pku-yuangroup

@pku-yuangroup source repo

604 skills
0 followers
1 day ago last updated

▌
Bio Genome Intervals Gtf Gff Handling · pku-yuangroup bundle
Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

0 installs
▌
Bio Cfdna Preprocessing · pku-yuangroup bundle
Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.

0 installs
▌
Bio Long Read Sequencing Long Read Qc · pku-yuangroup bundle
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 goal. Covers why read-only Qscore is an uncalibrated posterior (real accuracy needs a reference BAM), why the sequencing_summary.txt is required for run-health metrics, intent-conditioned filtering (preserve long reads and small replicons for assembly, filter almost nothing for variant calling), the chimera/internal-adapter trap that fabricates SVs, and PacBio rq-based HiFi QC. Use when judging a long-read run, computing read N50 or percent identity, filtering reads before assembly or variant calling, comparing barcodes/runs, or reading run-health red flags.

0 installs
▌
Bio Machine Learning Model Validation · pku-yuangroup bundle
Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.

0 installs
▌
Bio Metabolomics Msdial Preprocessing · pku-yuangroup bundle
Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.

0 installs
▌
Bio Metabolomics Statistical Analysis · pku-yuangroup bundle
Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA/HCA for QC), permutation-validated PLS-DA/OPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch/Mann-Whitney/ANOVA/LMM with covariate adjustment), and dependence-aware multiple testing. Use when testing which metabolites differ, building or validating a discriminant model, choosing a scaling, or correcting many correlated tests. For sample-wise normalization/drift correction see metabolomics/normalization-qc; for ML classifiers and selection-inside-CV leakage see machine-learning/biomarker-discovery and machine-learning/model-validation; for pathway interpretation see metabolomics/pathway-mapping; for design/power/multiplicity regime see experimental-design/multiple-testing.

0 installs
▌
Bio Metagenomics Abundance · pku-yuangroup bundle
Turns shotgun classifier output into a defensible abundance table with Bracken Bayesian re-estimation, then compositional treatment (CLR, zero handling), library-size normalization, reference-frame differential abundance, and optional absolute quantification. Covers why a relative-abundance change is not a change, why Bracken read fractions and MetaPhlAn percentages are different physical quantities, the silent -r read-length bias, the genome-size confound no library-size method fixes, and the rarefaction debate. Use when estimating species abundance from a Kraken2 report, normalizing a community count table, choosing a compositional transform, or converting relative to absolute load. For classification see kraken-classification; for diversity/ordination/DA mechanics see metagenome-visualization.

0 installs
▌
Bio Metagenomics Functional Profiling · pku-yuangroup bundle
Profiles the functional potential of shotgun metagenomes with HUMAnN 3's tiered search (MetaPhlAn prescreen, Bowtie2 pangenome, translated DIAMOND vs UniRef), giving gene-family (RPK) and MetaCyc pathway abundances stratified by species. Covers why a metagenome measures potential not activity, why dropping UNMAPPED/UNINTEGRATED biases everything, why stratification is an estimate, coverage-vs-abundance and MinPath/gap-fill, UniRef90-vs-50 and biome database bias, and the assembly/eggNOG/dbCAN/antiSMASH alternatives. Use when obtaining pathway or gene-family abundances, regrouping to KO/EC/GO, normalizing functional tables, or choosing read-based vs assembly-based functional profiling. For AMR genes see amr-detection; for host-gene enrichment see pathway-analysis.

0 installs
▌
Bio Microbiome Differential Abundance · pku-yuangroup bundle
Tests which individual taxa differ between groups on an amplicon ASV/feature table (phyloseq) using compositionally-aware methods - ALDEx2 (Dirichlet-MC CLR, conservative), ANCOM-BC2/ANCOMBC (sampling-fraction bias correction, structural zeros, passed_ss, default p_adj_method=holm), MaAsLin2/MaAsLin3 (multivariable GLM, random effects, prevalence/abundance split), LinDA (CLR mixed-model regression), ZicoSeq (permutation FDR), LEfSe, and q2-composition ancombc. Covers why the hit list depends more on the DA tool than the biology (Nearing benchmark) so the deliverable is a CONSENSUS of >=2 tools, why a relative change is not absolute without a load anchor, the prevalence-filter knob, BH/FDR plus an effect-size floor, and why DESeq2/edgeR misfire here. Use when finding differentially abundant taxa, handling covariates or longitudinal designs, or choosing a method. Whole-community diversity -> diversity-analysis; shotgun DA -> metagenomics/metagenome-visualization; CoDA theory -> metagenomics/abundance-estimation

0 installs
▌
Bio Proteomics Differential Abundance · pku-yuangroup bundle
Tests for differentially abundant proteins between conditions with limma/DEqMS empirical-Bayes moderation, proDA/msqrob2/MSstats missingness modeling, and Python Welch+BH alternatives. Frames missing values as left-censored MNAR (model, do not impute), makes variance moderation the load-bearing step at n=3-5, and prefers feature/peptide-level testing. Use when identifying proteins with significant abundance changes between experimental groups. Summarization and normalization mechanics are proteomics/quantification; volcano and MA plots are data-visualization/volcano-and-ma-plots; pathway enrichment of the hit list is pathway-analysis/go-enrichment.

