SMuRF: a novel tool to identify genomic regions enriched for somatic point mutations
Bibliographic record
Abstract
Abstract Motivation Single Nucleotide Variants (SNVs), including somatic point mutations and Single Nucleotide Polymorphisms (SNPs), in noncoding cis-regulatory elements (CREs) can affect gene regulation and lead to disease development (Zhou et al. , 2016; Zhang et al. , 2014). Others have previously developed methods to identify important clusters of somatic point mutations based on proximity (Weinhold et al. , 2014) or the enrichment of inherited risk-SNPs at CREs (Ahmed et al. , 2017). Here, we present SMuRF (Significantly Mutated Region Finder), a user-friendly command-line tool to identify these significantly mutated regions from user-defined genomic intervals and SNVs. Results SMuRF identified 72 significantly mutated CREs in liver cancer, including known mutated gene promoters as well as previously unreported regions. Availability The source code for SMuRF is open-source and freely available on GitHub ( https://github.com/LupienLabOrganization/SMuRF ) under the GNU GPLv3 license. SMuRF is implemented in Bash and R; it runs on any platform with Bash (≥4.1.2), R (≥3.3.0) and BEDTools (≥2.26.0). It requires the following R packages: GenomicRanges, gtools, gplots, ggplot2, data.table, psych, and dplyr. Supplementary Information Supplementary information available at Bioinformatics online. Contact paul.guilhamon@uhnresearch.ca ; mlupien@uhnres.utoronto.ca
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.037 |
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".