Nature Biotechnology

Reading a proteins sequence from tandem mass spectra without a reference is limited by single-spectrum accuracy, most acutely across the hypervariable complementaritydetermining regions of antibodies. Broadly specific proteases tile a protein with long, overlapping peptides, so every residue is covered by many independent de novo reads. borgonovo assembles their per-step probability profiles into a reference-free per-residue consensus, seeding templates from mass-closure-consistent reads and recruiting the rest by substitution- tolerant alignment and per-column voting. Re-decoding each spectrum with a prior from its consensus position lifts amino acid accuracy on placed spectra from 0.80 to 0.87. On the therapeutic antibody trastuzumab, nine proteases cover its heavy and light chains completely at 0.88 fixed-window identity, and 0.93 on the pruned assembly once local indels are accommodated. Applied unchanged to five secretome proteins and trastuzumab with three proteases, it reaches 0.87 mean fixed-window identity over 82% coverage. borgonovo is open source and works with most de novo sequencers, so redundant digestion turns any of them into a protein sequencer where no reference exists.

1Clinical metagenomic sequencing is increasingly used to detect pathogens directly from clinical samples, but sparse and uneven genome recovery caused by low microbial biomass and stochastic fragment loss makes species-level validation difficult. Under these conditions, the same observed coverage may arise either from genuine genome-wide representation or from reads confined to conserved loci shared among related taxa. Current heuristic thresholds based on read counts, breadth or related alignment summaries target the realised coverage rather than the generating process. We show that, under hidden fragment loss, coverage is the wrong inferential object for uniformly valid species-level inference. What remains informative is the spatial dispersion of surviving reads across genomic coordinates. This change of inferential object allows us to ask how much of the candidate genome must be represented in any source capable of reproducing the observed evidence. This yields the Metagenomic Alignment Validation Index (MAVI), a conservative lower bound on the fraction of the candidate genome required to explain the observed read dispersion. MAVI therefore provides an error-controlled, interpretable measure of genomic confidence: the minimum genomic content a competing source would need to share with the candidate to mimic the observed evidence. Evaluated across 643 long-read metagenomic samples, including 280 clinical samples, MAVI reproduced independently supported organism calls, resolved sparse low-coverage ambiguities and exposed workflow-level collapse of stochastic dispersion, without relying on heuristic coverage thresholds.

Plasmid construction underpins molecular biology and synthetic biology, yet validation is often limited to the inserted fragment rather than the whole plasmid, and around a third of laboratory-made plasmids carry sequence errors that can affect function. Sanger sequencing scales poorly across whole plasmids, while short-read approaches cannot resolve the repeated DNA parts, such as promoters, that are common in synthetic constructs. We present PICARD-seq, a rapid nanopore-based protocol that uses off-the-shelf Tn5 rapid barcoding reagents and a MinION to sequence pools of whole plasmids in under a day, and we systematically benchmark the computational pipelines available for analysing the resulting data. Using a curated set of 25 plasmids of known sequence spanning 3.0-20.6 kbp, including various dilution series and repetitive multi-part constructs, we ran five independent replicates of each pipeline. The ONT EPI2ME Clone Validation workflow was fast (13-18 min) but stochastic, varying between replicates for both plasmids assembled and what sequence was returned; Canu outperformed the default Flye assembler, and reducing the minimum coverage parameter from 60x to 20x substantially improved assembly of large, repetitive, and dilute samples. The ensemble assembler Autocycler was slower (81-111 min) but gave the highest and most consistent rate of recovering the expected sequence. Complementary read mapping with minimap2 distinguished genuine sequence differences from assembly artefacts. Applying PICARD-seq to problematic plasmids revealed backbone concatemers, a misincorporated promoter part, and a mixed population of rearranged molecules in a repetitive construct. PICARD-seq makes routine whole-plasmid validation practical and affordable for individual laboratories.

