bioSkills

Map translation initiation sites, including non-AUG and alternative starts, from initiation-drug ribosome profiling (TI-seq). Use when locating start codons, detecting near-cognate or upstream initiation, or analyzing harringtonine, lactimidomycin (GTI-seq/QTI-seq), or retapamulin (Ribo-RET) data.

Detect and quantify translated ORFs from Ribo-seq using 3-nucleotide periodicity, including uORFs, internal ORFs, dORFs, and novel ORFs. Use when finding actively translated regions beyond annotated CDS, classifying ORFs by the 2022 community standard, quantifying ORF-level translation, or choosing between periodicity-based callers.

Preprocess ribosome profiling reads with UMI handling, adapter trimming, contaminant/rRNA depletion, and footprint-aware alignment. Use when preparing Ribo-seq FASTQ for periodicity QC, ORF detection, translation efficiency, or stalling analysis, or when deciding how to deduplicate, which aligner to use, or how to size-select ribosome-protected fragments.

Validate Ribo-seq library quality by measuring 3-nucleotide periodicity and calibrating read-length-specific P-site offsets. Use when checking whether footprints capture genuine translation, determining P-site offsets for downstream ORF/TE/stalling analysis, or deciding which read lengths to keep.

Detect ribosome pausing and stalling at codon resolution from Ribo-seq, using local-relative occupancy metrics and A-site assignment. Use when studying elongation dynamics, codon dwell times, pause motifs, or ribosome collisions, and when judging whether a pause is real biology or a cycloheximide artifact.

Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions. Use when separating translational from transcriptional regulation, distinguishing genuine translational control from buffering, or choosing between riborex, Xtail, anota2seq, and DESeq2 interaction models.

End-to-end Ribo-seq analysis from FASTQ through periodicity QC, P-site calibration, ORF detection, translation efficiency, and stalling. Use when orchestrating a full ribosome profiling pipeline and deciding harvest/dedup/alignment options and which downstream analyses the library can support.

Select restriction enzymes for cloning or diagnostics using Biopython Bio.Restriction. Finds enzymes by cut frequency, overhang type, recognition-site length, commercial availability, compatible ends, and methylation sensitivity, and identifies isoschizomers and compatible pairs. Use when choosing which enzymes to use to linearize a vector, drop in an insert, set up a diagnostic digest, or pick a methylation-insensitive enzyme.

Predict restriction digest fragment sizes and gel patterns using Biopython Bio.Restriction. Computes fragment lengths and sequences for single and double digests on linear or circular DNA, and interprets them against an agarose gel. Use when predicting the fragments from a digest, planning a diagnostic digest to verify a clone, or matching observed gel bands to an expected pattern.

Design and validate Type IIS scarless DNA assembly (Golden Gate, MoClo) using Biopython Bio.Restriction. Screens parts for internal BsaI/BsmBI/BbsI/SapI sites (domestication), previews the fusion overhangs a digest exposes, and validates a fusion-overhang set for distinctness and fidelity. Use when designing a Golden Gate or MoClo assembly, domesticating a part by removing internal Type IIS sites, or choosing and checking fusion overhangs for one-pot assembly.

Build restriction maps showing enzyme cut positions and inter-site distances along DNA using Biopython Bio.Restriction. Produces text or graphical maps for linear and circular molecules, orders sites from single and double digests, and overlays GenBank features. Use when creating a restriction map of a sequence, ordering cut sites along a plasmid, or relating sites to annotated features.

Find restriction enzyme cut sites in DNA sequences using Biopython Bio.Restriction. Searches single enzymes, batches, or commercial enzyme sets and returns cut positions for linear or circular DNA. Use when locating where one or more restriction enzymes cut a sequence, screening a sequence for the presence or absence of a site, or counting how often an enzyme cuts.

Aggregates per-tool QC metrics (FastQC, fastp, alignment, quantification, variant calling, single-cell) into one interactive MultiQC report, and guides module scoping, sample-name resolution, large-cohort behavior, and turning the report into an actual QC gate. Use when summarizing QC across many samples, building a shareable quality report, or wiring automated QC into a pipeline.

Exports publication-ready figures with the correct vector/raster split, embedded editable fonts, color-space-robust palettes, and journal-correct sizing and resolution in matplotlib and ggplot2. Use when preparing figures for journal submission, exporting a dense single-cell or GWAS plot without producing an unopenable vector file, or fixing fonts and colors that break in print.

Runs parameterized Jupyter notebooks as reproducible batch report generators with papermill, renders them to HTML/PDF with nbconvert, aggregates results across samples, and makes notebook outputs trustworthy. Use when generating per-sample analysis reports, executing a notebook template across many datasets, or fixing notebooks that do not reproduce.

