Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis
Bibliographic record
Abstract
BACKGROUND: The use of whole genome sequence has increased recently with rapid progression of next-generation sequencing (NGS) technologies. However, storing raw sequence reads to perform large-scale genome analysis pose hardware challenges. Despite advancement in genome analytic platforms, efficient approaches remain relevant especially as applied to the human genome. In this study, an Integrated Genome Sizing (IGS) approach is adopted to speed up multiple whole genome analysis in high-performance computing (HPC) environment. The approach splits a genome (GRCh37) into 630 chunks (fragments) wherein multiple chunks can simultaneously be parallelized for sequence analyses across cohorts. RESULTS: IGS was integrated on Maha-Fs (HPC) system, to provide the parallelization required to analyze 2504 whole genomes. Using a single reference pilot genome, NA12878, we compared the NGS process time between Maha-Fs (NFS SATA hard disk drive) and SGI-UV300 (solid state drive memory). It was observed that SGI-UV300 was faster, having 32.5 mins of process time, while that of the Maha-Fs was 55.2 mins. CONCLUSIONS: The implementation of IGS can leverage the ability of HPC systems to analyze multiple genomes simultaneously. We believe this approach will accelerate research advancement in personalized genomic medicine. Our method is comparable to the fastest methods for sequence alignment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".