Microbiome sequencing doesn't count bacteria — it reports each taxon's share of a fixed pool of reads, and those shares are forced to sum to one. That single constraint quietly manufactures false findings: correlations biased negative even for independent taxa, and long lists of taxa that "significantly decrease" when the only real event was one organism blooming and squeezing everyone else's slice. Rarefying and subsetting don't fix it. This piece explains the constant-sum constraint, why log-ratio methods like ALDEx2 and ANCOM are the honest alternative, and why answering an absolute question needs an external anchor like qPCR or a spike-in.

A mislabeled tube, a pipetting error, a transposed row on a plate — and a sample produces a flawless VCF that belongs to someone else, or to two people at once. Every technical metric passes, because technically nothing failed: read quality, coverage, Ts/Tv are all normal. Standard QC is structurally blind to it, because those metrics ask "did the sequencing work?" not "is this the right person?" The only check that catches a swap is a genetic fingerprint — genotyping a panel of common SNPs to ask the genome who it belongs to. This piece explains why identity is a separate QC axis, and why tumor-normal work is especially exposed.

RNA velocity promises what a static snapshot can't give: the direction each cell is heading, inferred from the ratio of unspliced to spliced RNA. The arrows are visually compelling and often biologically right — but they rest on kinetic assumptions real data frequently violates (genes with multiple rate kinetics can produce distorted or even reversed velocity), the unspliced signal is faint and noisy, and the smooth streamline you see can be manufactured by the 2D projection rather than read from the biology. One critique produced a coherent-looking flow from three genes with no directional signal at all. This piece explains why a clean velocity stream is a hypothesis, not a measurement.

A single insertion or deletion can be written in several equally correct VCF representations — different position, different alleles, same biological edit. To a human they're obviously identical. To a computer doing an exact match against ClinVar, they're different variants, and the mismatch is completely silent: every job runs, every file is well-formed, and a pathogenic variant simply fails to annotate. This piece explains why VCF doesn't enforce a unique spelling, how left-alignment and parsimony produce one canonical form (and the uniqueness theorem that makes matching correct), why multiallelic decomposition is a required partner step, and why two different normalizers can still disagree on indels in repeats.

In metagenomic pathogen detection the organism you're hunting may be fewer than 100 reads out of tens of millions — and contamination, a conserved rRNA gene, or a dirty database entry can produce exactly that same handful. Read count alone cannot tell the real signal from the manufactured one. This piece explains why the instinctive metric fails at low abundance, why false-positive reads share a geometry (they pile up in the same few spots while real organisms spread across the genome), and how unique-k-mer counting operationalizes breadth of coverage — the KrakenUniq threshold of ~1000 unique k-mers eliminated many background pathogen identifications that a read-count filter would have passed.

Typing HLA is not variant calling. It's choosing two alleles out of thousands whose sequences differ by less than the ambiguity of a single short read — against a reference database that is itself incomplete. That's why a pipeline that nails the rest of the genome can quietly return the wrong HLA type. This piece walks through why the standard align-to-GRCh38-and-call workflow fails at the MHC, why four-digit resolution collapses on short reads (one early method got only ~32% of four-digit class I calls right), why low-SNP-density loci like DQA1/DPA1/DPB1 can't be phased, and how the working tools model the ambiguity instead of ignoring it.

A polygenic score trained in one population predicts poorly in another — that much is well known. What's less appreciated is that the loss isn't a step change between labeled groups. It's a smooth decline with genetic distance from the training data, and it happens inside a single ancestry label as surely as across the boundary between two. A large biobank analysis found accuracy correlates −0.95 with genetic distance across 84 traits, and the closest Hispanic/Latino individuals can be predicted as well as the farthest Europeans. This piece explains why the population label is the wrong unit — and what to report instead.

Point a germline pipeline at chrM and it runs without complaint and hands you a VCF. That's the problem. The mitochondrial genome breaks nearly every assumption a standard variant caller is built on — it's circular, it carries a variant at any fraction rather than two clean alleles, and, most treacherously, fragments of it are scattered across the nuclear genome as NUMTs. Reads from those nuclear copies mis-map onto the mitochondrial reference and manufacture low-heteroplasmy variants that were never in a mitochondrion. Worse, whether they clear your threshold depends on the sample's mtDNA copy number. This piece covers why a flat cutoff is the wrong instinct, and what actually controls the confound.

Moving a variant from GRCh37 to GRCh38 looks like arithmetic on a position — pass the coordinates through a chain file and you're done. It isn't that simple. A genome assembly is a specific reconstructed sequence, not a neutral ruler, and wherever two builds spell the same variant with different alleles, the standard liftover tools quietly drop it or convert it into a record that will never match a natively called genome. SNVs mostly survive; indels and short-tandem-repeat variants are where it breaks, silently. This piece walks through what a chain file actually encodes, why tool choice isn't cosmetic, and when you should realign instead of lift over.

It's tempting to treat panel, exome, and genome as a ladder — more territory, more diagnoses, strictly better if you can afford it. But breadth trades against depth, interpretability, turnaround, and the burden of uncertain and incidental findings — and the widest test isn't always the one that answers your question. A deep panel can out-diagnose an exome for a well-defined phenotype; a uniform genome can out-detect a patchy exome for the right coding variant. This piece frames test selection as a design decision driven by the question — the phenotype's specificity, the likely variant class, and the reporting strategy — not a reflex toward "more."

