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.
Database with annotations for human variation data with protein structural information and other functionally relevant information, if available. The mutations are organized by gene.
Proper citation: MutDB (RRID:SCR_003251) Copy
http://webdocs.cs.ualberta.ca/~bioinfo/PA/Sub/
Web server specialized to predict the subcellular localization of proteins using established machine learning techniques.
Proper citation: Proteome Analyst Specialized Subcellular Localization Server (RRID:SCR_003143) Copy
http://compbio.uthsc.edu/miRSNP/
Database of naturally occurring DNA variations in microRNA (miRNA) seed regions and miRNA target sites. MicroRNAs pair to the transcripts of protein-coding genes and cause translational repression or mRNA destabilization. SNPs and INDELs in miRNAs and their target sites may affect miRNA-mRNA interaction, and hence affect miRNA-mediated gene repression. The PolymiRTS database was created by scanning 3'UTRs of mRNAs in human and mouse for SNPs and INDELs in miRNA target sites. Then, the potential downstream effects of these polymorphisms on gene expression and higher-order phenotypes are identified. Specifically, genes containing PolymiRTSs, cis-acting expression QTLs, and physiological QTLs in mouse and the results of genome-wide association studies (GWAS) of human traits and diseases are linked in the database. The PolymiRTS database also includes polymorphisms in target sites that have been supported by a variety of experimental methods and polymorphisms in miRNA seed regions.
Proper citation: PolymiRTS (RRID:SCR_003389) Copy
http://nar.oxfordjournals.org/content/34/suppl_2/W635.long
THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 9, 2016. A web server that allows users to efficiently identify and prioritize high-risk SNPs according to their phenotypic risks and putative functional effects. A unique feature is that the functional effect information used for SNP prioritization is always up-to-date, because FASTSNP extracts the information from 11 external web servers at query time using a team of web wrapper agents. Moreover, FASTSNP is extendable by deploying more Web wrapper agents. FASTSNP provides three options for users to submit requests. If users already have some candidate SNPs on a candidate gene, they may use Query by Candidate Gene to select the specific SNPs on the gene to perform prioritization. If users have a specified SNP or a list of SNP rsid's needs to be prioritized, they can use Query by SNP option and upload the SNP list in an Excel-format file. Finally, if users have a novel SNP sequence, FASTSNP provides Novel SNP analysis. FASTSNP will generate a SNP Function Report for each SNP. Users can export SNP data to an excel file for further genotyping processes. Other features of FASTSNP include SNP quality checking and haplotype LD information.
Proper citation: FastSNP (RRID:SCR_003140) Copy
Database that catalogs experimentally verified pathogenicity, virulence and effector genes from fungal, Oomycete and bacterial pathogens, which infect animal, plant, fungal and insect hosts. It is an invaluable resource in the discovery of genes in medically and agronomically important pathogens, which may be potential targets for chemical intervention. In collaboration with the FRAC team, it also includes antifungal compounds and their target genes. Each entry is curated by domain experts and is supported by strong experimental evidence (gene disruption experiments, STM etc), as well as literature references in which the original experiments are described. Each gene is presented with its nucleotide and deduced amino acid sequence, as well as a detailed description of the predicted protein's function during the host infection process. To facilitate data interoperability, genes have been annotated using controlled vocabularies and links to external sources (Gene Ontology terms, EC Numbers, NCBI taxonomy, EMBL, PubMed and FRAC).
Proper citation: PHI-base (RRID:SCR_003331) Copy
One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. The T-profiler tool hosted on this website uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can submit their microarray data for analysis by clicking on one of the two organism-specific tabs above. Platform: Online tool
Proper citation: T-profiler (RRID:SCR_003452) Copy
Web server based on the Enhancer Identification (EI) method, to determine the chromosomal location and functional characteristics of distant regulatory elements (REs) in higher eukaryotic genomes. The server uses gene co-expression data, comparative genomics, and combinatorics of transcription factor binding sites (TFBSs) to find TFBS-association signatures that can be used for discriminating specific regulatory functions. DiRE's unique feature is the detection of REs outside of proximal promoter regions, as it takes advantage of the full gene locus to conduct the search. DiRE can predict common REs for any set of input genes for which the user has prior knowledge of co-expression, co-function, or other biologically meaningful grouping. The server predicts function-specific REs consisting of clusters of specifically-associated TFBSs, and it also scores the association of individual TFs with the biological function shared by the group of input genes. Its integration with the Array2BIO server allows users to start their analysis with raw microarray expression data.
Proper citation: Distant Regulatory Elements (RRID:SCR_003058) Copy
Database which contains the signal transduction proteins for complete and draft bacterial and archaeal genomes. The MiST2 database identifies and catalogs the repertoire of signal transduction proteins in microbial genomes.
Proper citation: MiST - Microbial Signal Transduction database (RRID:SCR_003166) Copy
http://technelysium.com.au/?page_id=13
Software ideal for the most basic of sequencing projects, where assembly of multiple sequences is not required., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Chromas (RRID:SCR_000598) Copy
http://www.sanger.ac.uk/science/tools/ssaha2-0
A program designed for the efficient mapping of sequence reads onto genomic references. The software is capable of reading most sequencing platforms and giving a range of outputs are supported.
