Claude Skills by GPTomics

Estimate divergence times under molecular-clock models with BEAST2, MCMCTree/PAML, TreePL, and LSD2, framing a date as a product of the calibration prior and the clock model far more than of the sequence data. Covers why branch length = rate x time is nonidentifiable so only calibrations convert relative rate-time into absolute age; why the effective (marginal) prior on a calibrated node differs from the density specified, mandating a sample-from-prior run; the fossil-as-minimum rule, soft bo...

Infers maximum-likelihood phylogenetic trees with IQ-TREE2 and RAxML-NG -- model selection (ModelFinder), branch support (UFBoot2, SH-aLRT), concordance factors (gCF/sCF), partitioning, topology tests, and long-branch-attraction control. Covers why an ML tree inherits every flaw of the assumed model and the fixed alignment, why reported support measures repeatability under resampling and not correctness, why UFBoot uses a >=95 cutoff and not the bootstrap-70 rule, and why a node with UFBoot 1...

Estimates species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER astral binary (ASTRAL-III/wASTRAL/ASTRAL-Pro), plus SVDQuartets, BPP, and StarBEAST2. Covers why a species tree is not a gene tree, why each locus has its own genealogy that disagrees by incomplete lineage sorting (ILS) even with zero error, why concatenation is statistically inconsistent and positively misleading in the anomaly zone where more loci converge on the wrong tree with full su...

Read, write, and convert phylogenetic tree files with Biopython Bio.Phylo, and choose an annotation-preserving parser (treeio, DendroPy) when metadata matters. Covers why a tree file is a lossy serialization, why format conversion silently drops BEAST/MrBayes node annotations (posteriors, HPD intervals, rates), the Newick support-vs-label ambiguity that mislabels bootstrap values, and the Nexus TRANSLATE and rooted/unrooted traps. Use when parsing Newick, Nexus, NHX, phyloXML, or NeXML, conve...

Edit phylogenetic tree structure with Biopython Bio.Phylo, and treat rooting as a separate statistical inference rather than a display choice. Covers why most inference returns an unrooted tree so placing the root creates every ancestor/descendant and basal claim; why a distant or lonely outgroup misroots inside the ingroup via long-branch attraction; the outgroup/midpoint/MAD/MinVar/non-reversible-likelihood rooting tradeoffs; why pruning must suppress degree-2 nodes and sum their branch len...

Draw and export phylogenetic trees with Bio.Phylo plus matplotlib, and route rich figures to ggtree, ETE4, or iTOL. Covers why a tree figure is an argument not a neutral picture, the cladogram-vs-phylogram-vs-chronogram choice that hides or reveals rate and time, how ladderization manufactures a false arrow of progress, why an unlabeled support number always flatters the result (bootstrap vs posterior vs SH-aLRT vs UFBoot are different scales), why Bio.Phylo silently drops BEAST HPD bars so a...

Computes linkage disequilibrium (r2, D', composite Rogers-Huff r2), prunes correlated variants, clumps GWAS summary statistics to lead SNPs, and defines haplotype blocks with PLINK 1.9/2.0 and scikit-allel. r2 and D' answer different questions - r2 (= chi2/N) is the tagging and GWAS-power currency, D' marks observed recombination and is upward-biased for rare variants. PLINK 2.0 has no bare --r2 (split into --r2-phased and --r2-unphased); pruning (--indep-pairwise, genotype-blind) and clumpin...

Gene and region-based rare-variant aggregation - burden/collapsing, SKAT, SKAT-O, ACAT-V/ACAT-O, annotation-weighted STAAR - with regenie (--vc-tests), SAIGE-GENE+, and the SKAT R package. Single-variant tests are powerless at low minor allele count, so rare variants are aggregated across a gene or region under an explicit mask (functional class plus a MAF cutoff). A burden test collapses variants into one score assuming a single effect direction (powerful when true, near-zero power when risk...

Scans genomes for natural selection with SFS tests (Tajima's D, Fay & Wu H, Zeng E, SweepFinder2 CLR), haplotype tests (iHS, nSL, XP-EHH, Rsb, H12), and differentiation (FST, PBS) using scikit-allel, selscan, and SweepFinder2. No single statistic separates selection from demography at one locus, so the deliverable is empirical genome-wide outliers plus multiple orthogonal signals, not an absolute cutoff. iHS detects incomplete sweeps and collapses to zero at fixation while XP-EHH catches fixe...

