Log: paper
← Log Tag: paper
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Jul 24, 2026 · 2 min read
How often is this variant seen
The problem. Most variants a pipeline calls are common and harmless. To find the few that matter, you need to know how often each one appears in the general population, and that...
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Jul 24, 2026 · 2 min read
Annotating variants against many databases
The problem. Knowing a variant’s effect on a gene is only the first question. The next ones are practical: is it already in dbSNP, how common is it in the population, does it si...
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Jul 24, 2026 · 2 min read
Predicting a variant's consequence
The problem. A VCF gives you positions and alleles, not meaning. To act on a variant you need to know which gene and transcript it lands in, whether it changes a protein, hits a...
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Jul 24, 2026 · 2 min read
Two signals for one rearrangement
The problem. Each clue to a structural variant is partial on its own. Read pairs that map at the wrong distance tell you a break is nearby but not exactly where. Split reads pin...
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Jul 24, 2026 · 2 min read
Structural variants, quickly
The problem. Small-variant callers stop at substitutions and short indels. Larger events, such as deletions, duplications, inversions, and translocations, leave a different fing...
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Jul 24, 2026 · 2 min read
Somatic variants from a tumor and its normal
The problem. Cancer exomes carry two kinds of signal at once: point mutations that appear in the tumor but not the normal, and larger gains and losses of whole regions. Early to...
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Jul 24, 2026 · 2 min read
Assembling the somatic mutation
The problem. Somatic mutations sit at low fraction in a mixed tumor sample, buried under sequencing error and normal contamination. The first MuTect found substitutions well but...
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Jul 24, 2026 · 2 min read
One fast caller for germline and somatic
The problem. Calling small variants has to fight the sequencer’s own error pattern, and indels are the hardest part, since each sample and each context makes errors at a differe...
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Jul 24, 2026 · 2 min read
A Bayesian take on variant calling
The problem. A variant caller has to weigh noisy reads and decide which differences from the reference are real. Many early callers looked at one base at a time. That misses the...
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Jul 24, 2026 · 2 min read
Calling variants across many samples at once
The problem. Early joint calling needed the raw reads of every sample in memory at the same time. Add one genome to a cohort of a thousand and you re-ran the whole set. That doe...
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Jul 23, 2026 · 2 min read
Linking omics through a graph
The problem. Integrating single-cell RNA with ATAC or methylation is harder than integrating batches of the same modality: the feature spaces differ (genes vs. peaks vs. methyla...
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Jul 23, 2026 · 2 min read
A statistical test for spatial genes
The problem. Identifying spatially variable genes is a hypothesis test, and tests are only trustworthy if they control false positives. Early approaches modeled transformed data...
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Jul 23, 2026 · 2 min read
Spatial genes at scale
The problem. A core spatial question is which genes show spatial structure, expression patterns that track tissue anatomy rather than scattering randomly. Gaussian processes mod...
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Jul 23, 2026 · 2 min read
Communication by optimal transport
The problem. Communication tools built for dissociated single cells ignore two facts that spatial data restores: signaling is local (a ligand reaches nearby cells, not the whole...
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Jul 23, 2026 · 2 min read
Which communication method to trust
The problem. After the wave of cell–cell communication tools (CellPhoneDB, CellChat, NicheNet, and more, which I read last week), a practical worry surfaced: do they agree? Each...
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Jul 23, 2026 · 2 min read
Search by shape, not sequence
The problem. AlphaFold and its kin produced hundreds of millions of predicted protein structures. Structure captures homology that sequence alone misses, distant relatives fold ...
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Jul 23, 2026 · 2 min read
Searching millions of sequences
The problem. Comparing protein sequences against huge databases is the bread-and-butter of bioinformatics, annotation, homology, clustering, but sequencing throughput outran sea...
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Jul 23, 2026 · 2 min read
Expression from a longer context
The problem. Regulation is long-range: enhancers can sit tens of kilobases from the gene they control. Convolutional models like DeepSEA see only a short window, so they miss di...
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Jul 23, 2026 · 2 min read
Reading regulation from sequence
The problem. Most disease-associated variants from GWAS fall in noncoding regions, where their effect is regulatory and invisible to protein-centric annotation. Predicting wheth...
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Jul 23, 2026 · 2 min read
Learning what a protein binds
The problem. A transcription factor or RNA-binding protein prefers certain sequence motifs, but the classic representation, a position weight matrix, is rigid: it assumes indepe...
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Jul 22, 2026 · 2 min read
Assembly through repeat graphs
The problem. Repeats are why genome assembly is hard: sequences longer than a read, repeated across the genome, collapse or tangle, and even long error-prone reads don’t trivial...
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Jul 22, 2026 · 2 min read
Assembling both haplotypes
The problem. Most assemblers collapse a diploid genome into a single mosaic consensus, discarding which variant came from which parent, exactly the haplotype information that ma...
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Jul 22, 2026 · 2 min read
Finding the somatic mutation
The problem. Calling somatic mutations isn’t germline calling. A tumor sample is a mix, normal-cell contamination, subclones at low fractions, so the real mutation may sit at a ...
