Claude Skills by GPTomics

Detect distant homologs using profile and structure-aware methods that go beyond standard BLAST. Use when sequence identity falls into the twilight zone (<35% pairwise), when BLAST fails to find homologs that should exist, when working at metagenomic scale (DIAMOND, MMseqs2), or when structure beats sequence (Foldseek). Covers PSI-BLAST (iterative PSSM), jackhmmer (iterative HMM), HHblits/HHsearch (profile-profile), DIAMOND, MMseqs2, and Foldseek (3Di structural alphabet, van Kempen 2024).

Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), a...

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimension...

Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner ...

Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fi...

Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critiq...

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Ca...

Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique th...

Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dor...

Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeR...

Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment...

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a de...

Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, t...

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus sing...

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden ...

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the criti...

Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS. Handles silent-miscounting traps (featureCounts -p v2.0.2 API break, STAR strandedness column choice, salmon NumReads-sum without tximport, RSEM non-integer expected_count, GENCODE _PAR_Y suffix, zero-length-transcript TPM divide-by-zero), and encodes the tximport countsFromAbundance decision tree with the "lengthScaled...

Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical r...

Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable)...

Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shu...

Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format prolifer...

Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction ...

Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering pop...

Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS...

Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or fla...

Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Event_length) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggr...

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels t...

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semant...

Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression netw...

Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes th...

Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression...

Build enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regu...

Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbatio...

Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driv...

Assesses the quality and completeness of a genome annotation with BUSCO (conserved single-copy ortholog recovery), OMArk (proteome completeness, consistency, and contamination), CheckM2 (prokaryotic completeness/contamination), and a gene-set sanity panel (gene count, mono-exonic fraction, protein-length distribution, mRNA:gene ratio, coding density). Covers the assembly-BUSCO-vs-proteome-BUSCO diagnostic, what BUSCO-Duplicated really means, why gene count is a vanity metric, and the QC of tr...

Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-...

Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bia...

Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when ad...

Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and wh...

Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotat...

Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat lands...

Decides whether and how to polish a draft genome assembly to raise consensus accuracy (QV) with read-type-matched tools - Racon and medaka (ONT consensus), dorado polish, Polypolish and pypolca (Illumina, repeat-aware), Pilon (legacy short-read), NextPolish/NextPolish2, Hapo-G (haplotype-aware), ntEdit, and DeepPolisher/PEPPER-Margin-DeepVariant for human. Covers the do-not-polish-HiFi rule, the medaka basecaller-model footgun, held-out Merqury QV as the only honest stop signal, and the haplo...

Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse). Covers why N50 is the most-gamed metric, why QV measured on the polishing reads is circular, distinguishing uncollapsed haplotigs from real WGD, and the EBP/VGP 6.C.Q40 standard. Use when jud...

Detects and removes contamination in genome assemblies via two disjoint workflows - foreign-sequence screening of a single-organism (eukaryote/isolate) assembly with NCBI FCS-GX (GenBank-submission-mandatory), FCS-adaptor, and BlobToolKit blob plots; and MAG/bin quality assessment with CheckM2 plus GUNC (chimerism) plus GTDB-Tk taxonomy, judged against MIMAG. Covers why CheckM2 alone is blind to disjoint-marker chimeras, the FCS-GX RAM wall, organelle/NUMT triage, strain heterogeneity, and th...

Profiles a genome from raw reads BEFORE assembly with a k-mer spectrum (KMC or Jellyfish histogram), then models it with GenomeScope2 to estimate genome size, heterozygosity, repeat content, and ploidy, and Smudgeplot to infer ploidy from heterozygous k-mer pairs (diploid AB vs triploid AAB vs tetraploid AABB). Covers choosing k via Merqury best_k.sh, the k-mer-coverage vs sequencing-coverage confusion, reading het/repeat/contamination/organelle peaks, why noisy ONT must not be used for count...

Assembles haplotype-resolved diploid and telomere-to-telomere (T2T) genomes from PacBio HiFi reads with hifiasm (HiFi-only, Hi-C, or trio phasing) and verkko (HiFi + ultralong ONT for T2T), extracting contigs from GFA and routing phasing QC to k-mer/trio metrics. Covers why a primary assembly is a haplotype mosaic that exists in no cell, partial-vs-full phasing (the .bp. vs .dip. filename convention), the purge-default trap on inbred samples, the --hom-cov coverage-estimate alarm, and verkko-...

Assembles genomes de novo from noisy long reads (Oxford Nanopore R9/R10/Dorado, PacBio CLR) with Flye (repeat graph), Canu (correct-trim-assemble OLC), NextDenovo, Shasta, Raven, wtdbg2, or miniasm, and reconciles bacterial assemblies into a consensus with Trycycler/Autocycler. Covers matching the input flag to the basecaller era (--nano-hq vs --nano-raw), why a raw long-read assembly is contiguous but low-QV and not finished until polished, haplotig false-duplication and purge_dups, coverage...

Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs ...

Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure...

Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC ...