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http://jexpress.bioinfo.no/site/

Gene expression analysis software using Java.

Proper citation: J-Express (RRID:SCR_003609) Copy   

•   Source: SciCrunch Registry

http://biorxiv.org/

A free archive and distribution service for unpublished preprints in the life sciences allowing authors to make their findings immediately available to the scientific community and receive feedback on draft manuscripts before they are submitted to journals. An article may be posted prior to, or concurrently with, submission to a journal but should not be posted if it has already been published. Once an article is published in a journal, bioRxiv will update the preprint with a link to the published version.

Proper citation: bioRxiv (RRID:SCR_003933) Copy   

•   Source: SciCrunch Registry

http://git-scm.com/

A free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. Git is easy to learn and has a tiny footprint with lightning fast performance. It outclasses SCM tools like Subversion, CVS, Perforce, and ClearCase with features like cheap local branching, convenient staging areas, and multiple workflows.

Proper citation: Git (RRID:SCR_003932) Copy   

•   Source: SciCrunch Registry

http://mendel.stanford.edu/sidowlab/downloads/quest/

A Kernel Density Estimator-based package for analysis of massively parallel sequencing data from chromatin immunoprecipitation (ChIP-seq) experiments.

Proper citation: Quantitative Enrichment of Sequence Tags (RRID:SCR_004065) Copy   

•   Source: SciCrunch Registry

http://www.brl.bcm.tmc.edu/pash/pashDownload.rhtml

Performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing.

Proper citation: Pash 3.0 (RRID:SCR_004078) Copy   

•   Source: SciCrunch Registry

http://epigraph.mpi-inf.mpg.de/WebGRAPH/

A software for genome and epigenome analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: EpiGRAPH (RRID:SCR_004326) Copy   

•   Source: SciCrunch Registry

http://pubchem.ncbi.nlm.nih.gov/

Collection of information about chemical structures and biological properties of small molecules and siRNA reagents hosted by the National Center for Biotechnology Information (NCBI).

Proper citation: PubChem (RRID:SCR_004284) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/insertionmapper/

A pipeline tool for the identification of targeted sequences from multidimensional high throughput sequencing data. It consists of four independently working modules: Data Preprocessing, Database Modeling, Dimension Deconvolution and Element Mapping. This pipeline tool is applicable to scenarios requiring analysis of the tremendous output of short reads produced in NGS sequencing experiments of targeted genome sequences.

Proper citation: InsertionMapper (RRID:SCR_004163) Copy   

•   Source: SciCrunch Registry

https://bcbio-nextgen.readthedocs.org/en/latest/

A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis.

Proper citation: bcbio-nextgen (RRID:SCR_004316) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/pdbe/

The European resource for the collection, organization and dissemination of data on biological macromolecular structures. In collaboration with the other worldwide Protein Data Bank (wwPDB) partners - the Research Collaboratory for Structural Bioinformatics (RCSB) and BioMagResBank (BMRB) in the USA and the Protein Data Bank of Japan (PDBj) - they work to collate, maintain and provide access to the global repository of macromolecular structure data. The main objectives of the work at PDBe are: * to provide an integrated resource of high-quality macromolecular structures and related data and make it available to the biomedical community via intuitive user interfaces. * to maintain in-house expertise in all the major structure-determination techniques (X-ray, NMR and EM) in order to stay abreast of technical and methodological developments in these fields, and to work with the community on issues of mutual interest (such as data representation, harvesting, formats and standards, or validation of structural data). * to provide high-quality deposition and annotation facilities for structural data as one of the wwPDB deposition sites. Several sophisticated tools are also available for the structural analysis of macromolecules.

Proper citation: PDBe - Protein Data Bank in Europe (RRID:SCR_004312) Copy   

•   Source: SciCrunch Registry

http://www.neuromatic.thinkrandom.com/

NeuroMatic is a collection of Igor Pro functions for analyzing electrophysiological data. By allowing users to organize their data into Sets and Groups, NeuroMatic makes it relatively easy to compute transformations and statistical analyses on their data, including scaling, alignment averaging, baseline subtraction, spike detection, stationarity analysis, rise-time computations, etc. Being open source and modular designed, NeuroMatic also allows users to develop their own analysis functions that can be easily incorporated into NeuroMatic's framework. Note, if you have reached this page in search of a freeware tool for neuronal reconstructions, you are more likely to be interested in Neuromantic, a software package that sounds like NeuroMatic, but is not quite the same. Features of NeuroMatic Include * Sorting, Scaling, Averaging, Interpolation * Max / Min / Mean / Level / Rise Time / FWHM / Slope Measurements * Stability / Stationarity Analysis * Event Detection * Waveform Template Matching * Spike Raster Plots * Interspike-Interval and Peri-Stimulus Time (PST) Histograms * Compact Easy-to-Use Interface * Modular design as a basis for your own procedures * Extra space for your own buttons and controls * Import functions for Axograph and Pclamp data * Automatic macro generation for batch processing Supporting Agencies: MRC, Wellcome Trust Spike, Event, Fit, NClamp, Acquisition, spike train, EPSP, IPSP, IPSC, EPSC

Proper citation: NeuroMatic (RRID:SCR_004186) Copy   

•   Source: SciCrunch Registry

http://genome.gsc.riken.jp/osc/english/dataresource/

A program to eliminate artifactual reads from next-generation sequencing data sets.

Proper citation: TagDust (RRID:SCR_004175) Copy   

•   Source: SciCrunch Registry

http://ratmine.mcw.edu/ratmine/begin.do

RatMine integrates data from RGD, UniProtKB, NCBI, KEGG and other sources to form a web-based data warehouse and tool set tailored for rat based data research. Search RatMine by entering names, identifiers, or keywords for genes, proteins, pathways, papers, etc. Additionally, we support programmatic access to our data through Application Programming Interface - choose from Perl or Java API. RatMine is a data warehouse that integrates many diverse biological data sets. The main focus is R. norvegicus genomics and proteomics. By integrating such data into one place it is possible to construct queries across domains of biological knowledge. The RatMine user interface is designed to go beyond simply looking up an identifier and viewing a report page. Some of the features include: * Quick Search is available just like on other sites, type in an identifier to see a report page. * Template queries are ''canned'' queries that provide a simple form to perform a specific task. You can create your own templates if you log in. * Lists lets you operate on whole lists of data at once. You can upload lists or save them from results tables. We also create useful public lists for everyone to use. * MyMine lets you create an account to save your own queries, bags and templates, as well as marking public templates as favorites.

Proper citation: RatMine (RRID:SCR_004190) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/resources/software/artemis/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Free genome browser and annotation tool that allows visualization of sequence features, next generation data and the results of analyses within the context of the sequence, and also its six-frame translation. Artemis is free software and is distributed under the terms of the GNU General Public License. Artemis is written in Java, and is available for UNIX, Macintosh and Windows systems. It can read EMBL and GENBANK database entries or sequence in FASTA, indexed FASTA or raw format. Other sequence features can be in EMBL, GENBANK or GFF format.

Proper citation: Artemis: Genome Browser and Annotation Tool (RRID:SCR_004267) Copy   

•   Source: SciCrunch Registry

https://github.com/alyssafrazee/derfinder

R package for differential expression analysis of RNA-seq data.

Proper citation: DER Finder (RRID:SCR_004250) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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

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

Proper citation: RAxML (RRID:SCR_006086) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry