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http://sourceforge.net/projects/as-peak/

A software that utilizes a peak detection algorithm to identify RNA-protein binding sites.

Proper citation: AS-Peak (RRID:SCR_000380) Copy   

•   Source: SciCrunch Registry

http://thomsonreuters.com/metadrug/

A leading systems pharmacology solution that incorporates extensive manually curated information on biological effects of small molecule compounds. Predictive and analytical algorithms look at chemical compounds from different angles in one integrated workflow are available for: * Individual previously described compounds to look up their known information and predict currently unknown properties * Individual newly synthesized or isolated compounds to predict their properties from its structures * Compound libraries to extract known and predict new properties of individual compounds and perform their comparison and prioritization

Proper citation: MetaDrug (RRID:SCR_000461) Copy   

•   Source: SciCrunch Registry

http://tango.crg.es/

A computer algorithm to predict aggregation nucleating regions in proteins as well the effect of mutations and environmental conditions on the aggregation propensity of these regions.

Proper citation: TANGO (RRID:SCR_001770) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Rebholz-srv/ebimed/

A web application that combines Information Retrieval and Extraction from Medline. EBIMed finds Medline abstracts in the same way PubMed does. Then it goes a step beyond and analyses them to offer a complete overview on associations between UniProt protein/gene names, GO annotations, Drugs and Species. The results are shown in a table that displays all the associations and links to the sentences that support them and to the original abstracts. By selecting relevant sentences and highlighting the biomedical terminology EBIMed enhances your ability to acquire knowledge, relate facts, discover implications and, overall, have a good overview economizing the effort in reading.

Proper citation: EBIMed (RRID:SCR_005314) Copy   

•   Source: SciCrunch Registry

http://medblast.sibsnet.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. An algorithm that finds articles most relevant to a genetic sequence. In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here, a new literature-mining tool MedBlast is developed, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. The genome sequencing projects generate such a large amount of data every day that many molecular biologists often encounter some sequences that they know nothing about. Literature is usually the principal resource of such information. It is relatively easy to mine the articles cited by the sequence annotation; however, it is a difficult task to retrieve those relevant articles without direct citation relationship. The related articles are those described in the given sequence (gene/protein), or its redundant sequences, or the close homologs in various species. They can be divided into two classes: direct references, which include those either cited by the sequence annotation or citing the sequence in its text; indirect references, those which contain gene symbols of the given sequence. A few additional issues make the task even more complicated: (1) symbols may have aliases; and (2) one sequence may have a couple of relatives that we want to take into account too, which include redundant (e.g. protein and gene sequences) and close homologs. Here the issues are addressed by the development of the software MedBlast, which can retrieve the related articles of the given sequence automatically. MedBlast uses BLAST to extend homology relationships, precompiled species-specific thesauruses, a useful semantics technique in natural language processing (NLP), to extend alias relationship, and EUtilities toolset to search and retrieve corresponding articles of each sequence from PubMed. MedBlast take a sequence in FASTA format as input. The program first uses BLAST to search the GenBank nucleic acid and protein non-redundant (nr) databases, to extend to those homologous and corresponding nucleic acid and protein sequences. Users can input the BLAST results directly, but it is recommended to input the result of both protein and nucleic acid nr databases. The hits with low e-values are chosen as the relatives because the low similarity hits often do not contain specific information. Very long sequences, e.g. 100k, which are usually genomic sequences, are discarded too, for they do not contain specific direct references. User can adjust these parameters to meet their own needs.

Proper citation: MedBlast (RRID:SCR_008202) Copy   

•   Source: SciCrunch Registry

http://www.poissonboltzmann.org/apbs/

APBS is a software package for modeling biomolecular solvation through solution of the Poisson-Boltzmann equation (PBE), one of the most popular continuum models for describing electrostatic interactions between molecular solutes in salty, aqueous media. APBS was designed to efficiently evaluate electrostatic properties for such simulations for a wide range of length scales to enable the investigation of molecules with tens to millions of atoms. It also provides implicit solvent models of nonpolar solvation which accurately account for both repulsive and attractive solute-solvent interactions. APBS uses FEtk (the Finite Element ToolKit) to solve the Poisson-Boltzmann equation numerically. FEtk is a portable collection of finite element modeling class libraries written in an object-oriented version of C. It is designed to solve general coupled systems of nonlinear partial differential equations using adaptive finite element methods, inexact Newton methods, and algebraic multilevel methods.

