Draft genome assemblies for tree pathogens Phytophthora pseudosyringae and Phytophthora boehmeriae.
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ABSTRACT:
SUBMITTER: Thorpe P
PROVIDER: S-EPMC8527500 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesEscallonia (Escalloniaceae) belongs to the Escalloniales, a diverse clade of flowering plants with unclear placement in the tree of life. Escallonia species show impressive morphological and ecological diversity and are widely distributed across three hotspots of biodiversity in the Neotropics. To shed light on the genomic substrate of this radiation and the phylogenetic placement of Escalloniales as well as to generate useful data for comparative evolutionary genomics across flowering plants, we produced and annotated draft genomes for two species of Escallonia.Data descriptionGenomic DNA from E. rubra and E. herrerae was sequenced with Oxford Nanopore sequencing chemistry, generating 3.4 and 12 million sequence reads with an average read length of 9.4 and 9.1 Kb (approximately 31 and 111 Gb of sequence data), respectively. In addition, we generated Illumina 100-bp paired-end short read data for E. rubra (approximately 75 Gb of sequence data). The Escallonia rubra genome was 566 Mb, with 3,233 contigs and an N50 of 285 Kb. The assembled genome for E. herrerae was 994 Mp, with 5,760 contigs and an N50 of 317 Kb. The genome sequences were annotated with 31,038 (E. rubra) and 47,905 (E. herrerea) protein-coding gene models supported by transcriptome/protein evidence and/or Pfam domain content. BUSCO assessments indicated completeness levels of approximately 98% for the genome assemblies and 88% for the genome annotations.
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Project description:Genome assembly of short reads from large plant genomes remains a challenge in computational biology despite major developments in next generation sequencing. Of late several draft assemblies have been reported in sequenced plant genomes. The reported draft genome assemblies of Cajanus cajan have different levels of genome completeness, a large number of repeats, gaps, and segmental duplications. Draft assemblies with portions of genome missing are shorter than the referenced original genome. These assemblies come with low map accuracy affecting further functional annotation and the prediction of gene components as desired by crop researchers. Genome coverage, i.e., the number of sequenced raw reads mapped onto a certain location of the genome is an important quality indicator of completeness and assembly quality in draft assemblies. The present work aimed to improve the coverage in reported de novo sequenced draft genomes (GCA_000340665.1 and GCA_000230855.2) of pigeonpea, a legume widely cultivated in India. The two recently sequenced assemblies, A1 and A2 comprised 72% and 75% of the estimated coverage of the genome, respectively. We employed an assembly reconciliation approach to compare the draft assemblies and merge them, filling the gaps by employing an algorithm size sorting mate-pair library to generate a high quality and near complete assembly with enhanced contiguity. The majority of gaps present within scaffolds were filled with right-sized mate-pair reads. The improved assembly reduced the number of gaps than those reported in draft assemblies resulting in an improved genome coverage of 82.4%. Map accuracy of the improved assembly was evaluated using various quality metrics and for the presence of specific trait-related functional genes. Employed pair-end and mate-pair local libraries helped us to reduce gaps, repeats, and other sequence errors resulting in lengthier scaffolds compared to the two draft assemblies. We reported the prediction of putative host resistance genes against Fusarium wilt disease by their performance and evaluated them both in wet laboratory and field phenotypic conditions.
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Project description:Short-read DNA sequencing instruments can yield over 1012 bases per run, typically composed of reads 150 bases long. Despite this high throughput, de novo assembly algorithms have difficulty reconstructing contiguous genome sequences using short reads due to both repetitive and difficult-to-sequence regions in these genomes. Some of the short read assembly challenges are mitigated by scaffolding assembled sequences using paired-end reads. However, unresolved sequences in these scaffolds appear as "gaps". Here, we introduce GapPredict - An implementation of a proof of concept that uses a character-level language model to predict unresolved nucleotides in scaffold gaps. We benchmarked GapPredict against the state-of-the-art gap-filling tool Sealer, and observed that the former can fill 65.6% of the sampled gaps that were left unfilled by the latter with high similarity to the reference genome, demonstrating the practical utility of deep learning approaches to the gap-filling problem in genome assembly.
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