Proper citation: Sequence Search and Alignment by Hashing Algorithm (RRID:SCR_000544) Copy
http://decgpu.sourceforge.net/homepage.htm
Software tool as parallel and distributed error correction algorithm for high-throughput short reads using CUDA and MPI parallel programming models.
Proper citation: DecGPU (RRID:SCR_000585) Copy
http://www.bioinf.uni-leipzig.de/Software/RNAplex/
Software tool to rapidly search for short interactions between two long RNAs.
Proper citation: RNAplex (RRID:SCR_002763) Copy
http://bamview.sourceforge.net/
A free interactive display of read alignments in BAM data files that can be launched with Java Web Start or downloaded. This interactive Java application for visualizing the large amounts of data stored for sequence reads which are aligned against a reference genome sequence can be used in a number of contexts including SNP calling and structural annotation. It has been integrated into Artemis so that the reads can be viewed in the context of the nucleotide sequence and genomic features. The source code is available as part of the Artemis code which can be downloaded from GitHub.
Proper citation: BamView (RRID:SCR_004207) Copy
http://noble.gs.washington.edu/proj/genomedata/
A format for efficient storage of multiple tracks of numeric data anchored to a genome. The format allows fast random access to hundreds of gigabytes of data, while retaining a small disk space footprint. They have also developed utilities to load data into this format. Retrieving data from this format is more than 2900 times faster than a naive approach using wiggle files. A reference implementation in Python and C components is available here under the GNU General Public License. The software has only been tested on Linux and Mac systems.
Proper citation: Genomedata (RRID:SCR_004544) Copy
https://github.com/Illumina/strelka/
Software for somatic single nucleotide variant (SNV) and small indel detection from sequencing data of matched tumor-normal samples. Strelka2 germline and somatic small variant caller.
Proper citation: Strelka2 (RRID:SCR_005109) Copy
http://alchemy.sourceforge.net/
ALCHEMY is a genotype calling algorithm for Affymetrix and Illumina products which is not based on clustering methods. Features include explicit handling of reduced heterozygosity due to inbreeding and accurate results with small sample sizes. ALCHEMY is a method for automated calling of diploid genotypes from raw intensity data produced by various high-throughput multiplexed SNP genotyping methods. It has been developed for and tested on Affymetrix GeneChip Arrays, Illumina GoldenGate, and Illumina Infinium based assays. Primary motivations for ALCHEMY''s development was the lack of available genotype calling methods which can perform well in the absence of heterozygous samples (due to panels of inbred lines being genotyped) or provide accurate calls with small sample batches. ALCHEMY differs from other genotype calling methods in that genotype inference is based on a parametric Bayesian model of the raw intensity data rather than a generalized clustering approach and the model incorporates population genetic principles such as Hardy-Weinberg equilibrium adjusted for inbreeding levels. ALCHEMY can simultaneously estimate individual sample inbreeding coefficients from the data and use them to improve statistical inference of diploid genotypes at individual SNPs. The main documentation for ALCHEMY is maintained on the sourceforge-hosted MediaWiki system. Features * Population genetic model based SNP genotype calling * Simultaneous estimation of per-sample inbreeding coefficients, allele frequencies, and genotypes * Bayesian model provides posterior probabilities of genotype correctness as quality measures * Growing number of scripts and supporting programs for validation of genotypes against control data and output reformating needs * Multithreaded program for parallel execution on multi-CPU/core systems * Non-clustering based methods can handle small sample sets for empirical optimization of sample preparation techniques and accurate calling of SNPs missing genotype classes ALCHEMY is written in C and developed on the GNU/Linux platform. It should compile on any current GNU/Linux distribution with the development packages for the GNU Scientific Library (gsl) and other development packages for standard system libraries. It may also compile and run on Mac OS X if gsl is installed.
Proper citation: ALCHEMY (RRID:SCR_005761) Copy
https://github.com/Gregor-Mendel-Institute/poolhap
Software tool for inferring haplotypes from pooled sequencing. Enables to infer strain numbers and haplotype frequencies in silico from sequences of pooled samples.
Proper citation: PoolHap (RRID:SCR_012129) Copy
http://wpicr.wpic.pitt.edu/WPICCompGen/fdr/
Software application (entry from Genetic Analysis Software)
Proper citation: WEIGHTED FDR (RRID:SCR_013442) Copy
https://imdevsoftware.wordpress.com/imdev/
A software application of RExcel that integrates R into Excel as an embedded additon for omics tasks and analysis. It can be used specifically for tasks concerning multivariate data visualization, exploration, and analysis. imDev has interactive modules for dimensional reduction, prediction, feature selection, analysis of correlation, and generation of networked structures, all of which provide an integrated environment for systems level analysis of multivariate data.
Proper citation: imDEV (RRID:SCR_014674) Copy
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
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.