Twardziok, S. O. | bioRxiv Science

bioRxiv2018

Chromosome-scale comparative sequence analysis unravels molecular mechanisms of genome evolution between two wheat cultivars

BackgroundRecent improvements in DNA sequencing and genome scaffolding have paved the way to generate high-quality de novo assemblies of pseudomolecules representing complete chromosomes of wheat and its wild relatives. These assemblies form the basis to compare the evolutionary dynamics of wheat genomes on a megabase-scale.\n\nResultsHere, we provide a comparative sequence analysis of the ~700-megabase chromosome 2D between two bread wheat genotypes - the old landrace Chinese Spring and the elite Swiss spring wheat line CH Campala Lr22a. There was a high degree of sequence conservation between the two chromosomes. Analysis of large structural variations revealed four large insertions/deletions (InDels) of >100 kb. Based on the molecular signatures at the breakpoints, unequal crossing over and double-strand break repair were identified as the evolutionary mechanisms that caused these InDels. Three of the large InDels affected copy number of NLRs, a gene family involved in plant immunity. Analysis of single nucleotide polymorphism (SNP) density revealed three haploblocks of ~8 Mb, ~9 Mb and ~48 Mb with a 35-fold increased SNP density compared to the rest of the chromosome.\n\nConclusionsThis comparative analysis of two high-quality chromosome assemblies enabled a comprehensive assessment of large structural variations. The insight obtained from this analysis will form the basis of future wheat pan-genome studies.

genomics↗

bioRxiv2017

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

MotivationGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.\n\nResultsHere, we present an extension of the gene prediction tool GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.\n\nAvailabilityGeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa.\n\nContactjens.keilwagen@julius-kuehn.de

bioinformatics↗