Builds publication-ready tables - descriptive Table 1, regression and differential-expression result tables, and supplementary tables - with gtsummary, gt, flextable, and kableExtra (R) or great_tables, pandas, and tableone (Python), choosing the right statistics and the right export format. Use when making a Table 1, exporting a formatted results table for a paper, or writing a gene-symbol-safe supplementary table.

Builds reproducible Quarto reports, presentations, and websites across R, Python, and Julia, with correct engine selection, cache-vs-freeze semantics, native cross-references, parameters, and environment pinning. Use when creating a Quarto report of an analysis, setting up freeze for CI, or debugging cross-references, caching, or working-directory issues.

Creates reproducible R Markdown analysis reports (HTML, PDF, Word) with knitr, covering the render pipeline, the interactive-vs-knit session trap, cache invalidation, bookdown cross-references, parameterization, and environment pinning. Use when generating an R-based analysis report, debugging a report that knits differently than it runs interactively, or fixing caching or cross-references.

End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.

End-to-end CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.

End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping with NovaSeq 10x MiSeq caveat (Schnell 2015), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition with MANDATORY PERMANOVA + PERMDISP pair (Anderson & Walsh 2013), constrained ordination, and the read-counts-not-abundance critique (Lamb 2019). Use when processing eDNA samples for biodiversity assessment, deciding ASV vs OTU, configuring OBITools3 v3, interpreting decontam screening, or reporting community comparisons with the dispersion confound check.

End-to-end 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-pipeline.

Removes sequencing adapters from FASTQ reads with Cutadapt and Trimmomatic, including paired-end read-through, small-RNA 3' adapters, amplicon primers, and anchored/linked adapters. Use when FastQC shows adapter content climbing toward the 3' end, when inserts are shorter than the read length (small-RNA, cfDNA, FFPE), or before assembly/k-mer analysis. For all-in-one trimming use fastp-workflow; for quality/length filtering use quality-filtering.

Detects contamination in sequencing reads - cross-species (FastQ Screen, Kraken2), vector/PhiX/adapter, rRNA, and same-species cross-sample/index-hopping and sample swaps (SNP fingerprints via verifyBamID2/NGSCheckMate/somalier). Use when suspecting cross-contamination, PDX host reads, microbial carry-over, or sample swaps, and to decide whether to report, filter, or align to a combined reference. For deep taxonomic profiling use metagenomics/kraken-classification.

Runs all-in-one FASTQ preprocessing with fastp in a single pass - adapter trimming via paired-end overlap analysis, quality/length filtering, 2-color poly-G removal, base correction, optional dedup/UMI/merge, and HTML/JSON reports. Use when preprocessing bulk Illumina data and wanting one fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps. For precise small-RNA/amplicon adapters use adapter-trimming; for molecule-accurate UMI dedup use umi-processing.

Filters reads by quality, length, N content, and complexity with Trimmomatic, fastp, and Cutadapt, including sliding-window trimming, per-read unqualified-base filtering, and 2-color poly-G removal. Use when reads have poor-quality tails, when an assembly or k-mer workflow needs clean input, or when a junk read subpopulation must be dropped. For adapter removal use adapter-trimming; for all-in-one preprocessing use fastp-workflow.

Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented sequences, and adapter content. Use when performing initial QC on raw sequencing reads, validating preprocessing, or judging a multi-sample cohort for outliers and batch effects. For long reads use NanoPlot; for adapter/quality remediation route to adapter-trimming, quality-filtering, or fastp-workflow.

Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity (TIN), and saturation - with RSeQC, Qualimap, RNA-SeQC, and Picard. Use when validating RNA-seq libraries before quantification or differential expression, diagnosing degradation or gDNA contamination, or determining library strandedness. For raw-FASTQ QC use quality-reports; for UMI dedup use umi-processing.

Extracts UMIs and collapses reads to original molecules with umi_tools (directional dedup) or builds error-corrected single-strand/duplex consensus reads with fgbio. Use when the library has UMIs and accurate molecule counting or below-sequencer-floor error correction is needed - single-cell, low-input RNA-seq, targeted panels, and ctDNA/liquid-biopsy rare-variant detection. For UMI extraction during QC use fastp-workflow; do not dedup non-UMI bulk RNA-seq.

End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.

