Claude Skills by GPTomics

Count reads per gene from aligned BAM files using Subread featureCounts. Use when turning STAR/HISAT2 BAMs into a gene-level count matrix for DESeq2/edgeR, deciding library strandedness, handling paired-end fragment counting, choosing how to treat multi-mapping and multi-overlapping reads, or diagnosing a low assignment rate from the summary file.

Import transcript-level quantifications from Salmon/kallisto/RSEM into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when summarizing transcript abundances to gene counts with the correct length offset, choosing a countsFromAbundance mode (full-length vs 3'-tag vs DTU), resolving transcript-ID version mismatches, or handing off to DESeq2/edgeR without double-applying the offset.

Tests whether a proposed or predicted RNA secondary structure is supported by evolutionary covariation using R-scape, which scores compensatory substitutions against a phylogeny-aware null and estimates the statistical power of the alignment. Use when validating a conserved-structure claim before trusting it (the test that found no support for HOTAIR/Xist/SRA lncRNA structures); separating real covariation from phylogenetic correlation; deciding whether an alignment even has the power to test...

Searches for non-coding RNA homologs and classifies RNA families with Infernal covariance models against Rfam, scoring sequence AND secondary-structure conservation jointly. Use when deciding whether a covariance model is the right tool versus BLAST/nhmmer (structured ncRNA versus lncRNA or mature miRNA); choosing the Rfam gathering threshold over a flat E-value; resolving clan overlaps; building and calibrating a custom CM from a structure-annotated alignment; or preferring a family-speciali...

Predicts RNA secondary structure with ViennaRNA, treating the Boltzmann ensemble (partition function, base-pair probabilities, centroid, MEA, stochastic samples) as the object rather than a single MFE fold. Covers consensus folding from alignments (RNAalifold), SHAPE-constrained folding, RNA-RNA interaction (RNAcofold/RNAduplex/RNAup), local and linear-time methods for long RNA, and pseudoknot-aware tools. Use when folding an RNA and choosing between MFE, centroid, MEA, or ensemble sampling; ...

Processes experimental RNA structure probing data (SHAPE-MaP, DMS-MaPseq) into per-nucleotide reactivity profiles with ShapeMapper2, then uses them as soft restraints on thermodynamic folding. Covers reagent and readout choice (SHAPE vs DMS, mutational-profiling vs RT-stop), the three control samples, per-transcript normalization, the Deigan vs Zarringhalam pseudo-energy models, in-cell versus in-vitro interpretation, and multi-conformation deconvolution. Use when converting probing reads to ...

Process many sequence files in batch (count, merge, split, convert, summarize) with memory-safe streaming and on-disk indexing using Biopython, pysam, or pyfastx. Use when iterating over a directory of FASTA/FASTQ files, merging or splitting datasets, building random access across many or huge files, or automating per-file operations without exhausting RAM.

Read, write, and index compressed sequence files (gzip, bzip2, xz, BGZF) with Biopython and bgzip/samtools. Use when working with .gz, .bz2, or .bgz sequence files, when random access into a compressed FASTA/FASTQ is needed, or when SeqIO.index/faidx/tabix rejects a plain .gz. Covers the BGZF-vs-gzip seekability asymmetry, the 'rt'-not-'rb' handle trap, virtual offsets, and gzip-to-BGZF conversion.

Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or trimming low-quality bases, generating quality reports, or deciding which FASTQ quality encoding a file uses before parsing.

Filter and select sequences by criteria (length, ID, GC content, N content, motifs, patterns, description) using Biopython, streaming so large files never load into RAM. Use when subsetting a FASTA/FASTQ file, removing unwanted or low-quality records, or selecting records by specific criteria. Use the paired-end-fastq skill instead whenever the input is paired R1/R2 reads.

Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL, Stockholm) and re-encode FASTQ quality offsets using Biopython Bio.SeqIO. Use when changing a file format for a downstream tool, fixing FASTQ quality encoding (Phred+33 vs Phred+64 vs Solexa), or when a conversion risks silently dropping annotations or quality scores.

Handle paired-end FASTQ files (R1/R2) using Biopython while keeping mates synchronized. Use when working with Illumina paired reads, synchronizing pairs, filtering both mates together with orphan routing, interleaving/deinterleaving, or matching mates by read name.

Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) with Biopython Bio.SeqIO, choosing between streaming, in-memory, and on-disk-indexed access. Use when parsing sequence files, iterating multi-record files, randomly accessing records by ID in large files, or maximizing parse throughput.

Calculate assembly and sequence statistics (N50/L50, auN, NG50/NGA50, length distribution, GC content with ambiguity handling, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing genome assemblies.

Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when saving sequences, creating new sequence files, or outputting modified records.

Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterologous expression, or studying synonymous codon selection.

Find sequence motifs, degenerate IUPAC patterns, and transcription-factor binding sites in DNA/RNA using Biopython and regex, including position weight matrix (PWM/PSSM) scoring. Use when locating regulatory elements, counting overlapping motif occurrences, scanning for binding-site matches above a significance threshold, or reading motif matrices from JASPAR/MEME/TRANSFAC files. For restriction enzyme sites, use restriction-analysis/restriction-sites.

Generate reverse complements and complements of DNA/RNA sequences using Biopython, including IUPAC ambiguity codes, gapped alignments, and minus-strand features. Use when working with the opposite strand, building reverse primers, normalizing strand orientation before alignment, or extracting a coding sequence from a minus-strand feature.

Create and manipulate Seq, MutableSeq, and SeqRecord objects using Biopython. Use when creating sequences from strings, modifying sequence data in-place, building annotated records for file output, or debugging post-1.78 Bio.Alphabet and immutability errors.

Calculate nucleotide and protein sequence properties (GC content, GC skew, molecular weight, melting temperature, isoelectric point, instability, hydropathy) with Biopython. Use when analyzing sequence composition, computing primer Tm, estimating DNA or protein mass, or profiling protein biophysical properties.

Slice, extract, and concatenate biological sequences and annotated records using Biopython. Use when extracting subsequences by position, splicing exons into a transcript, joining sequences, or carrying a sub-region of an annotated record (with quality scores and features) into a new record.

Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding. Use when converting a CDS or ORF to its amino-acid sequence, selecting a non-standard (mitochondrial, bacterial, ciliate) genetic code, validating a coding sequence, or scanning all reading frames.

Integrate multiple scRNA-seq samples or batches with Harmony, scVI/scANVI, Seurat (CCA/RPCA), fastMNN, Scanorama, or BBKNN. Resolves which method to use for the dataset size and design, how strongly to correct, when integration is the wrong move (confounded batch/biology), how to score integration with scIB metrics without gaming them, and why corrected expression must not be used for differential expression. Use when integrating batches or datasets, choosing an integration method, diagnosing...

Automated reference-based cell type annotation for single-cell RNA-seq using CellTypist, SingleR, Azimuth, scANVI, and scmap to transfer labels from a reference. Use when annotating cell types from a reference atlas or pretrained model, transferring labels onto a query, assessing prediction confidence and rejection, or triaging whether an unexpected cluster is a novel type versus a doublet, low-quality, or batch artifact.

Infers ligand-receptor cell-cell communication from scRNA-seq with a consensus-first workflow (LIANA), plus CellPhoneDB specificity tests, CellChat pathway probabilities, and NicheNet downstream ligand-activity. Use when ranking ligand-receptor interactions between cell types, comparing communication across conditions, asking which ligand drives a receiver response, or deciding which CCC method and resource to trust.

Dimensionality reduction and graph-based clustering for single-cell RNA-seq with Scanpy (Python) and Seurat (R). Resolves which algorithm to use (Leiden vs Louvain), how many PCs and neighbors to set, how to sweep and validate resolution, when a split is over-clustering, and why post-clustering marker p-values are not valid inference. Use when clustering cells, choosing a clustering resolution, deciding whether two clusters are one population, building a UMAP/tSNE, or judging whether clusters...

Infer large-scale copy-number alterations from tumor single-cell or single-nucleus RNA-seq to separate malignant from normal cells and call subclones, using inferCNV, copyKAT, Numbat, and SCEVAN. Use when separating malignant from normal cells in a tumor scRNA-seq dataset, inferring chromosome-arm CNVs or aneuploidy from expression, calling tumor subclones from single cells, choosing a CNV-inference method (reference-based vs reference-free, expression-only vs allele-aware), or deciding which...

Read, write, create, and convert single-cell objects across AnnData (Python), Seurat (R), and SingleCellExperiment (R). Use when loading 10X Cell Ranger output (raw vs filtered), importing or exporting h5ad/RDS/h5mu/zarr, building AnnData or Seurat objects from matrices, moving objects between Python and R, or debugging lost layers, transposed matrices, or mangled gene names during conversion.

Assign cells to their sample of origin from cell or nucleus hashing (CITE-seq HTOs, MULTI-seq lipid/cholesterol tags, CellPlex CMOs) and call cross-sample doublets using Seurat HTODemux/MULTIseqDemux, hashsolo, demuxEM, GMM-Demux, and demuxmix. Use when assigning pooled hashed cells back to their sample, calling cross-sample doublets from HTO counts, choosing a demultiplexing method, deciding between hashtag and genetic demultiplexing, or rescuing an oversized Negative pile from weak HTO stai...

