Claude Skills by swaruplab

Align short reads using Bowtie2 with local or end-to-end modes. Supports gapped alignment. Use when aligning ChIP-seq, ATAC-seq, or when flexible alignment modes are needed.

Align DNA short reads to reference genomes using bwa-mem2, the faster successor to BWA-MEM. Use when aligning DNA short reads to a reference genome.

Align RNA-seq reads with HISAT2, a memory-efficient splice-aware aligner. Use when STAR's memory requirements are too high or for general RNA-seq alignment.

Align RNA-seq reads with STAR (Spliced Transcripts Alignment to a Reference). Supports two-pass mode for novel splice junction discovery. Use when aligning RNA-seq data requiring splice-aware alignment.

Remove sequencing adapters from FASTQ files using Cutadapt and Trimmomatic. Supports single-end and paired-end reads, Illumina TruSeq, Nextera, and custom adapter sequences. Use when FastQC shows adapter contamination or before alignment of short reads.

Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.

All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.

Filter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream analysis.

Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.

RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.

Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.

ResolVI — variational autoencoder for denoising imaging-based spatial transcriptomics (Xenium, MERFISH, CosMx). Removes ambient background and resolves wrong segmentation by jointly modeling true cell expression, mis-assigned neighbor counts, and unspecific background. Outputs a latent representation, denoised expression, and (semi-supervised) cell-type predictions. Built on scvi-tools; GPU recommended.

Select restriction enzymes by criteria using Biopython Bio.Restriction. Find enzymes that cut once, don't cut, produce specific overhangs, are commercially available, or have compatible ends for cloning. Use when selecting restriction enzymes for cloning or analysis.

Analyze restriction digest fragments using Biopython Bio.Restriction. Predict fragment sizes, get fragment sequences, simulate gel electrophoresis patterns, and perform double digests. Use when analyzing restriction digest fragment patterns.

Create restriction maps showing enzyme cut positions on DNA sequences using Biopython Bio.Restriction. Visualize cut sites, calculate distances between sites, and generate text or graphical maps. Use when creating or analyzing restriction maps.

Find restriction enzyme cut sites in DNA sequences using Biopython Bio.Restriction. Search with single enzymes, batches of enzymes, or commercially available enzyme sets. Returns cut positions for linear or circular DNA. Use when finding restriction enzyme cut sites in sequences.

Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.

Preprocess ribosome profiling data including adapter trimming, size selection, rRNA removal, and alignment. Use when preparing Ribo-seq reads for downstream analysis of translation.

Validate Ribo-seq data quality by checking 3-nucleotide periodicity and calculating P-site offsets. Use when assessing library quality or determining read offsets for downstream analysis.

Detect ribosome pausing and stalling sites from Ribo-seq data at codon resolution. Use when studying translational regulation, identifying pause sites, or analyzing codon-specific translation dynamics.

Calculate translation efficiency (TE) as the ratio of ribosome occupancy to mRNA abundance. Use when comparing translational regulation between conditions or identifying genes with altered translation independent of transcription.

Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.

Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.

Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.

Import transcript-level quantifications from Salmon/kallisto into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when importing transcript counts into R for DESeq2/edgeR.

Searches for non-coding RNA homologs and classifies RNA families using Infernal covariance model searches against the Rfam database. Identifies structured RNAs by sequence and secondary structure conservation. Use when querying sequences against Rfam, building custom covariance models for novel RNA families, or classifying non-coding transcripts by family.

Predicts RNA secondary structures using minimum free energy folding and partition function analysis with ViennaRNA (RNAfold, RNAalifold, RNAcofold). Computes base-pair probabilities, centroid structures, and consensus structures from alignments. Use when predicting RNA folding, evaluating structural stability, or comparing structures across homologs.

Analyzes experimental RNA structure probing data from SHAPE-MaP and DMS-MaPseq experiments using ShapeMapper2. Converts mutation rates to per-nucleotide reactivity profiles that constrain structure prediction. Use when processing SHAPE-MaP or DMS-MaPseq sequencing data to obtain experimental RNA structure information.

AI-powered RNA velocity analysis for predicting cellular state transitions, differentiation trajectories, and dynamic gene regulation from single-cell RNA sequencing data.

Local Scanpy pipeline for single-cell RNA-seq QC, clustering, marker discovery, and optional two-group differential expression from raw-count .h5ad.

Execute the MAD-based single-cell RNA-seq QC workflow (scripts + Python API) to filter low-quality cells and emit reports plus filtered AnnData files.

Quantify transposable element expression from single-cell RNA/ATAC-seq BAM files at locus or family level.

Process multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.

Read and write compressed sequence files (gzip, bzip2, BGZF) using Biopython. Use when working with .gz or .bz2 sequence files. Use BGZF for indexable compressed files.

Work with FASTQ quality scores using Biopython. Use when analyzing read quality, filtering by quality, trimming low-quality bases, or generating quality reports.

Filter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.

Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when changing file formats or preparing data for different tools.

Handle paired-end FASTQ files (R1/R2) using Biopython. Use when working with Illumina paired reads, synchronizing pairs, interleaving/deinterleaving, or filtering paired data.

Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) using Biopython Bio.SeqIO. Use when parsing sequence files, iterating multi-sequence files, random access to large files, or high-performance parsing.

Calculate sequence statistics (N50, length distribution, GC content, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing 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, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis.

Find patterns, motifs, and subsequences in biological sequences using Biopython. Use when searching for transcription factor binding sites, regulatory elements, or any sequence pattern. For restriction enzyme analysis, use the restriction-analysis skill.

Generate reverse complements and complements of DNA/RNA sequences using Biopython. Use when working with opposite strands, primer design, or converting between template and coding strands.

Create and manipulate Seq, MutableSeq, and SeqRecord objects using Biopython. Use when creating sequences from strings, modifying sequence data in-place, or building annotated sequence records.

Calculate sequence properties like GC content, molecular weight, isoelectric point, and GC skew using Biopython. Use when analyzing sequence composition, computing physical properties, or comparing sequences.

Slice, extract, and concatenate biological sequences using Biopython. Use when extracting subsequences, joining sequences, or manipulating sequence regions by position.

Transcribe DNA to RNA and translate to protein using Biopython. Use when converting between DNA, RNA, and protein sequences, finding ORFs, or using alternative codon tables.

scRNA-seq analysis with Seurat v5 (R) — the standard R-based pipeline. Covers QC, normalization (LogNormalize + SCTransform), HVG selection, scaling, PCA, neighbors, leiden/Louvain clustering, UMAP/t-SNE, marker gene identification (FindMarkers / FindAllMarkers), and visualization (DimPlot / FeaturePlot / VlnPlot / DotPlot / DoHeatmap). Multi-sample integration via Seurat v5's IntegrateLayers — CCA, RPCA, Harmony, FastMNN, or scVI. Optional dependencies (presto, BPCells, glmGamPoi) substantia...

Advanced single-cell multi-omics analysis including scRNA-seq, scCITE-seq, scATAC-seq, and TARGET-seq. Use when analyzing single-cell data, cell type identification, trajectory analysis, differential expression, UMAP/clustering, integrating protein and RNA modalities (TotalVI), or working with Scanpy, Seurat, scvi-tools. Includes workflows for MPN, hematologic malignancies, megakaryocyte biology.