Biology subjects

Salzman, J.

Publications and source records attributed to Salzman, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Distinct virus-derived circular RNA molecule influences host response during SARS-CoV-2 infection

Virus-derived circular RNA molecules (VcircRNAs) are expressed by many RNA viruses during infection. Putative functions include modulating viral replication and interacting with the host immune response. Some function as non-coding RNA fragments that regulate gene expression through binding to complementary RNA sequences, whereas others contain internal ribosomal entry site (IRES) sequences or non-canonical modifications that allow them to be translated. Here, we confirm the expression of a distinct SARS-CoV-2 VcircRNA molecule, circ7b8N, that has not been previously identified. We found that circ7b8N is expressed and detectable in cell culture infections and in acute infections across SARS-CoV-2 variants and shows promise for detection in post-acute clinical samples. Conservation of circ7b8N junctions is limited to the nearest phylogenetic relatives within the betacoronavirus genus but are present in other human and bat-infecting coronaviruses. Host cell gene expression is modulated by the treatment with circ7b8N agnostic of viral infection. The discovery and subsequent confirmation of circ7b8N expressed by SARS-CoV-2 provides a new biomarker for infection, and its conservation across variants suggests functional importance. Author SummaryCircular RNAs are a well-documented class of molecules expressed by mammalian cells. However, circular RNA molecules expressed by RNA viruses remain largely uncharacterized regarding their generation, specific functions, and roles in host-pathogen interactions. Our computational predictions discovered thousands of distinct circular RNA molecules expressed by SARS-CoV-2. Among these, we confirmed the presence of circ7b8N, the most abundant SARS-CoV-2-derived circular RNA identified in our sequencing data. We found that circ7b8N localizes outside the nucleus and is detectable in clinical samples collected both during and after acute SARS-CoV-2 infection. Although overexpression of circ7b8N was not found to alter viral titers, it modulated the expression of host genes related to immune response activation and membrane remodeling. This suggests that circ7b8N may simultaneously provide pro- and anti-viral functions independent of influencing viral replication. Phylogenetic analyses of coronaviruses suggest that the expression of circ7b8N is a relatively recent evolutionary event, and it is conserved across SARS-CoV-2 variants from the first five years of the pandemic. The abundant presence of circ7b8N across variants in both sequencing data and clinical samples implies it plays a multifaceted role in SARS-CoV-2 pathogenesis.

microbiology↗

bioRxiv2026

FunctionaL Assigning Sequence Homing (FLASH) maps phenotype to sequence with deep and machine learning

Genome-wide association studies (GWAS) map genetic variation to a reference genome and correlate variants to phenotypes. Yet, GWAS and similar procedures have limitations, including an inability to predict phenotype on variants never seen during the discovery phase and difficulty integrating structural variants. Deep and machine learning alternatives have not been successful at consistent prediction of resistance phenotypes (Hu et al. 2024). Here, we introduce FLASH: a new interpretable, statistically-based deep learning framework that operates directly on raw sequencing reads. In over 35,000 isolates of bacteria, fungi and viruses, FLASH achieves uniformly high accuracy on independent test data, including on variation never seen in training, meeting or exceeding bespoke state of the art methods. FLASH identifies canonical drug targets ab initio and new pan-species predictors of virulence, including those lacking annotation and those only partially aligned to NCBI reference databases. Further, FLASH can predict phenotypes beyond the possibility of GWAS, such as bacterial host range of phage, a task that to our knowledge is impossible today. FLASH is simple to run, highly efficient and constitutes a new approach for predicting gene function and phenotype across the tree of life. It is especially valuable when bioethical concerns and the vast genetic complexity of pathogenic microbes limit the feasibility of experimental validation.

bioinformatics↗

bioRxiv2024

sc-SPLASH provides ultra-efficient reference-free discovery in barcoded single-cell sequencing

