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https://code.google.com/p/pepr-chip-seq/

A ChIP-Seq peak calling or differential binding analysis tool that is primarily designed for data with biological replicates. It uses a negative binomial distribution to model the read counts among the samples in the same group, and look for consistent differences between ChIP and control group or two ChIP groups run under different conditions.

Proper citation: PePr (RRID:SCR_005759) Copy   

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http://www.bioinf.uni-freiburg.de/Software/GraphProt/

Software for modeling binding preferences of RNA-binding proteins from high-throughput experiments such as CLIP-seq and RNAcompete.

Proper citation: GraphProt (RRID:SCR_005842) Copy   

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http://www.clipz.unibas.ch/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 20,2019.Database and analysis environment for experimentally determined binding sites of RNA-binding proteins. It supports the automatic functional annotation of short reads resulting primarily from crosslinking and immunoprecipitation experiments (CLIP) performed with RNA-binding proteins in order to identify the binding sites of these proteins. The functional annotation could be also applied to short reads resulting from other types of experiments such as mRNA-Seq, Digital Gene Expression, small RNA cloning, etc. The platform enables visualization and mining of individual data sets as well as analysis involving multiple experimental data sets. The platform can support collaborative projects involving multiple users and groups of users as well as public and private datasets.

Proper citation: CLIPZ (RRID:SCR_005755) Copy   

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http://europepmc.org/

Free access to biomedical literature resources including all of PubMed and PubMed Central, agricultural abstracts (from AGRICOLA), over 4 million international life science patents abstracts, National Health Service (NHS) clinical guidelines, and is supplemented with Chinese Biological Abstracts and the Citeseer database. As well as powerful search of abstracts and full text articles, it also includes: * article citations and sort order based on citation count * data citations mined from full text articles * links to and from related databases and institutional repositories * a tool to create bibliographies linked to your ORCID * named entity recognition of keywords and text-mining-based applications showcased in Europe PMC Labs * Tools for recipients of grants from one of the Europe PMC funders to deposit full-text manuscripts and link them to those specific grants. * Web services for programmatic access to all the above bibliographic information and 50,000 grants. * Search by publication date, relevance, or the number of times an article has been cited. * Links to public databases such as UniProt, Protein Data Bank (PDBe), and the European Nucleotide Archive (ENA) are provided. * Through textmining technologies, you can highlight and browse keywords such as gene names, organisms and diseases. * Search 40,000 biomedical research grants awarded to the 18,000 PIs supported by the Europe PMC funders. * Roadtest new tools based on Europe PMC content in Europe PMC labs. * In Europe PMC plus, PIs supported by the Europe PMC funders can link grants to publication information, view article citation and download statistics, and submit manuscripts.

Proper citation: Europe PubMed Central (RRID:SCR_005901) Copy   

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http://www-math.u-strasbg.fr/genpred/spip.php?article3

R software package to study, predict and simulate the diffusion of a signal through a temporal gene network. It predicts changes in gene expressions after a biological perturbation in the network and provides graphical outputs that allow monitoring the spread of a signal through the network., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Cascade (RRID:SCR_005861) Copy   

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http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape''''s versatile visualization environment to produce an intuitive and customizable visual representation of the results. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: BiNGO: A Biological Networks Gene Ontology tool (RRID:SCR_005736) Copy   

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http://www.nematodes.org/nematodegenomes/index.php/Main_Page

A collaborative wiki that collates information on completed, ongoing and planned genome and transcriptome sequencing projects on species from phylum Nematoda. The intention is to encourage genome sequencing across the diversity of the phylum Nematoda. Wiki includes: * Published complete nematode genomes: A dynamically generated table of all species for which the genome is published. * Nematode species with genomes in progress: A dynamically generated table of all species for which a genome project is underway. Users may add species to the list * Proposed nematode genome projects: To propose a species for genome sequencing, edit its species page, and set the genome project status to proposed. * BLAST server: Search a number of the nematode-genomes-in-progress with genes of your choice. Currently there are 12 draft genomes available... * Genomes with Data available: Genomes with data available for download. Users may add more data URLs to strain pages or update the URLs.