0 installs
▌
Bio Proteomics Peptide Identification · pku-yuangroup bundle
Peptide-spectrum matching from MS/MS with target-decoy FDR control, framing identification confidence as a property of a ranked list (q-value/PEP) rather than a raw engine score (XCorr, hyperscore, Andromeda, SpecEValue). Covers sequence-database search engines (Comet, MS-GF+, MSFragger, Sage, MaxQuant, MetaMorpheus), concatenated vs separate target-decoy competition, PEP vs q-value, the multi-level FDR cascade, open/mass-tolerant search, rescoring (Percolator, mokapot, MS2Rescore), and pyOpenMS SimpleSearchEngineAlgorithm + FalseDiscoveryRate. Use when identifying peptides from tandem mass spectra and deciding what FDR threshold to act on. Protein grouping and protein-level FDR are protein-inference; PTM site localization is ptm-analysis; DIA peptide-centric scoring is dia-analysis; intensity quant is quantification.

0 installs
▌
Bio Codon Usage · pku-yuangroup bundle
Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterologous expression, or studying synonymous codon selection.

0 installs
▌
Bio Seq Objects · pku-yuangroup bundle
Create and manipulate Seq, MutableSeq, and SeqRecord objects using Biopython. Use when creating sequences from strings, modifying sequence data in-place, building annotated records for file output, or debugging post-1.78 Bio.Alphabet and immutability errors.

0 installs
▌
Bio Small Rna Seq Smrna Preprocessing · pku-yuangroup bundle
Trims kit-specific 3' adapters, strips UMIs or 4N degenerate ends, size-selects, and collapses small RNA-seq reads (miRNA, piRNA, tRF) with cutadapt or fastp. Use when choosing the kit's 3' adapter; setting the size window (18-26 nt miRNA vs 24-32 nt piRNA); deciding whether a library carries a true UMI (QIAseq) versus a 4N debiasing spacer (NEXTflex); reading the read-length histogram to judge library quality; or deciding whether to collapse identical reads before mapping.

0 installs
▌
Bio Small Rna Seq Trf Pirna Profiling · pku-yuangroup bundle
Profiles non-miRNA small RNAs - tRNA-derived fragments (tRFs/tsRNAs), piRNAs, and rRNA/snoRNA-derived species - with MINTmap, unitas, SPORTS, and proTRAC. Use when annotating all small-RNA classes in a library; quantifying tRFs at locus resolution where tRNA loci are redundant (exclusive vs ambiguous); testing the piRNA ping-pong signature; deciding whether a species is a processed functional RNA or a degradation fragment; or judging whether the prep could even capture 5'-OH/cyclic-phosphate classes.

0 installs
▌
Bio Systems Biology Gene Essentiality · pku-yuangroup bundle
Performs in-silico single and double gene deletions, condition-dependent essentiality, and synthetic-lethality screens on genome-scale metabolic models with COBRApy, evaluating gene-protein-reaction rules and comparing FBA re-optimization against MOMA/ROOM minimal-adjustment. Use when predicting essential genes, finding synthetic-lethal pairs for drug targets, choosing a growth cutoff, deciding FBA vs MOMA vs ROOM for a knockout, making essentiality medium-specific to match an experiment, or validating predictions against Keio/Tn-seq/CRISPR screens with MCC.

0 installs
▌
Bio Workflow Management Cwl Workflows · pku-yuangroup bundle
Authors portable, strongly-typed bioinformatics pipelines in the Common Workflow Language (CWL v1.2) as CommandLineTool/Workflow/ExpressionTool documents, validated with cwltool and run at scale on Toil/Arvados/Calrissian. Use when deciding CWL (portability/provenance/regulated) vs Nextflow/WDL/Snakemake; declaring secondaryFiles for indexed companions (.bai/.fai/.dict/.tbi and the caret rule); putting resources/containers under requirements (must-hold) vs hints (advisory) to avoid silent OOM; choosing scatterMethod (dotproduct vs flat_/nested_crossproduct); preferring $(...) parameter refs over ${...} JavaScript for portability; pinning DockerRequirement images; or emitting a CWLProv provenance object for audited/clinical settings.

