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Found 6 result(s)
Intrepid Bioinformatics serves as a community for genetic researchers and scientific programmers who need to achieve meaningful use of their genetic research data – but can’t spend tremendous amounts of time or money in the process. The Intrepid Bioinformatics system automates time consuming manual processes, shortens workflow, and eliminates the threat of lost data in a faster, cheaper, and better environment than existing solutions. The system also provides the functionality and community features needed to analyze the large volumes of Next Generation Sequencing and Single Nucleotide Polymorphism data, which is generated for a wide range of purposes from disease tracking and animal breeding to medical diagnosis and treatment.
dbEST is a division of GenBank that contains sequence data and other information on "single-pass" cDNA sequences, or "Expressed Sequence Tags", from a number of organisms. Expressed Sequence Tags (ESTs) are short (usually about 300-500 bp), single-pass sequence reads from mRNA (cDNA). Typically they are produced in large batches. They represent a snapshot of genes expressed in a given tissue and/or at a given developmental stage. They are tags (some coding, others not) of expression for a given cDNA library. Most EST projects develop large numbers of sequences. These are commonly submitted to GenBank and dbEST as batches of dozens to thousands of entries, with a great deal of redundancy in the citation, submitter and library information. To improve the efficiency of the submission process for this type of data, we have designed a special streamlined submission process and data format. dbEST also includes sequences that are longer than the traditional ESTs, or are produced as single sequences or in small batches. Among these sequences are products of differential display experiments and RACE experiments. The thing that these sequences have in common with traditional ESTs, regardless of length, quality, or quantity, is that there is little information that can be annotated in the record. If a sequence is later characterized and annotated with biological features such as a coding region, 5'UTR, or 3'UTR, it should be submitted through the regular GenBank submissions procedure (via BankIt or Sequin), even if part of the sequence is already in dbEST. dbEST is reserved for single-pass reads. Assembled sequences should not be submitted to dbEST. GenBank will accept assembled EST submissions for the forthcoming TSA (Transcriptome Shotgun Assembly) division. The individual reads which make up the assembly should be submitted to dbEST, the Trace archive or the Short Read Archive (SRA) prior to the submission of the assemblies.
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The Alzheimer Disease & Frontotemporal Dementia Mutation Database (AD&FTDMDB) aims at collecting all known mutations in the genes related to Alzheimer disease (AD) and fromtotemporal dementias (FTD). Mutations are collected from the literature and from presentations at scientific meetings. In addition, mutations can be submitted to AD&FTDMDB at this web site.
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Mapping, copy number analysis, sequence and gene expression data generated by the High Resolution Analysis of Follicular Lymphoma Genomes project. The data will be available for 24 patients with follicular lymphoma. All data will be made as widely and freely available as possible while safeguarding the privacy of participants, and protecting confidential and proprietary data.The data from this project will be submitted to public genomic data sources. These sources will be listed on this web site as the data becomes available in these external data sources.
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The Parkinson disease Mutation Database (PDmutDB) aims at collecting all known mutations in the genes related to Parkinson disease (PD). Mutations are collected from the literature and from presentations at scientific meetings. In addition, mutations can be submitted to PDmutDB at this web site.
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>>>!!!<<<As stated 2017-05-23 Cancer GEnome Mine is no longer available >>>!!!<<< Cancer GEnome Mine is a public database for storing clinical information about tumor samples and microarray data, with emphasis on array comparative genomic hybridization (aCGH) and data mining of gene copy number changes.