Metagenomic sequencing allows for detection of any number of pathogenic viruses from a single sample without a priori knowledge of its contents. This has created a substantial opportunity to characterize pathogens in humans and their environment, but the field is held back by a lack of bioinformatics tools for taxonomic profiling and reconstructing virus genomes. The ideal profiler would simultaneously do two things: report viruses at single-read sensitivity so that ultra-low-abundance pathogens are not missed and hold near-perfect specificity so that a pathogen is never falsely reported. Here we present EsViritu, a taxonomic profiler that pairs an iterative greedy clustering algorithm and lowest-common-ancestor taxonomic refinement with a curated Virus Pathogen Database to reconstruct virus genomes and generate virome profiles directly from reads. We benchmarked EsViritu against four leading metagenomic profilers (Kraken2, Centrifuger, Metabuli, and Sylph), running each program across a full parameter sweep and against the same Virus Pathogen Database for an apples-to-apples comparison. Across synthetic positive controls, pathogen-free negative controls, and mutated mock communities, EsViritu was the most accurate tool at reconstructing community composition: it led or tied every tool on relative abundance accuracy at all taxonomic ranks and led or tied on detection across ranks. Importantly, other tools frequently report the presence of multiple related taxa when only reads for a single genome are present, but EsViritu faithfully records the correct, single taxon and genome. EsViritu achieves read-level sensitivity for sequences as low as 85% average nucleotide identity to the nearest reference and reports essentially no false positives in pathogen-free host and synthetic samples. EsViritu also returns reconstructed consensus genomes whose nucleotide identity to the nearest reference closely tracks ground truth, yielding both a profile and genomes from a single run.

Probe-based genomics technologies are extending molecular analysis into intact tissues and fixed cells, yet strategies to decode complex experimental conditions encoded in cellular RNA remain limited. Here we present a modular framework that integrates custom software tools with purpose-built cloning reagents to design, assemble, validate, and deploy combinatorial DNA barcodes. Combinatorial barcodes comprise spatially adjacent collections of known sequences, enabling millions of unique molecules to be efficiently distinguished using a limited set of probes. Our software tools integrate with optimized assembly plasmids and whole plasmid long-read sequencing for high-fidelity construction and structural validation of diverse combinatorial barcode architectures. Assembled barcode libraries are flexibly transferred into user-modified expression vectors to support diverse downstream experimental applications. We showcase the versatility of this framework by assembling two structurally distinct combinatorial barcode libraries, each containing millions of unique sequences. Following rabies virus-based delivery to the mouse brain, we validate in vivo decoding of a combinatorial barcode architecture capable of distinguishing ~16.3 million expressed RNAs through probe-based in situ sequencing. Our framework for flexible and accurate combinatorial barcode construction fills a technically demanding niche delivering cost-effective molecular reagents for multiplexed experimentation on current and evolving probe-based genomics platforms.

5-Hydroxymethylcytosine (5hmC) is a relatively stable chemical mark formed by oxidation of 5-methylcytosine in DNA, while its RNA counterparts, 5-hydroxymethylcytidine (hm5C) and 2'-O-methyl-5-hydroxymethylcytidine (hm5Cm), have recently emerged as potential regulators of post-transcriptional processes. Existing 5hmC enrichment methods rely on anti-bodies or enzymatic {beta}-glucosyltransferase labeling, are largely DNA-specific, and are not readily applicable to RNA. Here we report bisulfite-assisted labeling by thiol sequencing (BALT-seq), a one-step chemoselective strategy that installs a biotin-thioether handle directly onto hydroxymethylcytosine in DNA or RNA under mild acidic bisulfite conditions, without using enzymes. Mechanistic studies show that thiol capture of the bisulfite-activated 5hmC intermediate competes with canonical cytosine-5-methylenesulfonate formation, yielding a stable adduct that DNA polymerases bypass efficiently and that resists APOBEC-mediated deamination. In mouse embryonic stem cells, BALT-seq produces genome-wide 5hmC profiles highly concordant with 5hmC-Seal, TAB-seq, and oxBS-seq, recovers expected gene-body and chromatin-state enrichment, and extends to low-input cell-free DNA. On RNA, BALT-seq enriches hm5C/hm5Cm-containing transcripts more effectively than antibody-based hMeRIP and reveals structured modification landscapes across tRNA species and LINE/LTR repeat families. BALT-seq thus establishes a unified, enzyme-free chemical platform for hydroxymethylcytosine profiling across DNA, cell-free DNA, and RNA.

