A probabilistic approach for SNP discovery in high-throughput human resequencing data
Bibliographic record
Abstract
New high-throughput sequencing technologies are generating large amounts of sequence data, allowing the development of targeted large-scale resequencing studies. For these studies, accurate identification of polymorphic sites is crucial. Heterozygous sites are particularly difficult to identify, especially in regions of low coverage. We present a new strategy for identifying heterozygous sites in a single individual by using a machine learning approach that generates a heterozygosity score for each chromosomal position. Our approach also facilitates the identification of regions with unequal representation of two alleles and other poorly sequenced regions. The availability of confidence scores allows for a principled combination of sequencing results from multiple samples. We evaluate our method on a gold standard data genotype set from HapMap. We are able to classify sites in this data set as heterozygous or homozygous with 98.5% accuracy. In de novo data our probabilistic heterozygote detection ("ProbHD") is able to identify 93% of heterozygous sites at a <5% false call rate (FCR) as estimated based on independent genotyping results. In direct comparison of ProbHD with high-coverage 1000 Genomes sequencing available for a subset of our data, we observe >99.9% overall agreement for genotype calls and close to 90% agreement for heterozygote calls. Overall, our data indicate that high-throughput resequencing of human genomic regions requires careful attention to systematic biases in sample preparation as well as sequence contexts, and that their impact can be alleviated by machine learning-based sequence analyses allowing more accurate extraction of true DNA variants.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".