Designs and ranks PCR primer pairs for a target template with primer3-py (design_primers), returning pairs with nearest-neighbor Tm, GC, product size, and complementarity scores. Covers why primer3 is a LOCAL weighted-penalty minimizer over the single template supplied (so PRIMER_PAIR_0 is the lowest-penalty pair under the given bounds, never a genome-specificity guarantee), why Tm is a salt/concentration-dependent SantaLucia prediction not a fixed property, why the two primers must be Tm-mat...

Checks whether a PCR primer PAIR amplifies only the intended target genome-wide, using pair-aware in-silico PCR (MFEprimer-3.0, UCSC isPcr, NCBI Primer-BLAST) plus a primer3-py 3'-end-stability prefilter, against the correct database. Covers why plain BLAST is the wrong tool (it scores per-primer similarity, blind to 3'-terminal anchoring and to whether the two primers form a convergent amplicon in range), why a single 3'-terminal mismatch suppresses amplification while internal mismatches ar...

Validates chosen PCR/qPCR oligos for intramolecular thermodynamic liabilities with primer3-py - hairpins, self-dimers, cross-dimers (calc_hairpin/homodimer/heterodimer), and 3'-end stability (calc_end_stability) - returning ThermoResult dG/Tm and ASCII structures. Covers why a "dimer-free" verdict is a PREDICTION at the supplied salt/Mg/dNTP/oligo conditions and temp_c (so the same primer is fine or dimer-prone depending on conditions), why a 3'-END dimer or hairpin is the lethal class (polym...

Co-designs qPCR/RT-qPCR primers and hydrolysis (TaqMan) or molecular-beacon probes with primer3-py (PRIMER_PICK_INTERNAL_OLIGO, PRIMER_INTERNAL_* tags), for assays whose deliverable is a quantitative measurement device. Covers why amplification efficiency (90-110%, slope -3.6 to -3.1) and single-product specificity make the 2^-ddCq / Pfaffl math valid, why the short amplicon (70-150 bp), tight Tm, and zero-dimer requirement exist, the coupled probe rules (probe Tm 8-10 C above primers so it i...

Loads mass-spectrometry data into Python/R and strips the search engine's bookkeeping before any number is trusted -- removes decoys (REV__/Reverse), contaminants (CON__/Potential contaminant), Only-identified-by-site groups, and resolves semicolon razor/leading protein-ID ambiguity in MaxQuant proteinGroups.txt, DIA-NN report.parquet, and mzML/mzXML. Distinguishes Intensity (raw) vs LFQ intensity (MaxLFQ) vs iBAQ, treats a MaxQuant zero as missing (NaN, not log2(-inf)), and inherits the acqu...

Analyzes data-independent acquisition (DIA) proteomics by scoring reconstructed fragment-chromatogram peak groups against a decoy null with DIA-NN (library-free directDIA, library-based, or deep-learning predicted-library routes), Spectronaut, OpenSWATH, and EncyclopeDIA. Frames the deliverable around q-value LEVEL (precursor/peptide/protein-group) and CONTEXT (run vs experiment-wide/global) rather than a bare "1% FDR", and around the duty-cycle-vs-selectivity acquisition tradeoff (window des...

Peptide-spectrum matching from MS/MS with target-decoy FDR control, framing identification confidence as a property of a ranked list (q-value/PEP) rather than a raw engine score (XCorr, hyperscore, Andromeda, SpecEValue). Covers sequence-database search engines (Comet, MS-GF+, MSFragger, Sage, MaxQuant, MetaMorpheus), concatenated vs separate target-decoy competition, PEP vs q-value, the multi-level FDR cascade, open/mass-tolerant search, rescoring (Percolator, mokapot, MS2Rescore), and pyOpe...

Groups proteins from peptide identifications and controls protein-level FDR, framing inference as a chosen explanation (parsimony or a probability model) of underdetermined peptide evidence rather than a measurement. Reports protein GROUPS (proteins indistinguishable by observed peptides) with a leading protein, not flat lists. Covers shared-vs-unique peptides, indistinguishable/subsumable proteins, parsimony vs probabilistic (ProteinProphet, EPIFANY) vs razor inference, picked-protein and pi...

Quality control for bottom-up proteomics across three levels -- instrument/raw-signal (mass accuracy, RT/iRT fit, FWHM, TIC vs injection time, % MS2 identified), identification/run (missed cleavages, charge states, PTM handling artifacts, contaminants), and experiment/quantitative (replicate correlation on log2, CV on the linear scale, completeness, MNAR-vs-MCAR missingness, PCA/batch, TMT channel balance, DIA q-values). Frames QC as a control chart against a per-instrument rolling baseline, ...