RNA isoform dysregulation drives disease pathogenesis and is the target of FDA-approved splice-switching therapeutics. However, multiplexed sequencing methods discard splice junction information because only 3' termini are barcoded and counted. Here, we repurpose acylation and click chemistries to conjugate bioorthogonal barcodes (bobcodes) directly onto multiple internal positions along cellular RNAs. Bobcoded RNAs from multiple samples are pooled for multiplexed cDNA synthesis, during which reverse transcriptase switches from each RNA template onto its tethered bobcode with greater than 99% accuracy in species mixing experiments. Bobcode attachment intervals set cDNA insert sizes without a library fragmentation step, and priming with poly(dT) or random hexamers selects between 3'-end counting and full-length isoform capture. A bioorthogonal barcode-sequencing (BOB-seq v0.1) drug screen identifies transcriptome-wide on- and off-target RNA splicing effects and outperforms existing multiplexing RNA sequencing methods in workflow simplicity, sample-to-sample variability, and barcoding accuracy. Bobcodes add isoform resolution to scalable multiplexed RNA sequencing.

A cell-by-gene count matrix is the artifact of a single-cell RNA-seq experiment that is most often stored, shared, and reanalyzed. It is the output of a computation whose inputs are the sequenced molecules and a gene annotation, and while the molecules never change, the annotation is revised continually. Once the matrix has been produced, the evidence behind it can no longer be reinterpreted. Recovering that evidence means returning to raw reads or alignments that are large, costly to process, and frequently unavailable. We ask whether a concise representation of the molecules themselves can be extracted once and reused indefinitely, to quantify under any future annotation, to query and discover features that no annotation yet describes, and to pool evidence across cells and samples. Our starting observation is that the procedures that turn alignments into counts, gene assignment and UMI collapse, never read most of what an alignment file contains. They consume relations among molecules such as shared genomic geometry, shared placements, barcode identity, and the equality or near-equality of UMIs. We show that these relations form a statistic that is sufficient for such consumers, and we design a compact, seekable, content-authenticated archive that stores them while deferring every annotation-dependent decision to analysis time. Archives compose into content-addressed collections that route cohort queries to the molecules that can answer them without copying molecules. We implement these ideas in a tool called gravlax. Across four human 10x 3' datasets, gravlax archives require 11--18 bits per read and are 9.0--12.7x smaller than tag-preserving CRAM. Count matrices replayed from an archive deviate from direct STARsolo quantification by 0.24--0.75% of normalized UMI mass, whereas changing GENCODE v32 to v49 moves 2.12--4.64%, and quantification replay is 34--82x faster than STARsolo at matched thread budgets. A federated index over eight archives occupies 2.96% of their size, answers a 96-query junction panel 2.59x faster than the archives alone, and screens the cohort genome-wide for unannotated splice events that recur across donors in just 9 seconds. Because the molecules are retained, the archives also answer questions the matrix has discarded. An analysis of four peripheral-blood archives recovers a validated FYB1 immune-cell splicing switch, a cross-fitted fragment model appropriate for 3' chemistry reveals an eight-donor shift in NTRK2 terminal-isoform usage from astrocyte and neural-stem-cell populations to mature neurons, and pooling evidence across cells within the context of an expectation-maximization algorithm recovers 75--98% of withheld multi-gene molecule labels. Gravlax is open source, implemented in Rust, licensed under the BSD 3-clause license, and available at https://github.com/COMBINE-lab/gravlax.

Most human genes can end their messenger RNAs at more than one position, and the choice shifts between cell types. Single-cell sequencing reads that choice in individual cells, but two questions are seldom asked: are the reported sites real, and does the test for a cell-type difference hold its error rate? Here we introduce our tool PeakATail, which calls polyadenylation sites from reads still carrying a piece of the tail, ranks them by how many distinct molecules carry that evidence, discards sites where adenosine-rich genomic sequence mimics a tail, and pairs them with a differential test whose error rate is measured, not assumed. The evidence is scarce, so the tool is built to be right rather than complete. Across four libraries from two species, 71 to 83 percent of its calls fall within 100 bases of a curated atlas, over thirty times the shuffled-control rate; long reads from different donors support 76.5 percent of the human calls. The cost is sensitivity. Only one of six test configurations held its false-positive rate under shuffled labels; with it, 15,942 cell-type switches replicated across twelve lung cancer patients and none in ten shuffled-label runs. One pre-registered target was missed and is reported.

