Please use this identifier to cite or link to this item: http://hdl.handle.net/11349/20854
Title: Automation of functional annotation of genomes and transcriptomes
Automation of functional annotation of genomes and transcriptomes
Author: Cadavid Gutiérrez, Luis Fernando
Pérez Castillo, José Nelson
Rojas Quintero, Cristian Alejandro
Vera Parra, Nelson Enrique
Keywords: Annotator
Functional annotation
Gene ontology
High Throughput Sequencing.
Annotator
Functional annotation
Gene ontology
High Throughput Sequencing.
Publisher: Universidad Distrital Francisco José de Caldas. Colombia
Description: Functional annotation represents a means to investigate and classify genes and transcripts according to their function within a given organism.This paper presents Massive Automatic Functional Annotation (MAFA - Web), which is an online free bioinformatics tool that allows automation, unification and optimization of functional annotation processes when dealing with large volumes of sequences. MAFA includes tools for categorization and statistical analysis of associations between sequences. We have evaluated the performance of MAFA with a set of data taken from Diploria-Strigosatranscriptome (using an 8-core computer, namely E7450 @ 2,40GHZ with 256GB RAM), processing rates of 2,7 seconds per sequence (using Uniprot database) and 50,0 seconds per sequence (using Non-redundant from NCBI database) were found together with particular RAM usage patterns that depend on the database being processed (1GB for Uniprot database and 9GB for Non-redundant database).. Aviability: https://github.com/BioinfUD/MAFA. 
Functional annotation represents a means to investigate and classify genes and transcripts according to their function within a given organism.This paper presents Massive Automatic Functional Annotation (MAFA - Web), which is an online free bioinformatics tool that allows automation, unification and optimization of functional annotation processes when dealing with large volumes of sequences. MAFA includes tools for categorization and statistical analysis of associations between sequences. We have evaluated the performance of MAFA with a set of data taken from Diploria-Strigosatranscriptome (using an 8-core computer, namely E7450 @ 2,40GHZ with 256GB RAM), processing rates of 2,7 seconds per sequence (using Uniprot database) and 50,0 seconds per sequence (using Non-redundant from NCBI database) were found together with particular RAM usage patterns that depend on the database being processed (1GB for Uniprot database and 9GB for Non-redundant database). Aviability: https://github.com/BioinfUD/MAFA. 
URI: http://hdl.handle.net/11349/20854
Other Identifiers: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/9246
10.14483/22487638.9246
Appears in Collections:Tecnura

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