SEQSIM – A novel bioinformatics tool for comparisons of upstream gene regions – a case study of calcium binding protein spermatid associated 1 (CABS1)
Bibliographic record
Abstract
Abstract The regulation of gene expression is carefully overseen by upstream gene regions (UGRs) which include promoters, enhancers, and other regulatory elements. Understanding these regions is difficult using standard bioinformatic approaches due to the scale of the human genome. Here we present SEQSIM, a novel bioinformatics tool based on a modified Needleman-Wunsch algorithm that allows for fast, comprehensive, and accurate comparison of UGRs across the human genome. In this study, we detailed the applicability and validity of SEQSIM through an extensive case study of the calcium binding protein spermatid-associated 1 (CABS1). By analyzing 2000 base pairs upstream of every human gene, SEQSIM identified distinct clusters of UGRs, revealing conserved motifs and suggesting potential regulatory interactions. Our analysis identified 41 clusters, the second largest of which contains the CABS1 UGR. Studying the other members of the CABS1 cluster could offer new insights into its regulatory mechanisms and suggest broader implications for genes involved in similar pathways or functions. The development and implementation of SEQSIM represents a significant step forward for the genomics field, providing a powerful new tool to dissect the complexity of the human genome and gain a better understanding of how gene expression is regulated. The study not only shows that SEQSIM is an effective means to identify potential regulatory elements and gene clusters, but also opens up new lines of inquiry to understand overall genomic architecture.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".