Turns an enrichResult or gseaResult from clusterProfiler/enrichplot into a figure that collapses or shows gene-set redundancy, using dotplot, barplot, cnetplot, emapplot, treeplot, ridgeplot, gseaplot2, and upsetplot. Covers why a default top-20 GO dotplot is one biological theme drawn twenty times (the DAG/nesting guarantees redundant overlapping terms), so the figure is a modeling choice between SHOWING redundancy (pairwise_termsim -> emapplot/treeplot) and DELETING it (simplify/REVIGO); why cnetplot/emapplot/treeplot need pairwise_termsim first; why enrichplot ships no barplot for gseaResult (a bar cannot carry a signed NES); why GeneRatio is not fold enrichment; and why showCategory silently truncates. Use when plotting ORA or GSEA results, collapsing redundant GO terms visually, encoding a dotplot, or building a publication enrichment figure. Statistics come from go-enrichment and gsea; generic ggplot -> data-visualization/ggplot2-fundamentals.

Runs Gene Ontology over-representation analysis (ORA) on a gene LIST with clusterProfiler enrichGO, the one-sided hypergeometric/Fisher 2x2 test phyper(k-1, M, N-M, n, lower.tail=FALSE). Covers why the BACKGROUND universe (not the gene list) is the null and decides significance, why omitting universe= is a bug, why enrichGO defaults to ont='MF' not 'BP', why pvalueCutoff filters p.adjust not raw p, why ORA discards effect magnitude and inherits GO-DAG true-path redundancy (simplify, topGO), why RNA-seq gene-length bias inflates long-gene terms (GOseq Wallenius), plus GeneRatio/BgRatio, bitr ID mapping, minGSSize/maxGSSize, groupGO. Use when a pre-selected gene list (DE hits, co-expression module, screen, GWAS-mapped) needs GO annotation. For a ranked no-cutoff analysis see gsea; for other databases see kegg-pathways, reactome-pathways, wikipathways; DE source is differential-expression/de-results; plots in enrichment-visualization.

Tests a ranked gene vector for coordinated expression shifts in GO, KEGG, Reactome, or MSigDB gene sets with clusterProfiler's gseGO, gseKEGG, gsePathway, and GSEA (fgseaMultilevel engine), and scores per-sample pathway activity with ssGSEA and GSVA. Covers why a GSEA result is a deterministic function of three implicit choices (the ranking STATISTIC, the weight exponent p, and which LABELS are permuted), why the input must be a NAMED vector sorted DECREASING by a signed variance-calibrated metric (DESeq2 stat, limma t) not a raw p-value that erases direction, why preranked gene-permutation is anti-conservative for correlated sets (CAMERA is the fix), why nPerm is gone (eps governs tiny p), and why set.seed is required. Use when every gene carries a DE statistic, when a hard cutoff is arbitrary, or when ORA finds nothing. For gene-list ORA see go-enrichment; the ranking statistic comes from differential-expression/de-results.

Tests gene lists, ranked vectors, and fold-change vectors against KEGG pathways and modules with clusterProfiler enrichKEGG/enrichMKEGG (ORA), gseKEGG (GSEA), and SPIA/graphite (signed-topology perturbation) in R. Owns the third pathway-analysis generation because KEGG ships signed directed signaling topology (KGML). Covers why a KEGG result is a timestamped join against a live REST API (irreproducible unless pinned with a gson snapshot, not the stale 2012 KEGG.db), why enrichKEGG keyType is kegg/ncbi-geneid not OrgDb ENSEMBL/SYMBOL (zero hits), why organism is a KEGG code (hsa, pae) with prokaryotic locus tags, and why SPIA works only on signaling maps. Use when finding enriched KEGG pathways or modules, scoring signed pathway perturbation, analyzing prokaryotes or non-model organisms via locus tags or KO, comparing conditions with compareCluster, or overlaying data with pathview. The hypergeometric universe lives in go-enrichment; the GSEA engine in gsea.

Tests a gene list or ranked gene vector for over-representation or coordinated shifts in Reactome's curated, peer-reviewed, reaction-level pathways using ReactomePA's enrichPathway (ORA) and gsePathway (GSEA), reading the local reactome.db so a run is reproducible given the Bioconductor release. Covers why Reactome's atomic unit is the REACTION and pathways are nested containers so a parent and child enrich on the same genes and double-count one signal, why only human is curated and every other species is orthology-inferred, why enrichPathway has NO keyType argument and returns nothing unless genes are ENTREZ (bitr first), and why viewPathway draws a LOCAL reaction network from a pathway NAME. Use when reaction-level granularity, peer-reviewed curation, or an offline-reproducible database is wanted; for comparative multi-sample or multi-omics analysis use ReactomeGSA. The DE list comes from differential-expression; plots from enrichment-visualization.