Frames PTM/phosphoproteomics analysis as three stacked inference layers on a biased enrichment - chemistry selection, site localization (FLR), and protein-level-adjusted quantification with MSstatsPTM - plus kinase-activity and functional triage. Covers MaxQuant Phospho (STY)Sites multiplicity expansion, localization-probability filtering (class I, Ascore, ptmRS, DIA EG.PTMLocalizationProbabilities, DIA-NN PTM.Site.Confidence), false localization rate (LuciPHOr/DeepFLR), motif analysis with e...

Quantifies protein abundance from mass spectrometry using label-free (LFQ/MaxLFQ, DIA fragment-level), isobaric (TMT/iTRAQ reporter ions, MS2 vs SPS-MS3), and metabolic (SILAC) approaches, including peptide-to-protein summarization (Tukey median polish, MaxLFQ, msqrob), sample-loading and IRS cross-plex normalization, and isotopic impurity correction. Use when turning peptide/PSM/reporter signal into a protein-by-sample abundance matrix for downstream analysis. Statistical testing of that mat...

Builds and manages DIA spectral libraries as peptide query parameters (precursor m/z, a few fragment m/z plus relative intensities, normalized RT, optional CCS), covering experimental DDA, chromatogram, and in-silico predicted libraries via Koina-served Prosit, AlphaPeptDeep, MS2PIP, and DeepLC, with iRT/CiRT RT calibration, NCE tuning, format conversion (DIA-NN tsv/speclib/parquet, OpenSWATH pqp/TraML, Spectronaut, blib/dlib/elib), and library QC/merge. Use when generating, calibrating, conv...

Aligns DNA short reads to a reference with Bowtie2, choosing end-to-end (whole read must align) vs local (soft-clip read ends) mode and a sensitivity preset; the de-facto aligner for ChIP-seq, ATAC-seq, and CUT&RUN, where fragment-geometry flags (--no-mixed, --no-discordant, --dovetail, -X) and a tool-appropriate MAPQ filter feed the peak caller. Use when aligning ChIP/ATAC/CUT&RUN reads, when read ends are adapter-contaminated and need soft-clipping, or when a tunable sensitivity/speed prese...

Aligns DNA short reads (paired- or single-end) to a reference genome with bwa-mem2, the maintained successor to BWA-MEM, for WGS/WES and germline/somatic variant-calling pipelines; covers index build, read-group injection, the collate/fixmate/sort/markdup ordering, soft-clipping for SV split reads, ALT/decoy-aware mapping on GRCh38, -K determinism, and streaming straight to a sorted BAM. Use when mapping DNA short reads to a reference for variant calling, coverage, ChIP/ATAC (alongside bowtie...

Aligns RNA-seq reads to a genome with HISAT2, the splice-aware aligner whose hierarchical graph FM-index runs at roughly a quarter of STAR's memory (~7 GB for human), whose SNP/haplotype graph index reduces reference bias in the index itself, and whose MAPQ is GATK-friendly (60 for unique, no 255 problem). Use when RNA alignment must fit a memory-constrained machine, when feeding StringTie/Cufflinks transcript assembly via --dta, or when a SNP-aware graph index is wanted for allele-robust map...

Aligns RNA-seq reads to a genome with STAR, the fast splice-aware aligner whose splice-junction database (built from a GTF at sjdbOverhang = readlength-1) and two-pass mode set junction sensitivity, whose 255-for-unique MAPQ breaks GATK, and whose GeneCounts output reveals library strandedness. Use when RNA reads must be placed on the genome for novel-isoform discovery, fusion detection, RNA variant calling, coverage tracks, splicing QC, or single-cell (STARsolo). Memory-constrained RNA align...

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-proce...

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.

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.

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.

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.

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.

Quantify transcript expression from FASTQ with Salmon (selective alignment) or kallisto (pseudoalignment), bypassing genome mapping. Use when quantifying RNA-seq without alignment, deciding whether a decoy-aware index is required, detecting and verifying library strandedness, enabling GC and sequence bias correction, or choosing whether to generate inferential replicates (bootstraps/Gibbs) for transcript-level downstream testing.

Quality control and exploration of RNA-seq count matrices before differential expression. Use when checking library sizes and composition, choosing VST vs rlog for visualization, running PCA and sample correlation, detecting outliers with Cook's distance, deciding how to handle known vs unknown batch effects, screening for sample swaps, or judging whether a sample or design is too compromised to test.