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Bioinformatics

Genomics and biological data.

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bioinformatics
471

bio-gene-regulatory-networks-perturbation-simulation

Simulate transcription factor perturbation effects on cell state using CellOracle, which integrates GRN inference with in silico knockout and overexpression modeling. Predicts cell identity shifts and differentiation trajectory changes from TF perturbations. Use when predicting the effect of transcription factor knockouts, planning perturbation experiments, or identifying driver TFs for cell fate transitions.

GPTomics
GPTomics
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bioinformatics
471

bio-crispr-screens-base-editing-analysis

Analyzes base editing and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.

GPTomics
GPTomics
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bioinformatics
471

bio-spatial-transcriptomics-image-analysis

Process and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.

GPTomics
GPTomics
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bioinformatics
471

bio-spatial-transcriptomics-spatial-communication

Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.

GPTomics
GPTomics
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bioinformatics
471

bio-small-rna-seq-target-prediction

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

GPTomics
GPTomics
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bioinformatics
471

bio-spatial-transcriptomics-spatial-deconvolution

Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.

GPTomics
GPTomics
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bioinformatics
471

bio-spatial-transcriptomics-spatial-neighbors

Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.

GPTomics
GPTomics
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bioinformatics
471

bio-single-cell-scatac-analysis

Single-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data.

GPTomics
GPTomics
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bioinformatics
471

bio-small-rna-seq-mirge3-analysis

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.

GPTomics
GPTomics
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bioinformatics
471

bio-gene-regulatory-networks-multiomics-grn

Build enhancer-driven gene regulatory networks by integrating single-cell RNA-seq and ATAC-seq data using SCENIC+ to identify eRegulons linking transcription factors to enhancers and target genes. Use when analyzing 10x multiome or paired scRNA+scATAC data to infer cis-regulatory GRNs.

GPTomics
GPTomics
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bioinformatics
471

bio-methylation-calling

Extract methylation calls from Bismark BAM files using bismark_methylation_extractor. Generates per-cytosine reports for CpG, CHG, and CHH contexts. Use when extracting methylation levels from aligned bisulfite sequencing data for downstream analysis.

GPTomics
GPTomics
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bioinformatics
471

bio-small-rna-seq-smrna-preprocessing

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.

GPTomics
GPTomics
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bioinformatics
471

bio-clip-seq-binding-site-annotation

Annotate CLIP-seq binding sites to genomic features including 3'UTR, 5'UTR, CDS, introns, and ncRNAs. Use when characterizing where an RBP binds in transcripts.

GPTomics
GPTomics
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bioinformatics
471

bio-rna-quantification-tximport-workflow

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.

GPTomics
GPTomics
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bioinformatics
471

bio-multi-omics-mixomics-analysis

Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.

GPTomics
GPTomics
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bioinformatics
471

bio-genome-annotation-eukaryotic-gene-prediction

Predict protein-coding genes in eukaryotic genomes using BRAKER3 for combined RNA-seq and protein evidence, or GALBA for protein-only evidence. Runs Augustus with trained parameters for accurate gene models. Use when annotating a newly assembled eukaryotic genome or improving existing gene predictions.

GPTomics
GPTomics
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bioinformatics
471

bio-rna-quantification-featurecounts-counting

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.

GPTomics
GPTomics
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bioinformatics
471

bio-single-cell-preprocessing

Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.

GPTomics
GPTomics
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bioinformatics
471

bio-spatial-transcriptomics-spatial-domains

Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.

GPTomics
GPTomics
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bioinformatics
471

bio-metagenomics-functional-profiling

Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.

GPTomics
GPTomics
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bioinformatics
471

bio-single-cell-metabolite-communication

Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.

GPTomics
GPTomics
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bioinformatics
471

bio-experimental-design-batch-design

Designs experiments to minimize and account for batch effects using balanced layouts and blocking strategies. Use when planning multi-batch experiments, assigning samples to sequencing lanes, or designing studies where technical variation could confound biological signals.

GPTomics
GPTomics
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bioinformatics
471

bio-compressed-files

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.

GPTomics
GPTomics
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