Searching the RRID Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
A package of over twenty mass spectrometry-based tools primarily geared toward proteomic data analysis and database mining. It can be run from the command line, but is primarily used through a web browser, and there is a public website that allows anyone to use the software without local installation. Tandem mass spectrometry analysis tools are used for database searching and identification of peptides, including post-translationally modified peptides and cross-linked peptides. Support for isotope and label-free quantification from this type of data is provided. MS-Viewer software allows sharing and displaying of annotated spectra from many different tandem mass spectrometry data analysis packages. Other tools include software for analyzing peptide mass fingerprinting data (MS-Fit); prediction of theoretical fragmentation of peptides (MS-Product); theoretical chemical or enzymatic digestion of proteins (MS-Digest); and theoretical modeling of the isotope distribution of any chemical, including peptides (MS-Isotope). Searches using amino acid sequence can be used to identify homologous peptides in a database (MS-Pattern); the use of the combination of amino acid sequence and masses can be used for homologous peptide and protein identification using MS-Homology. Tandem mass spectrometry peak list files can be filtered for the presence of certain peaks or neutral losses using MS-Filter. Given a list of proteins, MS-Bridge can report all potential cross-linked peptide combinations of a specified mass. Given a precursor peptide mass and information about known amino acid presence, absence, or modifications, MS-Comp can report all amino acid combinations that could lead to the observed mass.
Proper citation: Protein Prospector (RRID:SCR_014558) Copy
https://atgu.mgh.harvard.edu/plinkseq/
An open-source C/C++ library for working with human genetic variation data. The specific focus is to provide a platform for analytic tool development for variation data from large-scale resequencing projects, particularly whole-exome and whole-genome studies. However, the library could in principle be applied to other types of genetic studies, including whole-genome association studies of common SNPs. (entry from Genetic Analysis Software)
Proper citation: PLINK/SEQ (RRID:SCR_013193) Copy
https://github.com/nservant/HiC-Pro
Software tool as optimized and flexible pipeline for Hi-C data processing. Used to process Hi-C data, from raw fastq files, paired end Illumina data, to normalized contact maps.
Proper citation: HiC-Pro (RRID:SCR_017643) Copy
Cloud based platform for simplified, standardized and reproducible microbiome data analysis. Allows users to process microbiome datasets through pipelines of existing software tools.
Proper citation: Nephele (RRID:SCR_016595) Copy
https://github.com/wilkinsonlab/epigenomics_pipeline
Software tool as epigenomics analysis pipeline for analysis of ChIP-Seq and RNA-Seq data using Docker images containing Galaxy and Jupyter.
Proper citation: Epigenomics Workflow on Galaxy and Jupyter (RRID:SCR_017544) Copy
https://bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html
Software package to arrange multiple heatmaps and support various annotation graphics. Used to visualize associations between different sources of data sets and to reveal potential patterns.
Proper citation: ComplexHeatmap (RRID:SCR_017270) Copy
https://swissmodel.expasy.org/
Software tool as fully automated protein structure homology modeling server, accessible via ExPASy web server, or from program DeepView Swiss Pdb-Viewer. Structural bioinformatics web-server dedicated to homology modeling of 3D protein structures. Used to make protein modelling accessible to all biochemists and molecular biologists.
Proper citation: SWISS-MODEL (RRID:SCR_018123) Copy
http://www.imgt.org/HighV-QUEST/home.action
Next generation B and T cell sequence alignment and characterization online surface by IMGT. Web portal for immunoglobulin (IG) or antibody and T cell receptor (TR) analysis from NGS high throughput and deep sequencing.
Proper citation: IMGT HighV-QUEST (RRID:SCR_018196) Copy
https://pm4ngs.readthedocs.io/
Software tool to generate standard organizational structure for Next Generation Sequencing data analysis. Includes directory structure for project, several Jupyter notebooks for data management and CWL workflows for pipeline execution.
Proper citation: PM4NGS (RRID:SCR_019164) Copy
Software application to organize and store in structured format signaling information published in scientific literature. Information is stored as binary causative relationships between biological entities and can be represented graphically as activity flow. Each relationship is linked to literature reporting experimental evidence. Each node is annotated with chemical inhibitors that modulate its activity. Signaling information is mapped to human proteome. SIGNOR 2.0 stores manually annotated causal relationships between proteins and other biologically relevant entities including chemicals, phenotypes, complexes, etc with compliance to FAIR data principles.
Proper citation: SIGNOR (RRID:SCR_018485) Copy
Software tool as a cross-platform NIfTI format image viewer. Used for viewing and exporting of brain images. MRIcroGL is a variant of MRIcron.
Proper citation: MRIcron (RRID:SCR_002403) Copy
http://webclu.bio.wzw.tum.de/profcom/
Profiling of Complex Functionality (ProfCom) is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of complex function. A complex function is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes. Platform: Online tool
Proper citation: ProfCom - Profiling of complex functionality (RRID:SCR_005797) Copy
http://estbioinfo.stat.ub.es/apli/serbgov131/index.php
SerbGO is a web-based tool intended to assist researchers determine which microarray tools for gene expression analysis which make use of the GO ontologies are best suited to their projects. SerbGO is a bidirectional application. The user can ask for some features by checking on the Query Form to get the appropriate tools for their interests. The user can also compare tools to check which features are implemented in each one. Platform: Online tool
Proper citation: SerbGO (RRID:SCR_005798) Copy
http://gopubmed.org/web/gopubmed/
A web server which allows users to explore PubMed search results with the Gene Ontology, a hierarchically structured vocabulary for molecular biology. GoPubMed submits a user''''s keywords to PubMed, retrieves the abstracts, detects Gene Ontology terms in the abstracts, displays the subset of Gene Ontology relevant to the original query, and allows the user to browse through the ontology displaying associated papers and their GO annotation. Platform: Online tool
Proper citation: GoPubMed (RRID:SCR_005823) Copy
http://www.ebi.ac.uk/expressionprofiler/
THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. The EP:GO browser is built into EBI's Expression Profiler, a set of tools for clustering, analysis and visualization of gene expression and other genomic data. With it, you can search for GO terms and identify gene associations for a node, with or without associated subnodes, for the organism of your choice.
