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Amazon listing builder and optimizer for sellers. Two modes: (A) Create — build keyword-optimized listings from scratch using keyword lists + product characteristics + AI copywriting, (B) Optimize — audit existing listings, find keyword gaps, score across 8 dimensions, and rewrite with missing keywords. Integrates with amazon-keyword-research for keyword input. Works on 12 Amazon marketplaces. No API key required. Use when: (1) creating a new Amazon listing from keywords, (2) auditing an existing listing for SEO and conversion, (3) checking keyword coverage in title/bullets/description, (4) generating listing copy with target keywords and tone, (5) comparing listings against competitors, (6) preparing a listing for launch or relaunch.

Mandatory filtering of degenerate and uninformative data points before statistical tests. Covers single-sequence alignments, empty files, constant-value features, zero-variance inputs, and all-NaN columns. For NaN-aware correlation computation, see the nan-safe-correlation skill. For broader statistical testing guidance, see the statistical-analysis skill.

Per-feature NaN-safe Spearman/Pearson correlation computation. Use when computing correlations across many features (genes, proteins, variants) with missing values. Covers why bulk matrix shortcuts fail with missing data, correct pairwise deletion, degenerate input filtering, and performance optimization for large datasets. For general statistical test selection use statistical-analysis; for model explainability use shap-model-explainability.

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Statistical modeling library for Python. Use for regression (OLS, WLS, GLM), discrete outcomes (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), and rigorous inference with detailed diagnostics, coefficient tables, and hypothesis tests. For ML-focused classification/regression use scikit-learn; for guided test selection use statistical-analysis.

Python bridge to ImageJ2/Fiji enabling macro execution, plugin calls (Bio-Formats, TrackMate, Analyze Particles), bidirectional NumPy↔ImagePlus/ImgLib2 data exchange, and ImageJ Ops from Python. Use for automating Fiji-specific workflows headlessly from Python scripts. Use scikit-image instead for pure Python pipelines that do not require Fiji plugins; use napari for interactive visualization.

Guide for choosing and creating scientific visualizations for publications and presentations. Covers selecting chart types for different data structures, color theory for accessibility and print, figure composition, journal-specific formatting requirements (Nature, Cell, ACS), and common pitfalls that undermine scientific credibility. Consult this guide when deciding how to visualize your data or preparing figures for submission.

Guide for annotating statistical significance (p-value asterisk notation) on comparison plots. Covers standard notation conventions (ns, *, **, ***, ****), when to annotate, matplotlib bracket+asterisk implementation, and integration with seaborn box/violin/bar plots. Use when generating publication-ready comparison figures that need significance markers to support statistical claims made in the analysis.

Gene regulatory network inference from expression data using GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load expression matrix, optionally filter by transcription factors, infer TF-target-importance links, filter and save network. Dask-parallelized for single-cell scale. Core component of the SCENIC pipeline.

Query uniformly processed RNA-seq gene expression profiles, tissue-specific expression patterns, and co-expression networks from the ARCHS4 database REST API. Retrieve z-score normalized expression across 1M+ human and mouse samples, find co-expressed genes, search samples by metadata, and download HDF5 expression matrices. For variant-level population genetics use gnomad-database; for pathway enrichment from gene lists use gget-genomic-databases (Enrichr).

Toolkit for genomic interval operations on BED, BAM, GFF, VCF files. Find overlapping regions, merge adjacent intervals, calculate coverage depth, extract FASTA sequences, find nearest features, and manipulate interval coordinates. Essential for ChIP-seq peak annotation, target region filtering, and genome arithmetic. Use tabix instead for indexed single-region queries; use deeptools for normalized bigWig coverage.

Automated cell type annotation for scRNA-seq data using pre-trained logistic regression models. CellTypist ships 45+ models covering immune cells, gut, lung, brain, fetal tissues, and cancer microenvironments. Inputs a normalized AnnData; outputs per-cell predicted labels, majority-vote cluster labels, and confidence scores. Use when you want fast, reproducible, reference-model-backed annotation without manual marker inspection.

Detect somatic copy number variants (CNVs) from WES, WGS, or targeted sequencing BAM files with CNVkit v0.9.x. Pipeline: calculate bin-level coverage in target/antitarget regions, normalize against a reference, segment copy ratios with CBS or HMM, call amplifications and deletions, generate scatter/diagram plots, estimate tumor purity and ploidy, and export to VCF/SEG. Both CLI and Python API (cnvlib) shown. Use GATK CNV instead for deep WGS with population-scale controls; use CNVkit for targeted or exome sequencing where antitarget bins are critical.