Proper citation: 959 Nematode Genomes (RRID:SCR_006068) Copy   

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http://mmdb.iab.keio.ac.jp/

MMMDB, Mouse Multiple tissue Metabolome DataBase, is a freely available metabolomic database containing a collection of metabolites measured from multiple tissues from single mice. The datases are collected using a single instrument and not integrated from literatures, which is useful for capturing the holistic overview of large metabolomic pathway. Currently data from cerabra, cerebella, thymus, spleen, lung, liver, kidney, heart, pancreas, testis, and plasma are provided. Non-targeted analyses were performed by capillary electropherograms time-of-flight mass spectrometry (CE-TOFMS) and, therefore, both identified metabolites and unknown (without matched standard) peaks were uploaded to this database. Not only quantified concentration but also processed raw data such as electropherogram, mass spectrometry, and annotation (such as isotope and fragment) are provided.

Proper citation: MMMDB - Mouse Multiple tissue Metabolome DataBase (RRID:SCR_006064) Copy   

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http://biodev.cea.fr/interevol/

InterEvol database is designed for the analysis of co-evolution events at the interface of known structures of hetero- and homo-oligomers. The database can be search and analyzed through 3 interconnected levels of analysis: * From a Keyword or the PDB entry of a complex, you can browse: ** structural homologs for every chain in other complexes ** structural interologs for every interface ** retrieve pre-computed sequence alignments in diverse species * From 1 or 2 sequences of interacting partners: ** build 2 multiple sequence alignments with the same species ordered in each ** query the InterEvol database with alignments using profile-profile comparison method * Visualize structure vs sequence alignment at the complex interface ** A dedicated Pymol plugin is provided ** Alignment views in Pymol are interactively restricted to the residues selected at the interface

Proper citation: InterEvol database (RRID:SCR_006054) Copy   

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https://github.com/stamatak/standard-RAxML

Software program for phylogenetic analyses of large datasets under maximum likelihood.

Proper citation: RAxML (RRID:SCR_006086) Copy   

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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   

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http://db-mml.sjtu.edu.cn/ICEberg/

ICEberg is an integrated database that provides comprehensive information about integrative and conjugative elements (ICEs) found in bacteria. ICEs are conjugative self-transmissible elements that can integrate into and excise from a host chromosome. An ICE contains three typical modules, integration and excision, conjugation, and regulation modules, that collectively promote vertical inheritance and periodic lateral gene flow. Many ICEs carry likely virulence determinants, antibiotic-resistant factors and/or genes coding for other beneficial traits. ICEberg offers a unique, highly organized, readily explorable archive of both predicted and experimentally supported ICE-relevant data. It currently contains details of 428 ICEs found in representatives of 124 bacterial species, and a collection of >400 directly related references. A broad range of similarity search, sequence alignment, genome context browser, phylogenetic and other functional analysis tools are readily accessible via ICEberg. ICEberg will facilitate efficient, multidisciplinary and innovative exploration of bacterial ICEs and be of particular interest to researchers in the broad fields of prokaryotic evolution, pathogenesis, biotechnology and metabolism. The ICEberg database will be maintained, updated and improved regularly to ensure its ongoing maximum utility to the research community.

Proper citation: ICEberg (RRID:SCR_006026) Copy   

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http://www.bioconductor.org/packages/2.14/bioc/html/h5vc.html

Software package that contains functions to interact with tally data from Next-Generation Sequencing (NGS) experiments that is stored in HDF5 files.

Proper citation: h5vc (RRID:SCR_006039) Copy   

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http://www.niehs.nih.gov/research/resources/software/biostatistics/eagleview/

An information-rich viewer for next-generation genome assembles with data integration capability. EagleView can display a dozen different types of information including base qualities, machine specific trace signals, and genome feature annotations. It provides an easy way for inspecting visually the quality of a genome assembly and validating polymorphism candidate sites (e.g., SNPs) reported by polymorphism discovery tools. It can also facilitate data interpretation and hypothesis generation. EagleView is a multi-platform application developed with C++ and is available for all three major platforms: Windows, Linux, and Mac OS.