Proper citation: Adaptive Poisson-Boltzmann Solver (RRID:SCR_008387) Copy   

•   Source: SciCrunch Registry

http://www.jcsg.org/

The JCSG is a multi-institutional consortium that aims to explore the expanding protein universe to find new challenges and opportunities to significantly contribute to new biology, chemistry and medicine through development of HT approaches to structural genomics. The mission of JCSG is to to operate a robust HT protein structure determination pipeline as a large-scale production center for PSI-2. A major goal is to ensure that innovative high-throughput approaches are developed that advance not only structural genomics, but also structural biology in general, via investigation of large numbers of high-value structures that populate protein fold and family space and by increasing the efficiency of structure determination at substantially reduced cost. The JCSG centralizes each core activity into single dedicated sites, each handling distinct, but interconnected objectives. This unique approach allows each specialized group to focus on its own area of expertise and provides well-defined interfaces among the groups. In addition, this approach addresses the requirements for the scalability needed to process large numbers of targets at a greatly reduced cost per target. JCSG production groups are: - Administrative Core - Bioinformatics Core - Crystallomics Core - Structure Determination Core - NMR Core JCSG is deeply committed to the development of new technologies that facilitate high throughput structural genomics. The areas of development include hardware, software, new experimental methods, and adaptation of existing technologies to advance genome research. In the hardware arena, their commitment is to the development of technologies that accelerate structure solution by increasing throughput rates at every stage of the production pipeline. Therefore, one major area of hardware development has been the implementation of robotics. In the software arena, they have developed enterprise resource software that track success, failures, and sample histories from target selection to PDB deposition, annotation and target management tools, and helper applications aimed at facilitating and automating multiple steps in the pipeline. Sponsors: The Joint Center for Structural Genomics is funded by the National Institute of General Medical Sciences (NIGMS), as part of the second phase of the Protein Structure Initiative (PSI) of the National Institutes of Health (U54 GM074898).

Proper citation: Joint Center for Structural Genomics (RRID:SCR_008251) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/gin.html

GIN-IE is a high precision system for extracting protein/gene interactions, interaction cue words, and directionality from the literature. Syntax-aware inferences about the roles of the entities are made by using the syntactic and dependency parse tree structures of the sentences. Negation and speculation are frequently occurring language phenomena that modify the factuality of the information contained in text. GIN-IE detects and distinguishes interactions that are extracted from negated or speculative sentences. GIN-IE has been integrated with the NCIBI PubMed daily update and processing pipeline. The extracted interactions are accessible through MimiWeb.

Proper citation: Gene Interaction Extraction from the Literature (RRID:SCR_008660) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.ingenuity.com/products/pathways_analysis.html

A web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.

Proper citation: Ingenuity Pathway Analysis (RRID:SCR_008653) Copy   

•   Source: SciCrunch Registry

http://www.molsoft.com/icm_browser.html

Molecular graphics environment which provides biologists and chemists with representations of proteins, DNA, RNA, and multiple sequence alignments. Users can build, annotate, and edit interactive views and slides of molecules. Users can also superimpose protein structures, search PDB, measure distanaces and angles, and view and make high resolution images of alignments.

Proper citation: ICM Browser (RRID:SCR_014878) Copy   

•   Source: SciCrunch Registry

http://weizhong-lab.ucsd.edu/cd-hit-otu/

Data analysis service and software program that perform Operantional Taxonomic Units (OTUs) finding. It uses a three-step clustering for identifying OTUs. The first-step clustering is raw read filtering and trimming. The second step is error-free reads picking.. At the last step, OTU clustering is done at different distanct cutoffs (0.01, 0.02, 0.03... 0.12).

Proper citation: CD-HIT-OTU (RRID:SCR_006983) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.albany.edu/~dmaps

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 6, 2016. DMAPS database contains pre-computed multiple structure alignments for protein chains in the Protein Data Bank (PDB). Automated structure alignments have been generated for classified protein families using CE-MC algorithm. Alignments have been built only for those families with at least three members. Currently, multiple structure alignments are available for 3050 SCOP-, 3087 CATH-, 664 ENZYME- and 1707 CE-based families. Users will be able to retrieve multiple alignments for a given PDB chain classified by one of these criteria.

Proper citation: DMAPS - A Database of Multiple Alignments for Protein Structures (RRID:SCR_007140) Copy   

•   Source: SciCrunch Registry

http://rebase.neb.com/rebase/

Database of information about restriction enzymes and related proteins containing published and unpublished references, recognition and cleavage sites, isoschizomers, commercial availability, methylation sensitivity, crystal, genome, and sequence data. DNA methyltransferases, homing endonucleases, nicking enzymes, specificity subunits and control proteins are also included. Several tools are available including REBsites, BLAST against REBASE, NEBcutter and REBpredictor. Putative DNA methyltransferases and restriction enzymes, as predicted from analysis of genomic sequences, are also listed. REBASE is updated daily and is constantly expanding. Users may submit new enzyme and/or sequence information, recommend references, or send them corrections to existing data. The contents of REBASE may be browsed from the web and selected compilations can be downloaded by ftp (ftp.neb.com). Additionally, monthly updates can be requested via email.,

Proper citation: REBASE (RRID:SCR_007886) Copy   

•   Source: SciCrunch Registry

http://signalink.org/

An integrated resource to analyze signaling pathway cross-talks, transcription factors, miRNAs and regulatory enzymes. The multi-layered database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The website allows the interactive exploration of how each signaling protein is regulated. Features * experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; * combines manual curation with large-scale datasets; * provides confidence scores for each interaction; * operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML).

