Dataset Information

Sat-BSA: an NGS-based method using local de novo assembly of long reads for rapid identification of genomic structural variations associated with agronomic traits.

ABSTRACT: Advances in next generation sequencing (NGS)-based methodologies have accelerated the identifications of simple genetic variants such as point mutations and small insertions/deletions (InDels). Structural variants (SVs) including large InDels and rearrangements provide vital sources of genetic diversity for plant breeding. However, their analysis remains a challenge due to their complex nature. Consequently, novel NGS-based approaches are needed to rapidly and accurately identify SVs. Here, we present an NGS-based bulked-segregant analysis (BSA) technique called Sat-BSA (SVs associated with traits) for identifying SVs controlling traits of interest in crops. Sat-BSA targets allele frequencies at all SNP positions to first identify candidate genomic regions associated with a trait, which is then reconstructed by long reads-based local de novo assembly. Finally, the association between SVs, RNA-seq-based gene expression patterns and trait is evaluated for multiple cultivars to narrow down the candidate genes. We applied Sat-BSA to segregating F₂ progeny obtained from crosses between turnip cultivars with different tuber colors and successfully isolated two genes harboring SVs that are responsible for tuber phenotypes. The current study demonstrates the utility of Sat-BSA for the identification of SVs associated with traits of interest in species with large and heterozygous genomes.

SUBMITTER: Segawa T

PROVIDER: S-EPMC8573553 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Sat-BSA: an NGS-based method using local <i>de novo</i> assembly of long reads for rapid identification of genomic structural variations associated with agronomic traits.

Segawa Tenta T Nishiyama Chisato C Tamiru-Oli Muluneh M Sugihara Yu Y Abe Akira A Sone Hinako H Itoh Noriaki N Asukai Mayu M Uemura Aiko A Oikawa Kaori K Utsushi Hiroe H Ikegami-Katayama Ayako A Imamura Tomohiro T Mori Masashi M Terauchi Ryohei R Takagi Hiroki H

Breeding science 20210612 3

Advances in next generation sequencing (NGS)-based methodologies have accelerated the identifications of simple genetic variants such as point mutations and small insertions/deletions (InDels). Structural variants (SVs) including large InDels and rearrangements provide vital sources of genetic diversity for plant breeding. However, their analysis remains a challenge due to their complex nature. Consequently, novel NGS-based approaches are needed to rapidly and accurately identify SVs. Here, we p ...[more]

PMID: 34776737

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Project description:BackgroundDe novo genome assembly using NGS data remains a computation-intensive task especially for large genomes. In practice, efficiency is often a primary concern and favors using a more efficient assembler like SOAPdenovo2. Yet SOAPdenovo2, based on de Bruijn graph, fails to take full advantage of longer NGS reads (say, 150 bp to 250 bp from Illumina HiSeq and MiSeq). Assemblers that are based on string graphs (e.g., SGA), though less popular and also very slow, are more favorable for longer reads.MethodsThis paper shows a new de novo assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.ResultsExperiments on two bacteria and four human datasets shows the advantage of BASE in both contig quality and speed in dealing with longer reads. In the experiment on bacteria, two datasets with read length of 100 bp and 250 bp were used.. Especially for the 250 bp dataset, BASE gives much better quality than SOAPdenovo2 and SGA and is simlilar to SPAdes. Regarding speed, BASE is consistently a few times faster than SPAdes and SGA, but still slower than SOAPdenovo2. BASE and Soapdenov2 are further compared using human datasets with read length 100 bp, 150 bp and 250 bp. BASE shows a higher N50 for all datasets, while the improvement becomes more significant when read length reaches 250 bp. Besides, BASE is more-meory efficent than SOAPdenovo2 when sequencing data with error rate.ConclusionsBASE is a practically efficient tool for constructing contig, with significant improvement in quality for long NGS reads. It is relatively easy to extend BASE to include scaffolding.

