MOTIVATION: Characterization of the coding sequences (CDSs) is an essential step in transcriptome annotation. Incorrect identification of CDSs can lead to the prediction of non-existent proteins that can eventually compromise knowledge if databases are populated with similar incorrect predictions made in different genomes. Also, the correct identification of CDSs is important for the characterization of the untranslated regions (UTRs), which are known to be important regulators of the mRNA translation process. Considering this, we present CodAn (Coding sequence Annotator), a new approach to predict confident CDS and UTR regions in full or partial transcriptome sequences in eukaryote species. RESULTS: Our analysis revealed that CodAn performs confident predictions on full-length and partial transcripts with the strand sense of the CDS known or unknown. The comparative analysis showed that CodAn presents better overall performance than other approaches, mainly when considering the correct identification of the full CDS (i.e. correct identification of the start and stop codons). In this sense, CodAn is the best tool to be used in projects involving transcriptomic data. AVAILABILITY: CodAn is freely available at https://github.com/pedronachtigall/CodAn. CONTACT: aland@usp.br. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.
MOTIVATION: Characterization of the coding sequences (CDSs) is an essential step in transcriptome annotation. Incorrect identification of CDSs can lead to the prediction of non-existent proteins that can eventually compromise knowledge if databases are populated with similar incorrect predictions made in different genomes. Also, the correct identification of CDSs is important for the characterization of the untranslated regions (UTRs), which are known to be important regulators of the mRNA translation process. Considering this, we present CodAn (Coding sequence Annotator), a new approach to predict confident CDS and UTR regions in full or partial transcriptome sequences in eukaryote species. RESULTS: Our analysis revealed that CodAn performs confident predictions on full-length and partial transcripts with the strand sense of the CDS known or unknown. The comparative analysis showed that CodAn presents better overall performance than other approaches, mainly when considering the correct identification of the full CDS (i.e. correct identification of the start and stop codons). In this sense, CodAn is the best tool to be used in projects involving transcriptomic data. AVAILABILITY: CodAn is freely available at https://github.com/pedronachtigall/CodAn. CONTACT: aland@usp.br. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.
Authors: Luciana A Freitas-de-Sousa; Pedro G Nachtigall; José A Portes-Junior; Matthew L Holding; Gunnar S Nystrom; Schyler A Ellsworth; Noranathan C Guimarães; Emilly Tioyama; Flora Ortiz; Bruno R Silva; Tobias S Kunz; Inácio L M Junqueira-de-Azevedo; Felipe G Grazziotin; Darin R Rokyta; Ana M Moura-da-Silva Journal: Toxins (Basel) Date: 2020-12-11 Impact factor: 4.546
Authors: Pedro G Nachtigall; Luciana A Freitas-de-Sousa; Andrew J Mason; Ana M Moura-da-Silva; Felipe G Grazziotin; Inácio L M Junqueira-de-Azevedo Journal: Toxins (Basel) Date: 2022-03-25 Impact factor: 5.075
Authors: Thaís A R Ramos; Nilbson R O Galindo; Raúl Arias-Carrasco; Cecília F da Silva; Vinicius Maracaja-Coutinho; Thaís G do Rêgo Journal: F1000Res Date: 2021-04-26