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Jul 22, 2026 · 2 min read
The fingerprints of mutation
The problem. A tumor genome carries thousands of somatic mutations, the accumulated residue of many processes: UV damage, tobacco carcinogens, defective repair, ageing, enzymati...
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Jul 22, 2026 · 2 min read
Significance for a gene set
The problem. A differential-expression analysis hands you a ranked list of thousands of genes. Individually, many real effects are modest and don’t clear a genome-wide threshold...
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Jul 22, 2026 · 2 min read
Calling peaks from the pileup
The problem. ChIP-seq (and later ATAC-seq) produces a genome-wide pileup of reads; the signal you want is where reads enrich, binding sites or open regions, above a noisy, non-u...
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Jul 22, 2026 · 2 min read
Borrowing microarray tools for RNA-seq
The problem. RNA-seq counts are heteroscedastic: a gene’s variance depends on its mean and on sequencing depth, which violates the equal-variance assumption of ordinary linear m...
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Jul 22, 2026 · 2 min read
Counting reads into genes
The problem. After alignment, you still have to decide which gene (or exon) each read belongs to and tally the totals, the count matrix every differential-expression tool consum...
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Jul 22, 2026 · 2 min read
Alignment over a graph genome
The problem. Aligning reads to a single linear reference bakes in reference bias: reads carrying an alternate allele map worse, or to the wrong place, precisely where the biolog...
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Jul 22, 2026 · 2 min read
Aligning the long reads
The problem. Short-read aligners like BWA were built for accurate ~100 bp reads. Nanopore and PacBio produce reads tens of kilobases long with much higher error rates, and the a...
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Jul 21, 2026 · 2 min read
Surgery on a reference model
The problem. Reference atlases keep growing, and every new dataset traditionally means re-running integration over the whole combined corpus, expensive, and it needs the origina...
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Jul 21, 2026 · 2 min read
Shared and dataset-specific factors
The problem. Most integration aims to erase differences between datasets so cells line up. But sometimes the differences are the biology, a disease sample versus control, a diff...
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Jul 21, 2026 · 2 min read
Normalizing counts by their model
The problem. The routine single-cell workflow normalises counts by library size and log-transforms them, but this is a rough fix: highly-expressed and lowly-expressed genes end ...
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Jul 21, 2026 · 2 min read
Matching neighbors across batches
The problem. Combine single-cell datasets from different runs, labs, or technologies and batch effects dominate: cells cluster by experiment, not biology. Earlier correction met...
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Jul 21, 2026 · 2 min read
Direction for the trajectory
The problem. Trajectory methods need you to tell them the direction (a start cell); RNA velocity gives direction but is noisy and can point wrong in messier systems. Regeneratio...
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Jul 21, 2026 · 2 min read
The probabilities of a cell's fate
The problem. Trajectory methods (Monocle, Slingshot, PAGA from the single-cell day) order cells along lineages, but a progenitor doesn’t follow one predetermined path, it faces ...
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Jul 21, 2026 · 2 min read
Regulatory networks from single cells
The problem. Clustering cells by raw expression groups them by correlated genes, but the biology that defines a cell state is the regulatory program behind it, which transcripti...
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Jul 21, 2026 · 2 min read
Many proteins, one microscope
The problem. Metal-tag imaging (imaging mass cytometry, MIBI) achieves high multiplexing but needs specialised, slow, destructive mass-spectrometry instruments. Most labs have a...
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Jul 21, 2026 · 2 min read
An ion beam reads tissue
The problem. Clinical pathology leans on immunohistochemistry, which typically visualises one or two markers per section. Understanding a tumor microenvironment, which immune ce...
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Jul 21, 2026 · 2 min read
Imaging proteins by mass
The problem. Fluorescence microscopy is limited by spectral overlap: you can stain only a handful of proteins at once before their colours collide. Tissue biology is driven by m...
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Jul 20, 2026 · 2 min read
RNA and protein in one model
The problem. CITE-seq protein counts carry heavy background, ambient antibody, non-specific binding, on top of batch effects, and the RNA and protein measurements need to inform...
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Jul 20, 2026 · 2 min read
Factors across many omics layers
The problem. When a study measures several modalities across several groups of cells, you want the major axes of variation that structure the whole dataset, and, above all, to k...
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Jul 20, 2026 · 2 min read
Weighting each modality per cell
The problem. CITE-seq gives every cell both a transcriptome and a surface-protein profile, but the two modalities aren’t equally informative for every cell, protein resolves som...
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Jul 20, 2026 · 2 min read
One graph for spatial analysis
The problem. Spatial analysis fragments into separate tasks, clustering spots into domains, integrating multiple tissue slices while correcting batch effects, and transferring c...
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Jul 20, 2026 · 2 min read
A workbench for spatial data
The problem. Spatial analysis had become a scatter of single-purpose scripts: one tool to find spatial domains, another for spatially variable genes, another for neighbourhood i...
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Jul 20, 2026 · 2 min read
A probabilistic take on spot deconvolution
The problem. Deconvolving spatial spots into cell types (SPOTlight) can be done by regression, but count data from sequencing is noisy and over-dispersed. A method that models t...
