| URL: | http://v2.sinex.cl/ |
| Full name: | SinEx DATABASE |
| Description: | A publicly available, searchable database that houses the DNA and protein sequence information of single exon genes (SEGs) and includes their functional predictions and the relative distribution of these functions within species. |
| Year founded: | 2016 |
| Last update: | 2020 |
| Version: | |
| Accessibility: |
Accessible
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| Country/Region: | Chile |
| Data type: | |
| Data object: | |
| Database category: | |
| Major species: |
NA
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| Keywords: |
| University/Institution: | Fundacion Ciencia & Vida |
| Address: | Center for Bioinformatics and Genome Biology, Fundacion Ciencia & Vida and Facultad de Ciencias Biologicas |
| City: | Santiago |
| Province/State: | Santiago |
| Country/Region: | Chile |
| Contact name (PI/Team): | David S. Holmes |
| Contact email (PI/Helpdesk): | dsholmes2000@yahoo.com |
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SinEx DB 2.0 update 2020: database for eukaryotic single-exon coding sequences. [PMID: 33507271]
Single-exon coding sequences (CDSs), also known as 'single-exon genes' (SEGs), are defined as nuclear, protein-coding genes that lack introns in their CDSs. They have been studied not only to determine their origin and evolution but also because their expression has been linked to several types of human cancers and neurological/developmental disorders, and many exhibit tissue-specific transcription. We developed SinEx DB that houses DNA and protein sequence information of SEGs from 10 mammalian genomes including human. SinEx DB includes their functional predictions (KOG (euKaryotic Orthologous Groups)) and the relative distribution of these functions within species. Here, we report SinEx 2.0, a major update of SinEx DB that includes information of the occurrence, distribution and functional prediction of SEGs from 60 completely sequenced eukaryotic genomes, representing animals, fungi, protists and plants. The information is stored in a relational database built with MySQL Server 5.7, and the complete dataset of SEG sequences and their GO (Gene Ontology) functional assignations are available for downloading. SinEx DB 2.0 was built with a novel pipeline that helps disambiguate single-exon isoforms from SEGs. SinEx DB 2.0 is the largest available database for SEGs and provides a rich source of information for advancing our understanding of the evolution, function of SEGs and their associations with disorders including cancers and neurological and developmental diseases. Database URL: http://v2.sinex.cl/. |
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SinEx DB: a database for single exon coding sequences in mammalian genomes. [PMID: 27278816]
Eukaryotic genes are typically interrupted by intragenic, noncoding sequences termed introns. However, some genes lack introns in their coding sequence (CDS) and are generally known as 'single exon genes' (SEGs). In this work, a SEG is defined as a nuclear, protein-coding gene that lacks introns in its CDS. Whereas, many public databases of Eukaryotic multi-exon genes are available, there are only two specialized databases for SEGs. The present work addresses the need for a more extensive and diverse database by creating SinEx DB, a publicly available, searchable database of predicted SEGs from 10 completely sequenced mammalian genomes including human. SinEx DB houses the DNA and protein sequence information of these SEGs and includes their functional predictions (KOG) and the relative distribution of these functions within species. The information is stored in a relational database built with My SQL Server 5.1.33 and the complete dataset of SEG sequences and their functional predictions are available for downloading. SinEx DB can be interrogated by: (i) a browsable phylogenetic schema, (ii) carrying out BLAST searches to the in-house SinEx DB of SEGs and (iii) via an advanced search mode in which the database can be searched by key words and any combination of searches by species and predicted functions. SinEx DB provides a rich source of information for advancing our understanding of the evolution and function of SEGs.Database URL: www.sinex.cl. © The Author(s) 2016. Published by Oxford University Press. |