A novel codon-based de Bruijn graph algorithm for gene construction from unassembled transcriptomes

Most gene prediction methods detect coding sequences from transcriptome assemblies in the absence of closely related reference genomes. Such methods are of limited application due to high transcript fragmentation and extensive assembly errors, which may lead to redundant or false coding sequence pre...

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Bibliographic Details
Published inGenome Biology Vol. 17; no. 1; p. 232
Main Authors Peng, Gongxin, Ji, Peifeng, Zhao, Fangqing
Format Journal Article
LanguageEnglish
Published England BioMed Central 17.11.2016
Subjects
Acids
Algorithms
Bioinformatics
Codon - genetics
Computational Biology
Datasets
Gene expression
Genes
Genomes
High-Throughput Nucleotide Sequencing
Insects
Learning algorithms
Method
Open Reading Frames
Pancreatitis-Associated Proteins
Phylogenetics
Sequence Analysis, RNA
Software
Transcription
Transcriptome - genetics
Gene prediction
de Bruijn graph
Transcriptome
Phylogenomics
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Summary:Most gene prediction methods detect coding sequences from transcriptome assemblies in the absence of closely related reference genomes. Such methods are of limited application due to high transcript fragmentation and extensive assembly errors, which may lead to redundant or false coding sequence predictions. We present inGAP-CDG, which can construct full-length and non-redundant coding sequences from unassembled transcriptomes by using a codon-based de Bruijn graph to simplify the assembly process and a machine learning-based approach to filter false positives. Compared with other methods, inGAP-CDG exhibits a significant increase in predicted coding sequence length and robustness to sequencing errors and varied read length.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-016-1094-x