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Jul 20, 2026 · 2 min read
Unmixing what a spot contains
The problem. Capture-based spatial transcriptomics, the Ståhl/Visium lineage, measures expression at spots that each contain several cells, not one. So a spot’s profile is a mix...
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Jul 20, 2026 · 2 min read
From ligand to downstream gene
The problem. Ligand–receptor tools (CellPhoneDB, CellChat) infer that a signal could pass between two cell types from co-expression. But co-expression isn’t proof of an active s...
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Jul 20, 2026 · 2 min read
Mapping a tissue's signaling network
The problem. Listing enriched ligand–receptor pairs (yesterday’s CellPhoneDB) tells you the edges but not the shape of the network. Which cell types are the hubs? Which pathways...
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Jul 20, 2026 · 2 min read
A dictionary of molecular conversations
The problem. Once you have cell types from a single-cell atlas, the next question is which ones signal to which, but a receptor is often a multi-subunit complex, and naïvely pai...
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Jul 19, 2026 · 2 min read
A generalist for cell segmentation
The problem. Imaging-based spatial (MERFISH, Xenium) and microscopy in general hit the same first obstacle: drawing accurate boundaries around each cell. Cells vary wildly in sh...
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Jul 19, 2026 · 2 min read
Mapping single cells onto tissue
The problem. Single-cell RNA-seq gives deep, whole-transcriptome cell-type detail but throws away location. Spatial methods keep location but are either coarse (capture) or targ...
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Jul 19, 2026 · 2 min read
Finding domains with a graph net
The problem. Detecting spatial domains, coherent tissue regions like cortical layers or tumor niches, needs more than expression clustering, which ignores where cells are. Neigh...
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Jul 19, 2026 · 2 min read
Genes that vary across space
The problem. Once you have spatial transcriptomics data, the first analytical question is: which genes show spatial structure, expression that varies with position in a non-rand...
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Jul 19, 2026 · 2 min read
A commercial imaging platform
The problem. Imaging-based methods (MERFISH, seqFISH+) are powerful but were largely bespoke, lab-built systems, hard to standardise, hard to scale across a facility. For spatia...
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Jul 19, 2026 · 2 min read
Spatial expression in the brain
The problem. The human dorsolateral prefrontal cortex has a clear laminar structure, cortical layers with distinct molecular identities. Recovering those layers de novo from spa...
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Jul 19, 2026 · 2 min read
A whole embryo in space
The problem. Capture-based spatial methods faced a scale-versus-resolution bind: fine resolution over a large tissue meant an enormous number of capture spots, and most platform...
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Jul 19, 2026 · 1 min read
Beads that map the tissue
The problem. Spot-based capture (Ståhl/Visium) is whole-transcriptome but coarse, 55-micron spots spanning several cells. Imaging is precise but targeted. Could a capture method...
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Jul 19, 2026 · 2 min read
Super-resolution across the transcriptome
The problem. Imaging methods like MERFISH give single-molecule resolution but on targeted panels; capture methods give whole-transcriptome coverage but coarse resolution. The ob...
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Jul 19, 2026 · 2 min read
Imaging transcripts in place
The problem. Spot-based spatial transcriptomics (Ståhl, day 8) captures the whole transcriptome but at spot resolution, each spot is several cells. To see which individual cell ...
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Jul 18, 2026 · 2 min read
An atlas of immune response
The problem. Methods are only worth as much as the biology they reveal. PBMCs (peripheral blood mononuclear cells) responding to LPS, a bacterial endotoxin that triggers innate ...
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Jul 18, 2026 · 2 min read
Single-cell counts from STAR
The problem. Turning droplet single-cell reads into a cell-by-gene count matrix means handling cell barcodes, UMIs, and multi-mapping, then de-duplicating molecules. The standar...
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Jul 18, 2026 · 2 min read
Stitching panoramas of cells
The problem. Integrating many single-cell datasets risks a specific failure: forcing alignment between datasets that don’t share cell types, blending distinct populations that s...
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Jul 18, 2026 · 2 min read
Annotating cells by reference
The problem. After clustering, someone has to say what each cluster is, which cell type. Done manually, that means hunting marker genes cluster by cluster: slow, subjective, and...
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Jul 18, 2026 · 2 min read
Correcting batch, fast
The problem. Combining single-cell datasets (Seurat’s anchors, day 10, solve this too) means removing batch effects without erasing biology. Anchor- and MNN-based methods work b...
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Jul 18, 2026 · 2 min read
Clusters and trajectories, reconciled
The problem. Single-cell analysis splits into two worldviews: clustering says cells fall into discrete types; trajectory inference says they lie on continuous paths. Real tissue...
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Jul 18, 2026 · 2 min read
Velocity for messier dynamics
The problem. The original RNA velocity model assumed genes reach a transcriptional steady state and that splicing rates are shared, convenient, but often false. In transient, fa...
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Jul 18, 2026 · 2 min read
The arrow of a cell
The problem. Pseudotime (Monocle, Slingshot) orders cells along a trajectory, but the direction of travel is assumed or inferred from side information, the data itself doesn’t s...
