Improving automated de-novo transcriptome definition in non-model organisms by integrating manually defined gene information

Abstract

Non-model organisms are of great ecological and economic significance, consequently the understanding of their unique metabolic pathways by investigating their gene expression profiles is essential. The bloom-forming alga Emiliania huxleyi is a cosmopolitan unicellular photoautotroph that plays a prominent role in the marine carbon and calcium cycle. Recently, genome sequences from several key marine phytoplankton species have been sequenced and partially assembled. Nevertheless, there are many challenges in defining genes in non-model organisms, where genomes are incomplete. With the advent of next generation sequencing technologies, cDNA short read sequences are generated in ever increasing amounts, and tools for building transcripts abound. However, quality control of the transcript building process is rarely performed if ever. We used 63 manually defined genes, several experimentally validated, in order to test the quality of the automated transcriptome definition. We found that the automated pipelines missed genes and artificially joined overlapping transcripts. In addition, E. huxleyi has a very high percentage of non-canonical splice junctions, and relatively high rates of intron readthrough, which caused unique issues with the currently available tools and may indicate unique transcription machinery. While individual tools missed transcripts, combining the results of several tools improved the completeness and quality considerably.