryotic PDB Chains (RRID:SCR_002831) Copy   

•   Source: SciCrunch Registry