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Jul 18, 2026 · 2 min read
Tracing lineages through clusters
The problem. Monocle showed you could order cells in pseudotime, but real differentiation branches: a progenitor becomes several fates. Inferring these branching lineages robust...
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Jul 18, 2026 · 2 min read
Ordering cells in time
The problem. A single-cell experiment is a snapshot: all cells captured at one instant. But biology is a process, cells differentiate, transition, progress. If a population is a...
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Jul 17, 2026 · 2 min read
Untangling mixed spectra
The problem. In metabolomics MS/MS, co-eluting compounds get fragmented together, so a single spectrum is often a blend of ions from several metabolites. Trying to match that ch...
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Jul 17, 2026 · 2 min read
Picking peaks from the noise
The problem. Raw LC-MS metabolomics data is a mess of noisy peaks whose retention times drift between runs. Before you can compare samples, you must detect real peaks, correct t...
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Jul 17, 2026 · 2 min read
A social network for molecules
The problem. Untargeted metabolomics detects thousands of small-molecule features, and most can’t be identified by matching to a reference spectrum, the databases are too incomp...
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Jul 17, 2026 · 1 min read
Deep proteomes by neural net
The problem. Classic proteomics (data-dependent acquisition) picks precursors to fragment one at a time, missing many peptides stochastically. Data-independent acquisition (DIA)...
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Jul 17, 2026 · 2 min read
The genome, finally complete
The problem. The human reference genome, for two decades, had holes: centromeres, segmental duplications, and repeat arrays that short reads (and even ordinary long reads) could...
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Jul 17, 2026 · 2 min read
Twelve years of SAMtools
The problem. Sequencing generates enormous alignments and variant sets, and without shared, efficient formats every tool would speak its own dialect. Aligners, callers, and view...
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Jul 17, 2026 · 2 min read
Containers that run anywhere
The problem. Containers (Docker) solved “ship the exact environment,” but Docker needs root-level daemon privileges, a non-starter on shared academic HPC systems, where you’re a...
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Jul 17, 2026 · 2 min read
Packaging the tools
The problem. Every bioinformatics tool has its own build system, dependencies, and version quirks. Historically, getting a dozen of them installed together, with compatible vers...
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Jul 17, 2026 · 2 min read
A community of pipelines
The problem. Nextflow (previous post) gives you portable pipelines, but everyone still wrote their own from scratch, with idiosyncratic structure, no shared testing, and wildly ...
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Jul 17, 2026 · 2 min read
Workflows that reproduce themselves
The problem. A bioinformatics analysis is a chain of a dozen tools, each with versions, parameters, and dependencies. Run it on a different machine, or six months later, and it ...
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Jul 16, 2026 · 2 min read
Freeing the raw files
The problem. Mass spectrometers write proprietary, vendor-specific binary files (Thermo’s .raw). Historically, converting these to open formats (mzML) for analysis required the ...
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Jul 16, 2026 · 2 min read
Catching the doublets
The problem. Droplet single-cell occasionally traps two cells in one droplet. The result is a barcode whose expression is a blend of two real cell types, and that blend can masq...
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Jul 16, 2026 · 1 min read
Long reads that are also accurate
The problem. Long reads (day 9’s nanopore) reach the repetitive, structural regions short reads can’t, but historically at a cost: high per-base error. Short reads were accurate...
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Jul 16, 2026 · 1 min read
The same aligner, only faster
The problem. BWA-MEM (day 6’s aligner, in my variant_calling_nf) is accurate and ubiquitous, but it was written before today’s wide multicore CPUs and SIMD vector units. As sequ...
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Jul 16, 2026 · 2 min read
From Louvain to Leiden
The problem. Louvain (day 6) is the community-detection method underneath most single-cell clustering. But it has a hidden flaw: it can produce communities that are internally d...
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Jul 16, 2026 · 1 min read
Stitching single-cell datasets together
The problem. Real single-cell studies combine many samples, batches, technologies, even species. Naively pooling them, batch effects dominate: cells cluster by where they were r...
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Jul 16, 2026 · 2 min read
Is that a cell or an empty drop?
The problem. In droplet single-cell (day 9’s 10x method), most droplets contain no cell, just ambient RNA floating in the suspension. These empty drops still capture stray trans...
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Jul 16, 2026 · 1 min read
The single-cell toolkit in Python
The problem. By 2018 single-cell datasets (see: Zheng’s droplet method) were exploding in size, but the analysis ecosystem was fragmented and largely R-based. Python needed a sc...
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Jul 16, 2026 · 2 min read
Finding the big rearrangements
The problem. SNVs and small indels are what GATK and DeepVariant call well from short reads. But a large fraction of consequential genetic variation is structural: kilobase dele...
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Jul 16, 2026 · 2 min read
Calling variants with a neural net
The problem. GATK’s best-practices caller (day 6) encodes hand-built statistical models of sequencing error, powerful, but each correction is a human-designed rule for a specifi...
