Aiming off the target: recycling target capture sequencing reads for investigating repetitive DNA. (29th May 2021) - Costa, Lucas; Marques, André; Buddenhagen, Chris; Thomas, William Wayt; Huettel, Bruno; Schubert, Veit; Dodsworth, Steven; Houben, Andreas; Souza, Gustavo; Pedrosa-Harand, Andrea
Abstract: Background and Aims: With the advance of high-throughput sequencing, reduced-representation methods such as target capture sequencing (TCS) emerged as cost-efficient ways of gathering genomic information, particularly from coding regions. As the off-target reads from such sequencing are expected to be similar to genome skimming (GS), we assessed the quality of repeat characterization in plant genomes using these data. Methods: Repeat composition obtained from TCS datasets of five Rhynchospora (Cyperaceae) species were compared with GS data from the same taxa. In addition, a FISH probe was designed based on the most abundant satellite found in the TCS dataset of Rhynchospora cephalotes . Finally, repeat-based phylogenies of the five Rhynchospora species were constructed based on the GS and TCS datasets and the topologies were compared with a gene-alignment-based phylogenetic tree. Key Results: All the major repetitive DNA families were identified in TCS, including repeats that showed abundances as low as 0.01 % in the GS data. Rank correlations between GS and TCS repeat abundances were moderately high ( r  = 0.58–0.85), increasing after filtering out the targeted loci from the raw TCS reads ( r  = 0.66–0.92). Repeat data obtained by TCS were also reliable in developing a cytogenetic probe of a new variant of the holocentromeric satellite Tyba. Repeat-based phylogenies from TCS data were congruent with those obtained from GS data and the gene-alignment tree. Conclusions: Our results show that off-target TCS reads can be recycled to identify repeats for cyto- and phylogenomic investigations. Given the growing availability of TCS reads, driven by global phylogenomic projects, our strategy represents a way to recycle genomic data and contribute to a better characterization of plant biodiversity.