Typical high-throughput single-cell RNA-sequencing (scRNA-seq) analyses are primarily conducted by (pseudo)alignment, through the lens of annotated gene models, and aimed at detecting differential gene expression. This misses diversity generated by other mechanisms that diversify the transcriptome such as splicing and V(D)J recombination, and is blind to sequences missing from imperfect reference genomes. Here, we present sc-SPLASH, a highly efficient pipeline that extends our SPLASH framework for statistics-first, reference-free discovery to barcoded scRNA-seq (10x Chromium) and spatial transcriptomics (10x Visium); we also provide its optimized module for preprocessing and k-mer counting in barcoded data, BKC, as a standalone tool. sc-SPLASH rediscovers known biology including V(D)J recombination and cell-type-specific alternative splicing in human and trans-splicing in tunicate (Ciona) and when applied to spatial datasets, detects sequence variation including tumor-specific somatic mutation. In sponge (Spongilla) and tunicate (Ciona), we uncover secreted repeat proteins expressed in immune-type cells and regulated during development; the sponge genes were absent from the reference assembly. sc-SPLASH provides a powerful alternative tool for exploring transcriptomes that is applicable to the breadth of lifes diversity.

bioinformatics↗

bioRxiv2024

SPLASH-structure: a statistical approach to identify RNA secondary structures from raw sequencing data, bypassing multiple sequence alignment

RNA secondary and tertiary structures are essential to life. Experimental methods to detect RNA structure, such as X-ray crystallography and chemical probing, are incisive but suffer from low throughput and dimensionality. Computational approaches, leveraging evolutionary signals from correlated mutations, provide an alternative means to infer RNA structures. However, these methods require assembly and face challenges due to statistical biases inherent in multiple sequence alignment (MSA). Furthermore, these methods cannot exploit a given RNA elements full spectrum of natural sequence variations. Here, we introduce STRUCT (Statistical Testing of RNA Units with Covariation Traits), an assembly-free, MSA-free, and metadata-free statistical method for identifying conserved RNA structures from raw sequencing data, quantifying base-pair covariations or stem variation exclusion in the putative RNA structures. We show STRUCT rediscovers known HIV structural elements and identifies conserved rRNA structures in metatranscriptomics samples. Moreover, STRUCT finds viral structures in mosquito metatranscriptomics samples de novo, including previously unannotated viral genomes, highlighting the methods potential for viral discovery. STRUCT is an ultra-fast, easy-to-use, and robust tool that excels in high-throughput RNA structure prediction and hypothesis generation, presenting a novel approach for discovering structural RNA elements.

genomics↗

bioRxiv2024

A reference-free algorithm discovers regulation in the plant transcriptome

Most plant genomes and their regulation remain unknown. We used SPLASH - a new, reference-genome free sequence variation detection algorithm - to analyze transcriptional and post-transcriptional regulation from RNA-seq data. We discovered differential homolog expression during maize pollen development, and imbibition-dependent cryptic splicing in Arabidopsis seeds. SPLASH enables discovery of novel regulatory mechanisms, including differential regulation of genes from hybrid parental haplotypes, without the use of alignment to a reference genome.

bioinformatics↗

bioRxiv2024

Ultra-efficient, unified discovery from microbial sequencing with SPLASH and precise statistical assembly

Bacteria comprise > 12% of Earths biomass and profoundly impact human and planetary health.1 Many key biological functions of microbes, and functions differentiating strains, are conferred or modified by genome plasticity including mobilization of genetic elements, phage integration, and CRISPR arrays. Characterizing each of these processes is time-consuming and requires custom bioinformatic workflows ill-suited to enable discovery of new sources of genetic diversity or to uncover which elements are active. Further, strain typing of bacterial species and approaches to discriminate sub-populations remain time-consuming and resource intensive. Here, we show that SPLASH, our published approach for reference-free discovery and analysis directly from raw reads, and an improved statistical assembly algorithm, compactors, unify diverse tasks in microbial sequence analysis: discovering new mobile elements and CRISPR arrays missing from any reference, and generating rapid, metadata-free strain typing of diverse bacteria. SPLASH and compactors together constitute a new general discovery tool for biological discovery in the microbial world.

