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Claude Skills by GPTomics
github.com/GPTomics537 skills2 installs584 views
- Base Editing DesignDesigns cytosine (CBE, C-to-T) and adenine (ABE, A-to-G) base-editor guides by positioning the target base at the activity-peak of the editing window (protospacer positions ~5-7, PAM-distal numbering), minimizing bystander edits for product purity, reading dinucleotide context (APOBEC1 TC favored / GC disfavored), and selecting the editor variant (BE4max, ABEmax, ABE8e, YE1/SECURE, TadCBE, CGBE, SpG/SpRY-BE). Covers knockout by premature stop (CRISPR-STOP/iSTOP) and splice-site disruption, th...Votes: 0GitHub stars: 1,198
- Grna DesignDesigns and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift ou...Votes: 0GitHub stars: 1,198
- Hdr Template DesignDesigns donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate en...Votes: 0GitHub stars: 1,198
- Off Target PredictionNominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas...Votes: 0GitHub stars: 1,198
- Prime Editing DesignDesigns pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point...Votes: 0GitHub stars: 1,198
- Bed File BasicsHandles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, valid...Votes: 0GitHub stars: 1,198
- Bedgraph HandlingGenerates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGrap...Votes: 0GitHub stars: 1,198
- Bigwig TracksReads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values(...Votes: 0GitHub stars: 1,198
- Coverage AnalysisComputes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, r...Votes: 0GitHub stars: 1,198
- Gtf Gff HandlingParses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codo...Votes: 0GitHub stars: 1,198
- Interval ArithmeticPerforms set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlap...Votes: 0GitHub stars: 1,198
- Overlap SignificanceTests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hyperge...Votes: 0GitHub stars: 1,198
- Proximity OperationsPerforms proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window searches, strand-aware promoter construction, and interval extension. Covers the closest -d/-D a/b/ref/-t/-k/-io/-iu/-id flags, the -D ref strand sign-flip, silent chromosome-end clipping in slop/flank, -t all tie double-counting, and the critical distinction between a geometry answer (nearest TSS) and a...Votes: 0GitHub stars: 1,198
- Compartment AnalysisDetects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing w...Votes: 0GitHub stars: 1,198
- Contact PairsTurns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked. Covers the bwa mem -SP5M / bwa-mem2 / chromap --preset hic alignment idiom (mates mapped as independent single-end reads), pairtools parse vs parse2 and the walks-policy choice (5unique pairwise vs all for Pore-C/Micro-C concatemers), pair-type classification (keep UU and rescued UC), dedup (PCR vs optical/by-tile), select by pair_type/MAPQ/distance, restriction-fragment h...Votes: 0GitHub stars: 1,198
- Hic Data IoLoads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chr...Votes: 0GitHub stars: 1,198
- Hic DifferentialCompares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding ...Votes: 0GitHub stars: 1,198
- Hic VisualizationRenders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condit...Votes: 0GitHub stars: 1,198
- Hichip Plac LoopsCalls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-s...Votes: 0GitHub stars: 1,198
- Loop CallingDetects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions ...Votes: 0GitHub stars: 1,198
- Matrix OperationsBalances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max...Votes: 0GitHub stars: 1,198
- Tad DetectionDetects TAD boundaries from balanced Hi-C contact matrices via the diamond-window insulation score (cooltools insulation) and HiCExplorer hicFindTADs, returning a continuous log2 insulation track, valley-prominence boundary_strength, and Li/Otsu-thresholded is_boundary flags across a list of window sizes. Covers the multi-scale window sweep (sub-TAD to compartment-domain), why the boundary is reproducible but the domain partition is not, cross-condition comparison via differential SCORE not d...Votes: 0GitHub stars: 1,198