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Jul 15, 2026 · 2 min read
The embedding that replaced t-SNE
The problem. t-SNE (day 6) gave the field a way to see high-dimensional single-cell data, but at a cost: it’s slow on large datasets, scales awkwardly to millions of cells, and ...
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Jul 15, 2026 · 2 min read
Cataloguing the switches
The problem. Transcription factors are the master switches of gene expression, but the human set was surprisingly poorly defined: estimates of how many there are, which proteins...
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Jul 15, 2026 · 2 min read
Assembling a chromosome, end to end
The problem. Even with long reads, some regions resist assembly: the human X chromosome’s centromere is a vast array of near-identical repeats where reads can’t be uniquely plac...
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Jul 15, 2026 · 2 min read
Sequencing a human genome on nanopore
The problem. Short reads (the Illumina data my pipelines assume) map beautifully to unique sequence but fail in repeats, segmental duplications, and structural rearrangements, r...
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Jul 15, 2026 · 2 min read
Cleaning reads before anything else
The problem. Raw sequencing reads carry adapter contamination, low-quality tails, and other artefacts. Historically you chained several tools, one to trim adapters, one to filte...
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Jul 15, 2026 · 1 min read
Single cells by the million
The problem. Early single-cell RNA-seq was low-throughput and expensive, hundreds of cells, plate-based, laborious. To see cell-type heterogeneity in a real tissue you need thou...
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Jul 15, 2026 · 2 min read
The architecture behind the foundation models
The problem. Sequence models built on recurrence (RNNs, LSTMs) process tokens one after another, which is slow to train and struggles to link distant positions, the gradient has...
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Jul 15, 2026 · 2 min read
Protein and RNA from one cell
The problem. Single-cell RNA-seq measures transcripts, but cell identity, especially for immune cells, is often defined by surface proteins, the markers flow cytometry has used ...
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Jul 15, 2026 · 1 min read
Bias-aware transcript quantification
The problem. Fast pseudoalignment (kallisto, yesterday) gives you transcript abundances cheaply, but RNA-seq has systematic biases, GC content, fragment-length distribution, pos...
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Jul 15, 2026 · 1 min read
Searching spectra at speed
The problem. Identifying peptides means matching each observed spectrum against theoretical spectra from a protein database. If you also want to catch post-translational modific...
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Jul 14, 2026 · 1 min read
The other variant benchmark
The problem. A truth set is only as trustworthy as the assumptions behind it. If everyone validates variant callers against one reference (say, GIAB), systematic blind spots in ...
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Jul 14, 2026 · 1 min read
A near-complete proteome
The problem. Proteomics has long lagged genomics in completeness: a single mass-spec run samples only a fraction of the proteins present, and getting near-comprehensive coverage...
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Jul 14, 2026 · 2 min read
A truth set for variant calling
The problem. You can run GATK and get a VCF, but how do you know it’s correct? Variant calling has no ground truth in a typical sample, you can’t independently confirm millions ...
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Jul 14, 2026 · 1 min read
The first spatial transcriptome
The problem. Bulk and single-cell RNA-seq tell you what is expressed and, for single-cell, in which cell type, but both discard where the cells were. Tissue is organised; functi...
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Jul 14, 2026 · 1 min read
A toolkit for mass spectrometry
The problem. Mass-spectrometry analysis had powerful but often monolithic, closed tools. Building a custom, reproducible workflow, peak picking, then feature detection, then ide...
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Jul 14, 2026 · 2 min read
One report for the whole pipeline
The problem. A modern bioinformatics run scatters quality metrics across dozens of files: FastQC per sample, alignment stats from the aligner, duplication rates, counts summarie...
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Jul 14, 2026 · 2 min read
Quantification without alignment
The problem. The classic RNA-seq route, align every read to the genome with STAR, then count, is accurate but heavy: full alignment is slow and produces information you throw aw...
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Jul 14, 2026 · 1 min read
Reading the urinary metabolome
The problem. Metabolites, the small-molecule end products of cellular activity, vary with age, sex, and body mass, but untangling those effects from a noisy untargeted LC-MS dat...
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Jul 14, 2026 · 2 min read
Deep learning, in one review
The problem. By 2015 deep networks had gone from fringe to dominant across vision and speech, but the ideas were scattered across a decade of conference papers. The field needed...
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Jul 14, 2026 · 1 min read
A catalog of human variation
The problem. A variant caller tells you where a sample differs from the reference, but not whether that difference is a mundane common polymorphism or something rare and interes...
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Jul 13, 2026 · 1 min read
The airway dataset everyone learns on
The problem. Glucocorticoids are frontline asthma drugs, but which genes they act on in airway smooth muscle, and how, wasn’t fully mapped. Himes and colleagues used RNA-seq to ...
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Jul 13, 2026 · 1 min read
Counting proteins without labels
The problem. To compare protein abundance across samples by mass spectrometry, the reliable route was chemical labelling (isotope tags), which is costly and limits sample number...
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Jul 13, 2026 · 2 min read
Aligning RNA reads across splice junctions
The problem. DNA aligners like BWA assume a read maps to one contiguous stretch of genome. RNA reads don’t: a read can straddle a splice junction, with its two halves landing on...