bioinformatics↗

bioRxiv2023

SPLASH: a statistical, reference-free genomic algorithm unifies biological discovery

The authors have withdrawn this manuscript due to a duplicate posting of manuscript number BIORXIV/2022/497555. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. The correct preprint can be found at doi: https://doi.org/10.1101/2022.06.24.497555

genomics↗

bioRxiv2023

NOMAD2 provides ultra-efficient, scalable, and unsupervised discovery on raw sequencing reads

SPLASH is an unsupervised, reference-free, and unifying algorithm that discovers regulated sequence variation through statistical analysis of k-mer composition, subsuming many application-specific methods. Here, we introduce SPLASH2, a fast, scalable implementation of SPLASH based on an efficient k-mer counting approach. SPLASH2 enables rapid analysis of massive datasets from a wide range of sequencing technologies and biological contexts, delivering unparalleled scale and speed. The SPLASH2 algorithm unveils new biology (without tuning) in single-cell RNA-sequencing data from human muscle cells, as well as bulk RNA-seq from the entire Cancer Cell Line Encyclopedia (CCLE), including substantial unannotated alternative splicing in cancer transcriptome. The same untuned SPLASH2 algorithm recovers the BCR-ABL gene fusion, and detects circRNA sensitively and specifically, underscoring SPLASH2s unmatched precision and scalability across diverse RNA-seq detection tasks.

bioinformatics↗

bioRxiv2023

An interpretable, finite sample valid alternative to Pearson's X2 for scientific discovery

Contingency tables, data represented as counts matrices, are ubiquitous across quantitative research and data-science applications. Existing statistical tests are insufficient however, as none are simultaneously computationally efficient and statistically valid for a finite number of observations. In this work, motivated by a recent application in reference-free genomic inference (1), we develop OASIS (Optimized Adaptive Statistic for Inferring Structure), a family of statistical tests for contingency tables. OASIS constructs a test-statistic which is linear in the normalized data matrix, providing closed form p-value bounds through classical concentration inequalities. In the process, OASIS provides a decomposition of the table, lending interpretability to its rejection of the null. We derive the asymptotic distribution of the OASIS test statistic, showing that these finitesample bounds correctly characterize the test statistics p-value up to a variance term. Experiments on genomic sequencing data highlight the power and interpretability of OASIS. The same method based on OASIS significance calls detects SARS-CoV-2 and Mycobacterium Tuberculosis strains de novo, which cannot be achieved with current approaches. We demonstrate in simulations that OASIS is robust to overdispersion, a common feature in genomic data like single cell RNA-sequencing, where under accepted noise models OASIS still provides good control of the false discovery rate, while Pearsons X2 test consistently rejects the null. Additionally, we show on synthetic data that OASIS is more powerful than Pearsons X2 test in certain regimes, including for some important two group alternatives, which we corroborate with approximate power calculations. Significance StatementContingency tables are pervasive across quantitative research and data-science applications. Existing statistical tests fall short, however; none provide robust, computationally efficient inference and control Type I error. In this work, motivated by a recent advance in reference-free inference for genomics, we propose a family of tests on contingency tables called OASIS. OASIS utilizes a linear test-statistic, enabling the computation of closed form p-value bounds, as well as a standard asymptotic normality result. OASIS provides a partitioning of the table for rejected hypotheses, lending interpretability to its rejection of the null. In genomic applications, OASIS performs reference-free and metadata-free variant detection in SARS-CoV-2 and M. Tuberculosis, and demonstrates robust performance for single cell RNA-sequencing, all tasks without existing solutions.

bioinformatics↗

bioRxiv2023

Analysis of RNA processing directly from spatial transcriptomics data reveals previously unknown regulation

