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http://www.dsitissuebank.org

Donor Services of Indiana (DSI) in Fort Wayne is the nonprofit tissue bank established to provide high-quality human tissue and eye tissue for transplant to patients in our region, and for use in medical research. The program depends on contributions made by generous people who have consented to donation after the death of a family member. Bone, tendons, skin grafts and heart valves can significantly improve the quality of life for transplant recipients by preventing amputation, restoring mobility, relieving pain and sometimes saving lives. DSI adheres to the strict guidelines of the American Association of Tissue Banks and is widely recognized for its leadership in tissue banking. Transplant tissue provided by DSI is from regional donors evaluated and procured by our clinical staff, tested under our rigorous protocols, and distributed, tracked and followed by our professionals. DSI provides tissues back to the community from which they came and is equipped to deliver needed tissues on an urgent basis for medical emergencies, surgeries and procedures.

Proper citation: Donor Services of Indiana (RRID:SCR_004838) Copy   

•   Source: SciCrunch Registry

http://amphoranet.pitgroup.org/

Webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data. It is capable of assigning a probability-weighted taxonomic group for each phylogenetic marker gene found in the input metagenomic sample.

Proper citation: AmphoraNet (RRID:SCR_005009) Copy   

•   Source: SciCrunch Registry

http://mltreemap.org/

Data analysis service that analyzes DNA sequences and determines their most likely phylogenetic origin. Its main use is in metagenomics projects, where DNA is isolated directly from natural environments and sequenced (the organisms from which the DNA originates are often entirely undescribed). It will search such sequences for suitable marker genes, and will use maximum likelihood analysis to place them in the ''''Tree of Life''''. This placement is more reliable than simply assessing the closest relative of a sequence using BLAST. More importantly, MLTreeMap decides not only who is the closest relative of your query sequence, but also how deep in the tree of life it probably branched off. Additionally, MLTreeMap searches the sequences for genes, which are coding for key enzymes of important functional pathways, such as RuBisCo, methane monooxygenase or nitrogenase. In case of a positive hit, MLTreeMap uses maximum likelihood analysis to place them in the respective ''''gene-family tree''''.

Proper citation: MLTreeMap (RRID:SCR_004792) Copy   

•   Source: SciCrunch Registry

http://stellabase.org

StellaBase is a genomics database of Nematostella vectensis. It allows users to query the assembled Nematostella genome, a confirmed gene library, and a predicted genome using both keyword and homology based search functions. Data provided by these searches will elucidate gene family evolution in early animals. Unique research tools, including a Nematostella genetic stock library, a primer library, a literature repository and a gene expression library will provide support to the burgeoning Nematostella research community. Supported by: National Science Foundation (Grant No. 0212773 to JRF)

Proper citation: StellaBase: Nematostella vactensis genomics database (RRID:SCR_005153) Copy   

•   Source: SciCrunch Registry

http://www.donatelifenm.org/

New Mexico Donor Services (NMDS) is committed to saving and improving lives, connecting one life to another through donation and transplantation. NMDS and New Mexico hospitals share responsibility to ensure that an individual''s decision to be a donor is followed or their family is given the option to donate organs and/or tissue. Hospitals identify potential donors, make timely referrals, and manage the patients to allow NMDS to evaluate the patient for donor suitability and check donor status on their driver''s license or ID. Organs are distributed to waiting recipients through the national organ transplant list maintained by the United Network for Organ Sharing (UNOS) based on medical factors such as blood type, size and tissue match. It is illegal to distribute organs based on wealth or celebrity status. Tissue is distributed based on patient need, medical criteria and availability.

