bio-epitranscriptomics-m6a-peak-calling — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited bio-epitranscriptomics-m6a-peak-calling (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Reference examples tested with: MACS3 3.0+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Call m6A peaks from my MeRIP-seq data" → Identify m6A-modified RNA regions by comparing immunoprecipitated (IP) and input samples using statistical enrichment testing.
exomePeak2::exomePeak2() for GC-bias aware peak callingmacs3 callpeak as an alternative broad peak callerGoal: Identify m6A-enriched regions by comparing IP and input samples with GC-bias correction and replicate-aware statistical testing.
Approach: Provide IP and input BAM files along with a gene annotation to exomePeak2, which models read counts in sliding windows across the transcriptome and calls significant enrichment peaks.
library(exomePeak2)
# Peak calling with biological replicates
result <- exomePeak2(
bam_ip = c('IP_rep1.bam', 'IP_rep2.bam'),
bam_input = c('Input_rep1.bam', 'Input_rep2.bam'),
gff = 'genes.gtf',
genome = 'hg38',
paired_end = TRUE
)
# Export peaks
exportResults(result, format = 'BED')# Call peaks treating input as control
macs3 callpeak \
-t IP_rep1.bam IP_rep2.bam \
-c Input_rep1.bam Input_rep2.bam \
-f BAMPE \
-g hs \
-n m6a_peaks \
--nomodel \
--extsize 150 \
-q 0.05library(MeTPeak)
# GTF-aware peak calling
metpeak(
IP_BAM = c('IP_rep1.bam', 'IP_rep2.bam'),
INPUT_BAM = c('Input_rep1.bam', 'Input_rep2.bam'),
GENE_ANNO_GTF = 'genes.gtf',
OUTPUT_DIR = 'metpeak_output'
)# Filter by fold enrichment and q-value
# FC > 2, q < 0.05 typical thresholds
awk '$7 > 2 && $9 < 0.05' peaks.xls > filtered_peaks.bed~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.