Detection of large sequence insertions by a hybrid approach that combine de novo assembly and resequencing of medium-coverage genome sequences
Bibliographic record
Abstract
Large sequence insertion (LSI) is one of the structural variations (SVs) that may cause phenotypic differences in plants. To identify the LSIs using medium-coverage sequencing data of four wild soybean (Glycine soja) genotypes, we designed a hybrid approach combining de novo assembly and read mapping. Total reads and reads with both ends unmapped were independently assembled into "ordinary contigs" and "orphan contigs", respectively, and subjected to pairwise alignment and stringent filtering. This approach predicted 24 LSIs averaging 2682 bp in size, with no overlap with SVs detected by Pindel, BreakDancer, or ScanIndel, and they were validated by PCR. Compared with the soybean (Glycine max) reference genome, 20 LSIs were located outside genic regions. One of the four LSIs within a genic region, LSI05, is located in the coding DNA sequence region of a protein kinase superfamily gene (Glyma.08G123500). It caused delayed translation initiation and loss of 24 amino acids in the wild soybean genotype CW12. LSI05 was more frequently observed in 29 G. soja accessions than in 34 G. max accessions. Identified LSIs would be genomic resources harboring novel gene contents for studying SVs and improving crops. Moreover, our cost-efficient approach may be applicable to other plant species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".