0 installs
▌
Bio Workflow Management Wdl Workflows · pku-yuangroup bundle
Authors bioinformatics pipelines in WDL (Workflow Description Language) run by Cromwell or miniwdl, targeting the GATK/Broad and Terra/AnVIL/BioData Catalyst cloud ecosystem, with tasks, workflows, scatter-gather parallelism, structs, and a runtime block that sizes the cloud VM. Use when deciding to target Terra/AnVIL/GATK/WARP (chosen for the ecosystem, not the language); sizing runtime disks dynamically for a fresh-per-task cloud VM (ceil(size(f)*factor)+buffer); choosing preemptible vs on-demand VMs by task length and idempotency; picking Cromwell (production, cloud, call-caching) vs miniwdl (local dev, miniwdl check linting, readable errors); enabling and debugging call-caching silent-miss modes; pinning Docker by digest for reproducibility and cache stability; or scattering an array for parallel fan-out.

0 installs
▌
Bio Workflows Clinical Trial Pipeline · pku-yuangroup bundle
End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.

2 installs
▌
Bio Workflows Crispr Editing Pipeline · pku-yuangroup bundle
Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.

0 installs
▌
Bio Alignment Indexing · pku-yuangroup bundle
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.

0 installs
▌
Bio Duplicate Handling · pku-yuangroup bundle
Mark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant calling or when duplicate reads would bias analysis.

0 installs
▌
Bio Sashimi Plots · pku-yuangroup bundle
Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/MntJULiP/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

0 installs
▌
Bio Causal Genomics Mediation Analysis · pku-yuangroup bundle
Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.

0 installs
▌
Bio Generative Design · pku-yuangroup bundle
Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.

0 installs
▌
Bio Scaffold Analysis · pku-yuangroup bundle
Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.

0 installs
▌
Bio Virtual Screening · pku-yuangroup bundle
Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.

0 installs
▌
Bio Comparative Genomics Hgt Detection · pku-yuangroup bundle
Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.

0 installs
▌
Bio Ortholog Inference · pku-yuangroup bundle
Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when batch-mapping gene IDs across species, or when comparing the resources for consensus calls. Encodes confidence-level semantics, 1:1 vs 1:many vs many:many, HomoloGene deprecation, and when to defect to de novo computation.

0 installs
▌
Bio Differential Expression De Results · pku-yuangroup bundle
Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

0 installs
▌
Bio Epitranscriptomics M6anet Analysis · pku-yuangroup bundle
Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.

0 installs
▌
Bio Experimental Design Power Analysis · pku-yuangroup bundle
Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

0 installs
▌
Bio Genome Annotation Ncrna Annotation · pku-yuangroup bundle
Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.

0 installs
▌
Bio Genome Assembly Assembly Polishing · pku-yuangroup bundle
Decides whether and how to polish a draft genome assembly to raise consensus accuracy (QV) with read-type-matched tools - Racon and medaka (ONT consensus), dorado polish, Polypolish and pypolca (Illumina, repeat-aware), Pilon (legacy short-read), NextPolish/NextPolish2, Hapo-G (haplotype-aware), ntEdit, and DeepPolisher/PEPPER-Margin-DeepVariant for human. Covers the do-not-polish-HiFi rule, the medaka basecaller-model footgun, held-out Merqury QV as the only honest stop signal, and the haplotype-collapse trap. Use when correcting homopolymer indels or residual SNPs in a long-read assembly, deciding if a HiFi assembly needs polishing, or choosing an ONT vs hybrid vs short-read polishing chain.

0 installs
▌
Bio Genome Assembly Long Read Assembly · pku-yuangroup bundle
Assembles genomes de novo from noisy long reads (Oxford Nanopore R9/R10/Dorado, PacBio CLR) with Flye (repeat graph), Canu (correct-trim-assemble OLC), NextDenovo, Shasta, Raven, wtdbg2, or miniasm, and reconciles bacterial assemblies into a consensus with Trycycler/Autocycler. Covers matching the input flag to the basecaller era (--nano-hq vs --nano-raw), why a raw long-read assembly is contiguous but low-QV and not finished until polished, haplotig false-duplication and purge_dups, coverage and read-N50 as non-substitutable inputs, and mid-read adapter de-chimerization. Use when assembling a bacterial or eukaryotic genome from ONT or PacBio noisy reads, choosing a long-read assembler, or diagnosing an over-collapsed or duplicated assembly. For PacBio HiFi use hifi-assembly instead.

0 installs
▌
Bio Genome Intervals Bedgraph Handling · pku-yuangroup bundle
Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.

0 installs
▌
Bio Genome Intervals Coverage Analysis · pku-yuangroup bundle
Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.

0 installs
▌
Bio Hi C Analysis Compartment Analysis · pku-yuangroup bundle
Detects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing with bioframe.frac_gc, resolution choice (100kb-1Mb), saddle plots and saddle_strength for compartmentalization strength, the cohesin-loss-strengthens-compartments result, subcompartments (SNIPER/Calder/dcHiC), and cross-condition compartment switching. Use when calling A/B compartments, computing E1/eigenvectors, phasing the eigenvector, building saddle plots, choosing a compartment resolution, quantifying compartment strength, or comparing compartmentalization across conditions.

