genBlastA: Enabling BLAST to identify homologous gene sequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BLAST is an extensively used local similarity search tool for identifying homologous sequences. When a gene sequence (either protein sequence or nucleotide sequence) is used as a query to search for homologous sequences in a genome, the search results, represented as a list of high-scoring pairs (HSPs), are fragments of candidate genes rather than full-length candidate genes. Relevant HSPs ("signals"), which represent candidate genes in the target genome sequences, are buried within a report that contains also hundreds to thousands of random HSPs ("noises"). Consequently, BLAST results are often overwhelming and confusing even to experienced users. For effective use of BLAST, a program is needed for extracting relevant HSPs that represent candidate homologous genes from the entire HSP report. To achieve this goal, we have designed a graph-based algorithm, genBlastA, which automatically filters HSPs into well-defined groups, each representing a candidate gene in the target genome. The novelty of genBlastA is an edge length metric that reflects a set of biologically motivated requirements so that each shortest path corresponds to an HSP group representing a homologous gene. We have demonstrated that this novel algorithm is both efficient and accurate for identifying homologous sequences, and that it outperforms existing approaches with similar functionalities.
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.
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 | 0.001 | 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 | 0.001 | 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 it