Whole‐genome sequencing and genome regions of special interest: Lessons from major histocompatibility complex, sex determination, and plant self‐incompatibility
Bibliographic record
Abstract
Whole-genome sequencing of non-model organisms is now widely accessible and has allowed a range of questions in the field of molecular ecology to be investigated with greater power. However, some genomic regions that are of high biological interest remain problematic for assembly and data-handling. Three such regions are the major histocompatibility complex (MHC), sex-determining regions (SDRs) and the plant self-incompatibility locus (S-locus). Using these as examples, we illustrate the challenges of both assembling and resequencing these highly polymorphic regions and how bioinformatic and technological developments are enabling new approaches to their study. Mapping short-read sequences against multiple alternative references improves genotyping comprehensiveness at the S-locus thereby contributing to more accurate assessments of allelic frequencies. Long-read sequencing, producing reads of several tens to hundreds of kilobase pairs in length, facilitates the assembly of such regions as single sequences can span the multiple duplicated gene copies of the MHC region, and sequence through repetitive stretches and translocations in SDRs and S-locus haplotypes. These advances are adding value to short-read genome resequencing approaches by allowing, for example, more accurate haplotype phasing across longer regions. Finally, we assessed further technical improvements, such as nanopore adaptive sequencing and bioinformatic tools using pangenomes, which have the potential to further expand our knowledge of a number of genomic regions that remain challenging to study with classical resequencing approaches.
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 | 0.000 | 0.000 |
| 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".