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https://networkx.github.io/

Software Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Proper citation: NetworkX (RRID:SCR_016864) Copy   

•   Source: SciCrunch Registry

http://www.biojava.org

Project dedicated to providing Java framework for processing biological data. It provides analytical and statistical routines, parsers for common file formats and allows the manipulation of sequences and 3D structures. The goal of the biojava project is to facilitate rapid application development for bioinformatics. Sponsor: BioJava is not formally funded by any grants. Through the OBF they have received sponsorship from Sun Microsystems, Apple Computers and NESCent. The initial development of the phylogenetics module was undertaken as a Google Summer of Code 2007 project in collaboration with NESCent.

Proper citation: BioJava Project (RRID:SCR_007180) Copy   

•   Source: SciCrunch Registry

http://nirlweb.duhs.duke.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Neuropsychiatric Imaging Research Laboratory (NIRL) analyze magnetic resonance images to research numerous psychiatric disorders including depression, bipolar disorder, and post traumatic stress disorder. NIRL also develop new methods for MR image processing to improve quality and reliability of research in the field of neuroimaging. The laboratory computer resources include Sun MicroSystems SPARC workstations, Windows PCs, over 3 terabytes of online hard disk space, and a web server system. The lab has a site filtered anonymous ftp server system for data transfer. There are individual offices for visiting fellows and analysts for image processing as well as shared work-study rooms and conference facilities.

Proper citation: Duke University Medical Center Neuropsychiatric Imaging Research Laboratory (RRID:SCR_007124) Copy   

•   Source: SciCrunch Registry

https://commonfund.nih.gov/hmp/

NIH Project to generate resources to characterize the human microbiota and to analyze its role in human health and disease at several different sites on the human body, including nasal passages, oral cavities, skin, gastrointestinal tract, and urogenital tract using metagenomic and traditional approach to genomic DNA sequencing studies.HMP was supported by the Common Fund from 2007 to 2016.

Proper citation: Human Microbiome Project (RRID:SCR_012956) Copy   

•   Source: SciCrunch Registry

http://www.usadellab.org/cms/index.php?page=trimmomatic

Software Java pipeline for trimming tasks for Illumina paired end and single ended data. Flexible Trimmer for Illumina Sequence Data. Pair aware preprocessing tool optimized for Illumina next generation sequencing data. Includes several processing steps for read trimming and filtering. Operating systems Unix/Linux, Mac OS, Windows.

Proper citation: Trimmomatic (RRID:SCR_011848) Copy   

•   Source: SciCrunch Registry

http://snpselector.duhs.duke.edu/hqsnp36.html

This is the HQSNP DB (high-quality SNP database) developed by CHG bioinformatics group. The high-quality SNP is defined as a SNP having allele frequency or genotyping data. The majority of the HQSNPs come from HapMap, others come from JSNP (Japanese SNP database), TSC (The SNP Consortium), Affymetrix 120K SNP, and Perlegen SNP. There are four kinds of SNP search you can do: * Get SNPs by dbSNP rs#: Choose this search if you have already selected a list of SNPs and you just want to get the SNP information. The program will generate a Excel file containing the SNP flanking sequence, variation, quality, function, etc. In the Excel file, there are 10 highlighted fields. You can send only those highlighted information to Illumina to get SNP pre-score. (The same fields are presented in other types of searches as well.) * Get gene SNPs by gene names: Choose this search if you have a list of gene names and you want to get the SNP information in these genes. The gene name can be official gene symbol, Ensembl gene ID, RefSeq accession ID, LocusLink number, etc. * Get gene SNPs by genome regions: Choose this search if you have a list of genome regions and you want to get all gene SNP information in these regions. The software will find all the Ensembl genes in the regions and find SNPs associated to each Ensembl gene. * Get genome scan SNPs by genome regions: Choose this search if you have a list of genome regions and you want to get evenly spaced SNPs in these regions. A SNP selection tool (SNPselector) was built upon HQSNP. It took snp ID list, gene name list, or genome region list as input and searched SNPs for genome scan or gene assoctiation study. It could take an optional ABI SNP file (exported from ABI SNP search web page) as input for checking whether the candidate SNP is available from ABI. It could also take an optional Illumina SNP pre-score file as input to select SNP for Illumina SNP assay. It generated results sorted by tag SNP in LD block, SNP quality, SNP function, SNP regulatory potential, and SNP mutation risk. SNPselector is now retired from public use (as of September 30, 2010).

