Skills for biological image analysis: cell/nucleus segmentation, image restoration, and spatial data processing.
Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation.
Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation.
Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization.
Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization.
Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation.
Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation.
Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches.
Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches.
Quality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC.
Quality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC.
Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens.
Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens.
Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features.
Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features.
Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy.
Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy.
Predict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition.
Predict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition.
Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP).
Detect and polarize interdomain horizontal gene transfer (virus <-> eukaryote/bacteria/archaea, and cross-cellular HGT) using reciprocal best hits, transfer-direction inference, a…
Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential…
Create a Linktree-style bio link hub page as a single self-contained HTML file. Triggers on: "create a bio link page", "make a linktree", "link in bio page", "bio link", "link…
Cell-free DNA analysis pipeline from plasma sequencing to tumor monitoring. Preprocesses cfDNA reads, analyzes fragment patterns, estimates tumor fraction from sWGS, and — from…
Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid,…
Assesses Oxford Nanopore and PacBio long-read quality with NanoPlot, cramino, NanoComp, pycoQC/toulligQC, and seqkit, and filters reads with chopper/Filtlong for the downstream…
Polishes Oxford Nanopore draft assemblies to higher consensus accuracy with medaka, a basecaller-model-specific neural consensus net, produces haploid variant calls (VCF) for…
Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without b — from…
Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without b — from…
Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM,…
Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions.
Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions.
Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read character — from…
Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read character — from…
Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex re — from…
Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex re — from…
Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models.
Implements nested cross-validation and stratified splits for unbiased model evaluation on biomedical datasets.
Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs.
Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions.
Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines.
Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation.
Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation.
Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment.
Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment.
Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis.
Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis.
Targeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment.
Targeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment.
XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling.
XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling.
Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates.
Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates.
Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database.
Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database.
Marker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers.
Marker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers.
Performs probe filtering and sample-level QC on Illumina Infinium methylation arrays (450K / EPIC / EPICv2) to decide which probes and samples to trust.
Analyzes cfDNA methylation patterns for cancer detection using cfMeDIP-seq or bisulfite sequencing with MethylDackel.
Analyzes cfDNA methylation patterns for cancer detection using cfMeDIP-seq or bisulfite sequencing with MethylDackel.