Claude Skills by mdbabumiamssm

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.

Essential guidelines for writing modern, type-safe, and idiomatic Python 3 code.

Standards for efficient, readable, and performant data manipulation using Python''s Pandas library.

Guidelines for building scalable, SEO-friendly applications with Next.js (App Router).

AI-powered integration of cryo-EM structural data with generative AI and molecular dynamics for structure-based drug design targeting flexible proteins and membrane complexes.

AI-powered time-resolved cryo-EM analysis for capturing protein dynamics, drug-binding kinetics, and conformational transitions for dynamics-based drug discovery.

Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).

Perform geometric calculations on protein structures using Biopython Bio.PDB. Use when measuring distances, angles, and dihedrals, superimposing structures, calculating RMSD, or computing solvent accessible surface area (SASA).

Predict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1. Use when predicting structures for novel proteins, protein complexes, or when comparing predictions across multiple methods.

Parse and write protein structure files using Biopython Bio.PDB. Use when reading PDB, mmCIF, and MMTF files, downloading structures from RCSB PDB, or writing structures to various formats.

Modify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.

Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.

Build tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA.

Perform flux balance analysis (FBA) and flux variability analysis (FVA) on genome-scale metabolic models using COBRApy. Predict growth rates, metabolic fluxes, and optimal resource utilization. Use when predicting metabolic phenotypes or optimizing flux distributions.

Perform in silico gene knockout analysis and synthetic lethality screens using COBRApy single and double deletions. Predict essential genes and identify synthetic lethal pairs for drug target discovery. Use when identifying essential genes or finding synthetic lethal drug targets.

Build genome-scale metabolic models from genome sequences using CarveMe and gapseq for automated reconstruction. Generate draft models ready for curation and analysis. Use when creating metabolic models for organisms without existing models.

Validate, gap-fill, and curate genome-scale metabolic models using memote for quality scores and COBRApy for manual curation. Ensure models meet SBML standards and produce biologically meaningful predictions. Use when improving draft models or preparing models for publication.

Extract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results.

Load gene expression count matrices from various formats including CSV, TSV, featureCounts, Salmon, kallisto, and 10X. Use when importing quantification results for downstream analysis.

Convert between gene identifier systems including Ensembl, Entrez, HGNC symbols, and UniProt. Use when mapping IDs for pathway analysis or matching different data sources.

Merge sample metadata with count matrices and add gene annotations. Use when preparing data for differential expression analysis or visualization.

Work with sparse matrices for memory-efficient storage of count data. Use when dealing with single-cell data or large bulk RNA-seq datasets where most values are zero.

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.

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.

Fast miRNA quantification with isomiR detection and A-to-I editing analysis using miRge3. Use when quantifying known miRNAs quickly or analyzing isomiR variants and RNA editing.

Preprocess small RNA sequencing data with adapter trimming and size selection optimized for miRNA, piRNA, and other small RNAs. Use when preparing small RNA-seq reads for downstream quantification or discovery analysis.

Predict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs.

Create portable, standards-based bioinformatics pipelines with Common Workflow Language (CWL). Use when building workflows that need maximum portability across execution platforms, sharing pipelines with collaborators using different systems, or contributing to community workflow registries.

Create scalable, containerized bioinformatics pipelines with Nextflow DSL2 supporting Docker, Singularity, and cloud execution. Use when building portable pipelines with container support, running workflows on cloud platforms (AWS, Google Cloud), or leveraging nf-core community pipelines.

Build reproducible bioinformatics pipelines with Snakemake using rules, wildcards, and automatic dependency resolution. Use when creating Python-based workflows, automating multi-step analyses with make-like dependency tracking, or running pipelines on HPC clusters with SLURM.

Create portable bioinformatics pipelines with Workflow Description Language (WDL) using Cromwell or miniwdl execution engines. Use when running GATK best practices pipelines, working with Terra/AnVIL platforms, or building workflows for cloud execution on Google Cloud or AWS.

End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3, and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.

End-to-end CLIP-seq analysis from FASTQ to binding sites and motif enrichment. Use when analyzing protein-RNA interactions from CLIP-based methods.

End-to-end CRISPR experiment design from target selection to delivery-ready constructs. Covers guide RNA design, off-target assessment, and specialized editing strategies including knockouts, base editing, and HDR knockins. Use when designing complete CRISPR editing experiments for gene knockout, correction, or tagging.

Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.

End-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller. Use when calling variants from raw sequencing reads.

End-to-end genome assembly workflow from reads to polished assembly with QC. Supports short reads (SPAdes), long reads (Flye), and hybrid approaches. Use when assembling genomes from raw reads.

End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.

End-to-end workflow for detecting structural variants from long-read sequencing data. Covers ONT/PacBio alignment with minimap2 and SV calling with Sniffles or cuteSV. Use when detecting structural variants from long reads.

End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.

End-to-end metabolomics workflow from raw MS data to pathway analysis. Orchestrates XCMS preprocessing, annotation, normalization, statistical analysis, and pathway mapping. Use when processing LC-MS metabolomics data.

End-to-end metagenomics workflow from FASTQ to taxonomic and functional profiles. Covers Kraken2 classification, Bracken abundance estimation, and HUMAnN functional profiling. Use when profiling metagenomic samples.

End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.

End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains.

End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with MSstats or limma. Use when processing mass spectrometry proteomics.