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.
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
Align and QC MeRIP-seq IP and input samples for m6A analysis. Use when preparing MeRIP-seq data for peak calling or differential methylation analysis.
Discovers periodic signals of unknown period in time-series omics data using Lomb-Scargle periodograms (scipy), autocorrelation, and wavelet time-frequency decomposition (pywt). Identifies dominant frequencies, handles irregularly sampled data, and detects transient periodicity. Use when searching for periodic patterns of unknown period length, analyzing cell cycle oscillations, or processing unevenly spaced time-series. Not for testing known 24-hour rhythms (see temporal-genomics/circadian-rhythms).
Perform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.
Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.
End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.
End-to-end RNA-seq workflow from FASTQ files to differential expression results. Covers QC, quantification (Salmon or STAR+featureCounts), and DESeq2 analysis with visualization. Use when running RNA-seq from FASTQ to DE results.
Estimate divergence times using molecular clock models with BEAST2, MCMCTree, and TreePL. Use when dating speciation events, calibrating phylogenies with fossils, choosing between strict and relaxed clock models, or estimating evolutionary rates across lineages.
Statistical analysis for metabolomics data. Covers preprocessing (log2 transformation, normalization), limma moderated testing with empirical Bayes, Welch's t-tests with BH correction, fold change estimation, and multivariate methods (PCA, PLS-DA, OPLS-DA). Use when identifying differentially abundant metabolites or building classification models.
Run remote BLAST searches against NCBI databases using Biopython Bio.Blast. Use when identifying unknown sequences, finding homologs, or searching for sequence similarity against NCBI's nr/nt databases.
Generate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks.
Find homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.
End-to-end Hi-C analysis workflow from contact pairs to compartments, TADs, and loops. Covers cooler matrices, cooltools analysis, and visualization. Use when processing Hi-C data to compartments and TADs.
End-to-end Ribo-seq analysis from FASTQ to translation efficiency and ORF detection. Use when analyzing ribosome profiling data to study translation.
Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.
Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.
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.
High-quality genome assembly from PacBio HiFi reads using hifiasm with phasing support. Use when building reference-quality diploid assemblies from HiFi data, especially with trio or Hi-C phasing for fully resolved haplotypes.
Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.
Access 1000+ scientific tools through ToolUniverse for drug discovery, protein analysis, genomics, literature search, clinical data, ADMET prediction, molecular docking, and more. Use when the user needs biomedical or scientific research capabilities.
Strategies for scientific computing, numerical methods, bioinformatics/DNA tasks, logic circuit design, algorithmic challenges, and ML training tasks.
This skill should be used when the user asks for "deep research", "research team", "comprehensive analysis", "research report", "investigate thoroughly", "compare X vs Y in depth", or needs synthesis across multiple sources with verification. It spawns a coordinated team of researcher agents across multiple rounds, with the lead triaging findings and creating targeted follow-up tasks. Scales from Focused (2 researchers, 1-2 rounds) to Comprehensive (4 researchers, 3-4 rounds with cross-verification). Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.
Problem-solving strategies for interpolation in numerical methods
