SeqEntropy: Genome-Wide Assessment of Repeats for Short Read Sequencing
Bibliographic record
Abstract
BACKGROUND: Recent studies on genome assembly from short-read sequencing data reported the limitation of this technology to reconstruct the entire genome even at very high depth coverage. We investigated the limitation from the perspective of information theory to evaluate the effect of repeats on short-read genome assembly using idealized (error-free) reads at different lengths. METHODOLOGY/PRINCIPAL FINDINGS: We define a metric H(k) to be the entropy of sequencing reads at a read length k and use the relative loss of entropy ΔH(k) to measure the impact of repeats for the reconstruction of whole-genome from sequences of length k. In our experiments, we found that entropy loss correlates well with de-novo assembly coverage of a genome, and a score of ΔH(k)>1% indicates a severe loss in genome reconstruction fidelity. The minimal read lengths to achieve ΔH(k)<1% are different for various organisms and are independent of the genome size. For example, in order to meet the threshold of ΔH(k)<1%, a read length of 60 bp is needed for the sequencing of human genome (3.2 10(9) bp) and 320 bp for the sequencing of fruit fly (1.8×10(8) bp). We also calculated the ΔH(k) scores for 2725 prokaryotic chromosomes and plasmids at several read lengths. Our results indicate that the levels of repeats in different genomes are diverse and the entropy of sequencing reads provides a measurement for the repeat structures. CONCLUSIONS/SIGNIFICANCE: The proposed entropy-based measurement, which can be calculated in seconds to minutes in most cases, provides a rapid quantitative evaluation on the limitation of idealized short-read genome sequencing. Moreover, the calculation can be parallelized to scale up to large euakryotic genomes. This approach may be useful to tune the sequencing parameters to achieve better genome assemblies when a closely related genome is already available.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".