Proper citation: High Quality SNP Database (RRID:SCR_007230) Copy   

•   Source: SciCrunch Registry

http://cmckb.cellmigration.org

It is a database of keys facts about proteins, families, and complexes involved in cell migration. This ongoing project provides a large amount of automated and curated data, collected from numerous online resources that are updated monthly. These data include names, synonyms, sequence information, summaries, CMC research data, reagents, structures, as well as protein family and complex details. CMKB''s ultimate goal is to create a database that will enable the cell migration community to conveniently access significant information about molecules of interest. This will also serve as a stepping stone to pathway analysis and demonstrate how these molecules coordinate with one another during cell adhesion and movement. Sponsors: This resource is supported by the Cell Migration Consortium.

Proper citation: CMKB (RRID:SCR_007229) Copy   

•   Source: SciCrunch Registry

http://www.oreganno.org/oregano/

Open source, open access database and literature curation system for community based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. Automatically cross referenced against PubMED, Entrez Gene, EnsEMBL, dbSNP, eVOC: Cell type ontology, and Taxonomy database. Community driven resource for curated regulatory annotation.

Proper citation: Open Regulatory Annotation Database (RRID:SCR_007835) Copy   

•   Source: SciCrunch Registry

https://leger2.helmholtz-hzi.de/cgi-bin/expLeger.pl

Knowledge database and visualization tool for comparative genomics of pathogenic and non-pathogenic Listeria species.Provides information on gene functions (as annotated or supposed by literature from homologous organisms) , protein expression levels under defined experimental conditions ,subcellular localization of proteins (expected and/or experimentally validated) , biological meaning of genes and proteins based on KEGG, InterPro and Gene Ontology.

Proper citation: LEGER: the post-genome Database for Listeria Research (RRID:SCR_007760) Copy   

•   Source: SciCrunch Registry

http://www.genepath.org/

GenePath is a web-enabled intelligent assistant for the analysis of genetic data and for discovery of genetic networks. GenePath uses abductive inference to elucidate network constraints and logic to derive consistent networks. Typically, it starts with a set of genetic experiments, uses a set of embedded rules (patterns) to infer relations between genes and outcome, and based on these relations constructs a genetic network.

Proper citation: GenePath (RRID:SCR_007974) Copy   

•   Source: SciCrunch Registry

https://github.com/alleninstitute/cocoframer

Software R package contains various functions for using the data from the Allen Mouse Brain Atlas that is registered to the Common Coordinate Framework. The functionality includes retrieving 3D, CCF aligned, gridded ISH data from the Allen Brain Atlas API, rendering 2D plots of slices of ISH data, retrieving the Mouse Brain Atlas structural ontology, and generating 3D plots of brain structures like those presented in the Allen Brain Explorer.

Proper citation: Cocoframer (RRID:SCR_023817) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpr/lung

Data set of a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, researchers analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct sub-classes of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

Proper citation: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes (RRID:SCR_003010) Copy   

•   Source: SciCrunch Registry

http://eyebrowse.cit.nih.gov/

EyeBrowse displays expressed sequence tag (EST) cDNA clones from eye tissues (derived from NEIBank and other sources) aligned with current versions of the human, rhesus, mouse, rat, dog, cow, chicken, or zebrafish genomes, including reference sequences for known genes. This gives a simplified view of gene expression activity from different parts of the eye across the genome. The data can be interrogated in several ways. Specific gene names can be entered into the search window. Alternatively, regions of the genome can be displayed. For example, entering two STS markers separated by a semicolon (e.g. RH18061;RH80175) allows the display of the entire chromosomal region associated with the mapping of a specific disease locus. ESTs for each tissue can then be displayed to help in the selection of candidate genes. In addition, sequences can be entered into a BLAT search and rapidly aligned on the genome, again showing eye derived ESTs for the same region. EyeBrowse includes a custom track display SAGE data for human eye tissues derived from the EyeSAGE project. The track shows the normalized sum of SAGE tag counts from all published eye-related SAGE datasets centered on the position of each identifiable Unigene cluster. This indicates relative activity of each gene locus in eye. Clicking on the vertical count bar for a particular location will bring up a display listing gene details and linking to specific SAGE counts for each eye SAGE library and comparisons with normalized sums for neural and non-neural tissues. To view or alter settings for the EyeSAGE track on EyeBrowse, click on the vertical gray bar at the left of the display. Other custom tracks display known eye disease genes and mapped intervals for candidate loci for retinal disease, cataract, myopia and cornea disease. These link back to further information at NEIBank. For mouse, there is custom track data for ChIP-on-Chip of RNA-Polymerase-II during photoreceptor maturation.