Proper citation: NMDS - New Mexico Donor Services (RRID:SCR_005033) Copy   

•   Source: SciCrunch Registry

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

Database that aggregates sample information for reference samples (e.g. Coriell Cell lines) and samples for which data exist in one of the EBI''''s assay databases such as ArrayExpress, the European Nucleotide Archive or PRoteomics Identificates DatabasE. It provides links to assays for specific samples, and accepts direct submissions of sample information. The goals of the BioSample Database include: # recording and linking of sample information consistently within EBI databases such as ENA, ArrayExpress and PRIDE; # minimizing data entry efforts for EBI database submitters by enabling submitting sample descriptions once and referencing them later in data submissions to assay databases and # supporting cross database queries by sample characteristics. The database includes a growing set of reference samples, such as cell lines, which are repeatedly used in experiments and can be easily referenced from any database by their accession numbers. Accession numbers for the reference samples will be exchanged with a similar database at NCBI. The samples in the database can be queried by their attributes, such as sample types, disease names or sample providers. A simple tab-delimited format facilitates submissions of sample information to the database, initially via email to biosamples (at) ebi.ac.uk. Current data sources: * European Nucleotide Archive (424,811 samples) * PRIDE (17,001 samples) * ArrayExpress (1,187,884 samples) * ENCODE cell lines (119 samples) * CORIELL cell lines (27,002 samples) * Thousand Genome (2,628 samples) * HapMap (1,417 samples) * IMSR (248,660 samples)

Proper citation: BioSample Database at EBI (RRID:SCR_004856) Copy   

•   Source: SciCrunch Registry

http://www.lifelineofohio.org/

An independent, non-profit organization, Lifeline of Ohio (LOOP), promotes and coordinates the donation of human organs and tissue for transplantation. Its mission is to educate and empower central and southeastern Ohioans about organ and tissue donation while also facilitating the donation process. Lifeline of Ohio, a Donate Life Organization, has been approved by the Centers for Medicare and Medicaid Services (CMS) as the designated organ procurement organization (OPO) serving 37 Ohio counties along with Wood and Hancock counties in West Virginia. Accredited by both the Association of Organ Procurement Organizations (AOPO) and the American Association of Tissue Banks (AATB), Lifeline of Ohio provides services to 70 hospitals and the communities they serve through its procurement and tissue coordinators, and other professional staff.

Proper citation: Lifeline of Ohio (RRID:SCR_005027) Copy   

•   Source: SciCrunch Registry

http://sidirect2.rnai.jp/

siDirect 2.0 provides functional and off-target minimized siRNA design for mammalian RNAi. The previous version of our software designed functional siRNAs by considering the relationship between siRNA sequence and RNAi activity, and provided them along with the enumeration of potential off-target gene candidates by using a fast and sensitive homology search algorithm. In the new version, the siRNA design algorithm is extensively updated to eliminate off-target effects by reflecting our recent finding that the capability of siRNA to induce off-target effect is highly correlated to the thermodynamic stability, or the melting temperature (Tm), of the seed-target duplex, which is formed between the nucleotides positioned at 2-8 from the 5'' end of the siRNA guide strand and its target mRNA. Selection of siRNAs with lower seed-target duplex stabilities (benchmark Tm < 21.5 degrees C) followed by the elimination of unrelated transcripts with nearly perfect match should minimize the off-target effects. siDirect 2.0 provides functional, target-specific siRNA design with the updated algorithm which significantly reduces off-target silencing. When the candidate functional siRNAs could form seed-target duplexes with Tm values below 21.5 degrees C, and their 19-nt regions spanning positions 2-20 of both strands have at least two mismatches to any other non-targeted transcripts, siDirect 2.0 can design at least one qualified siRNA for > 94% of human mRNA sequences in RefSeq. Enter an accession number and retrieve sequence or Paste in a nucleotide sequence.

Proper citation: siDirect (RRID:SCR_004853) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/biosample

Database containing descriptions of biological source materials used in experimental assays. Sources include: GenBank, Sequence Read Archive (SRA), Coriell, ATCC. Submissions are supported by a web-based Submission Portal that guides users through a series of forms for input of rich metadata describing their samples. As the capacity and complexity of biological data sets expands, databases face new challenges in ensuring that the information is adequately organized and described. The NCBI BioSample database is being developed to help address the challenges by providing the means by which data generators can organize and describe a broad range of sample types, and link to corresponding sets of experimental data in archival databases.

