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http://elm.eu.org

Computational biology resource for investigating candidate functional sites in eukarytic proteins. Functional sites which fit to the description linear motif are currently specified as patterns using Regular Expression rules. To improve the predictive power, context-based rules and logical filters are being developed and applied to reduce the amount of false positives. The current version of the ELM server provides core functionality including filtering by cell compartment, phylogeny, globular domain clash (using the SMART/Pfam databases) and structure. In addition, both the known ELM instances and any positionally conserved matches in sequences similar to ELM instance sequences are identified and displayed (see ELM instance mapper). Although the ELM resource contains a large collection of functional site motifs, the current set of motifs is not exhaustive.

Proper citation: Eukaryotic Linear Motif (RRID:SCR_003085) Copy   

•   Source: SciCrunch Registry

http://www.bork.embl.de/j/

The main focus of this Computational Biology group is to predict function and to gain insights into evolution by comparative analysis of complex molecular data. The group currently works on three different scales: * genes and proteins, * protein networks and cellular processes, and * phenotypes and environments. They require both tool development and applications. Some selected projects include comparative gene, genome and metagenome analysis, mapping interactions to proteins and pathways as well as the study of temporal and spatial protein network aspects. All are geared towards the bridging of genotype and phenotype through a better understanding of molecular and cellular processes. The services - resources & tools, developed by Bork Group, are mainly designed and maintained for research & academic purposes. Most of services are published and documented in one or more papers. All our tools can be completely customized and integrated into your existing framework. This service is provided by the company biobyte solutions GmbH. Please visit their tools and services pages for full details and more information. Standard commercial licenses for our tools are also available through biobyte solutions GmbH. The group is partially associated with Max Delbr��ck Center for Molecular Medicine (MDC), Berlin.

Proper citation: EMBL - Bork Group (RRID:SCR_000810) Copy   

•   Source: SciCrunch Registry

https://tobiasrausch.com/delly/

Integrated structural variant prediction software that can detect deletions, tandem duplications, inversions and translocations at single-nucleotide resolution in short-read massively parallel sequencing data. It uses paired-ends and split-reads to sensitively and accurately delineate genomic rearrangements throughout genome.

Proper citation: DELLY (RRID:SCR_004603) Copy   

•   Source: SciCrunch Registry

http://www-huber.embl.de/users/anders/HTSeq/doc/count.html

Script distributed with the HT-Seq Python framework for processing RNA-seq or DNA-seq data., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: htseq-count (RRID:SCR_011867) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.11/bioc/html/easyRNASeq.html

Software that calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as ''RPKM'' or by the ''DESeq'' or ''edgeR'' package.

Proper citation: easyRNASeq (RRID:SCR_012020) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/st/emboss_transeq/

Software that translates nucleic acid sequences to their corresponding peptide sequences. It can translate to the three forward and three reverse frames, and output multiple frame translations at once.

Proper citation: Transeq (RRID:SCR_015647) Copy   

•   Source: SciCrunch Registry

http://biomodels.net/

For computational modeling to become more widely used in biological research, researchers must be able to exchange and share their results. The development and broad acceptance of common model representation formats such as SBML is a crucial step in that direction, allowing researchers to exchange and build upon each other''s work with greater ease and accuracy. The BioModels.net project is another step: an international effort to: 1. define agreed-upon standards for model curation 2. define agreed-upon vocabularies for annotating models with connections to biological data resources 3. provide a free, centralized, publicly-accessible database of annotated, computational models in SBML and other structured formats To facilitate assembling useful collections of quantitative models of biological phenomena, it is crucial to establish standards for the vocabularies used in model annotations as well as criteria for minimum quality levels of those models. The BioModels.net project aims to bring together a community of interested researchers to address these issues. We are working towards defining these standards through white papers and process definitions. All of the products of our efforts are open and freely available through this site.

Proper citation: BioModels.net (RRID:SCR_010525) Copy   

•   Source: SciCrunch Registry

http://www.embl.de/~korbel/CopySeq/

A computational tool that analyzes the depth-of-coverage of high-throughput DNA sequencing reads, and can integrate paired-end and breakpoint junction analysis based CNV-analysis approaches, to infer locus copy-number genotypes.

Proper citation: CopySeq (RRID:SCR_010758) Copy   

•   Source: SciCrunch Registry

http://foldx.crg.es/

A computer algorithm that provides a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF has been tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins . FoldX uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FoldX have been weighted using empirical data obtained from protein engineering experiments.

Proper citation: FoldX (RRID:SCR_008522) Copy   

•   Source: SciCrunch Registry

http://coot.embl.de/g2d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

Proper citation: Candidate Genes to Inherited Diseases (RRID:SCR_008190) Copy   

•   Source: SciCrunch Registry

http://projects.villa-bosch.de/dbase/dsmm/

DSMM provides an easily-searchable source of information about movies showing biomolecular motions that have been generated by computer simulation. All of the movies are available through the internet. Molecules simulated include proteins, DNA, RNA, sugars and lipids. Simulation techniques include Molecular Dynamics, Brownian Dynamics and automated docking procedures.

Proper citation: DSMM - a Database of Simulated Molecular Motions (RRID:SCR_007631) 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://string.embl.de/

Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)

Proper citation: STRING (RRID:SCR_005223) Copy   

•   Source: SciCrunch Registry

http://swift.cmbi.kun.nl/swift/FUNPEP/gergo/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 6, 2016. As a part of the FUNPEP project, we made a small collection of peptides, which are known to form these amyloid plaques (Known amyloidogenic peptides). Sequences, including respective animal analogues, were extracted from SWISSPROT, and aligned. These sequences and some words about the peptides can be found under the links in the table below. Some molecular modelling was also perfomed, to show some possible structures of amyloids. The peptides on these pages were not chosen because of some kind of sequence similarity, what is more, they hardly have any. Their common, and very starnge property is the ability to form amyloid plaques (or fibrils). The exact structure and the formation of these supermolacular structures are still subject of research, but there are lots of promising results.

Proper citation: FUNPEP (RRID:SCR_007667) Copy   

•   Source: SciCrunch Registry

http://phenomenal-h2020.eu/

Cloud based standardised European e-infrastructure for metabolomics and phenomics data processing, analysis and information mining on public or private cloud providers. Used for large scale computing for medical metabolomics.

Proper citation: PhenoMeNal (RRID:SCR_016605) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/blast2/index.html

It is used to compare a novel sequence with those contained in nucleotide and protein databases by aligning the novel sequence with previously characterized genes.

Proper citation: Washington University Basic Local Alignment Search Tool (RRID:SCR_008285) Copy   

•   Source: SciCrunch Registry

http://www.russelllab.org/miRNAs/

Data set of 2003 and 2005 miRNA-Target predictions for Drosophila miRNAs.

Proper citation: miRNA (RRID:SCR_010849) Copy   

•   Source: SciCrunch Registry