NGS analysis CLI toolkit for ChIP-seq, RNA-seq, ATAC-seq. BAM→bigWig conversion with normalization (RPGC, CPM, RPKM), sample correlation/PCA, heatmaps and profile plots around genomic features, enrichment fingerprints. For alignment use STAR/BWA; for peak calling use MACS2.

Differential expression analysis for bulk RNA-seq using R/Bioconductor DESeq2. Negative binomial GLM with empirical Bayes shrinkage, Wald and LRT tests, multi-factor designs, interaction terms, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap visualization. The R gold standard for DE analysis with native Bioconductor integration. Use pydeseq2-differential-expression for Python-based pipelines; use edgeR for TMM normalization.

European Nucleotide Archive (ENA) REST API access for genomic sequences, raw reads, assemblies, and annotations. Portal API search with query syntax, Browser API retrieval (XML/FASTA/EMBL), file reports for FASTQ/BAM download URLs, taxonomy queries, cross-references. For multi-database Python queries prefer bioservices; for NCBI-specific queries use pubmed-database or Biopython Entrez.

Query Ensembl REST API for gene/transcript/variant annotations across 300+ species. Retrieve gene info by symbol/ID, sequence, cross-references (HGNC, RefSeq, UniProt), variants, regulatory features, comparative genomics. For bulk local access use pyensembl; for pathway lookups use kegg-database or reactome-database.

ETE Toolkit (ETE3) is a Python environment for phylogenetic tree analysis, manipulation, and visualization. Parse Newick/NHX/PhyloXML trees, traverse and annotate nodes, render publication-quality figures with TreeStyle/NodeStyle, integrate NCBI taxonomy for taxon-aware operations, and run PhyloTree workflows for comparative genomics. Use for building species trees, gene family evolution analysis, and annotated tree figures.

All-in-one FASTQ quality control and adapter trimming tool. Automatically detects and removes Illumina adapters, filters low-quality reads, corrects paired-end overlaps, and generates HTML+JSON QC reports in a single fast pass. 3-10× faster than Trim Galore/Trimmomatic. Use as the first step before STAR, BWA-MEM2, or Salmon alignment in any NGS pipeline.

Query NCBI Gene via E-utilities for curated gene records across 1M+ taxa. Retrieve official gene symbols, aliases, RefSeq accessions, summary descriptions, genomic coordinates, GO annotations, and interaction data. Use for gene ID resolution, cross-species queries, and gene function summaries. For sequence retrieval use Ensembl; for expression data use geo-database.

Query gnomAD v4 population variant frequencies via GraphQL API. Retrieve allele counts and frequencies stratified by ancestry group (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID), gene-level constraint metrics (pLI, LOEUF, missense z-score), and read depth coverage. Identify variants with low population frequency or under evolutionary constraint. For clinical pathogenicity classifications use clinvar-database; for GWAS associations use gwas-database.

Gene set enrichment analysis (GSEA) and over-representation analysis (ORA) for RNA-seq and proteomics data. Wraps Enrichr API for ORA against MSigDB, KEGG, GO, and 200+ gene set databases; implements preranked GSEA for ranked gene lists from differential expression. Outputs enrichment tables and GSEA running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation of differential expression results.

Query the JASPAR 2024 TF binding profile database via REST API and pyJASPAR. Retrieve position frequency matrices (PFMs) and position weight matrices (PWMs) by TF name, JASPAR ID, species, or structural class. Scan DNA sequences for transcription factor binding sites (TFBS). Browse profiles by taxon (Homo sapiens, Mus musculus) or TF family (bHLH, zinc finger). Use for motif enrichment input, TFBS scanning, and regulatory sequence analysis. For ChIP-seq peak-based motif discovery use homer-motif-analysis; for regulatory variant scoring use regulomedb-database.

Query disease-gene-phenotype associations, entity details, and cross-species orthology from the Monarch Initiative knowledge graph REST API. Retrieve MONDO disease-to-gene and disease-to-phenotype mappings, HP phenotype profiles, and cross-species phenotype comparisons. Use for rare disease gene prioritization, phenotype-based candidate gene ranking, and building disease-phenotype networks. For GWAS associations use gwas-database; for clinical pathogenicity use clinvar-database.