Image-based transcriptomic approaches can define, discover, and chart cell types and states within an array of tissues. However, measurement quality depends on RNA integrity, and the modern tissue preservation toolbox was not designed to protect this highly labile molecule. Here we leverage MERFISH to show that tissue-dependent differences in endogenous RNase activity can shape spatial transcriptomics data quality for different preservation methods and that RNase-activity-guided protocol optimization can improve data quality. In parallel, we introduce an RNA-aware pan-tissue preservation approach, Rapid Inhibition and Permanent Inactivation of Nucleases (RIPIN), that rapidly stabilizes samples with a broad-spectrum RNase inhibitor while permitting slow, chemical inactivation. RIPIN produces high-quality MERFISH measurements in all profiled human and mouse tissues, is compatible with clinical workflows, and is easily integrated with frozen or paraffin sectioning. By highlighting how RNA integrity can be lost during tissue processing, our work may inspire the next generation of RNA-aware histology methods.

Microbial metagenomes encode vast catalytic diversity, but recovering enzymes of novel, yet-undescribed functionality typically requires whole-community assembly plus a means of identifying active proteins ab initio. The first step of the task is compute-intensive and misses low-abundance sequences. The second is complicated by our inability to predict novel functions. REBEAN, our DNA language model, sidesteps the latter by assigning each sequencing read a high-level Enzyme Commission (EC) class or a non-enzyme label without alignment. Here we build REFinder, a pipeline that addresses both steps by routing REBEAN-annotated reads to assemble only the putative enzymatic reads that have identified catalytic signatures. In our evaluation of 50 microbiome metagenomes, REFinder identified 1.2 to 2.1 fold more enzymes than could be recovered via homology-based annotation of the proteins from the corresponding full assemblies. Moreover, it was as much as 6.4-fold cheaper computationally than full assembly. Across all samples, REFinder identified at least three fourths and as many as 90% of the homology-accessible enzymes identified via full assembly of the complete metagenomes. Notably, a fraction of these, 22% to 45% per EC class, carried no similarity to Swiss-Prot proteins, i.e. a set of enzymes homology cannot annotate. For roughly two fifths of the over thirteen thousand such novel oxidoreductases from two saliva samples, ESMFold predicted structures aligned with TM-score[≥]0.7 to a characterized enzyme structure in the PDB - a substantial structural similarity without sequence homology. These results illustrate that targeted, alignment-free assembly turns even well-mapped microbiomes into a source of thousands of previously invisible but credible novel enzymes, raising our expectations for exploration of environmental microbiomes.

Virtual-cell models increasingly learn from large perturbation atlases, but their view of cis regulation remains limited to endogenous genes embedded in broad native regulatory contexts. Here we introduce Therna Biosciences' Chronos platform and its first public dataset release, comprising two complementary modules of a massively parallel synthetic gene atlas: Penta-47x27K for 5' UTRs/internal-promoter elements and Tria-47x28K for 3' UTR stability elements. Together, these datasets measure ~60,000 compact cis-regulatory elements across approximately 50 cell lines in one pooled experiment. The choice of cell lines here, provided to us courtesy of Tahoe Tx, was rooted in our aim to make this dataset maximally useful for the virtual-cell modeling community. At Therna, we routinely apply Chronos -- part of our RNA-Logix(TM) platform -- beyond cancer cell-line pools, to systems such as primary cells and organoids, via LNP-formulated mRNA delivery. Chronos captures both transcriptional and post-transcriptional gene expression control. Episomal DNA delivery measures DNA-normalized mRNA output, while direct RNA delivery with longitudinal sampling reliably quantifies RNA decay. The RNA-delivery arm uses chemically modified synthetic mRNA incorporating N1-methylpseudouridine, a modification widely used in mRNA therapeutics. To resolve high-complexity libraries of up to 30,000 elements, Chronos pushes the sensitivity limits of single-cell RNA sequencing to quantify individual RNA molecules within single cells. By massively expanding gene regulatory networks with synthetic genes whose variable regulatory code is short and defined, Chronos provides an auxiliary cis-regulatory lens for virtual-cell modeling and enables context-specific cis-trans regulatory interactions to be learned directly.

