HSDatabase – a database of highly similar duplicate genes from plants, animals, and algae
Bibliographic record
Abstract
Abstract Gene duplication is an important evolutionary mechanism capable of providing new genetic material, which can help organisms adapt to various environmental conditions. Recent studies, for example, have indicated that highly similar duplicated genes (HSDs) are involved in adaptation to extreme conditions via gene dosage. However, HSDs in most genomes remain uncharacterized. Here, we collected and curated HSDs in nuclear genomes from a diversity of species and indexed them in an online, open-access sequence repository called HSDatabase. Currently, this database contains 117,864 curated HSDs from 40 eukaryotic genomes, and it includes information on the total HSD number, gene copy number/length, and alignments of gene copies. HSDatabase also allows users to download sequences of gene copies, access genome browsers, and link out to other databases, such as Pfam and KEGG. What’s more, a built-in Basic Local Alignment Search Tool (BLAST) option is available to conveniently explore potential homologous sequences of interest within and across species. HSDatabase is presented with a user-friendly interface and provides easy access to the source data. It can be used on its own for comparative analyses of gene duplicates or in conjunction with HSDFinder, a newly developed bioinformatics tool for identifying, annotating, categorizing, and visualizing HSDs. Database URL http://hsdfinder.com/database/
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.018 |
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".