Project description:BackgroundFlax (Linum usitatissimum. L) is an ancient oilseed and natural fiber crop. It could be divided into three categories by use, namely oil flax, fiber flax and oil-fiber dual purpose (OF). Cultivated flax is widely used in the food and textile industry. It is of great significance to elucidate the genetic characteristics of flax collections for accelerating the process of breeding improvement in this dual purpose crop. With the development of next-generation sequencing, we can use new methods, such as SLAF-seq (specific-locus amplified fragment sequencing), to decode unknown genomes of species. In this study, a high-through sequencing of flax collections using SLAF-seq was conducted. The evolutionary tendency was defined and candidate genes associated with agronomic traits of flax species were identified by Genome-Wide Association Studying (GWAS).ResultsA flax collection consisting of 224 varieties were sequenced by SLAF-seq. In total, 346,639 SLAF tags were developed from all accessions, with an average sequencing depth of 7.19 for each accession. A total of 584,987 SNPs (single nucleotide polymorphism) with an MAF > 0.05 were identified from these SLAFs. The population structure division and phylogenetic analysis indicated a strong divergence among three kinds of flax groups. The genome-wide variation uncovered that oil flax had the highest genetic diversity and was considered to be the ancestor of fiber flax and oil-fiber flax. Sixteen associated peak SNPs for six traits were obtained by GWAS of oil-related traits using EMMAX (efficient mixed-model association eXpedited). Candidate genes and their related pathway were evaluated. A new GWAS was developed for fiber properties using the GLM (General linear model) model and a number of loci were identified.ConclusionsTo our knowledge, this is the first study on discovery multiple loci for important agronomic traits of flax species using GWAS strategy. These results will provide the highest possibility of incorporating both high fiber and good oil traits in a single variety.

Project description:BackgroundThe main challenge in de novo genome assembly of DNA-seq data is certainly to deal with repeats that are longer than the reads. In de novo transcriptome assembly of RNA-seq reads, on the other hand, this problem has been underestimated so far. Even though we have fewer and shorter repeated sequences in transcriptomics, they do create ambiguities and confuse assemblers if not addressed properly. Most transcriptome assemblers of short reads are based on de Bruijn graphs (DBG) and have no clear and explicit model for repeats in RNA-seq data, relying instead on heuristics to deal with them.ResultsThe results of this work are threefold. First, we introduce a formal model for representing high copy-number and low-divergence repeats in RNA-seq data and exploit its properties to infer a combinatorial characteristic of repeat-associated subgraphs. We show that the problem of identifying such subgraphs in a DBG is NP-complete. Second, we show that in the specific case of local assembly of alternative splicing (AS) events, we can implicitly avoid such subgraphs, and we present an efficient algorithm to enumerate AS events that are not included in repeats. Using simulated data, we show that this strategy is significantly more sensitive and precise than the previous version of KisSplice (Sacomoto et al. in WABI, pp 99-111, 1), Trinity (Grabherr et al. in Nat Biotechnol 29(7):644-652, 2), and Oases (Schulz et al. in Bioinformatics 28(8):1086-1092, 3), for the specific task of calling AS events. Third, we turn our focus to full-length transcriptome assembly, and we show that exploring the topology of DBGs can improve de novo transcriptome evaluation methods. Based on the observation that repeats create complicated regions in a DBG, and when assemblers try to traverse these regions, they can infer erroneous transcripts, we propose a measure to flag transcripts traversing such troublesome regions, thereby giving a confidence level for each transcript. The originality of our work when compared to other transcriptome evaluation methods is that we use only the topology of the DBG, and not read nor coverage information. We show that our simple method gives better results than Rsem-Eval (Li et al. in Genome Biol 15(12):553, 4) and TransRate (Smith-Unna et al. in Genome Res 26(8):1134-1144, 5) on both real and simulated datasets for detecting chimeras, and therefore is able to capture assembly errors missed by these methods.

Dataset Information

Sat-BSA: an NGS-based method using local de novo assembly of long reads for rapid identification of genomic structural variations associated with agronomic traits.

Publications

Sat-BSA: an NGS-based method using local <i>de novo</i> assembly of long reads for rapid identification of genomic structural variations associated with agronomic traits.

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