Local alignment tools struggle at querying against thousands of bacterial genomes, because the reference indexes are memory-intensive and base-level dynamic programming is slow. We present alamem (https://github.com/yunwilliamyu/alamem), an approximate local aligner that streams unprocessed reference databases and estimates average nucleotide identity (ANI) from Maximal-Exact-Match (MEM) chaining statistics. Alamem can query a bacterial genome against >85,000 prokaryotic genomes in under 30 seconds (with multithreading) using 4 GB of memory, making it over 16x faster and 21x more memory-efficient than existing local aligners using their default running modes. On top of alamem, we built alasight (https://github.com/graceoualline/alasight) for detecting signatures of horizontal gene transfer (HGT) by finding pairs of alamem hits to divergent genomes. Alasight achieves over an order of magnitude better sensitivity on data from real bovid microbiomes than existing mobile genetic element-detection software and provides depth and sparsity information supporting each putative HGT region.

Genomic and metagenomic studies have revealed that microbes have the capacity to produce an enormous diversity of secondary metabolites. These compounds play important roles in microbial interactions and are also a major source of medicines and other useful natural products, yet only a small fraction of this biosynthetic potential has been experimentally characterized. BGC Atlas was developed to explore this largely uncharacterized diversity by placing biosynthetic gene clusters (BGCs) into genomic and environmental context. Here, we present BGC Atlas v2, which expands the collection nearly ninefold to more than 16 million predicted BGCs and extends it from metagenomic assemblies to MAGs, single-amplified genomes and isolate genomes. The new release provides taxonomic assignments for nearly all BGCs, harmonized environmental metadata, nested searches across biosynthetic, taxonomic, environmental and geographic properties, and protein-sequence searches against BGC-encoded genes. Despite its scale, the collection reveals how much microbial biosynthetic diversity remains unexplored. By connecting pathways to related families, organisms and environments, BGC Atlas v2 supports natural-product discovery and investigations of microbial biosynthetic diversity across taxa and ecosystems. BGC Atlas v2 is freely available at https://bgc-atlas.cs.uni-tuebingen.de.

The spatial organization of post-transcriptional regulation is a fundamental yet difficult to access layer of tissue biology. MicroRNAs (miRNAs) are small RNAs with a key role in post-transcriptional regulation, but their short length has excluded them from spatial profiling technologies, leaving them largely unexplored in spatial transcriptomics. Here, we introduce miR-Space, a method that converts individual miRNAs into extended, uniquely barcoded molecules directly in tissue, enabling their spatial detection by in situ sequencing. Across 30 mouse and human brain sections, miR-Space enabled highly multiplexed miRNA profiling at single-molecule and single-cell resolution, joint analysis with mRNA, and implementation on the automated Xenium platform. miR-Space resolved major anatomical regions and cell populations from spatial miRNA expression, identified reproducible cell-associated miRNA signatures, and uncovered previously unknown spatial and cellular distributions of multiple miRNAs. Together, these capabilities establish miR-Space as a framework for integrating miRNAs into spatial transcriptomics, enabling spatial miRNomics at anatomical and single-cell resolution.

Long-read RNA sequencing directly resolves the full structures of RNA transcripts. Advances in throughput now enable the generation of deeply sequenced cohorts of hundreds of samples, making joint transcript discovery across large datasets possible. However, existing transcript identification methods were designed for small datasets, which limits their applicability at this scale. Here, we present Isocall, a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples. Isocall converts aligned full-length non-concatemer reads into compact per-sample transcript profiles, merges these profiles across samples, and jointly identifies known and novel transcripts supported by reads in the analyzed dataset. Filtering is tunable: presets provide coarse control and individual parameters, including relative abundance and internal priming thresholds, provide fine control. Isocall demonstrated high precision in our accuracy benchmarks, including the WTC11 SIRV spike-in controls, for which Isocall reported 0-2 false-positive transcripts per sample across the three SIRV mixes at default settings. To demonstrate scalability, we applied Isocall to 206 samples, totalling 3.5 billion raw reads, from the Human Pangenome Reference Consortium. After parallelized pbmm2 alignment and Isocall profile, the call step performed joint calling across the entire dataset in 25 minutes, using 1.3 GB of peak memory and 8 threads. Finally, in Genome in a Bottle samples with matched SNP genotypes, splice-site polymorphisms provide an additional measure of call accuracy: Isocall recovered 337 polymorphic splice sites, including a de novo donor site in BTN3A1 that corresponds to a complete isoform switch on the mutant allele.