0 installs
▌
Bio Imaging Mass Cytometry Phenotyping · pku-yuangroup bundle
Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

0 installs
▌
Bio Machine Learning Omics Classifiers · pku-yuangroup bundle
Builds diagnostic and prognostic classifiers on omics feature matrices with regularized logistic regression, random forest, and gradient-boosted trees, handling the p>>n regime, batch shortcut learning, class imbalance, and probability calibration. Use when building a classifier from expression, methylation, or variant data, choosing an algorithm for high-dimensional small-n data, or diagnosing a suspiciously perfect AUC. For unbiased evaluation see machine-learning/model-validation; for feature selection see machine-learning/biomarker-discovery; for time-to-event outcomes see machine-learning/survival-analysis.

0 installs
▌
Bio Machine Learning Survival Analysis · pku-yuangroup bundle
Builds and validates predictive time-to-event models on clinical and omics data with penalized Cox, random survival forests, gradient-boosted and deep survival models, and prediction-grade evaluation (Uno's C, time-dependent AUC, integrated Brier, calibration, competing risks). Use when building an individualized risk predictor or prognostic omics signature, choosing a survival model, or evaluating one beyond the C-index. For Kaplan-Meier, log-rank, and classical Cox hazard-ratio inference in a trial see clinical-biostatistics/survival-analysis.

0 installs
▌
Bio Metabolomics Metabolite Annotation · pku-yuangroup bundle
Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.

0 installs
▌
Bio Metagenomics Kraken · pku-yuangroup bundle
Classifies shotgun metagenomic reads to taxa with Kraken2's minimizer/LCA matching against a chosen reference database, then hands off to Bracken for abundance re-estimation. Covers why the database (not the algorithm) decides what can be detected, the --confidence and --minimum-hit-groups precision levers, unique-minimizer false-positive control, host-read removal, and why raw Kraken2 read counts are not abundances. Use when profiling who-is-there from shotgun reads, choosing a Kraken2 database, setting a confidence threshold, controlling false positives, or feeding reports to Bracken. For marker-gene profiling see metaphlan-profiling; for abundance mechanics see abundance-estimation; for assembly/MAG recovery see genome-assembly/metagenome-assembly.

0 installs
▌
Bio Methylation Dmr Detection · pku-yuangroup bundle
Detects differentially methylated regions (DMRs) from short-read bisulfite (WGBS/RRBS), array, and long-read methylation count tables using dmrseq (permutation region-FDR over the region selection), DSS callDMR (beta-binomial), methylKit tiles, bsseq BSmooth, DMRcate Gaussian-kernel smoothing, metilene, and comb-p. Covers why a DMR is DEFINED by arbitrary thresholds (min-CpGs, max-gap, delta-beta, q) and a smoothing bandwidth, why selecting extreme runs of CpGs then testing them on the same data is post-selection inference, why region q-values are not comparable across tools, and a single-sample domain-segmentation section (PMD, UMR/LMR, MethylSeekR, solo-WCGW) that must run before focal calling on cancer/aging genomes. Use when calling region-level methylation differences, choosing a DMR caller, controlling region-level FDR, or segmenting megabase methylation domains. For per-site testing see differential-cpg-testing; for the methylKit object model see methylkit-analysis.

0 installs
▌
Bio Pathway Reactome · pku-yuangroup bundle
Tests a gene list or ranked gene vector for over-representation or coordinated shifts in Reactome's curated, peer-reviewed, reaction-level pathways using ReactomePA's enrichPathway (ORA) and gsePathway (GSEA), reading the local reactome.db so a run is reproducible given the Bioconductor release. Covers why Reactome's atomic unit is the REACTION and pathways are nested containers so a parent and child enrich on the same genes and double-count one signal, why only human is curated and every other species is orthology-inferred, why enrichPathway has NO keyType argument and returns nothing unless genes are ENTREZ (bitr first), and why viewPathway draws a LOCAL reaction network from a pathway NAME. Use when reaction-level granularity, peer-reviewed curation, or an offline-reproducible database is wanted; for comparative multi-sample or multi-omics analysis use ReactomeGSA. The DE list comes from differential-expression; plots from enrichment-visualization.

0 installs
▌
Bio Rna Quantification Count Matrix Qc · pku-yuangroup bundle
Quality control and exploration of RNA-seq count matrices before differential expression. Use when checking library sizes and composition, choosing VST vs rlog for visualization, running PCA and sample correlation, detecting outliers with Cook's distance, deciding how to handle known vs unknown batch effects, screening for sample swaps, or judging whether a sample or design is too compromised to test.

0 installs
▌
Bio Rna Structure Covariation Analysis · pku-yuangroup bundle
Tests whether a proposed or predicted RNA secondary structure is supported by evolutionary covariation using R-scape, which scores compensatory substitutions against a phylogeny-aware null and estimates the statistical power of the alignment. Use when validating a conserved-structure claim before trusting it (the test that found no support for HOTAIR/Xist/SRA lncRNA structures); separating real covariation from phylogenetic correlation; deciding whether an alignment even has the power to test structure; or building a covariation-supported consensus (CaCoFold) to seed a covariance model or folding.

0 installs
▌
Bio Motif Search · pku-yuangroup bundle
Find sequence motifs, degenerate IUPAC patterns, and transcription-factor binding sites in DNA/RNA using Biopython and regex, including position weight matrix (PWM/PSSM) scoring. Use when locating regulatory elements, counting overlapping motif occurrences, scanning for binding-site matches above a significance threshold, or reading motif matrices from JASPAR/MEME/TRANSFAC files. For restriction enzyme sites, use restriction-analysis/restriction-sites.

0 installs
▌
Bio Single Cell Differential Abundance · pku-yuangroup bundle
Test whether cell-type proportions or composition changed between conditions in single-cell data using Milo (miloR), scCODA, sccomp, and propeller. Use when comparing cell-type proportions / composition between conditions, asking which populations expanded or contracted with treatment or disease, running neighborhood-level (cluster-free) abundance testing, or guarding against compositional shifts that masquerade as differential expression.

0 installs
▌
Bio Single Cell Hashing Demultiplexing · pku-yuangroup bundle
Assign cells to their sample of origin from cell or nucleus hashing (CITE-seq HTOs, MULTI-seq lipid/cholesterol tags, CellPlex CMOs) and call cross-sample doublets using Seurat HTODemux/MULTIseqDemux, hashsolo, demuxEM, GMM-Demux, and demuxmix. Use when assigning pooled hashed cells back to their sample, calling cross-sample doublets from HTO counts, choosing a demultiplexing method, deciding between hashtag and genetic demultiplexing, or rescuing an oversized Negative pile from weak HTO staining or ambient spillover.

0 installs
▌
Bio Single Cell Multimodal Integration · pku-yuangroup bundle
Integrate multimodal single-cell data (CITE-seq RNA+protein, 10x Multiome RNA+ATAC, unpaired/diagonal RNA+ATAC) and choose the right joint method. Use when classifying an integration task by anchor structure (paired vs unpaired), denoising CITE-seq ADT background before joint embedding, picking between WNN, totalVI, MultiVI, MOFA+, GLUE, or Seurat v5 bridge integration, or diagnosing why a modality dominates a joint clustering.

0 installs
▌
Bio Tcr Bcr Analysis Vdjtools Analysis · pku-yuangroup bundle
Computes immune-repertoire diversity, clonal structure, overlap, and segment usage from TCR/BCR clonotype tables with VDJtools (immunarch as the modern R alternative). Use when deciding which diversity estimator answers a question (q=0 observed richness/chao1/chaoE, q=1 shannonWienerIndex, q=2 inverseSimpson as a Hill profile); normalizing sequencing depth before any cross-sample claim (DownSample or the resampled CalcDiversityStats table); choosing an overlap metric (depth-robust MorisitaHorn/F2 vs depth-biased Jaccard/public counts) and a clonotype match key (-i nt/aa, +/-V/J); summarizing clonality as 1 - normalizedShannonWienerIndex; reading spectratype and V-J usage under primer bias; interpreting public clonotypes; and choosing VDJtools (stable Java CLI) vs immunarch (active tidy R).

0 installs
▌
Bio Variant Annotation · pku-yuangroup bundle
Annotates VCF variants with functional consequences, population frequencies, and pathogenicity scores using bcftools annotate/csq, Ensembl VEP, SnpEff, and ANNOVAR. Use when deciding which annotation engine and version to pin, which transcript set to report on (RefSeq vs Ensembl vs MANE Select/Plus Clinical, and why VEP --pick is dangerous clinically), how to reconcile HGVS 3'-shifting with VCF left-alignment, which consequence plus NMD status governs PVS1 eligibility, which single calibrated predictor to use for PP3/BP4 (REVEL, AlphaMissense, CADD, SpliceAI deltas), or how to read gnomAD v2/v3/v4 grpmax filtering allele frequency instead of one global AF cutoff. Not for ACMG combining rules or final classification (see variant-calling/clinical-interpretation).

0 installs
▌
Bio Workflows Causal Genomics Pipeline · pku-yuangroup bundle
End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

0 installs
▌
Bio Workflows Genome Assembly Pipeline · pku-yuangroup bundle
Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it. Profiles the genome first (k-mer spectrum -> size, heterozygosity, ploidy), QCs reads, chooses an assembly path by data type (SPAdes for Illumina, Flye for noisy long reads, hifiasm for HiFi, metaFlye for communities), polishes only when needed, decontaminates, scaffolds with Hi-C, and finishes with three-axis QC (contiguity + completeness + correctness). Use when assembling a genome from raw reads and deciding which assembler, whether to polish, and how to prove the result is good.

0 installs
▌
Bio Workflows Somatic Variant Pipeline · pku-yuangroup bundle
Chains a somatic (tumor-normal) SNV/indel and structural-variant pipeline end to end with GATK Mutect2 (or Strelka2), wiring the somatic-specific machinery - panel-of-normals and gnomAD germline-resource priors, GetPileupSummaries/CalculateContamination, and LearnReadOrientationModel FFPE/oxoG orientation-bias filtering fed into FilterMutectCalls. Use when calling somatic mutations from a tumor-normal pair (or tumor-only with PoN caveats), deciding which artifact filter removes which class of false positive, reasoning about VAF/purity/ploidy and clonal-vs-subclonal detection, adding somatic SV/CNV or TMB/MSI/signatures, or routing variants to AMP/ASCO/CAP tier and oncogenicity interpretation (never germline ACMG).

0 installs
▌
Bio Alignment Filtering · pku-yuangroup bundle
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.

0 installs
▌
Bio Atac Seq Differential Accessibility · pku-yuangroup bundle
Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.

0 installs
▌
Bio Causal Genomics Genetic Correlation · pku-yuangroup bundle
Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

0 installs
▌
Bio Clinical Databases Pharmacogenomics · pku-yuangroup bundle
Queries PharmGKB / CPIC / DPWG for drug-gene interactions; calls CYP2D6/CYP2C9/CYP2C19/DPYD/TPMT/NUDT15/UGT1A1/SLCO1B1 star alleles and phenotype with PharmCAT, Cyrius (CYP2D6 structural variants), Aldy, Stargazer; applies Caudle 2020 activity-score translation. Use when implementing pharmacogenomic-guided prescribing, applying CPIC vs DPWG guidance, screening HLA risk alleles for ICI / antiepileptics / abacavir, or interpreting compound TPMT+NUDT15 thiopurine risk.

0 installs
▌
Bio Copy Number Copy Ratio Segmentation · pku-yuangroup bundle
Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

0 installs
▌
Bio Crispr Screens Perturb Seq Analysis · pku-yuangroup bundle
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

0 installs
▌
Bio Epitranscriptomics M6a Differential · pku-yuangroup bundle
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).

0 installs
▌
Bio Epitranscriptomics M6a Peak Calling · pku-yuangroup bundle
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.

0 installs
▌
Bio Genome Annotation Repeat Annotation · pku-yuangroup bundle
Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.

0 installs
▌
Bio Genome Assembly Metagenome Assembly · pku-yuangroup bundle
Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.

0 installs
▌
Bio Genome Assembly Short Read Assembly · pku-yuangroup bundle
Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

0 installs
▌
Bio Analytical Validation · pku-yuangroup bundle
Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.

0 installs
▌
Bio Metagenomics Contamination Controls · pku-yuangroup bundle
Cleans a shotgun metagenome of everything that is not the target community before profiling - host-read depletion (Hostile, bowtie2/T2T-CHM13), reagent/kitome contamination control with blanks and decontam, mock-community validation, and depth-adequacy checks (Nonpareil). Covers why a metagenomic result is a position in a choice-chain rather than a direct observation, why extraction is the experiment, why a low-biomass community can be entirely kitome, why absence means not-detectable-by-this-chain, and why a confident classifier call can still be wrong when the reference is contaminated. Use when designing controls, removing host reads, identifying reagent contaminants, validating with mocks, or judging whether a low-biomass result is real. For adapter/quality trimming see read-qc; for MAG-level decontamination see genome-assembly/metagenome-assembly.

0 installs
▌
Bio Phasing Imputation Reference Panels · pku-yuangroup bundle
Selects and prepares the reference panel that phasing/imputation copies haplotypes from (1000 Genomes, HRC, TOPMed, HGDP+1kGP/gnomAD, CAAPA), matching panel ancestry to the target, reconciling genome build and chromosome naming, and running the strand/allele harmonization gate. Covers why ancestry-match beats panel size (imputation can only copy haplotypes the panel contains), why palindromic A/T and C/G SNPs flip strand without erroring, why liftover is a strand-flip generator in between-build inverted regions, that HRC is SNP-only and TOPMed is never downloadable (governance can override accuracy), and panel formats (msav, bref3, imp5). Use when choosing a panel for a target ancestry, preparing or converting a panel, aligning study data, or deciding between downloadable and server-only panels. Phasing is haplotype-phasing; imputation is genotype-imputation; PCA for ancestry is population-genetics/population-structure; HLA panels are clinical-databases/hla-typing.

0 installs
▌
Bio Phylo Distance Calculations · pku-yuangroup bundle
Build model-corrected evolutionary distance matrices and distance trees (NJ, BIONJ, FastME, UPGMA) with Biopython Bio.Phylo plus R ape/phangorn/FastME. Covers why a distance is a model-corrected estimate of substitutions per site that undercounts raw because of multiple/back/parallel hits (saturation); why the matrix discards the per-site information ML keeps; the LogDet/paralinear fix for compositional heterogeneity; the UPGMA molecular-clock trap; and the Bio.Phylo landmine that DistanceCalculator offers only identity/matrix distances, not JC/K80/TN93. Use when computing a distance matrix, building a fast NJ/FastME tree, seeding an ML search, barcoding, or testing substitution saturation before a deep tree. Routes ML and starting-tree work to modern-tree-inference, alignment quality to alignment/alignment-io, and tree I/O to tree-io.

0 installs
▌
Bio Phylo Modern Tree Inference · pku-yuangroup bundle
Infers maximum-likelihood phylogenetic trees with IQ-TREE2 and RAxML-NG -- model selection (ModelFinder), branch support (UFBoot2, SH-aLRT), concordance factors (gCF/sCF), partitioning, topology tests, and long-branch-attraction control. Covers why an ML tree inherits every flaw of the assumed model and the fixed alignment, why reported support measures repeatability under resampling and not correctness, why UFBoot uses a >=95 cutoff and not the bootstrap-70 rule, and why a node with UFBoot 100 but gCF ~35 is essentially unresolved ILS rather than a clade. Use when inferring an ML tree, selecting a substitution or partition model, choosing or interpreting support measures, testing an a-priori topology, or diagnosing LBA. Routes model-free distance trees to distance-calculations, posteriors to bayesian-inference, and species trees under ILS to species-trees.

0 installs
▌
Bio Temporal Genomics Circadian Rhythms · pku-yuangroup bundle
Tests and estimates rhythmicity at a PRE-SPECIFIED period (canonically 24h) in time-series omics using cosinor regression (CosinorPy), JTK_CYCLE/ARSER/Lomb-Scargle meta-analysis (MetaCycle meta2d), and non-parametric tests for asymmetric waveforms (RAIN, DiscoRhythm); estimates phase (acrophase), amplitude, and MESOR, and controls FDR with an effect-size (rAMP) filter against over-detection. Use when testing for 24-hour or other known-period oscillations in a single condition (circadian, feeding-fasting, or light-dark experiments) and estimating their phase/amplitude. Not for unknown-period discovery (see temporal-genomics/periodicity-detection) or comparing rhythms between conditions (see temporal-genomics/differential-rhythmicity).

0 installs
▌
Bio Consensus Sequences · pku-yuangroup bundle
Generate consensus FASTA sequences by applying VCF variants onto a reference with bcftools consensus, or build viral/amplicon consensus with iVar. Use when reconstructing a sample-specific reference or haplotype, deciding -H haplotype vs IUPAC vs all-ALT projection, masking no-coverage sites so a consensus does not manufacture false reference calls, or setting iVar min-depth/min-frequency policy for surveillance genomes.

0 installs
▌
Bio Alignment Validation · pku-yuangroup bundle
Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.

0 installs
▌
Bio Reference Operations · pku-yuangroup bundle
Generate consensus sequences and manage reference files using samtools. Use when creating consensus from alignments, indexing references, or creating sequence dictionaries.

0 installs
▌
Bio Causal Genomics Pleiotropy Detection · pku-yuangroup bundle
Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.

0 installs
▌
Bio Substructure Search · pku-yuangroup bundle
Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.

0 installs
▌
Bio Clinical Databases Myvariant Queries · pku-yuangroup bundle
Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.

0 installs
▌
Bio Crispr Screens Base Editing Analysis · pku-yuangroup bundle
Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

0 installs
▌
Bio Crispr Screens Combinatorial Screens · pku-yuangroup bundle
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

0 installs
▌
Bio Crispr Screens Prime Editing Screens · pku-yuangroup bundle
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

0 installs
▌
Bio Differential Expression Edger Basics · pku-yuangroup bundle
Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

0 installs
▌
Bio Experimental Design Multiple Testing · pku-yuangroup bundle
Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.

0 installs
▌
Bio Flow Cytometry Differential Analysis · pku-yuangroup bundle
Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.

0 installs
▌
Bio Genome Intervals Interval Arithmetic · pku-yuangroup bundle
Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

0 installs
▌
Bio Immunoinformatics Epitope Prediction · pku-yuangroup bundle
Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

0 installs
▌
Bio Long Read Sequencing Clair3 Variants · pku-yuangroup bundle
Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification. Covers why the model string is the experiment (no auto-detection, silent degradation on mismatch), why ONT homopolymer/STR indels are the residual error whole-genome F1 hides, and the somatic/trio/RNA boundary to the ClairS/Clair3-Trio family. Use when calling germline SNVs/indels from ONT or HiFi BAMs, choosing a Clair3 model, phasing variants, or benchmarking long-read calls.

0 installs
▌
Bio Long Read Sequencing Isoseq Analysis · pku-yuangroup bundle
Discovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-split step, orthogonal CAGE/poly-A/short-read-junction validation, and why long-read isoform quantification needs EM. Use when building a full-length isoform catalog, classifying/filtering long-read transcripts, running Iso-Seq or ONT cDNA/dRNA analysis, or judging novel-isoform reliability.

0 installs
▌
Bio Machine Learning Biomarker Discovery · pku-yuangroup bundle
Selects biomarker features from high-dimensional omics data using Boruta all-relevant selection, mRMR, LASSO/elastic-net, and stability selection, while controlling the leakage, irreproducibility, and correlated-feature traps that make most published signatures fail to replicate. Use when identifying candidate biomarkers, deciding between an all-relevant and a minimal-optimal selector, or judging whether a selected gene set is reproducible. For unbiased performance estimation of the resulting model see machine-learning/model-validation; for interpreting a trained model see machine-learning/prediction-explanation.

0 installs
▌
Bio Phasing Imputation Haplotype Phasing · pku-yuangroup bundle
Estimates haplotype phase from population linkage disequilibrium with SHAPEIT5, SHAPEIT4, Eagle2, or Beagle - turning unphased genotypes (0/1) into phased haplotypes (0|1) for imputation input, compound-heterozygote calls, HLA typing, or population genetics. Covers why statistical phase is an INFERENCE (not a measurement) whose error concentrates at rare variants, why a genome-wide switch-error rate hides catastrophic rare-variant error and must be reported MAC-stratified, the SHAPEIT5 common-scaffold-then-rare design (phase_common, ligate, phase_rare, switch), reference-based vs within-cohort phasing, the build-matched genetic map, chrX male-haploid handling, and the switch-vs-flip-vs-Hamming distinction. Use when phasing genotypes before imputation, for compound-het/ASE/HLA, or benchmarking against trios. Read-backed / molecular phasing (long reads, Hi-C) is long-read-sequencing/haplotype-phasing; panel choice is reference-panels; imputation is genotype-imputation.

0 installs
▌
Bio Rna Quantification Tximport Workflow · pku-yuangroup bundle
Import transcript-level quantifications from Salmon/kallisto/RSEM into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when summarizing transcript abundances to gene counts with the correct length offset, choosing a countsFromAbundance mode (full-length vs 3'-tag vs DTU), resolving transcript-ID version mismatches, or handing off to DESeq2/edgeR without double-applying the offset.

0 installs
▌
Bio Single Cell Metabolite Communication · pku-yuangroup bundle
Infers metabolite-mediated cell-cell communication from scRNA-seq by scoring enzyme-to-sensor pairs (MEBOCOST), with metabolic flux (scFEA), FBA state (Compass), and neurotransmitter (NeuronChat) alternatives. Use when studying metabolic crosstalk between cell types, predicting metabolite secretion and sensing, or deciding which metabolic-communication method fits and how speculative the result is.

0 installs
▌
Bio Gatk Variant Calling · pku-yuangroup bundle
Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).

0 installs
▌
Bio Workflows Genome Annotation Pipeline · pku-yuangroup bundle
Orchestrates genome annotation from assembled contigs to functional annotation, forking prokaryotic (Bakta one-step, genetic-code table from GTDB-Tk) vs eukaryotic (RepeatMask -> BRAKER3 -> functional -> ncRNA), then eggNOG/InterProScan functional assignment and Infernal/tRNAscan ncRNA. Use when committing the pro-vs-eukaryotic path and the genetic-code table from taxonomy (never guessing), annotating ONLY a decontaminated QC-passed assembly (CheckM2 before prokaryotic annotation is non-negotiable), committing the evidence set (RNA-seq + protein drives BRAKER3 training), soft-masking with a curated repeat library before gene prediction, or pinning the tool + DB version for any pangenome comparison. Hands mechanism to the genome-annotation component skills; not a re-teach of any single step.

0 installs
▌
Bio Conformer Generation · pku-yuangroup bundle
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

0 installs
▌
Bio Chemoinformatics Ml Docking Rescoring · pku-yuangroup bundle
Version Compatibility

0 installs
▌
Bio Reaction Enumeration · pku-yuangroup bundle
Enumerates virtual chemical libraries via reaction SMARTS transformations using RDKit and reaction templates, with explicit handling of atom mapping, RDChiral template extraction, product validation, RECAP/BRICS fragmentation, R-group decomposition, matched molecular pair analysis (MMPA), and Free-Wilson analysis. Use when generating combinatorial libraries from building blocks, enumerating analog series, deriving structure-activity rules, or extracting transformations from reaction data.

0 installs
▌
Bio Similarity Searching · pku-yuangroup bundle
Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, MHFP6 LSH forest, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.

0 installs
▌
Bio Chipseq Chromatin State Segmentation · pku-yuangroup bundle
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

0 installs

604 published skills · page 4 of 7