bioinformatics
471
bio-expression-matrix-sparse-handling
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.
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bioinformatics
471
bio-genome-intervals-gtf-gff-handling
Parse, query, and convert GTF and GFF3 annotation files. Extract gene, transcript, and exon coordinates using gffread, gtfparse, and gffutils. Use when extracting specific features from gene annotations or converting between annotation formats.
GPTomics
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bioinformatics
471
bio-epitranscriptomics-modification-visualization
Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.
GPTomics
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bioinformatics
471
bio-variant-calling-filtering-best-practices
Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.
GPTomics
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scientific-computing
471
bio-variant-calling-deepvariant
Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller or when highest germline calling accuracy is required.
GPTomics
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computational-chemistry
471
bio-ribo-seq-ribosome-periodicity
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.
GPTomics
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scientific-computing
471
bio-chipseq-differential-binding
Identifies differentially bound ChIP-seq regions between conditions using DiffBind (from BAMs), DESeq2, or PyDESeq2 (from count matrices). Handles normalization, statistical testing, and fold-change estimation with ChIP-seq-specific considerations. Use when comparing ChIP-seq binding between experimental conditions.
GPTomics
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bioinformatics
471
bio-vcf-manipulation
Merge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.
GPTomics
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scientific-computing
471
bio-data-visualization-genome-tracks
Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.
GPTomics
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bioinformatics
471
bio-variant-calling
Call SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments.
GPTomics
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scientific-computing
471
bio-variant-annotation
Comprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance with MANE transcript selection and pathogenicity scoring. Use when annotating variants with functional and clinical information.
GPTomics
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bioinformatics
471
bio-vcf-statistics
Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.
GPTomics
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bioinformatics
471
bio-variant-calling-joint-calling
Joint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples.
GPTomics
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bioinformatics
471
bio-ribo-seq-ribosome-stalling
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.
GPTomics
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bioinformatics
471
bio-consensus-sequences
Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
GPTomics
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bioinformatics
471
bio-systems-biology-flux-balance-analysis
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.
GPTomics
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scientific-computing
471
bio-pdb-structure-navigation
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.
GPTomics
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scientific-computing
471
bio-pdb-geometric-analysis
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).
GPTomics
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scientific-computing
471
bio-structural-biology-alphafold-predictions
Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).
GPTomics
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bioinformatics
471
bio-spatial-transcriptomics-spatial-visualization
Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.
GPTomics
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computational-chemistry
471
bio-rna-structure-structure-probing
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.
GPTomics
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bioinformatics
471
bio-filter-sequences
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.
GPTomics
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bioinformatics
471
bio-ribo-seq-orf-detection
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.
GPTomics
research
scientific-computing
471
bio-structural-biology-modern-structure-prediction
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.
GPTomics
research