Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms
Berat Z Haznedaroglu1, Darryl Reeves2, Hamid Rismani-Yazdi13 and Jordan Peccia1*
BMC Bioinformatics 2012, 13:170 doi:10.1186/1471-2105-13-170
Background
The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single k-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of k-mer selection on the annotation output. This study provides an in-depth k-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a de novo transcriptome assembly.
See suggested Work-flow in Supplementary information
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