Reconstructs single-cell lineage trees and clonal relationships from CRISPR/Cas9 scars, static expressed barcodes (LARRY/CellTag), or somatic mtDNA mutations using Cassiopeia, Startle, and CoSpar. Use when building a phylogeny from barcode scars, choosing a tree-reconstruction solver, handling homoplasy and dropout, grouping clones from mtDNA, integrating clone with transcriptomic state, or judging whether a state-based fate call is trustworthy.

Detect cluster marker genes and assign manual cell type labels in single-cell RNA-seq using Scanpy (Python) and Seurat (R). Use when finding genes that distinguish clusters, ranking markers for annotation, scoring gene signatures, hand-labeling clusters, or deciding between Wilcoxon marker ranking and pseudobulk condition DE.

Infers metabolite-mediated cell-cell communication from scRNA-seq by scoring enzyme-to-sensor pairs (MEBOCOST), with metabolic flux (scFEA), FBA state (Compass), and neurotransmitter (NeuronChat) alternatives. Use when studying metabolic crosstalk between cell types, predicting metabolite secretion and sensing, or deciding which metabolic-communication method fits and how speculative the result is.

Integrate multimodal single-cell data (CITE-seq RNA+protein, 10x Multiome RNA+ATAC, unpaired/diagonal RNA+ATAC) and choose the right joint method. Use when classifying an integration task by anchor structure (paired vs unpaired), denoising CITE-seq ADT background before joint embedding, picking between WNN, totalVI, MultiVI, MOFA+, GLUE, or Seurat v5 bridge integration, or diagnosing why a modality dominates a joint clustering.

Analyze Perturb-seq / CROP-seq single-cell CRISPR screens. Use when assigning guides as a mixture problem, removing non-perturbed escaper cells with Mixscape, choosing a calibrated test (SCEPTRE conditional resampling) over naive DE, quantifying effect size with E-distance, separating compositional shifts from within-state expression change, or judging whether a perturbation-prediction foundation model actually beats a baseline.

Quality control, ambient-RNA handling, normalization, and feature selection for single-cell RNA-seq using Scanpy (Python) and Seurat (R). Use when filtering low-quality cells with MAD-adaptive thresholds, setting tissue-aware mito cutoffs, removing ambient RNA (SoupX/CellBender/DecontX), choosing a normalization (shifted-log vs scran vs sctransform vs Pearson residuals), selecting highly variable genes, or deciding whether to scale and regress out covariates.

Analyze single-cell ATAC-seq with Signac/ArchR (R) and SnapATAC2 (Python alternative). Use when processing scATAC fragments, choosing a framework, calling consensus peaks, running TF-IDF/LSI while diagnosing the depth component, scoring chromVAR motif deviations against GC-matched backgrounds, detecting homotypic vs heterotypic doublets, or deciding whether to binarize the count matrix.

Infers developmental trajectories, pseudotime, RNA velocity, and directed fate probabilities from single-cell data using PAGA, Slingshot, Monocle3, DPT, Palantir, scVelo, and CellRank 2. Use when ordering cells along a differentiation continuum, choosing a trajectory method by topology, rooting pseudotime, estimating RNA velocity direction, computing fate probabilities near a bifurcation, or judging whether an inferred trajectory is real.

Tests miRNAs for differential expression with DESeq2 or edgeR using small-RNA-aware normalization and filtering. Use when deciding which normalization survives a library dominated by a few hyper-abundant miRNAs (compositional fragility); choosing DESeq2 vs edgeR vs a compositional method; setting a lower prefilter than mRNA; handling biofluid data with no endogenous normalizer; or remembering that RPM is for display and TDMD can make a miRNA drop without transcriptional repression.

Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature. Use when deciding whether a study needs de novo discovery at all versus known-miRNA quantification; choosing the species and related-species miRBase references; reading the miRDeep2 score as a signal-to-noise hypothesis rather than a fixed cutoff; or filtering novel candidates against tRNA/rRNA loci to reject the classic false positives.

Quantifies known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries. Use when choosing miRBase versus MirGeneDB as the reference; deciding whether to collapse isomiRs to the parent miRNA or keep 5'-isomiRs separate (they shift the seed and retarget); confirming the organism is among the six supported species; or remembering that RPM output is for display only and raw counts go to DESeq2/edgeR.

Trims kit-specific 3' adapters, strips UMIs or 4N degenerate ends, size-selects, and collapses small RNA-seq reads (miRNA, piRNA, tRF) with cutadapt or fastp. Use when choosing the kit's 3' adapter; setting the size window (18-26 nt miRNA vs 24-32 nt piRNA); deciding whether a library carries a true UMI (QIAseq) versus a 4N debiasing spacer (NEXTflex); reading the read-length histogram to judge library quality; or deciding whether to collapse identical reads before mapping.

