Genome Homology Visualization by Short Similar Substring Enumeration (Acceleration and Visualization of Computation for Enumeration Problems)
Bibliographic record
Abstract
Finding similar substrings/substructures is a central task in analyzing huge amounts of genome data.In the sense of complexity theory, the existence of polynomial time algorithms for such problems is usually trivial since the number of substrings is bounded by the square of their lengths.However, straightforward algorithms do not work for practical huge databases because of their computation time of high degree order.This paper addresses the problems of finding pairs of strings with small Hamming distances from huge databa.es composed of short strings of a fixed length.Using this, we compare two genomc scqucnces by solving this problcm for all the fixed- length substrings taken from the sequences.We focus on the practical efficiency of algorithms, and propose an algorithm running in almost linear tlme of the database size.When there are so many similar pairs so that the visualization is impossible, we propose to use a filtering algorithm to remove the pairs which are not parts of similar long sequences.Computational experiments for genome sequcnces show the efficiency of thc method.An implementation is available at the author's homepagel 1 IntroductionIn this paper, we consider the problem of enumerating all pairs of similar strings in a set $S$ of strings of the same length $l$ .We can approach to general substring comparison problems through this problem sinoe such non-short similar strings must include several such short similar substrings.As a similarity measure, we use Hamming distance.Thus the definition of the problem is as follows.Short Hamming Distance String Pair Enumeration Problem Input: a set $S$ of strings of the same length $l$ , a distance threshold $d$ Output: all pairs of strings $S_{1}$ and $S_{2}$ such that the Hamming distance between $S_{1}$ and $S_{2}$ is at most $d$ .We here call a pair of strings with Hamming distance at most $d$ similar string pair.We consider the
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| 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".