Establishing evidenced-based best practice for the de novo assembly and evaluation of transcriptomes from non-model organisms

Characterizing transcriptomes in both model and non-model organisms has resulted in a massive increase in 2 our understanding of biological phenomena. This boon, largely made possible via high-throughput 3 sequencing, means that studies of functional, evolutionary and population genomics are now being done by 4 hundreds or even thousands of labs around the world. For many, these studies begin with a de novo 5 transcriptome assembly, which is a technically complicated process involving several discrete steps. Each 6 step may be accomplished in one of several different ways, using different software packages, each producing 7 different results. This analytical complexity begs the question – Which method(s) are optimal? Using 8 reference and non-reference based evaluative methods, I propose a set of guidelines that aim to standardize 9 and facilitate the process of transcriptome assembly. These recommendations include the generation of 10 between 20 million and 40 million sequencing reads from single individual where possible, error correction of 11 reads, gentle quality trimming, assembly filtering using Transrate and/or gene expression, annotation using 12 dammit, and appropriate reporting. These recommendations have been extensively benchmarked and 13 applied to publicly available transcriptomes, resulting in improvements in both content and contiguity. To 14 facilitate the implementation of the proposed standardized methods, I have released a set of version 15 controlled open-sourced code, The Oyster River Protocol for Transcriptome Assembly, available at 16 http://oyster-river-protocol.rtfd.org/. 17