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Calling large indels in 1047 Arabidopsis with IndelEnsembler.

ABSTRACT: Large indels greatly impact the observable phenotypes in different organisms including plants and human. Hence, extracting large indels with high precision and sensitivity is important. Here, we developed IndelEnsembler to detect large indels in 1047 Arabidopsis whole-genome sequencing data. IndelEnsembler identified 34 093 deletions, 12 913 tandem duplications and 9773 insertions. Our large indel dataset was more comprehensive and accurate compared with the previous dataset of AthCNV (1). We captured nearly twice of the ground truth deletions and on average 27% more ground truth duplications compared with AthCNV, though our dataset has less number of large indels compared with AthCNV. Our large indels were positively correlated with transposon elements across the Arabidopsis genome. The non-homologous recombination events were the major formation mechanism of deletions in Arabidopsis genome. The Neighbor joining (NJ) tree constructed based on IndelEnsembler's deletions clearly divided the geographic subgroups of 1047 Arabidopsis. More importantly, our large indels represent a previously unassessed source of genetic variation. Approximately 49% of the deletions have low linkage disequilibrium (LD) with surrounding single nucleotide polymorphisms. Some of them could affect trait performance. For instance, using deletion-based genome-wide association study (DEL-GWAS), the accessions containing a 182-bp deletion in AT1G11520 had delayed flowering time and all accessions in north Sweden had the 182-bp deletion. We also found the accessions with 65-bp deletion in the first exon of AT4G00650 (FRI) flowered earlier than those without it. These two deletions cannot be detected in AthCNV and, interestingly, they do not co-occur in any Arabidopsis thaliana accession. By SNP-GWAS, surrounding SNPs of these two deletions do not correlate with flowering time. This example demonstrated that existing large indel datasets miss phenotypic variations and our large indel dataset filled in the gap.

SUBMITTER: Liu DX

PROVIDER: S-EPMC8565333 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Thellungiella halophila (also known as T. salsuginea) is a model halophyte with a small size, short life cycle, and small genome. Thellungiella genes exhibit a high degree of sequence identity with Arabidopsis genes (90% at the cDNA level). We previously generated a full-length enriched cDNA library of T. halophila from various tissues and from whole plants treated with salinity, chilling, freezing stress, or ABA. We determined the DNA sequences of 20 000 cDNAs at both the 5'- and 3' ends, and identified 9569 distinct genes. RESULTS: Here, we completely sequenced 1047 Thellungiella full-length cDNAs representing abiotic-stress-related genes, transcription factor genes, and protein phosphatase 2C genes. The predicted coding sequences, 5'-UTRs, and 3'-UTRs were compared with those of orthologous genes from Arabidopsis for length, sequence similarity, and structure. The 5'-UTR sequences of Thellungiella and Arabidopsis orthologs shared a significant level of similarity, although the motifs were rearranged. While examining the stress-related Thellungiella coding sequences, we found a short splicing variant of T. halophila salt overly sensitive 1 (ThSOS1), designated ThSOS1S. ThSOS1S contains the transmembrane domain of ThSOS1 but lacks the C-terminal hydrophilic region. The expression level of ThSOS1S under normal growth conditions was higher than that of ThSOS1. We also compared the expression levels of Na+-transport-system genes between Thellungiella and Arabidopsis by using full-length cDNAs from each species as probes. Several genes that play essential roles in Na+ excretion, compartmentation, and diffusion (SOS1, SOS2, NHX1, and HKT1) were expressed at higher levels in Thellungiella than in Arabidopsis. CONCLUSIONS: The full-length cDNA sequences obtained in this study will be essential for the ongoing annotation of the Thellungiella genome, especially for further improvement of gene prediction. Moreover, they will enable us to find splicing variants such as ThSOS1S (AB562331).

Project description:BACKGROUND:Discovering single nucleotide polymorphisms (SNPs) from agriculture crop genome sequences has been a widely used strategy for developing genetic markers for several applications including marker-assisted breeding, population diversity studies for eco-geographical adaption, genotyping crop germplasm collections, and others. Accurately detecting SNPs from large polyploid crop genomes such as wheat is crucial and challenging. A few variant calling methods have been previously developed but they show a low concordance between their variant calls. A gold standard of variant sets generated from one human individual sample was established for variant calling tool evaluations, however hitherto no gold standard of crop variant set is available for wheat use. The intent of this study was to evaluate seven SNP variant calling tools (FreeBayes, GATK, Platypus, Samtools/mpileup, SNVer, VarScan, VarDict) with the two most popular mapping tools (BWA-mem and Bowtie2) on wheat whole exome capture (WEC) re-sequencing data from allohexaploid wheat. RESULTS:We found the BWA-mem mapping tool had both a higher mapping rate and a higher accuracy rate than Bowtie2. With the same mapping quality (MQ) cutoff, BWA-mem detected more variant bases in mapping reads than Bowtie2. The reads preprocessed with quality trimming or duplicate removal did not significantly affect the final mapping performance in terms of mapped reads. Based on the concordance and receiver operating characteristic (ROC), the Samtools/mpileup variant calling tool with BWA-mem mapping of raw sequence reads outperformed other tests followed by FreeBayes and GATK in terms of specificity and sensitivity. VarDict and VarScan were the poorest performing variant calling tools with the wheat WEC sequence data. CONCLUSION:The BWA-mem and Samtools/mpileup pipeline, with no need to preprocess the raw read data before mapping onto the reference genome, was ascertained the optimum for SNP calling for the complex wheat genome re-sequencing. These results also provide useful guidelines for reliable variant identification from deep sequencing of other large polyploid crop genomes.

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Calling large indels in 1047 Arabidopsis with IndelEnsembler.

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