Proper citation: SignaLink (RRID:SCR_003569) Copy   

•   Source: SciCrunch Registry

http://abc.med.cornell.edu/pdzbase

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022.A manually curated protein-protein interaction database developed specifically for interactions involving PDZ domains. It currently contains 339 experimentally determined protein protein interactions.

Proper citation: PDZBase (RRID:SCR_003568) Copy   

•   Source: SciCrunch Registry

http://www.jbldesign.com/jmogil/enter.html

Database of genes regulated by pain derived from published manuscripts describing results of pain-relevant knockout studies. The database has two levels of exploration: across-gene and within-gene. The across-gene level, the PainGenesdbSelector, is encountered first. All genes in the database can be accessed and sorted by their gene name, protein name, common names and acronyms, or genomic position (by navigating a graphic representation of the mouse genome). The gene and protein names can be selected from an alphabetical list, or by typing a text string into a search box.

Proper citation: Pain Genes database (RRID:SCR_004771) Copy   

•   Source: SciCrunch Registry

http://ahdb.ee.ncku.edu.tw/

Database of apo and holo structure pairs of proteins before and after binding. Various protein functions have been shown directly associated with conformational transitions triggered by binding other molecules. Tertiary structures determined in the unbound and bound state are usually named apo and holo structures, respectively. AH-DB is the largest database of apo-holo structure pairs and provides a sophisticated interface to search and view the collected data. It contains 746314 apo-holo pairs of 3638 proteins from 702 organisms.

Proper citation: Apo and Holo structures DataBase (RRID:SCR_004800) Copy   

•   Source: SciCrunch Registry

http://floresta.eead.csic.es/footprintdb/index.php

Database with 2797 unique DNA-binding proteins (mostly transcription factors, TFs), 4196 Position Weight Matrices (PWMs) and 13161 DNA Binding Sites extracted from the literature and other repositories. The binding interfaces of (most) proteins in the database are inferred from the collection of protein-DNA complexes described in 3D-footprint. The database predicts transcription factors which bind a specific DNA site or motif and DNA motifs or sites likely to be recognized by a specific DNA-binding protein.

Proper citation: footprintDB (RRID:SCR_005368) Copy   

•   Source: SciCrunch Registry

http://deepbase.sysu.edu.cn/chipbase/

A database for decoding transcription factor binding maps, expression profiles and transcriptional regulation of long non-coding RNAs (lncRNAs, lincRNAs), microRNAs, other ncRNAs (snoRNAs, tRNAs, snRNAs, etc.) and protein-coding genes from ChIP-Seq data. ChIPBase currently includes millions of transcription factor binding sites (TFBSs) among 6 species. ChIPBase provides several web-based tools and browsers to explore TF-lncRNA, TF-miRNA, TF-mRNA, TF-ncRNA and TF-miRNA-mRNA regulatory networks., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ChIPBase (RRID:SCR_005404) Copy   

•   Source: SciCrunch Registry

http://gila.bioengr.uic.edu/snp/toposnp

A topographic database for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include disease- associated nsSNPs derived from the Online Mendelian Inheritance in Man (OMIM) database and other nsSNPs derived from dbSNP, a resource at the National Center for Biotechnology Information that catalogs SNPs. TopoSNP further classifies each nsSNP site into three categories based on their geometric location: those located in a surface pocket or an interior void of the protein, those on a convex region or a shallow depressed region, and those that are completely buried in the interior of the protein structure. These unique geometric descriptions provide more detailed mapping of nsSNPs to protein structures. It also includes relative entropy of SNPs calculated from multiple sequence alignment as obtained from the Pfam database (a database of protein families and conserved protein motifs) as well as manually adjusted multiple alignments obtained from ClustalW. These structural and conservational data can be useful for studying whether nsSNPs in coding regions are likely to lead to phenotypic changes. TopoSNP includes an interactive structural visualization web interface, as well as downloadable batch data.

Proper citation: TopoSNP (RRID:SCR_005572) Copy   

•   Source: SciCrunch Registry