- Cell SegmentationSegment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting ...Votes: 0GitHub stars: 1,198
- Data PreprocessingLoad and preprocess imaging mass cytometry (IMC) and MIBI data from raw MCD/TXT through hot-pixel removal, spillover compensation, and variance-stabilizing transformation, covering readimc/steinbock ingestion, NNLS spillover compensation (CATALYST), IMC-Denoise, and the IMC arcsinh-cofactor question. Use when starting analysis from raw MCD files, building per-channel TIFF stacks, compensating channel spillover, choosing an arcsinh cofactor, or preparing single-cell intensities for phenotyping.Votes: 0GitHub stars: 1,198
- Differential AnalysisCompare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-...Votes: 0GitHub stars: 1,198
- Interactive AnnotationInteractive cell annotation and image QC for IMC/MIBI using napari, napari-imc, Mantis Viewer, and cytomapper, covering the pixels-to-cell-table bridge, overlaying masks to catch segmentation/spillover artifacts, inter-annotator variability as the accuracy ceiling, contrast-as-threshold, and building class-balanced ground-truth label sets. Use when manually labeling cells, generating training data for a classifier, QC-ing segmentation on the image, confirming clusters are spatially real, or c...Votes: 0GitHub stars: 1,198
- PhenotypingAssign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive popula...Votes: 0GitHub stars: 1,198
- Quality MetricsQuality control for IMC/MIBI data across pixel, channel, image, slide, and batch levels, covering Poisson-count SNR (cell-level Gaussian-mixture and empty-channel comparison), spillover-matrix QC (the three physical sources), drift and the missing EQ-bead analog, acquisition artifacts, and sample-of-origin batch effects. Use when deciding whether to keep or drop a channel, ROI, or slide, distinguishing a dim antibody from a failed one, reading a spillover matrix, or diagnosing batch-driven cl...Votes: 0GitHub stars: 1,198
- Spatial AnalysisAnalyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a de...Votes: 0GitHub stars: 1,198
- Epitope PredictionPredict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity...Votes: 0GitHub stars: 1,198
- Immunogenicity ScoringRank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator ...Votes: 0GitHub stars: 1,198
- Mhc Binding PredictionPredict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a bin...Votes: 0GitHub stars: 1,198
- Mhc Class Ii PredictionPredict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR...Votes: 0GitHub stars: 1,198
- Neoantigen PredictionIdentify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tier...Votes: 0GitHub stars: 1,198
- Tcr Epitope BindingInfer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there i...Votes: 0GitHub stars: 1,198
- Analytical ValidationTreats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD r...Votes: 0GitHub stars: 1,198
- Cfdna PreprocessingDecides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coinci...Votes: 0GitHub stars: 1,198
- Ctdna Mutation DetectionDetects somatic mutations in circulating tumor DNA, treating low-VAF detection as a signal-versus-noise problem set by error suppression and molecules sampled, not by the choice of caller. Distinguishes de novo CALLING (scanning a panel for unknown variants, bounded by per-locus error and multiple testing) from tumor-informed DETECTION (tracking a pre-specified variant set, where panel integration reaches single-ppm). Covers VarDict and Mutect2 for de novo calling, UMI-aware callers, and a py...Votes: 0GitHub stars: 1,198
- Fragment AnalysisExtracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline;...Votes: 0GitHub stars: 1,198
- Longitudinal MonitoringTracks ctDNA across serial liquid-biopsy timepoints for molecular residual disease (MRD) and treatment-response monitoring, treating MRD as a binary integrated detection call across the patient's full variant set (with a defined LoD95 and per-sample specificity) rather than a per-timepoint VAF threshold, and handling undetectable samples as left-censored at the per-sample limit of detection rather than true zeros. Covers tumor-informed bespoke vs tumor-naive design, landmark vs surveillance s...Votes: 0GitHub stars: 1,198