Proper citation: NCBI BioSample (RRID:SCR_004854) Copy   

•   Source: SciCrunch Registry

http://rnp.uthscsa.edu/rnp/SRPDB/SRPDB.html

It provides aligned, annotated and phylogenetically ordered sequences related to structure and function of SRP. SRPDB assists the study of structure and function of signal recognition particles (SRP), and provides annotated SRP, RNA, and SRP protein sequences phylogenetically ordered and aligned. Included are representative RNA secondary structure diagrams where each base pair is proven by comparative sequence analysis, information about other proteins that play a role in SRP-mediated protein translocation, as well as structural information about components of SRP. Where possible, links to primary sources were established.

Proper citation: SRPDB (RRID:SCR_005142) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi

A web server and database that organizes, analyzes and predicts interactions between proteins and other biomolecules. For a given protein sequence or structure query, it reports protein-protein, protein-small molecule, protein nucleic acids and protein-ion interactions observed in experimentally-determined structural biological assemblies. It also infers/predicts interacting partners and binding sites by homology, by inspecting the protein complexes formed by close homologs of a given query. To ensure biological relevance of inferred binding sites, the IBIS algorithm clusters binding sites formed by homologs based on binding site sequence and structure conservation.

Proper citation: IBIS: Inferred Biomolecular Interactions Server (RRID:SCR_004886) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/jpopgen/dbNSFP

A database for functional prediction and annotation of all potential non-synonymous single-nucleotide variants (nsSNVs) in the human genome. Version 2.0 is based on the Gencode release 9 / Ensembl version 64 and includes a total of 87,347,043 nsSNVs and 2,270,742 essential splice site SNVs. It compiles prediction scores from six prediction algorithms (SIFT, Polyphen2, LRT, MutationTaster, MutationAssessor and FATHMM), three conservation scores (PhyloP, GERP++ and SiPhy) and other related information including allele frequencies observed in the 1000 Genomes Project phase 1 data and the NHLBI Exome Sequencing Project, various gene IDs from different databases, functional descriptions of genes, gene expression and gene interaction information, etc. Some dbNSFP contents (may not be up-to-date though) can also be accessed through variant tools, ANNOVAR, KGGSeq, UCSC Genome Browser''s Variant Annotation Integrator, Ensembl Variant Effect Predictor and HGMD.

Proper citation: dbNSFP (RRID:SCR_005178) Copy   

•   Source: SciCrunch Registry

http://mlstoslo.uio.no/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11,2023. SuperCAT hosts typing databases for the Bacillus cereus group of bacteria. The databases contain MultiLocus Sequence Typing (MLST), MultiLocus Enzyme Electrophoresis (MLEE), and Amplified Fragment Length Polymorphism (AFLP) phylogenetic data. multilocus, sequence, Bacillus cereus, bacteria, Genomics, non-vertebrate, taxonomy, identification

Proper citation: SuperCAT (RRID:SCR_004882) Copy   

•   Source: SciCrunch Registry

http://blogs.wsj.com/health/

Health Blog offers news and analysis on health and the business of health. The blog is written by Katherine Hobson and includes contributions from staffers at The Wall Street Journal, WSJ.com and Dow Jones Newswires. A searchable interface allows the user to find topics of interest. Katherine Hobson has been writing about health and business for more than 15 years, including stints covering cancer, nutrition, exercise science, the U.S. economy and the U.K. beer industry.

Proper citation: Wall Street Journal Health Blog (RRID:SCR_004914) Copy   

•   Source: SciCrunch Registry

http://solgenomics.net/

A clade oriented, community curated database containing genomic, genetic, phenotypic and taxonomic information for plant genomes. Genomic information is presented in a comparative format and tied to important plant model species such as Arabidopsis. SGN provides tools such as: BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an intron detection tool, an advanced Alignment Analyzer, and a browser for phylogenetic trees. The SGN code and database are developed as an open source project, and is based on database schemas developed by the GMOD project and SGN-specific extensions.

Proper citation: SGN (RRID:SCR_004933) Copy   

•   Source: SciCrunch Registry

http://www.dbs.ifi.lmu.de/~bundschu/LHGDN.html

A text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a textual source of interest. The semantic gene-disease relations were extracted with F-measures of 78. More specifically, the textual source utilized here originates from Entrez Gene''''s GeneRIF (Gene Reference Into Function) database (Mitchell, et al., 2003). LHGDN was created based on a GeneRIF version from March 31st, 2009, consisting of 414241 phrases. These phrases were further restricted to the organism Homo sapiens, which resulted in a total of 178004 phrases. We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph. We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining.

