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Found 8 result(s)

TopFIND

The public knowledgebase for protein termini and protease processing

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TopFIND is a protein-centric database for the annotation of protein termini currently in its third version. Non-canonical protein termini can be the result of multiple different biological processes, including pre-translational processes such as alternative splicing and alternative translation initiation or post-translational protein processing by proteases that cleave proteases as part of protein maturation or as a regulatory modification. Accordingly, protein termini evidence in TopFIND is inferred from other databases such as ENSEMBL transcripts, TISdb for alternative translation initiation, MEROPS for protein cleavage by proteases, and UniProt for canonical and protein isoform start sites.

ProteomicsDB

PrDB

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ProteomicsDB started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data.

National Genomics Data Center

国家基因组科学数据中心

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The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), advances life & health sciences by providing open access to a suite of resources, with the aim to translate big data into big discoveries and support worldwide activities in both academia and industry.

GeneMANIA

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GeneMANI helps you predict the function of your favourite genes and gene sets. GeneMania, a real-time multiple association network integration algorithm for predicting gene function.

MicrosporidiaDB

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MicrosporidiaDB belongs to the EuPathDB family of databases and is an integrated genomic and functional genomic database for the phylum Microsporidia. In its first iteration (released in early 2010), MicrosporidiaDB contains the genomes of two Encephalitozoon species (see below). MicrosporidiaDB integrates whole genome sequence and annotation and will rapidly expand to include experimental data and environmental isolate sequences provided by community researchers. The database includes supplemental bioinformatics analyses and a web interface for data-mining.

PhenoM

Phenomics of yeast Mutants

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<<<!!!<<< This repository is no longer available. >>>!!!>>>

OMICtools

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>>>!!!<<< OMICtools is no longer online >>>!!!<<< We founded OMICtools in 2012 with the vision to drive progress in life science. We wanted to empower life science practitioners all over the world to achieve breakthroughs by getting data to talk. While we made tremendous progress over the past three years, developing a bioinformatics database of software and dynamic protocols, attracting more than 1.5M visitors a year, we lacked the financial support we needed to continue. We certainly gave it our all. We'd like to thank everyone who believed in us and supported us on this journey: all our users, our community, our friends, families and employees (who we consider as our extended family!). omicX will probably shut down its operations within the next few weeks. The team and I remain firmly committed to our vision, particularly at this very difficult time. It is now, more than ever before, that researchers need access to a resource that pools collective scientific intelligence. We have accumulated an awful lot of experience which we are keen to share. If your institution would be interested in taking over our website and database, to provide researchers with continued access to the platform, or you simply want to stay in touch with the omicX team, contact us at contact@omictools.com or at carine.toutain@fhbx.eu.

GeneLab

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GeneLab is an interactive, open-access resource where scientists can upload, download, store, search, share, transfer, and analyze omics data from spaceflight and corresponding analogue experiments. Users can explore GeneLab datasets in the Data Repository, analyze data using the Analysis Platform, and create collaborative projects using the Collaborative Workspace. GeneLab promises to facilitate and improve information sharing, foster innovation, and increase the pace of scientific discovery from extremely rare and valuable space biology experiments. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight. GeneLab helps scientists understand how the fundamental building blocks of life itself – DNA, RNA, proteins, and metabolites – change from exposure to microgravity, radiation, and other aspects of the space environment. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data alongside essential metadata describing each spaceflight and space-relevant experiment. By carefully curating and implementing best practices for data standards, users can combine individual GeneLab datasets to gain new, comprehensive insights about the effects of spaceflight on biology. In this way, GeneLab extends the scientific knowledge gained from each biological experiment conducted in space, allowing scientists from around the world to make novel discoveries and develop new hypotheses from these priceless data.