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.
Center for Computational Biology as a joint research center in the McKusick-Nathans Institute of Genetic Medicine, spanning the School of Medicine, the Whiting School of Engineering, the Bloomberg School of Public Health, and the Krieger School of Arts & Sciences. Multidisciplinary center dedicated to research on genomics, genetics, DNA sequencing technology, and computational methods for DNA and RNA sequence analysis.
Proper citation: Center for Computational Biology at JHU (RRID:SCR_016680) Copy
Ratings or validation data are available for this resource
Human and mouse genome annotation project which aims to identify all gene features in the human genome using computational analysis, manual annotation, and experimental validation.
Proper citation: GENCODE (RRID:SCR_014966) Copy
http://www.evocontology.org/site/Main/EvocOntologyDotOrg
THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented September 6, 2016. Set of orthogonal controlled vocabularies that unifies gene expression data by facilitating a link between the genome sequence and expression phenotype information. The system associates labelled target cDNAs for microarray experiments, or cDNA libraries and their associated transcripts with controlled terms in a set of hierarchical vocabularies. eVOC consists of four orthogonal controlled vocabularies suitable for describing the domains of human gene expression data including Anatomical System, Cell Type, Pathology and Developmental Stage. The four core eVOC ontologies provide an appropriate set of detailed human terms that describe the sample source of human experimental material such as cDNA and SAGE libraries. These expression terms are linked to libraries and transcripts allowing the assessment of tissue expression profiles, differential gene expression levels and the physical distribution of expression across the genome. Analysis is currently possible using EST and SAGE data, with microarray data being incorporated. The eVOC data is increasingly being accepted as a standard for describing gene expression and eVOC ontologies are integrated with the Ensembl EnsMart database, the Alternate Transcript Diversity Project and the UniProt Knowledgebase. Several groups are currently working to provide shared development of this resource such that it is of maximum use in unifying transcript expression information.
Proper citation: eVOC (RRID:SCR_010704) Copy
Software tool to identify known and novel miRNA genes in seven animal clades by analyzing sequenced RNAs. Used for discovering known and novel miRNAs from small RNA sequencing data.
Proper citation: miRDeep (RRID:SCR_010829) Copy
http://mafft.cbrc.jp/alignment/server/
Software package as multiple alignment program for amino acid or nucleotide sequences. Can align up to 500 sequences or maximum file size of 1 MB. First version of MAFFT used algorithm based on progressive alignment, in which sequences were clustered with help of Fast Fourier Transform. Subsequent versions have added other algorithms and modes of operation, including options for faster alignment of large numbers of sequences, higher accuracy alignments, alignment of non-coding RNA sequences, and addition of new sequences to existing alignments.
Proper citation: MAFFT (RRID:SCR_011811) Copy
https://github.com/MikkelSchubert/adapterremoval
Software program to remove residual adapter sequences from next generation sequencing reads. Used for cleaning of next-generation sequencing reads. AdapterRemoval v2 introduces improvements in throughput, through use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support; handles datasets containing reads or read-pairs with different adapters or adapter pairs; provides simultaneous demultiplexing and adapter trimming; has ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets; provides native gzip and bzip2 support.
Proper citation: AdapterRemoval (RRID:SCR_011834) Copy
Core facility provides researchers with access to high-throughput sequencing technologies. The staff provide consultation on experimental design, library preparation, and data analysis. The Sequencing Core Facility works closely with Bioinformatics staff in the Center for Quantitative Biology to provide researchers with computing power and consulting services to analyze sequencing data.
Proper citation: Princeton High Throughput Sequencing and Microarray Facility (RRID:SCR_012619) Copy
http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml
This resource gives information about the U.S. Human Genome Project, which was was a 13-year effort to to discover all the estimated 20,000-25,000 human genes and make them accessible for further biological study. The primary project goals were to: - identify all the approximately 20,000-25,000 genes in human DNA, - determine the sequences of the 3 billion chemical base pairs that make up human DNA, - store this information in databases, - improve tools for data analysis, - transfer related technologies to the private sector, and - address the ethical, legal, and social issues (ELSI) that may arise from the project. To help achieve these goals, researchers also studied the genetic makeup of several nonhuman organisms. These include the common human gut bacterium Escherichia coli, the fruit fly, and the laboratory mouse. These parallel studies helped to develop technology and interpret human gene function. Sponsors: The DOE Human Genome Program and the NIH National Human Genome Research Institute (NHGRI) together sponsored the U.S. Human Genome Project.
