Orthology Guided Assembly in highly heterozygous crops: creating a reference transcriptome to uncover genetic diversity in Lolium perenne. Issue 5 (21st February 2013) - Ruttink, Tom; Sterck, Lieven; Rohde, Antje; Bendixen, Christian; Rouzé, Pierre; Asp, Torben; Van de Peer, Yves; Roldan‐Ruiz, Isabel
Summary Despite current advances in next‐generation sequencing data analysis procedures, de novo assembly of a reference sequence required for SNP discovery and expression analysis is still a major challenge in genetically uncharacterized, highly heterozygous species. High levels of polymorphism inherent to outbreeding crop species hamper De Bruijn Graph‐based de novo assembly algorithms, causing transcript fragmentation and the redundant assembly of allelic contigs. If multiple genotypes are sequenced to study genetic diversity, primary de novo assembly is best performed per genotype to limit the level of polymorphism and avoid transcript fragmentation. Here, we propose an Orthology Guided Assembly procedure that first uses sequence similarity (tBLASTn) to proteins of a model species to select allelic and fragmented contigs from all genotypes and then performs CAP3 clustering on a gene‐by‐gene basis. Thus, we simultaneously annotate putative orthologues for each protein of the model species, resolve allelic redundancy and fragmentation and create a de novo transcript sequence representing the consensus of all alleles present in the sequenced genotypes. We demonstrate the procedure using RNA‐seq data from 14 genotypes of Lolium perenne to generate a reference transcriptome for gene discovery and translational research, to reveal the transcriptome‐wide distribution and density of SNPs in an outbreeding crop and to illustrate the effect of polymorphisms on the assembly procedure. The results presented here illustrate that constructing a non‐redundant reference sequence is essential for comparative genomics, orthology‐based annotation and candidate gene selection but also for read mapping and subsequent polymorphism discovery and/or read count‐based gene expression analysis.