Agent skills by GPTomics, page 6

Performs 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…

GPTomics/bioSkills

Performs proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window…

GPTomics/bioSkills

Detects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigscis), then orients…

GPTomics/bioSkills

Turns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked.

GPTomics/bioSkills

Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk.

GPTomics/bioSkills

Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener.

GPTomics/bioSkills

Balances Hi-C contact matrices (ICE via cooler.balancecooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expectedcis per-diagonal P(s), expectedtrans scalar), builds…

GPTomics/bioSkills

Detects 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…

GPTomics/bioSkills

Segment 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…

GPTomics/bioSkills

Load 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…

GPTomics/bioSkills

Compare 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…

GPTomics/bioSkills

Interactive 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…

GPTomics/bioSkills

Assign 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)…

GPTomics/bioSkills

Quality 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…

GPTomics/bioSkills

Analyze 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…

GPTomics/bioSkills

Predict 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.

GPTomics/bioSkills

Rank 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)…

GPTomics/bioSkills

Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes.

GPTomics/bioSkills

Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0.

GPTomics/bioSkills

Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers.

GPTomics/bioSkills

Infer 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…

GPTomics/bioSkills

Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG5hmCG, 6mA, m6A) at…

GPTomics/bioSkills

Calls 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…

GPTomics/bioSkills

Phases small variants, SVs, and methylation from Oxford Nanopore and PacBio long reads (read-backed/physical phasing) with WhatsHap, LongPhase, or HiPhase, and haplotags the BAM (HP/PS tags) for…

GPTomics/bioSkills

Discovers, 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…

GPTomics/bioSkills

Aligns 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)…

GPTomics/bioSkills

Assesses 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.

GPTomics/bioSkills

Polishes Oxford Nanopore draft assemblies to higher consensus accuracy with medaka, a basecaller-model-specific neural consensus net, produces haploid variant calls (VCF) for microbial…

GPTomics/bioSkills

Calls 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…

GPTomics/bioSkills

Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based…

GPTomics/bioSkills

Tracks 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…

GPTomics/bioSkills

Maps 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…

GPTomics/bioSkills

Selects biomarker features from high-dimensional omics data using Boruta all-relevant selection, mRMR, LASSO/elastic-net, and stability selection, while controlling the leakage, irreproducibility…

GPTomics/bioSkills

Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net…

GPTomics/bioSkills

Builds diagnostic and prognostic classifiers on omics feature matrices with regularized logistic regression, random forest, and gradient-boosted trees, handling the pn regime, batch shortcut…

GPTomics/bioSkills

Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the…

GPTomics/bioSkills

Builds and validates predictive time-to-event models on clinical and omics data with penalized Cox, random survival forests, gradient-boosted and deep survival models, and prediction-grade…

GPTomics/bioSkills

Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool…

GPTomics/bioSkills

Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against…

GPTomics/bioSkills

Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and…

GPTomics/bioSkills

Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering.

GPTomics/bioSkills

Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement…

GPTomics/bioSkills

Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA/QEA), mummichog/PSEA on raw m/z peaks, and network-diffusion enrichment (FELLA), with…

GPTomics/bioSkills

Decision-grade statistical analysis for metabolomics intensity tables.

GPTomics/bioSkills

Designs and validates quantitative targeted metabolomics assays (MRM/SRM on triple-quadrupole, PRM on high-resolution instruments) to report absolute concentrations.

GPTomics/bioSkills

Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time…

GPTomics/bioSkills

Turns shotgun classifier output into a defensible abundance table with Bracken Bayesian re-estimation, then compositional treatment (CLR, zero handling), library-size normalization, reference-frame…

GPTomics/bioSkills

Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling…

GPTomics/bioSkills