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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.    Results  Analyses of single  k -mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of  k -mers ( k- 19 to  k- 63). For each  k -mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other  k -mer assemblies. Producing a non-redundant CA of  k -mers 19 to 63 resulted in a more complete functional annotation than any single  k -mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual  k -mers ( k- 19 to  k- 63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented.    Conclusions  This study demonstrated that different  k -mer choices result in various quantities of unique contigs per single  k -mer assembly which affects biological information that is retrievable from the transcriptome. This undesirable effect can be minimized, but not eliminated, with clustering of multi- k  assemblies with redundancy removal. The complete extraction of biological information in  de novo  transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual  k -mer assemblies but not in the CA.

Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms