Research

Chooses a bulk multi-omics integration strategy before any tool runs by mapping the biological question (subtype discovery, shared axis of variation, predictive signature, pairwise correlation) to a method class, naming the sample correspondence (paired-vertical, horizontal, mosaic, diagonal), enforcing the n<<p discipline that makes a held-out cohort the endpoint instead of in-cohort cross-validation, and running the per-view variance-imbalance diagnostic. Covers the early/mixed/intermediate...

Assigns taxonomy to amplicon ASVs/OTUs (16S, ITS, 18S) with a classifier conditioned on a reference database and primer region - DADA2 assignTaxonomy + addSpecies (RDP naive Bayes), DECIPHER IDTAXA, and QIIME2 q2-feature-classifier (classify-sklearn naive Bayes, classify-consensus-vsearch alignment-consensus). Covers region-specific training (extract-reads, fit-classifier-naive-bayes), why a full-length classifier fabricates calls on a V4 read, the scikit-learn version-pinning trap on pre-tra...

Predicts community functional POTENTIAL from 16S/ITS amplicon ASVs with PICRUSt2 (or q2-picrust2) by phylogenetic interpolation of reference-genome gene content - EPA-ng placement, gappa, castor hidden-state prediction of KO/EC/Pfam copy number, 16S copy-number normalization, and MinPath MetaCyc/KEGG pathways - gated by the NSTI quality index. Covers why predicted function is taxonomy re-encoded (never measured gene content and never activity), the mandatory NSTI report (--max_nsti 2 silently...

Alpha and beta diversity of an amplicon (16S/ITS) ASV/OTU community table - observed features, Shannon, Pielou evenness, Faith PD, Bray-Curtis, Jaccard, weighted/unweighted/generalized UniFrac, Aitchison/RPCA - via QIIME2 core-metrics-phylogenetic, phyloseq/vegan, and scikit-bio. Covers the three knobs that set the answer before it is seen (rarefaction sampling depth, the tree, the metric), why core-metrics silently deletes samples below the sampling depth, why de novo trees lose to SEPP frag...

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 ...

Infers exact amplicon sequence variants (ASVs) from demultiplexed 16S rRNA or ITS amplicon FASTQ with DADA2 - removing primers with cutadapt (--discard-untrimmed), learning a per-run error model (filterAndTrim -> learnErrors -> dada -> mergePairs), merging run-level tables with mergeSequenceTables, then one removeBimeraDenovo. Covers why primers come OFF before truncation, why the error model is per-run, truncLen as a merge-overlap detection budget (V4 vs V3-V4), DADA2 vs Deblur and q2-dada2 ...

Extracts per-cytosine methylation calls from aligned bisulfite/EM-seq reads with bismark_methylation_extractor (Bismark BAM) or the aligner-agnostic MethylDackel/BISCUIT (bwa-meth BAM), producing the beta value M/(M+U) as a coverage file, bedGraph, or genome-wide cytosine report across CpG/CHG/CHH context. Covers conversion-rate QC as the first gate, the 5mC vs 5hmC summed caveat, variant-aware calling so a C/T SNP does not masquerade as unmethylation, paired-end --no_overlap double-counting,...

Designs and defends an epigenome-wide association study (EWAS) on 450K/EPIC array or bisulfite methylation - the layer deciding whether a hit is credible. Covers the confounding hierarchy (cell composition covariates as the dominant confounder, batch/Sentrix chip/array position, age/sex, smoking AHRR cg05575921, ancestry/mQTL, reverse causation), chip randomization (no-rescue theorem), surrogate variable analysis sva/SmartSVA, ComBat, RUVm, over-correction, genomic inflation lambda vs GWAS ge...

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 d...

Tests individual CpG sites for differential methylation (DMC/DMP) from bisulfite sequencing counts or array/continuous beta-value matrices. Covers the count-vs-continuous fork that dictates the model, beta-value vs M-value logit (Du 2010), beta-binomial overdispersion count models (DSS, methylKit, MOABS, RADMeth) for sequencing, limma moderated-t on M-values (eBayes trend/robust) for arrays, the bare-beta Welch t-test caveat, coverage-as-precision coupling, delta-beta effect size, BH-FDR with...