- Methylation Based DetectionDetects cancer and infers tissue-of-origin from cfDNA methylation by choosing conversion chemistry (bisulfite vs EM-seq vs TAPS vs cfMeDIP), calling read-level methylation haplotypes rather than averaged beta values, and deconvolving a hematopoietic-dominated cfDNA mixture against a methylation atlas via NNLS/quadratic programming. Encodes the GRAIL/CCGA thesis that thousands of tissue-specific markers make methylation outperform sparse mutations for multi-cancer early detection (MCED) and lo...Votes: 0GitHub stars: 1,198
- BasecallingBasecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG_5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to...Votes: 0GitHub stars: 1,198
- Clair3 VariantsCalls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification. Covers why the model string is the experiment (no auto-detection, silent degradation on mismatch), why ONT homopolymer/STR indels are the residual error whole-genome F1 hides, and the s...Votes: 0GitHub stars: 1,198
- Isoseq AnalysisDiscovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-sp...Votes: 0GitHub stars: 1,198
- Long Read AlignmentAligns Oxford Nanopore and PacBio long reads (and assemblies) to a reference with minimap2 using the error-rate-matched preset (map-ont, lr:hq, map-hifi, map-pb, splice/splice:hq, asm5/10/20, ava), producing a sorted/indexed BAM for variant, SV, methylation, or isoform analysis. Covers why the preset rewrites the scoring/chaining model, why SV calling rides on supplementary not secondary alignments, carrying MM/ML methylation tags through with -y, the multi-part-index MAPQ trap, and when to s...Votes: 0GitHub stars: 1,198
- Long Read QcAssesses Oxford Nanopore and PacBio long-read quality with NanoPlot, cramino, NanoComp, pycoQC/toulligQC, and seqkit, and filters reads with chopper/Filtlong for the downstream goal. Covers why read-only Qscore is an uncalibrated posterior (real accuracy needs a reference BAM), why the sequencing_summary.txt is required for run-health metrics, intent-conditioned filtering (preserve long reads and small replicons for assembly, filter almost nothing for variant calling), the chimera/internal-ad...Votes: 0GitHub stars: 1,198
- Medaka PolishingPolishes Oxford Nanopore draft assemblies to higher consensus accuracy with medaka, a basecaller-model-specific neural consensus net, produces haploid variant calls (VCF) for microbial, mitochondrial, or viral samples, and generates amplicon/viral consensus sequences. Covers the model-matching footgun that silently degrades output, why Racon-first is obsolete and medaka runs directly on Flye output as a single pass, why HiFi must never be fed to medaka, the v1->v2 subcommand renames, and the ...Votes: 0GitHub stars: 1,198
- Nanopore MethylationCalls DNA base modifications (5mC, 5hmC, 6mA, 4mC) directly from Oxford Nanopore and PacBio HiFi long reads encoded as MM/ML SAM tags, piles them into per-site bedMethyl with modkit (or pb-CpG-tools for PacBio), and produces phased allele-specific methylation. Covers why methylation is a basecalling decision that cannot be recovered later, the MM/ML tag-drop failure that silently zeroes methylation through alignment, the MM ? vs . no-call semantics, 5mC/5hmC resolution vs bisulfite, modkit's ...Votes: 0GitHub stars: 1,198
- Structural VariantsDetects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based callers, joint-genotypes cohorts via the Sniffles2 .snf workflow, and benchmarks with Truvari against GIAB. Covers why an SV call is a representation artifact (the tandem-repeat BED, aligner, and Truvari params set precision/recall as much as the caller), the cuteSV per-platform parameter trap, soft-...Votes: 0GitHub stars: 1,198
- Atlas MappingMaps query single-cell data onto reference atlases and transfers cell-type labels using scArches surgery (scVI/scANVI), Symphony, Azimuth, CellTypist, scPoli, popV, and foundation models, with explicit out-of-distribution and label-transfer uncertainty. Use when annotating new single-cell datasets against a pre-trained reference, deciding which mapping method fits, or judging whether transferred labels are trustworthy. For de novo clustering and manual annotation see single-cell/cell-annotati...Votes: 0GitHub stars: 1,198