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Jul 13, 2026 · 2 min read
Reading open chromatin
The problem. Which parts of the genome are available to be read, nucleosome-free, bound by regulators, poised for transcription, is central to what a cell is doing. Earlier meth...
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Jul 13, 2026 · 2 min read
The network that started deep learning
The problem. For decades, image classification meant hand-engineered features fed to a classifier, and progress was incremental. Neural networks were an old idea widely consider...
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Jul 13, 2026 · 1 min read
An encyclopedia of the genome
The problem. The human genome sequence told us the letters, but most of it doesn’t code for protein. What is the rest doing? Which stretches are promoters, enhancers, transcript...
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Jul 13, 2026 · 1 min read
Annotating what a variant does
The problem. A variant caller hands you a position and an allele, chr7, this base is A not G. That’s a coordinate, not a consequence. Is it in a gene? Does it change an amino ac...
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Jul 13, 2026 · 1 min read
Differential expression, exon by exon
The problem. Standard differential-expression asks whether a gene goes up or down. But regulation often happens at finer grain: a splicing factor can shift which exons are inclu...
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Jul 13, 2026 · 1 min read
Does RNA-seq beat the microarray?
The problem. Around 2010 the field was mid-transition from microarrays to RNA-seq, and the honest question was empirical: does sequencing actually detect more, and more reliable...
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Jul 13, 2026 · 2 min read
The other differential-expression engine
The problem. Like DESeq, edgeR faces the fact that RNA-seq counts are discrete and overdispersed, and that most experiments have too few replicates to estimate each gene’s varia...
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Jul 12, 2026 · 2 min read
The variant-calling standard
The problem. Once BWA has aligned your reads, you still have to call variants, decide where the sample differs from the reference, through a haze of sequencing errors, alignment...
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Jul 12, 2026 · 2 min read
Before DESeq2, there was DESeq
The problem. RNA-seq gives you counts of reads per gene, and counts don’t behave like the continuous, normally-distributed data of the microarray era. They’re discrete, and thei...
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Jul 12, 2026 · 2 min read
Mapping reads to the genome
The problem. Next-gen sequencing produces tens of millions of short reads per run; every one has to be placed against a three-billion-base reference, tolerating sequencing error...
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Jul 12, 2026 · 2 min read
Seeing high-dimensional data
The problem. A single cell is a point in tens of thousands of dimensions (one per gene). You cannot see that, and naive projections (like PCA) preserve global variance but smear...
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Jul 12, 2026 · 2 min read
Turning spectra into proteins
The problem. A mass spectrometer emits raw spectra, not proteins. Between them sits a gauntlet: calibrate masses, identify peptides, control error, roll peptides up to proteins,...
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Jul 12, 2026 · 2 min read
Finding communities in a graph
The problem. Given a network, nodes and weighted edges, how do you find its communities: groups of nodes more connected to each other than to the rest? On large graphs this is c...
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Jul 12, 2026 · 2 min read
Rescoring the proteome
The problem. A shotgun proteomics run produces hundreds of thousands of spectra, each matched to a candidate peptide by a search engine. But any single score is a noisy signal, ...
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Jul 12, 2026 · 2 min read
The rules of cancer
The problem. By 2000, cancer research was drowning in molecular detail, hundreds of oncogenes and tumor suppressors, thousands of mutations, with no organizing framework. Every ...
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Jul 12, 2026 · 2 min read
The index that makes search cheap
The problem. Yesterday’s Burrows–Wheeler transform compresses text beautifully, but compression alone isn’t enough: you want to search a huge string, find every occurrence of a ...
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Jul 12, 2026 · 2 min read
Taming the multiple-testing problem
The problem. Test one hypothesis at p < 0.05 and you accept a 5% chance of a false positive. Test 20,000 genes at once and you’d expect a thousand false positives by chance a...
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Jul 11, 2026 · 2 min read
The transform that makes aligners fast
The problem. This one starts in computer science, not biology: how do you compress text well and still search it efficiently? A permutation that groups similar contexts together...
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Jul 11, 2026 · 2 min read
Making billions of copies
The problem. For most of DNA’s history the limiting reagent was DNA itself. Studying, sequencing, or manipulating a specific stretch meant you needed enough of it, and biology o...
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Jul 11, 2026 · 2 min read
How we learned to read DNA
The problem. You could know DNA carried a linear code (Crick) without being able to read it. Until the late 1970s, determining the actual base-by-base sequence of a piece of DNA...
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Jul 11, 2026 · 2 min read
The cell that swallowed a cell
The problem. Eukaryotic cells are radically more complex than bacteria, and two of their organelles, mitochondria and chloroplasts, are strangely bacterium-like: their own membr...
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Jul 11, 2026 · 2 min read
The central dogma, first stated
The problem. By 1958 DNA’s structure was known, but how a gene becomes a protein was open. What determines a protein’s sequence? What is the flow of information between nucleic ...