Proper citation: EagleView (RRID:SCR_006859) Copy   

•   Source: SciCrunch Registry

http://bar.utoronto.ca/welcome.htm

Web-based tools for working with functional genomics and other data, including Gene Expression and Protein Tools, Molecular Markers and Mapping Tools, and Other Genomic Tools. Most are designed with the plant (mainly Arabidopsis) researcher in mind, but a couple of them can be useful to the wider research community, e.g. Mouse eFP Browser or BlastDigester. The associated paper for most tools is available.

Proper citation: BAR (RRID:SCR_006748) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/bigpre/

A quality assessment software package for next-genomics sequencing data.

Proper citation: BIGpre (RRID:SCR_006781) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/2.12/bioc/html/seqbias.html

Software package that implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence.

Proper citation: seqbias (RRID:SCR_006832) Copy   

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http://phenom.ccbr.utoronto.ca/index.jsp

Database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae, it allows storing, retrieving, visualizing and data mining the quantitative single-cell measurements extracted from micrographs of the temperature-sensitive (ts) mutant cells. PhenoM allows users to rapidly search and retrieve raw images and their quantified morphological data for genes of interest. The database also provides several data-mining tools, including a PhenoBlast module for phenotypic comparison between mutant strains and a Gene Ontology module for functional enrichment analysis of gene sets showing similar morphological alterations. About one-fifth of the genes in the budding yeast are essential for haploid viability and cannot be functionally assessed using standard genetic approaches such as gene deletion. To facilitate genetic analysis of essential genes, we and others have assembled collections of yeast strains expressing temperature-sensitive (ts) alleles of essential genes. To explore the phenotypes caused by essential gene mutation we used a panel of genetically engineered fluorescent markers to explore the morphology of cells in the ts strain collection using high-throughput microscopy. The database contains quantitative measurements of 1,909,914 cells and 78,194 morphological images for 775 temperature-sensitive mutants spanning 491 different essential genes in permissive temperature (26* C) and restrictive temperature (32* C). The morphological images were generated by high-content screening (HCS) technology.

Proper citation: PhenoM - Phenomics of yeast Mutants (RRID:SCR_006970) Copy   

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http://tritrypdb.org/tritrypdb/

An integrated genomic and functional genomic database providing access to genome-scale datasets for kinetoplastid parasites, and supporting a variety of complex queries driven by research and development needs. Currently, TriTrypDB integrates datasets from Leishmania braziliensis, L. infantum, L. major, L. tarentolae, Trypanosoma brucei and T. cruzi. Users may examine individual genes or chromosomal spans in their genomic context, including syntenic alignments with other kinetoplastid organisms. Data within TriTrypDB can be interrogated utilizing a sophisticated search strategy system that enables a user to construct complex queries combining multiple data types. All search strategies are stored, allowing future access and integrated searches. ''''User Comments'''' may be added to any gene page, enhancing available annotation; such comments become immediately searchable via the text search, and are forwarded to curators for incorporation into the reference annotation when appropriate. TriTrypDB provides programmatic access to its searches, via REST Web Services. The result of a web service request is a list of records (genes, ESTs, etc) in either XML or JSON format. REST services can be executed in a browser by typing a specific URL. TriTrypDB and its continued development are possible through the collaborative efforts between EuPathDB, GeneDB and colleagues at the Seattle Biomedical Research Institute (SBRI).

Proper citation: TriTrypDB (RRID:SCR_007043) Copy   

•   Source: SciCrunch Registry

http://bioinfo.cau.edu.cn/agriGO/

A web-based tool and database for the gene ontology analysis. Its focus is on agricultural species and is user-friendly. The agriGO is designed to provide deep support to agricultural community in the realm of ontology analysis. Compared to other available GO analysis tools, unique advantages and features of agriGO are: # The agriGO especially focuses on agricultural species. It supports 45 species and 292 datatypes currently. And agriGO is designed as an user-friendly web server. # New tools including PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA) were developed. The arrival of these tools provides users with possibilities for data mining and systematic result exploration and will allow better data analysis and interpretation. # The exploratory capability and result visualization are enhanced. Results are provided in different formats: HTML tables, tabulated text files, hierarchical tree graphs, and flash bar graphs. # In agriGO, PAGE and SEACOMPARE can be used to carry out cross-comparisons of results derived from different data sets, which is very important when studying multiple groups of experiments, such as in time-course research. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: agriGO (RRID:SCR_006989) Copy   

•   Source: SciCrunch Registry