Tests a gene list (ORA, enrichWP) or a ranked gene vector (GSEA, gseWP) against the WikiPathways community-curated pathway collection with clusterProfiler and rWikiPathways. Covers why a WikiPathways result is a snapshot of a live, monthly-updated database (enrichWP/gseWP/gson_WP silently pull data.wikipathways.org/current/), why reproducibility requires pinning a dated GMT via downloadPathwayArchive(date=, format='gmt'), why the WP GMT is Entrez-keyed so symbols and Ensembl silently overlap nothing, why universe=NULL gives a biased all-WP-genes background, how to split the name%version%wpid%org term, and why WikiPathways (CC0, no peer review) complements KEGG/Reactome. Use when running open community-pathway enrichment, covering a non-model WP species, catching disease/drug pathways missing from KEGG/Reactome, or needing a reproducible dated analysis. The gene list comes from differential-expression/de-results; visualize with enrichment-visualization.

Imputes untyped genotypes against a phased reference panel with Beagle, Minimac4, or IMPUTE5 (array data) or from genotype likelihoods with GLIMPSE2, QUILT2, or STITCH (low-coverage WGS), producing per-variant dosages (DS) with a self-estimated quality (Beagle DR2, Minimac R2, IMPUTE INFO). Covers why the honest output is a dosage posterior not a hard call, why GWAS regresses on DS, why the quality metric is an ESTIMATE of r2 from posterior spread (not validation against truth), the DS/GP/HDS fields, the phasing prerequisite, chunking, chrX ploidy, the Michigan/TOPMed servers (the only access to HRC/TOPMed), and low-coverage WGS as the modern array replacement. Use when increasing variant density for GWAS, harmonizing arrays, inferring untyped variants, or imputing low-coverage sequence. Phase first with haplotype-phasing; prepare the panel with reference-panels; filter with imputation-qc; the GWAS test is population-genetics/association-testing; end-to-end orchestration is workflows/gwas-pipeline.

Estimates haplotype phase from population linkage disequilibrium with SHAPEIT5, SHAPEIT4, Eagle2, or Beagle - turning unphased genotypes (0/1) into phased haplotypes (0|1) for imputation input, compound-heterozygote calls, HLA typing, or population genetics. Covers why statistical phase is an INFERENCE (not a measurement) whose error concentrates at rare variants, why a genome-wide switch-error rate hides catastrophic rare-variant error and must be reported MAC-stratified, the SHAPEIT5 common-scaffold-then-rare design (phase_common, ligate, phase_rare, switch), reference-based vs within-cohort phasing, the build-matched genetic map, chrX male-haploid handling, and the switch-vs-flip-vs-Hamming distinction. Use when phasing genotypes before imputation, for compound-het/ASE/HLA, or benchmarking against trios. Read-backed / molecular phasing (long reads, Hi-C) is long-read-sequencing/haplotype-phasing; panel choice is reference-panels; imputation is genotype-imputation.

Assesses and filters phasing/imputation output - the quality metrics (Beagle DR2, Minimac R2 and EmpRsq, IMPUTE/GLIMPSE INFO), MAF-stratified filtering, true accuracy by masking, the differential-imputation confound, dosage-based downstream usage, and phasing switch-error QC. Covers why every routine quality score is an ESTIMATE of r2 from the posterior spread (not validation against truth), why it is confounded with MAF so a flat INFO>=0.3 cutoff is a hidden rare-variant filter, why concordance lies for rare variants while masked dosage-r2 by MAF is the gold standard, why separate case/control imputation manufactures false GWAS hits, and that the field name tells the tool (DR2=Beagle, R2=Minimac, INFO=GLIMPSE/IMPUTE). Use when filtering imputed variants before GWAS, validating accuracy, benchmarking phasing against trios, or diagnosing inflated association. Imputation is genotype-imputation; phasing is haplotype-phasing; panel ancestry is reference-panels; the test is population-genetics/association-testing.

Selects and prepares the reference panel that phasing/imputation copies haplotypes from (1000 Genomes, HRC, TOPMed, HGDP+1kGP/gnomAD, CAAPA), matching panel ancestry to the target, reconciling genome build and chromosome naming, and running the strand/allele harmonization gate. Covers why ancestry-match beats panel size (imputation can only copy haplotypes the panel contains), why palindromic A/T and C/G SNPs flip strand without erroring, why liftover is a strand-flip generator in between-build inverted regions, that HRC is SNP-only and TOPMed is never downloadable (governance can override accuracy), and panel formats (msav, bref3, imp5). Use when choosing a panel for a target ancestry, preparing or converting a panel, aligning study data, or deciding between downloadable and server-only panels. Phasing is haplotype-phasing; imputation is genotype-imputation; PCA for ancestry is population-genetics/population-structure; HLA panels are clinical-databases/hla-typing.