CodAn: predictive models for precise identification of coding regions in eukaryotic transcripts

Abstract

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.

A simple guide to de novo transcriptome assembly and annotation

“…A core element in the downstream analysis for RNA-seq data involves the translation of assembled sequences into their corresponding amino acid sequences, and on the nucleotide level into the protein coding sequences (CDS) not containing any untranslated regions (UTRs). A correct characterization of CDS is not only important for profiling the protein-coding fraction of a transcriptome, but also for an accurate classification of UTRs and non-coding sequences/regions which may be of interest in the context of gene regulation [ 146 ].…”

Size Matters: An Evaluation of the Molecular Basis of Ontogenetic Modifications in the Composition of Bothrops jararacussu Snake Venom

“…In addition, we searched for putative regulatory elements that may be acting in the transcriptional and post-transcriptional levels of the transcripts. First, to detect the transcriptional factors (TFs) GRHL1 and NFI in the transcriptome assembly, we performed a coding sequence prediction using CodAn (v1.0; [ 93 ]) with the vertebrate full model. Next, we performed a BLAST search using a database containing the GRHL1 and NFI peptide sequences available from Swiss-Prot, Ensembl, and previously published snake transcriptome assemblies from the TSA database [ 9 , 87 , 94 , 95 ].…”

The computational approaches of lncRNA identification based on coding potential: Status quo and challenges

“…Certainly, characterization of coding potential has its own significance for genome annotation, so as to partition different functional regions on the genomes. Prodigal [60] , TransDecoder [48] , GeneMarkS-T [132] and CodAn [101] are such approaches that were developed for precise identification of coding regions in transcirpts, these methods have an important referential value for lncRNA identification. For example, using these tools, we can further determine the ORF-related features which were usually as a vital parameter during lncRNA identification.…”