Annotate prokaryotic genome assemblies (bacteria, archaea, viruses) with Prokka's BLAST/HMM-based pipeline. Identifies CDS, rRNA, tRNA, tmRNA, and signal peptides against Pfam, TIGRFAMs, and RefSeq databases. Produces GFF3, GenBank, protein FASTA, and TSV outputs. Use PGAP instead when submitting to NCBI GenBank; use Bakta for faster annotation with NCBI-compatible outputs on modern assemblies.

Bulk RNA-seq differential expression analysis with PyDESeq2. Load count matrices, normalize, fit negative binomial models, Wald test with BH-FDR correction, LFC shrinkage, volcano/MA plots. Use for two-group comparisons, multi-factor designs with batch correction, and multiple contrast testing.

Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences. Region queries, coverage/pileup analysis, variant filtering, read group extraction. Python wrapper for htslib with samtools/bcftools CLI access. For alignment pipelines use STAR/BWA; for variant calling use GATK/DeepVariant.

Query the EBI QuickGO REST API for Gene Ontology terms and protein GO annotations. Fetch GO term metadata by ID, search terms by keyword, retrieve ancestor/descendant hierarchies, and download GO annotations filtered by taxon ID, evidence code, and GO aspect. Use for GO term resolution, ontology traversal, and annotation-set retrieval before enrichment analysis. For enrichment analysis itself use gseapy-gene-enrichment; for protein function annotations use uniprot-protein-database.

Query the ReMap 2022 TF ChIP-seq binding peak database via REST API and BED file downloads. Retrieve all TF binding peaks overlapping a genomic region (chr:start-end), find TF peaks near a gene by name, list TFs available for a species, filter peaks by regulatory biotype (promoter, enhancer), and download peak BED files for a TF-cell type pair. Use for TF co-occupancy analysis, regulatory region annotation, and building TF binding atlases. For JASPAR motif matrices use jaspar-database; for ENCODE regulatory tracks use encode-database.

Command-line toolkit for SAM/BAM/CRAM alignment file manipulation. Sort, index, convert, filter, and QC sequencing alignments. Core commands: view (filter/convert), sort, index, flagstat, stats, depth, markdup, merge. Required for all NGS pipelines between alignment and variant calling or peak calling. Use pysam for Python-native BAM access; use deeptools for normalized coverage tracks.

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Guide to mandatory quality filtering of raw/unfiltered VCF files before computing summary statistics such as Ts/Tv ratio, variant counts, or allele frequency distributions. Covers detection of raw VCFs via FILTER column and QUAL distribution inspection, QUAL-based filtering with bcftools, interpretation of Ts/Tv ratios, and when NOT to filter. Essential reading before any variant-level QC task. Cross-references: bcftools-variant-manipulation for advanced filtering expressions, gatk-variant-calling for upstream caller configuration, samtools-bam-processing for alignment QC prior to variant calling.

Decision guide for finding or designing sgRNAs using a three-tiered strategy: (1) validated sequences from Addgene or literature, (2) pre-computed designs from the Broad Institute CRISPick database, (3) de novo design with CRISPOR/Benchling as a last resort. Covers PAM requirements for SpCas9, SaCas9, AsCas12a, and enAsCas12a; sgRNA quality metrics; application-specific targeting rules for knockout, CRISPRa, CRISPRi, base editing, and prime editing; and computational filtering criteria. Use when planning any CRISPR experiment and unsure which sgRNA source or design approach to use.

Parse and query the Human Metabolome Database (HMDB) local XML for metabolite information, chemical properties, biological context, disease associations, spectral data, and cross-database mapping. No public REST API — primary access via downloaded XML (~6 GB). For drug-focused queries use drugbank-database-access; for live compound lookups use pubchem-compound-search.

Mass spectrometry spectral matching and metabolite identification with matchms. Use for importing spectra (mzML, MGF, MSP, JSON), filtering/normalizing peaks, computing spectral similarity (cosine, modified cosine, fingerprint), building reproducible processing pipelines, and identifying unknown metabolites from spectral libraries. For full LC-MS/MS proteomics pipelines, use pyopenms instead.

MaxQuant + Perseus proteomics pipeline: configure and run MaxQuant for label-free quantification (LFQ) and SILAC; parse proteinGroups.txt in Python; filter contaminants/reverse decoys; log2-transform and median-normalize LFQ intensities; impute MNAR missing values; t-test with FDR correction; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo instrument-native processing; FragPipe/MSFragger for GPU-accelerated database search.