Bacterial conjugation and CRISPR base editing offer complementary routes for programmable DNA transfer and heritable genomic writing, but their combined potential for information encoding in living cells remains unexplored. Here, we introduce CONCORD, a bacterial encoding platform that couples multiplexed bacterial conjugation to genomic base editing for synthesis-free in vivo DNA data writing. A recipient Writer strain expresses the base-editing machinery and is equipped with a writing template, whereas reusable donor Guide strains deliver the encoding sgRNA signals by conjugation. Using this setup, we encode binary information into genomic DNA in a high-throughput manner. Moreover, we systematically evaluated multiple orthogonal base editors for an expanded encoding repertoire and improved data recovery through redundancy encoding. We deploy the system to encode 45,689 bits into DNA including two culturally and historically significant artifacts: a greeting from NASA's Voyager Golden Record and a photograph of Navajo petroglyphs and recovered these datasets with 100% and 99.5% accuracy, respectively, without computational error correction. Connecting and engineering the unique mating and immune system of microbial cells we have established a framework for programmable information writing in bacterial populations, with applications for biological event recording and sustainable DNA data storage.

Recent efforts have demonstrated that systematic analysis of the millions of metagenomes in the Sequence Read Archive (SRA) enables groundbreaking biological discoveries. However, SRA-scale comparison of whole metagenomes has remained elusive. Here, we introduce the most comprehensive resource to date for experiment-to-experiment analysis of all SRA metagenomes, comprising FracMinHash sketches (470GB), hypergen sketches (19GB), and an all-pairs Jaccard similarity matrix (90GB), which we release online at https://github.com/RolandFaure/SRA-metagenome-sketches-databases. These resources enable a wide range of analyses, which we demonstrate with several applications. First, we use the FracMinHash sketches to uncover temporal trends, finding that the amount of newly discovered microbial sequence continues to grow, rather than saturate or shrink. Second, we use the hypergen sketches, which enable nearest-neighbor search, to identify co-assembly candidates and thereby improve assembly quality. Third, we use the similarity matrix to analyze all SRA metagenomes, identifying duplicated submissions and over 50K candidates for metadata correction. Together, our resources unlock systematic analysis of metagenomic data at unprecedented scale.

Steady-state RNA abundance measurements mask the synthesis and decay rates that shape gene expression. Analog intrinsic recoding sequencing (AIR-seq) repurposes the base-pairing properties of N4-hydroxycytidine (NHC) to mark newly synthesized RNA with C-to-T and T-to-C mismatches in standard RNA-seq libraries, eliminating chemical conversion and enrichment common to RNA metabolic labeling methods. NHC-AIR-seq resolves bulk and single-cell RNA dynamics, improves RNA velocity inference, reveals hidden regulation, and brings RNA kinetics into routine transcriptomic workflows.

Spatial transcriptomics offers a glimpse into the immunology of tissues. However, limitations in spatial transcriptomics preclude the detection of highly diverse, low-abundance, and previously unknown sequences, including immune repertoires and microbiota. Here, we introduce Archimap, a spatial transcriptomic platform that simultaneously profiles spatial transcriptomes, immune repertoires, and microbiota from formalin-fixed paraffin-embedded (FFPE) tissues. Using Archimap, we profile the spatial localization of TCRs, BCRs, and the microbiota landscape in archived clinical tissues at single-cell resolution. Through comprehensive benchmarking, we validate Archimaps performance and fidelity. Archimap in situ assembles the immune complex and reconstructs the clonal evolution of antibodies. Together, Archimap shows the power of in situ discovery of functional immune repertoires for their antitumor immunity.