Estimates cell-type composition from bulk DNA methylation and uses it to defuse the single biggest EWAS confounder. Covers reference-based deconvolution (Houseman constrained-projection, minfi estimateCellCounts2 with FlowSorted.Blood.EPIC + IDOL-optimized libraries, EpiDISH RPC/CBS/CP, 12-cell extended, cord-blood nRBC references, EpiSCORE/hepidish for solid tissue), reference-free correction (ReFACTor, RefFreeEWAS, SVA), using fractions as covariates vs the compositionality/collinearity tra...

Aligns bisulfite-converted (WGBS, RRBS, PBAT) and enzymatic (EM-seq) short reads to an in-silico C->T/G->A-converted reference with Bismark (Bowtie2 or HISAT2), preparing the genome index, choosing the directional vs non-directional vs PBAT strand flag, deduplicating WGBS/EM-seq (never RRBS), and bounding bisulfite conversion efficiency with unmethylated lambda and methylated pUC19 spike-ins. Covers why the library protocol (not the aligner) decides whether calls are meaningful, why incomplet...

Turns raw Illumina Infinium methylation BeadChip IDATs (450K, EPIC, EPICv2) into a defensible beta/M matrix with sesame (openSesame/SigDF) or minfi (RGChannelSet -> MethylSet -> GenomicRatioSet). Covers Type I vs Type II probe chemistry and why raw Type II beta is compressed, the signal-to-beta math (beta = M/(M+U+100)) and M-value logit, detection-p / pOOBAH masking including the out-of-band deletion-artifact catch, dye-bias correction, and the normalization decision (noob, funnorm, quantile...

Resolves and compares bacterial strains below the species level from shotgun metagenomes with inStrain (popANI/conANI microdiversity), StrainPhlAn (marker-SNV consensus phylogeny and nGD), MIDAS2, metaSNV, and StrainGE, plus genome-vs-genome ANI (skani/fastANI/MASH) for isolate/MAG comparison. Covers why a strain is a threshold not a thing, why ANI answers same-genome while popANI/nGD answer same-population-in-situ, the 99.999% popANI and per-species nGD definitions, the coverage detection li...

Profiles shotgun metagenomes to species/SGB relative abundance with MetaPhlAn 4's clade-specific marker genes (bowtie2 short reads, minimap2 long reads). Covers why a MetaPhlAn percentage is a cell fraction (genome-size-normalized taxonomic abundance) and must never be merged with Kraken/Bracken read fractions, kSGB vs uSGB units for quantifying database-absent taxa, the unknown-fraction rescaling and its version-default flip, --index pinning as a batch variable, and when mOTUs3 or sourmash g...

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...

Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling with AMRFinderPlus/ABRicate on assembled contigs or MAGs. Covers why an ARG hit is a sequence match not a phenotype, why a metagenomic ARG has no host and no genomic context until assembly (and assembly breaks at ARGs), per-gene curated thresholds vs a flat 80/80, gene-fraction false-positive contr...

Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabo...

Designs and validates quantitative targeted metabolomics assays (MRM/SRM on triple-quadrupole, PRM on high-resolution instruments) to report absolute concentrations. Covers the internal-standard strategy (external cal -> global IS -> standard addition -> stable-isotope-labeled IS), weighted calibration judged by back-calculated %RE not R-squared, ion-ratio quantifier/qualifier confirmation, matrix-effect/recovery characterization, and ICH M10 method validation. Use when quantifying a closed p...

Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA/QEA), mummichog/PSEA on raw m/z peaks, and network-diffusion enrichment (FELLA), with correct background-set construction and honest interpretive ceilings. Use when interpreting differential metabolites or an untargeted LC-MS feature table in pathway context, choosing ORA vs MSEA vs mummichog vs topology, or setting the reference/background set. For annotation confidence levels feed...

Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak/feature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metab...

Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, or deciding whether an attribution ranking can be trusted as biology. For validated feature selection see machine-learning/biomarker-discovery; explanations are not a selection method.

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...

Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based callers, joint-genotypes cohorts via the Sniffles2 .snf workflow, and benchmarks with Truvari against GIAB. Covers why an SV call is a representation artifact (the tandem-repeat BED, aligner, and Truvari params set precision/recall as much as the caller), the cuteSV per-platform parameter trap, soft-...