A setback into a success: What can batch effects tell us about best practices in genomics?
Bibliographic record
Abstract
The increasing access to high-throughput sequencing is certainly one of the major changes that molecular ecology has gone through over the last decade. With the positive trend towards more open science, most sequencing data sets are now available on public databases, which holds amazing potential, but also risks of introducing batch effects in studies combining data sets. In this issue of Molecular Ecology Resources, Lou and Therkildsen (2022) offer a timely discussion on the matter by analyzing an imperfect low-coverage Whole Genome Sequencing data set, in which they test the effects of differences in sequencing choices, DNA degradation, and read depth on routine population genomics analyses. Through a series of diagnostic tools, they uncover multiple factors producing technical artefacts that can bias estimates of genetic diversity, inference of population structure, and selection scans. For each confounding factor, they demonstrate the effectiveness of mitigation approaches and suggest other avenues to deal with the issue. In this perspective, we highlight considerations regarding (1) effects that arise from differences between batches of sequencing; (2) unavoidable heterogeneity within data sets; and (3) more general concerns around the use of next-generation sequencing in population genomics. Altogether, by exploring what may have appeared at first glimpse as a "failed" sequencing project, Lou and Therkildsen (2022) end up setting a standard of best practices to make the most of heterogeneous whole-genome sequences, opening a promising avenue towards efficient reuse of published data sets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".