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Jul 11, 2026 · 2 min read
The double helix, in two pages
The problem. Avery had shown DNA carries heredity, but a carrier has to do two impossible-sounding things at once: store an enormous amount of information and copy it faithfully...
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Jul 11, 2026 · 2 min read
The transforming principle is DNA
The problem. By the 1940s genes were known to be real, Mendel’s units, mapped onto chromosomes, but nobody knew what they were made of. The smart money was on protein, with its ...
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Jul 11, 2026 · 2 min read
Inheritance, quantified
The problem. Darwin had a mechanism that required heritable variation but no theory of heredity, inheritance looked like blending, which would dilute any new variant away. Nobod...
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Jul 11, 2026 · 2 min read
Descent with modification
The problem. Nineteenth-century biology had a catalogue of life but no mechanism, no account of why organisms are so exquisitely suited to their conditions, or how the diversity...
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Jul 11, 2026 · 2 min read
The database behind the interview
The problem. Predicting microRNA targets computationally is easy and mostly wrong, sequence complementarity throws off huge false-positive lists. What the field actually needs i...
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Jul 10, 2026 · 2 min read
The single-cell best-practices bible
The problem. Single-cell tooling moves so fast that “best practice” is a moving target, and the 2019 tutorials, good as they were, no longer cover multimodal data, the deep-gene...
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Jul 10, 2026 · 2 min read
Semi-supervised cell annotation
The problem. You often have some annotated cells, a reference atlas, a few labeled samples, and a pile of unlabeled ones. Purely unsupervised embedding (scVI) ignores the labels...
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Jul 10, 2026 · 2 min read
A latent space for single cells
The problem. Single-cell counts are sparse, noisy, and shot through with technical variation, dropout, library-size differences, batch effects. The usual pipeline patches each w...
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Jul 10, 2026 · 2 min read
A common container for spatial omics
The problem. Every spatial platform ships its own file layout, its own coordinate conventions, its own idea of what an “image” and a “cell” are. Before you analyze anything you ...
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Jul 10, 2026 · 2 min read
The Scanpy of spatial
The problem. Single-cell analysis has Scanpy: a mature, composable toolkit everyone shares. Spatial analysis, for a while, was a pile of one-off scripts. What you want is the sa...
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Jul 10, 2026 · 2 min read
Benchmarking the niche finders
The problem. Once you have BANKSY, and a dozen other spatial-domain and niche-identification methods, the same question that hit deconvolution returns: which one do you trust, a...
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Jul 10, 2026 · 2 min read
One embedding for cells and domains
The problem. Spatial analysis usually splits into two tasks with two toolchains: cell typing (what is this cell?) and domain segmentation (what tissue region is it in?). Standar...
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Jul 10, 2026 · 2 min read
Cleaning up missegmented transcripts
The problem. No segmentation is perfect. Transcripts from one cell get assigned to its neighbor, and the result is contamination: a cell’s expression profile is polluted by what...
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Jul 10, 2026 · 2 min read
Segmentation as a graph problem
The problem. Baysor made molecule-level segmentation the right frame, but imaging panels keep growing, millions of transcripts, whole slides, and the probabilistic-model approac...
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Jul 10, 2026 · 2 min read
Where does one cell end?
The problem. In imaging assays you don’t get cells, you get a cloud of transcript coordinates. Assigning each molecule to the right cell is the load-bearing step, and doing it o...
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Jul 9, 2026 · 2 min read
Deconvolution, robustly
The problem. Deconvolution leans on a single-cell reference, but the reference and the spatial assay are measured on different platforms, with different biases. Ignore that gap ...
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Jul 9, 2026 · 2 min read
How cell2location puts cell types back on the map
The problem. A Visium spot is a bag of several cells’ transcripts. To recover which cell types are where, you need to map a single-cell reference onto each spot, and do it in a ...
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Jul 9, 2026 · 2 min read
Which deconvolution tool should you trust?
The problem. Spot-based assays like Visium capture several cells per spot, so nearly every downstream question depends on deconvolution, estimating the cell-type mixture inside ...
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Jul 9, 2026 · 2 min read
A QC checklist for Xenium
The problem. Vendor pipelines get you from a Xenium run to a cell-by-gene matrix, but they don’t tell you whether to trust it, or what to check before analysis. This paper is th...
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Jul 9, 2026 · 2 min read
Cross-checking spatial platforms against the references
The problem. Head-to-head imaging benchmarks tell you how the platforms compare to each other, but not whether any of them is right. On tumor tissue especially, heterogeneous, c...
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Jul 9, 2026 · 2 min read
Xenium vs. MERSCOPE vs. CosMx, by the numbers
The problem. The three imaging spatial platforms, 10x Xenium, Vizgen MERSCOPE, NanoString CosMx, all promise single-cell-resolution, in-situ transcript detection, and vendors al...
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Jul 9, 2026 · 2 min read
Pseudotime, but in tissue space
The problem. Trajectory inference in dissociated single-cell data reconstructs a process, differentiation, activation, from a static snapshot, but it throws away where the cells...
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Jul 9, 2026 · 2 min read
The 2025 state of spatial omics
The problem. The founding spatial methods (Visium, the first imaging assays) are what most tutorials teach, but the technology has moved fast. This review is the 2025 snapshot o...