Proper citation: EyeBrowse (RRID:SCR_008000) Copy   

•   Source: SciCrunch Registry

https://cancer.dartmouth.edu/researchers/bioinformatics-resource.html

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on July 29,2022. Core to support the implementation of bioinformatics resources for cancer research at Dartmouth. Provides consultation and collaboration for research projects of NCCC members, regular workshops, seminars, services including applied bioinformatics and data mining, computer programming and software engineering, database development and programming and high performance computing and systems administration.

Proper citation: Dartmouth-Hitchcock Bioinformatics Shared Resource (RRID:SCR_009758) Copy   

•   Source: SciCrunch Registry

http://howard.eagle-i.net/i/00000134-a517-1426-bf4c-ca4080000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27,2023. Core for bioinformatics consultation and software access. Laboratory of Molecular Computations and Bioinformatics (LMCB) is a resource facility dedicated to the support of computational biomedical research at Howard University. Provides molecular modeling, molecular dynamics, bioinformatics, and computational quantum chemistry capabilities and support to a variety of research projects at Howard University.

Proper citation: Howard University Center for Computational Biology and Bioinformatics Core Facility (RRID:SCR_009864) Copy   

•   Source: SciCrunch Registry

http://harvard.eagle-i.net/i/0000012e-5e87-861a-55da-381e80000000

Core for data driven projects related to basic, clinical and translational research, with a particular emphasis on diabetes. Aims to ensure that researchers take advantage of the most modern and robust methods available in the field of Bioinformatics and Biostatistics.

Proper citation: Harvard Bioinformatics Core at Joslin Diabetes Center (RRID:SCR_009827) Copy   

•   Source: SciCrunch Registry

http://www.ukbiobank.ac.uk/

Biobank provides data collected at Assessment Center and via online questionnaires on participants aged 40-69 years recruited throughout United Kingdom and provides summary information to improve prevention, diagnosis and treatment of serious and life threatening illnesses.

Proper citation: UK Biobank (RRID:SCR_012815) Copy   

•   Source: SciCrunch Registry

http://www.sbpdiscovery.org/technology/sr/Pages/LaJolla_Crystallography.aspx

Facility equipped with X-ray crystallography equipment, such as the Rigaku FR-E SuperBright Ultra high-intensity rotating anode generator. The instrument is inteded for self-usage by trained users. It utilizes both ports with two independent diffraction stations that can run simultaneously. Each station is equipped with an X-stream cryogenic system for standard low temperature (~100 K) data collection. The system is used to collect high resolution diffraction data from native and inhibitor-bound crystals or to test and optimize diffraction quality (cryo-protectant conditions, etc.) and screen for further data collection at synchrotron sources.

Proper citation: Sanford Burnham Prebys Medical Discovery Institute X-ray Crystallography Facility (RRID:SCR_014860) Copy   

•   Source: SciCrunch Registry

http://casestudies.brain-map.org/celltax

Cellular Taxonomy of Mouse Visual Cortex by analyzing gene expression patterns at single cell level. Construction of cellular taxonomy of one cortical region, primary visual cortex, in adult mice done on basis of single cell RNA sequencing.

Proper citation: CellTax vignette (RRID:SCR_017000) Copy   

•   Source: SciCrunch Registry

https://biosciencecores.umd.edu/proteomics.html

Facility is equipped withThermoFisher Orbitrap Fusion Lumos Tribrid mass spectrometer that is interfaced to Dionex Ultimate3000 RSLCnano HPLC system. Facility also maintains 2 separate data stations dedicated for proteomics data processing, database searching, and generation of reports.

Proper citation: Maryland University Proteomics Core Facility (RRID:SCR_017739) Copy   

•   Source: SciCrunch Registry