Proper citation: Human Genome Project Information (RRID:SCR_013028) Copy
http://sift.bii.a-star.edu.sg/
Data analysis service to predict whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations. (entry from Genetic Analysis Software) Web service is also available.
Proper citation: SIFT (RRID:SCR_012813) Copy
http://www.mrc-lmb.cam.ac.uk/genomes/dolop/
DOLOP is an exclusive knowledge base for bacterial lipoproteins by processing information from 510 entries to provide a list of 199 distinct lipoproteins with relevant links to molecular details. Features include functional classification, predictive algorithm for query sequences, primary sequence analysis and lists of predicted lipoproteins from 43 completed bacterial genomes along with interactive information exchange facility. This website along will have additional information on the biosynthetic pathway, supplementary material and other related figures. DOLOP also contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. Additionally, the website features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins.
Proper citation: DOLOP: A Database of Bacterial Lipoproteins (RRID:SCR_013487) Copy
http://genetics.bwh.harvard.edu/pph2/
Software tool which predicts possible impact of amino acid substitution on structure and function of human protein using straightforward physical and comparative considerations. PolyPhen-2 is new development of PolyPhen tool for annotating coding nonsynonymous SNPs.
Proper citation: PolyPhen: Polymorphism Phenotyping (RRID:SCR_013189) Copy
Software application that provides sequence editing, primer design, internet database searching, protein analysis, sequence confirmation, multiple sequence alignment, phylogenetic reconstruction, coding region analysis, agarose gel simulation and a variety of other functions.
Proper citation: MacVector (RRID:SCR_015700) Copy
https://github.com/sanger-pathogens/ariba
Analysis software that identifies antibiotic resistance genes by running local assemblies. It can also be used for MLST calling.
Proper citation: Ariba (RRID:SCR_015976) Copy
Alignment analysis software tool for comparative mapping between two genome assemblies or between two different genomes. It can cache intermediate results to speed a comparisons of multiple sequences.
Proper citation: Atac (RRID:SCR_015980) Copy
https://github.com/kdmurray91/axe
Software for sequencing data analysis and demultiplexing. It can be used in situations where sequence reads contain the barcodes that uniquely distinguish samples.
Proper citation: Axe (RRID:SCR_015984) Copy
https://github.com/pezmaster31/bamtools/wiki
Software that provides both a C++ API and a command-line toolkit for reading, writing, and manipulating genome sequence alignment files in the BAM and SAM formats. It is used for research analysis and management of data produced by sequencing technologies.
Proper citation: Bamtools (RRID:SCR_015987) Copy
http://www.sanger.ac.uk/science/tools/seqtools
Software for sequence alignments that displays multiple match sequences aligned against a single genomic reference sequence. It can be used for manipulation, display and annotation of genomic data, to check the quality of an alignment, to find missing/misaligned sequence, and to identify splice sites and polyA sites.
Proper citation: Blixem (RRID:SCR_015994) Copy
http://sing.ei.uvigo.es/ALTER/
Web application to perform program-oriented conversion of DNA and protein alignments and transform between multiple sequence alignment formats. ALTER focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats.
Proper citation: ALTER (RRID:SCR_015968) Copy
http://code.google.com/p/amap-align/
Source code that performs multiple alignment of peptidic sequences. It utilizes posterior decoding and a sequence-annealing alignment, instead of the traditional progressive alignment method.
Proper citation: AMAP (RRID:SCR_015969) Copy
http://mbio-serv2.mbioekol.lu.se/ARAGORN/
Software that detects tRNA genes and tmRNA genes in nucleotide sequences. The program employs heuristic algorithms to predict tRNA secondary structure, based on homology with recognized tRNA consensus sequences and ability to form a base‐paired cloverleaf., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Aragorn (RRID:SCR_015974) 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 SPARC SAWG Resources search. From here you can search through a compilation of resources used by SPARC SAWG and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that SPARC SAWG 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 SPARC SAWG then you can log in from here to get additional features in SPARC SAWG 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 SPARC SAWG 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 SPARC SAWG 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.