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Jul 9, 2026 · 2 min read
From spatial map to clinical decision
The problem. Most spatial-methods reviews are written for method developers. This one asks a different question: what has to be true for spatial transcriptomics to inform clinic...
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Jul 9, 2026 · 2 min read
A field map for spatial transcriptomics
The problem. Spatial transcriptomics adds coordinates to expression, and with them a new stack of computational problems that don’t exist in dissociated single-cell data. Before...
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Jul 8, 2026 · 2 min read
Where the software lives in the DBTL loop
The problem. Synthetic biology is organized around the Design–Build–Test–Learn cycle, but “where exactly does software add value?” is usually hand-waved. This review answers it ...
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Jul 8, 2026 · 2 min read
The single-cell checklist
The problem. Single-cell analysis has a dozen decision points before you ever see a UMAP, and most of them silently determine the result. This tutorial is the field’s shared ans...
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Jul 8, 2026 · 2 min read
Best practices, audited against my own pipeline
The problem. Everyone builds an RNA-seq workflow; far fewer can defend each choice. This review is the field’s consensus map, QC through interpretation, and I read it as an audi...
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Jul 8, 2026 · 2 min read
What DESeq2 is actually doing to my counts
The problem. RNA-seq counts are discrete, over-dispersed, and, with two or three replicates per group, desperately under-powered per gene. Naive per-gene tests either miss real ...
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Jul 8, 2026 · 2 min read
The lab that runs itself (almost)
The problem. LLMs can talk about experiments; can they run them, plan a protocol, write the instrument code, execute on real hardware, read the result, and iterate, without a hu...
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Jul 8, 2026 · 2 min read
Two recipes for a single-cell foundation model
The problem. If Geneformer showed pretraining can work for single cells, the next question is how, what objective, what tokenization, and does the design choice change which tas...
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Jul 8, 2026 · 2 min read
Foundation models meet the single cell
The problem. Single-cell RNA-seq gives you a matrix of ~20,000 genes × thousands of cells, but most biological questions come with tiny labeled datasets. Could you pretrain on t...
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Jul 8, 2026 · 2 min read
A foundation model for DNA
The problem. Protein language models learned to read amino acids. But DNA is the more general substrate, genes, regulatory elements, ncRNAs, whole operons, and modeling it at si...
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Jul 8, 2026 · 2 min read
The poster child, revisited
The problem. Artemisinin is the frontline antimalarial, but it was sourced from a plant (Artemisia annua) with volatile supply and price. Could you engineer a microbe to make it...
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Jul 8, 2026 · 2 min read
DBTL, fully worked
The problem. DBTL is easy to draw and hard to run as one integrated system. Carbonell et al. built the whole loop, design through machine-learning-guided redesign, inside a biof...
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Jul 7, 2026 · 2 min read
Generating proteins that never existed
The problem. Prediction and inverse folding both start from something, a sequence, or a backbone you already drew. Design’s most ambitious version starts from intent: “give me a...
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Jul 7, 2026 · 2 min read
Inverse folding
The problem. Structure prediction runs sequence → structure. Protein design needs the inverse: you have a backbone you want (a fold, a binder, a scaffold), and you need an amino...
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Jul 7, 2026 · 2 min read
Proteins as a language
The problem. AlphaFold2 is accurate but leans on a multiple-sequence alignment per target, which is slow to build and thin for orphan or designed sequences. Could a model learn ...
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Jul 7, 2026 · 2 min read
The structure problem, mostly solved
The problem. Predicting a protein’s 3D structure from its sequence was a 50-year open problem. Physics-based folding didn’t scale; earlier ML got close-ish. Then at CASP14, Alph...
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Jul 7, 2026 · 2 min read
Find-and-replace on a genome
The problem. The genetic code is redundant, 64 codons for 20 amino acids plus stop, so in principle you don’t need all 64. But “in principle” is cheap. Could you actually rewrit...
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Jul 7, 2026 · 2 min read
The edit button
The problem. Targeted DNA cutting existed before CRISPR, zinc fingers, TALENs, but each new target meant engineering a new protein. That’s slow and expensive. The question was w...
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Jul 7, 2026 · 2 min read
A field map for synthetic biology
The problem. By 2010 synthetic biology had gone from two proof-of-concept circuits to a sprawl of switches, oscillators, logic gates, and early applications. Someone needed to d...
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Jul 7, 2026 · 2 min read
Standard parts for living systems
The problem. After the toggle switch and repressilator, building anything bigger was miserable: every part behaved differently in every new context, and each project started fro...
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Jul 7, 2026 · 2 min read
A clock built from three genes
The problem. The toggle switch showed a cell could hold a state. Elowitz and Leibler asked the harder dynamical question: could you make a cell cycle through states on a schedul...
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Jul 7, 2026 · 2 min read
Biology's first flip-flop
The problem. By 2000, molecular biology could describe gene regulation exquisitely but rarely design it. Gardner, Cantor and Collins asked a pointed engineering question: can yo...