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http://sv.gersteinlab.org/pemer/
Software package as computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Package is composed of three modules, PEMer workflow, SV-Simulation and BreakDB. PEMer workflow is a sensitive software for detecting SVs from paired-end sequence reads. SV-Simulation randomly introduces SVs into a given genome and generates simulated paired-end reads from novel genome.
Proper citation: PEMer (RRID:SCR_005263) Copy
http://ogeedb.embl.de/#summary
Online GEne Essentiality database containing genes that were tested experimentally for essentiality and their features; it also provides a set of tools to systematically explore and analyze these data. The main purpose of this project is to better understand gene essentiality by facilitating the comparisons of the differences and similarities between essential and non-essential genes. This is achieved by collecting not only experimentally tested essential and non-essential genes, but also associated gene features such as expression profiles, duplication status, conservation across species, evolutionary origins and involvement in embryonic development. We focus on large-scale experiments and complement our data with text-mining results. Genes are organized into data sets according to their sources. Genes with variable essentiality status across data sets are tagged as conditionally essential, highlighting the complex interplay between gene functions and environments. Linked tools allow the user to compare gene essentiality among different gene groups, or compare features of essential genes to non-essential genes, and visualize the results. Why is it different from existing databases? * we included both essential and non-essential genes so that we could better understand the gene essentiality by comparing the similarities and differences between the two gene sets; * we compiled a list of features for each gene, including whether they are duplicates or involved in development, the number of other homologous genes in the same genome, as well as their earliest expression stages during development. These features are keys to understand the essentiality of genes; * we also provide a set of tools to explore our data and visualize the results. For example, users can simply divide genes into two groups according to whether they are duplicates, calculate the proportion of essential genes (PE%) in each group and then visualize the results in a bar plot; or they can classify genes into multiple groups according to their earliest expression stages during evolution, compare the essentiality of genes that were expressed earlier with those were latter, and plot the results in a line chart.
Proper citation: OGEE - Online GEne Essentiality database (RRID:SCR_006080) Copy
http://bioinformatics.biol.uoa.gr/human_gpdb/
A publicly accessible, relational database of human G-Proteins and their interactions with human GPCRs and Effectors. Advanced data integration techniques make Human-gpDB very rich in context since all of the bioentities are linked to a rich variety of external data sources. High quality visualization methods make the networks more informative and the extraction of information easier. Human-gpDB is currently a very useful tool for drug targeting investigation. The sequences of G-Proteins and GPCRs are classified according to a hierarchy of different classes, families and sub-families, whereas the Effectors sequences are classified in families, subfamilies and types, based on extensive literature search. The classification of GPCRs follows the IUPHAR classification, while the Effectors classification is a unique feature and is based on their function. The database currently holds information about 713 human GPCRs, 36 human G-Proteins and 99 human Effectors. The collection of the information about the interactions between these molecules was done manually and the current status of Human-gpDB reveals information about 1663 connections between GPCRs and G-Proteins and 1618 connections between G-Proteins and Effectors.
Proper citation: Human-gpDB (RRID:SCR_006223) Copy
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
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
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
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
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
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
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
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
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
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
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
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
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
http://www.russelllab.org/miRNAs/
Data set of 2003 and 2005 miRNA-Target predictions for Drosophila miRNAs.
Proper citation: miRNA (RRID:SCR_010849) Copy
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