Proper citation: Expression Profiler (RRID:SCR_005821) Copy
http://bioinformatics.biol.uoa.gr/PRED-TMBB/
A web tool, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the gram-negative bacteria outer membrane proteins, and of discriminating such proteins from water-soluble ones when screening large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of the correct prediction rather than the likelihood of the sequences. The training is performed on a non-redundant database consisting of 16 outer membrane proteins (OMP''s) with their structures known at atomic resolution. We show that we can achieve predictions at least as good comparing with other existing methods, using as input only the amino-acid sequence, without the need of evolutionary information included in multiple alignments. The method is also powerful when used for discrimination purposes, as it can discriminate with a high accuracy the outer membrane proteins from water soluble in large datasets, making it a quite reliable solution for screening entire genomes. This web-server can help you run a discriminating process on any amino-acid sequence and thereafter localize the transmembrane strands and find the topology of the loops.
Proper citation: PRED-TMBB (RRID:SCR_006190) Copy
http://bioapps.rit.albany.edu/MITOPRED/
THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It predicts nuclear-encoded mitochondrial proteins from all eukaryotic species including plants. Prediction is based on the occurrence patterns of Pfam domains (version 16.0) in different cellular locations, amino acid composition and pI value differences between mitochondrial and non-mitochondrial locations. Additionally, you may download MITOPRED predictions for complete proteomes. Re-calculated predictions are instantly accessible for proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Homo sapiens, Mus musculus and Arabidopsis species as well as all the eukaryotic sequences in the Swiss-Prot and TrEMBL databases. Queries, at different confidence levels, can be made through four distinct options: (i) entering Swiss-Prot/TrEMBL accession numbers; (ii) uploading a local file with such accession numbers; (iii) entering protein sequences; (iv) uploading a local file containing protein sequences in FASTA format. The Mitopred algorithm works based on the differences in the Pfam domain occurrence patters and amino acid composition differences in different cellular compartments. Location specific Pfam domains have been determined from the entire eukaryotic set of Swissprot database. Similarly, differences in the amino acid composition between mitochondrial and non-mitochondrial sequences were pre-calculated. This information is used to calculate location-specific amino acid weights that are used to calculate amino acid score. Similarly, pI average values of the N-terminal 25 residues in different cellular location were also determined. This knowledge-base is accessed by the program during execution.
Proper citation: mitopred (RRID:SCR_006135) Copy
http://katahdin.mssm.edu/kismeth/revpage.pl
A web-based tool for bisulfite sequencing analysis that was designed to be used with plants, since it considers potential cytosine methylation in any sequence context (CG, CHG, and CHH). It provides a tool for the design of bisulfite primers as well as several tools for the analysis of the bisulfite sequencing results. Kismeth is not limited to data from plants, as it can be used with data from any species.
Proper citation: Kismeth (RRID:SCR_005444) Copy
http://genetrail.bioinf.uni-sb.de/
A web-based application that analyzes gene sets for statistically significant accumulations of genes that belong to some functional category. Considered category types are: KEGG Pathways, TRANSPATH Pathways, TRANSFAC Transcription Factor, GeneOntology Categories, Genomic Localization, Protein-Protein Interactions, Coiled-coil domains, Granzyme-B clevage sites, and ELR/RGD motifs. The web server provides two statistical approaches, "Over-Representation Analysis" (ORA) comparing a reference set of genes to a test set, and "Gene Set Enrichment Analysis" (GSEA) scoring sorted lists of genes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: GeneTrail (RRID:SCR_006250) Copy
http://omicslab.genetics.ac.cn/GOEAST/
Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods. Compared with available GO analysis tools, GOEAST has the following unique features: * GOEAST supports analysis for data from various resources, such as expression data obtained using Affymetrix, illumina, Agilent or customized microarray platforms. GOEAST also supports non-microarray based experimental data. The web-based feature makes GOEAST very user friendly; users only have to provide a list of genes in correct formats. * GOEAST provides visualizable analysis results, by generating graphs exhibiting enriched GO terms as well as their relationships in the whole GO hierarchy. * Note that GOEAST generates separate graph for each of the three GO categories, namely biological process, molecular function and cellular component. * GOEAST allows comparison of results from multiple experiments (see Multi-GOEAST tool). The displayed color of each GO term node in graphs generated by Multi-GOEAST is the combination of different colors used in individual GOEAST analysis. Platform: Online tool
Proper citation: GOEAST - Gene Ontology Enrichment Analysis Software Toolkit (RRID:SCR_006580) Copy
Can't find your Tool?
We recommend that you click next to the search bar to check some helpful tips on searches and refine your search firstly. Alternatively, please register your tool with the SciCrunch Registry by adding a little information to a web form, logging in will enable users to create a provisional RRID, but it not required to submit.
Welcome to the NIF Resources search. From here you can search through a compilation of resources used by NIF and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that NIF has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on NIF then you can log in from here to get additional features in NIF such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into NIF you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the sources that were queried against in your search that you can investigate further.
Here are the categories present within NIF that you can filter your data on
Here are the subcategories present within this category that you can filter your data on
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