Mass spectrometry data processing with PyOpenMS. Use for LC-MS/MS proteomics and metabolomics workflows — mzML/mzXML file I/O, signal processing (smoothing, peak picking, centroiding), feature detection and linking across samples, peptide/protein identification with FDR control, untargeted metabolomics pipelines. For simple spectral matching and metabolite ID, use matchms instead.

LLM-driven hypothesis generation and testing on tabular datasets. Three methods: HypoGeniC (data-driven), HypoRefine (literature+data synergy), Union (mechanistic combination). Iterative refinement, Redis caching, multi-hypothesis inference. For manual hypothesis formulation use hypothesis-generation knowhow; for creative ideation use scientific-brainstorming.

Graph and network analysis toolkit: create, manipulate, and analyze complex networks. Four graph types (directed, undirected, multi-edge), centrality measures, shortest paths, community detection, graph generators, I/O (GraphML, GML, edge list, pandas, NumPy), visualization with matplotlib. For large-scale graphs (100K+ nodes) use igraph or graph-tool; for graph neural networks use PyG.

NeuroKit2 is a Python toolkit for neurophysiological signal processing. Process ECG (heart rate, HRV, R-peak detection), EEG (complexity, power spectral density), EMG (muscle activation onset), EDA/GSR (skin conductance, SCR decomposition), PPG (photoplethysmography), and RSP (respiration) signals. Simulate synthetic signals for testing. Alternatives: BioSPPy (older, less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP without biosignal abstraction).

pymoo is a Python framework for single- and multi-objective optimization using evolutionary algorithms. Define problems as vectorized objective functions and constraints, then solve with NSGA-II, NSGA-III, MOEA/D, genetic algorithms, or differential evolution. Analyze Pareto fronts, visualize trade-off surfaces, and customize operators and callbacks. Ideal for engineering design, hyperparameter search, process optimization, and any problem with multiple conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient-based), platypus (fewer algorithms), jMetalPy (Java-based, more algorithms).

Unified Python framework for extracellular electrophysiology. Load recordings from 20+ formats (SpikeGLX, OpenEphys, NWB, Intan, Maxwell, Blackrock), preprocess signals, run 10+ spike sorters (Kilosort4, SpykingCircus2, Tridesclous, MountainSort5) with a single API, compute quality metrics (SNR, ISI violations, firing rate, amplitude cutoff), compare sorter outputs, and export to NWB or Phy. Use for format-agnostic and multi-sorter workflows. For a Neuropixels-specific Kilosort4 pipeline with PSTH and population decoding, use neuropixels-analysis instead.

Use HuggingFace Transformers with biomedical language models for scientific NLP tasks. Load BioBERT, PubMedBERT, BioGPT, and BioMedLM for named entity recognition (genes, diseases, chemicals), relation extraction, question answering on biomedical literature, text classification, and abstract summarization. Covers model loading, tokenization of biomedical text, inference pipelines, and fine-tuning on domain-specific datasets. Alternatives: spaCy with en_core_sci_lg (rule-based NER), Stanza (Stanford NLP, biomedical models), NLTK (classical NLP).

General scientific figure quality checklist for generated plots. Covers visual inspection for overlapping labels, clipped text, missing axes/legends, empty plot areas, overcrowded data, and resolution/format best practices across journals.

Figure and image preparation guide for The Lancet. Covers resolution (300+ DPI at 120% size), file formats (PowerPoint, Word, SVG preferred), column widths (75/154 mm), Times New Roman font, and Lancet in-house redraw policy.

Figure and image preparation guide for Nature journal. Covers resolution (300+ DPI), file formats (AI, EPS, TIFF), RGB color mode, Helvetica/Arial fonts, lowercase panel labels, and image integrity requirements.

Figure and image preparation guide for PNAS. Covers resolution (300-1000 PPI by type), file formats (TIFF, EPS, PDF), strict RGB-only color mode, Arial/Helvetica fonts, italicized uppercase panel labels, and automated image screening.

Systematic strategies for searching, retrieving, and analyzing scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers the PICO framework for clinical question formulation, three-tiered search strategy (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH vocabulary, boolean query construction, and full-text extraction workflows. Consult this guide when planning a literature search, constructing database queries, or deciding which search tier to use for a given research question.