N50 is the number everyone quotes to prove a genome assembly is good: the bigger it is, the more contiguous the assembly. But N50 measures only how long the pieces are — not whether they're joined correctly. And the fastest way to raise it is to make exactly the kind of aggressive join that produces a chimera. A misjoin fuses two sequences that don't belong adjacent into one longer contig, which raises N50 — the structural error and the metric improvement are the same event. This piece covers the three Cs of assembly quality, why contiguity isn't correctness, and how to actually verify structure.

"Sequenced to 100× mean coverage" sounds like every base was read a hundred times. It wasn't. Mean depth is an average over a distribution, and two samples with the same average can differ enormously in whether a given clinically important base was covered at all — because sequencing dropout is systematic, not random. GC-extreme regions, promoters and first exons, long exons, and repeats drop out in the same places every run, invisible in the mean. This piece covers why the average reassures while the distribution decides which variants get called, and which uniformity metrics — percent-above-threshold, Fold-80, evenness — actually capture what the mean hides.

A phased genome sounds like a solved genome — two clean parental haplotypes, allele by allele. In reality "phased" means the chromosome was broken into blocks: contiguous stretches the method declares internally phased, separated by gaps it couldn't resolve, each carrying its own error rate, with no reliable phase relationship between blocks. The single number usually reported — block N50 — describes only how long the blocks are, not whether they're correct. And a switch error, one wrong junction, inverts every allele downstream of it, silently flipping a compound-heterozygote call from benign to disease-causing. This piece covers switch vs. flip errors, block boundaries, and the resolution "phased" hides.

The GWAS threshold of 5×10⁻⁸ feels like a constant of nature. It isn't. It's a Bonferroni correction — 0.05 divided by roughly a million effectively independent tests, a count derived from the genome's linkage-disequilibrium structure. That works because the dependence is understood and confined to one data type. Multi-omic integration removes both comforts: you're testing SNPs, transcripts, proteins, and metabolites with different feature counts, different correlation structures, and correlations between them. The number of tests — the denominator every correction depends on — stops having a clean answer, and the same biological signal gets counted once, thrice, or not at all. This piece covers why.

Combining single-cell datasets from different runs, labs, or chemistries means removing the technical batch effects between them. But technical and biological variation are entangled in the same numbers, and no algorithm separates them perfectly. Integration is a balancing act between two goals that pull in opposite directions — mixing the batches together vs. conserving real biology — with a dangerous failure mode: push too hard toward blending, and you don't just remove noise, you erase genuine differences. And because the output is a clean, well-mixed embedding, the erasure is invisible. This piece covers the tradeoff, why over-correction is common, and the confounding trap where batch IS the biology.

Clustering gives you groups of cells; annotation gives them names — and that naming step feels like the objective payoff of the whole experiment. But every annotation method assigns a label by relating your cells to prior knowledge: a marker list, a reference atlas, a training set. It inherits that prior's limits without flagging them. The sharpest failure is structural: a standard classifier assigns every cell to one of the labels it was trained on, so a genuinely novel population gets forced to the nearest known type — confidently — instead of flagged as unknown. This piece covers the forced-label problem, why the reference isn't ground truth, and what defensible annotation requires.

Single-cell analysis answers "how many cell types are in this sample?" with an unsupervised clustering step — and it's the step most easily mistaken for objective. The number of clusters isn't something the data determines; it's set directly by a resolution parameter the analyst chooses. Turn it up and the same cells split into more groups, each with its own confident marker genes. Worse, those markers are tested on the same data used to draw the clusters — a circularity that inflates p-values even when there's only one true population. This piece covers over-clustering, the double-dipping problem, and how to tell a real cluster from a manufactured one.

The entire premise of single-cell RNA-seq is the word single: one droplet, one cell, one barcode. Doublets break it. When two cells share a droplet, their combined mRNA gets one barcode, and the analysis sees a single "cell" with a blended transcriptome. The danger is that the blend doesn't look like noise — it looks like a discovery. Two different cell types merged into one barcode land between their real clusters and read as a novel intermediate or transitional cell type that doesn't exist. This piece covers why the doublet rate is set when you load the chip, why detection is blind to same-type doublets, and what actually works.

Bisulfite conversion is an elegant trick: it rewrites unmethylated cytosines as thymines and leaves methylated ones untouched, turning an invisible epigenetic mark into a readable C-versus-T base change. But the same reaction fragments the DNA with heat and acid, collapses the four-letter genome to three letters (wrecking alignment), breaks strand complementarity, and — most dangerously — makes an unmethylated cytosine that escaped conversion indistinguishable from a truly methylated one. Incomplete conversion is a false-positive engine that only a spiked-in control can quantify. This piece covers why the chemistry that reveals methylation also corrupts the data, and what a trustworthy methylation pipeline requires.

Allele-specific expression asks a surgical question: within one sample, at a heterozygous site, is the maternal copy of a gene expressed as much as the paternal one? Because both alleles share a cell, any consistent imbalance points to a cis cause — a regulatory variant, imprinting, nonsense-mediated decay, X-inactivation — which makes ASE powerful where standard differential expression is blind. But the measurement has a thumb on the scale: reads are aligned to a reference that contains only one allele, so alternate-allele reads mismap more often and the reference count inflates. This piece covers reference bias, why correcting it is necessary but not sufficient, and what a trustworthy ASE pipeline requires.