Predicts and prioritizes miRNA target genes with seed-based tools (miRanda, TargetScan, miRDB) and experimentally validated databases (miRTarBase, multiMiR). Use when deciding that a predicted target is a hypothesis not a finding; ranking by the right score (weighted context++, mirSVR, miRDB); raising confidence by intersecting predictions with inversely-correlated mRNA DE; weighing validated (CLIP/reporter) over predicted evidence; or avoiding the circular enrichment of unfiltered target lists.

Profiles non-miRNA small RNAs - tRNA-derived fragments (tRFs/tsRNAs), piRNAs, and rRNA/snoRNA-derived species - with MINTmap, unitas, SPORTS, and proTRAC. Use when annotating all small-RNA classes in a library; quantifying tRFs at locus resolution where tRNA loci are redundant (exclusive vs ambiguous); testing the piRNA ping-pong signature; deciding whether a species is a processed functional RNA or a degradation fragment; or judging whether the prep could even capture 5'-OH/cyclic-phosphate ...

Reconstructs single cells from sub-cellular spatial capture units (Visium HD 2um bins, Stereo-seq DNB spots, Slide-seqV2 beads) by aggregating bins UP into cells rather than deconvolving a mixture DOWN. Use when choosing a bin size and recognizing the sparsity-vs-mixture dilemma (2um bins are too sparse to cluster, but binning to 8/16um re-creates the multi-cell mixture deconvolution was meant to escape); deciding between morphology-driven cell reconstruction (Bin2cell -- StarDist/Cellpose nu...

Segments cells/nuclei and extracts image features from imaging spatial transcriptomics (Xenium, MERFISH/MERSCOPE, CosMx) and H&E/IF tissue images using Cellpose, StarDist, Baysor, and Squidpy. Use when choosing a segmentation strategy (DAPI nucleus + expansion vs membrane-stain whole-cell vs transcript-aware Baysor/proseg vs segmentation-free SSAM) given the available stain; judging whether transcript spillover is fabricating false co-expression and short-range cell-cell signal; and deciding ...

Maps cell-cell communication and ligand-receptor co-expression in spatial transcriptomics (Visium, Xenium, MERFISH, CosMx, Slide-seq) with Squidpy ligrec, COMMOT, stLearn, CellChat-spatial, and NicheNet. Use when choosing a method by whether spatial distance is actually modeled (squidpy ligrec is space-blind cluster-permutation vs COMMOT optimal-transport is distance-aware vs stLearn neighborhood vs CellChat-spatial filter) and by secreted-vs-contact-dependent range; choosing the ligand-recep...

Loads spatial transcriptomics data from Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq/Curio, and Stereo-seq into AnnData or SpatialData using spatialdata-io and Squidpy. Use when deciding which platform class is in hand (imaging/in-situ vs sequencing/capture), which reader matches the platform (spatialdata_io.xenium/merscope/cosmx vs squidpy.read.visium/vizgen/nanostring), whether to work from the per-transcript molecule table (the re-segmentable source of truth) or the segmen...

Estimates per-spot cell type composition of spatial transcriptomics mixtures (Visium, Slide-seq, Stereo-seq) from an scRNA-seq reference with cell2location, RCTD, SPOTlight, stereoscope, SpatialDWLS, or reference-free STdeconvolve. Use when deciding whether a platform even needs deconvolution (the resolution fork -- a 55um Visium spot is a 1-10-cell MIXTURE -> deconvolve, but a Xenium/MERFISH/CosMx cell is already single -> segment instead, and running deconvolution there invents fractions th...

Identify spatially coherent tissue domains (regions like cortical layers, tumor vs stroma) in Visium, Visium HD, Xenium, MERFISH, Slide-seq, and Stereo-seq data with Squidpy, BANKSY, BayesSpace, STAGATE, and GraphST. Use when distinguishing a domain (a region with many cell types) from a cell type (one cell's identity) and a niche (local cell-type composition); choosing a domain method by tissue geometry (laminar/continuous vs high-resolution imaging vs non-contiguous); tuning the spatial-wei...

Integrates spatial RNA with a second modality (protein, ATAC, or histone marks) on spatial CITE-seq, DBiT-seq, spatial-ATAC, or Visium CytAssist data. Use when deciding vertical (same-pixel co-profiling -> WNN/MOFA joint factors) versus diagonal (serial adjacent sections -> registration via PASTE/STalign) integration; recognizing that modalities from serial sections are DIFFERENT cells so joint same-cell methods do not apply; handling a bounded antibody/feature panel where absence is uninform...