Proper citation: Literature-derived human gene-disease network (RRID:SCR_005653) Copy   

•   Source: SciCrunch Registry

http://www.phenologs.org/

Database for identifying orthologous phenotypes (phenologs). Mapping between genotype and phenotype is often non-obvious, complicating prediction of genes underlying specific phenotypes. This problem can be addressed through comparative analyses of phenotypes. We define phenologs based upon overlapping sets of orthologous genes associated with each phenotype. Comparisons of >189,000 human, mouse, yeast, and worm gene-phenotype associations reveal many significant phenologs, including novel non-obvious human disease models. For example, phenologs suggest a yeast model for mammalian angiogenesis defects and an invertebrate model for vertebrate neural tube birth defects. Phenologs thus create a rich framework for comparing mutational phenotypes, identify adaptive reuse of gene systems, and suggest new disease genes. To search for phenologs, go to the basic search page and enter a list of genes in the box provided, using Entrez gene identifiers for mouse/human genes, locus ids for yeast (e.g., YHR200W), or sequence names for worm (e.g., B0205.3). It is expected that this list of genes will all be associated with a particular system, trait, mutational phenotype, or disease. The search will return all identified model organism/human mutational phenotypes that show any overlap with the input set of the genes, ranked according to their hypergeometric probability scores. Clicking on a particular phenolog will result in a list of genes associated with the phenotype, from which potential new candidate genes can identified. Currently known phenotypes in the database are available from the link labeled ''Find phenotypes'', where the associated gene can be submitted as queries, or alternately, can be searched directly from the link provided.

Proper citation: Phenologs (RRID:SCR_005529) Copy   

•   Source: SciCrunch Registry

http://www.bumc.bu.edu/busm-pathology/pathology-core-services/biospecimen-archive-research-core-barc/

Biospecimen repository of normal and diseased human material from a variety of tissues and conditions along with clinical annotation. Both frozen aliquots and paraffin embedded tissue are available. Biospecimens are available to qualified researchers with IRB approval. * Preliminary inquires please contact Cheryl Spencer at cheryl.spencer (at) bmc.org

Proper citation: Boston University Biospecimen Archive Research Core (RRID:SCR_005363) Copy   

•   Source: SciCrunch Registry

http://highwire.stanford.edu/

Service that partners with independent scholarly publishers, societies, associations, and university presses to facilitate the digital dissemination of 1779 journals, reference works, books, and proceedings. It also offers a complete manuscript submission, tracking, peer review, and publishing system for journal editors.

Proper citation: HighWire (RRID:SCR_005316) Copy   

•   Source: SciCrunch Registry

http://maize.tigr.org

A database of maize genomic sequences, searchable by BLAST, by repeat sequences, and sequence name, gene name, locus, or other landmark. TIGR is a member of the Consortium for Maize Genomics. The Consortium received a funding award from the National Science Foundation in September 2002, to evaluate two gene-enrichment techniques, methylation filtration and high Cot selection, to sequence the maize 'genespace'. Draft assemblies of 287 maize BAC clones selected by the maize community and the Consortium were also produced in the Consortium project. We have recently developed an improved version of the TIGR annotation pipeline optimized for maize genomic assemblies. The latest maize genomic assemblies obtained by gene-enrichment (AZM5) and the 287 maize draft BAC assemblies have been annotated using the improved pipeline. Gene model annotation and functional annotation can be accessed via the TIGR maize BLAST server or the TIGR maize gbrowse display. The first version of the Maize Repeat Database contained 485 characterized maize repeat sequences from the TIGR Cereal Repeat Database. To these we added repetitive sequences downloaded from GenBank and a file of retrotransposon sequences compiled by Phillip SanMiguel (Purdue University). In addition we searched our maize genomic assemblies (AZMs) to identify new repeats. Any sequence within an AZM that showed at least 80% identity over a minimum stretch of 100 bp with an entry in the TIGR Cereal Repeat Database was coded accordingly and added to the Maize Repeat Database.

Proper citation: TIGR Maize database (RRID:SCR_005431) Copy   

•   Source: SciCrunch Registry