Technical advances have led to an explosion in the amount of biological data available in recent years, especially in the field of RNA sequencing. Specifically, spatial transcriptomics (ST) datasets, which allow each RNA molecule to be mapped to the 2D location it originated from within a tissue, have become readily available. Due to computational challenges, ST data has rarely been used to study RNA processing such as splicing or differential UTR usage. We apply the ReadZS and the SpliZ, methods developed to analyze RNA process in scRNA-seq data, to analyze spatial localization of RNA processing directly from ST data for the first time. Using Moran's I metric for spatial autocorrelation, we identify genes with spatially regulated RNA processing in the mouse brain and kidney, re-discovering known spatial regulation in Myl6 and identifying previously-unknown spatial regulation in genes such as Rps24, Gng13, Slc8a1, Gpm6a, Gpx3, ActB, Rps8, and S100A9. The rich set of discoveries made here from commonly used reference datasets provides a small taste of what can be learned by applying this technique more broadly to the large quantity of Visium data currently being created.

bioinformatics↗

bioRxiv2022

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Myriad mechanisms diversify the sequence content of eukaryotic transcripts at both the DNA and RNA levels, leading to profound functional consequences. Examples of this diversity include RNA splicing and V(D)J recombination. Currently, these mechanisms are detected using fragmented bioinformatic tools that require predefining a form of transcript diversification and rely on alignment to an incomplete reference genome, filtering out unaligned sequences, potentially crucial for novel discoveries. Here, we present SPLASH+, significantly advancing biological discovery possible with SPLASH, our recently introduced efficient, reference-free statistical approach. Integrating a micro-assembly and biological interpretation framework, SPLASH+ enables new discoveries including broad and novel examples of transcript diversification in single cells de novo, without the need for cell type metadata, which is impossible with current algorithms. Applied to 10,326 primary human single cells across 19 tissues profiled with SmartSeq2, SPLASH+ discovers a set of splicing and histone regulators with highly conserved intronic regions that are themselves subject to complex splicing regulation. Additionally, it reveals unreported transcript diversity in the heat shock protein HSP90AA1, as well as diversification in centromeric RNA expression, V(D)J recombination, RNA editing, and repeat expansion, all missed by existing methods. SPLASH+ is highly efficient, enabling the discovery of an unprecedented breadth of RNA regulation and diversification in single cells through a new automated paradigm of unbiased transcriptomic analysis.

bioinformatics↗

bioRxiv2022

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant tool differentiators. Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.

genomics↗

bioRxiv2022

Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation

Targeted low-throughput studies have previously identified subcellular RNA localization as necessary for cellular functions including polarization, and translocation. Further, these studies link localization to RNA isoform expression, especially 3 Untranslated Region (UTR) regulation. The recent introduction of genome-wide spatial transcriptomics techniques enable the potential to test if subcellular localization is regulated in situ pervasively. In order to do this, robust statistical measures of subcellular localization and alternative poly-adenylation (APA) at single cell resolution are needed. Developing a new statistical framework called SPRAWL, we detect extensive cell-type specific subcellular RNA localization regulation in the mouse brain and to a lesser extent mouse liver. We integrated SPRAWL with a new approach to measure cell-type specific regulation of alternative 3 UTR processing and detected examples of significant correlations between 3 UTR length and subcellular localization. Included examples, Timp3, Slc32a1, Cxcl14, and Nxph1 have subcellular localization in the brain highly correlated with regulated 3 UTR processing that includes use of unannotated, but highly conserved, 3 ends. Together, SPRAWL provides a statistical framework to integrate multi-omic single-cell resolved measurements of gene-isoform pairs to prioritize an otherwise impossibly large list of candidate functional 3 UTRs for functional prediction and study. SPRAWL predicts 3 UTR regulation of subcellular localization may be more pervasive than currently known.

bioinformatics↗

bioRxiv2022

Mouse lemur transcriptomic atlas elucidates primate genes, physiology, disease, and evolution

Mouse lemurs (Microcebus spp.) are an emerging primate model organism. However, little is known about their genetics or cellular and molecular biology. In the accompanying paper, we used large-scale single cell RNA-sequencing of 27 organs and tissues to identify over 750 molecular cell types, characterize their full transcriptomic profiles, and study evolution of primate cell types. Here we use the atlas to characterize mouse lemur genes, mutations, physiology, and disease. We uncover thousands of previously unidentified lemur genes and hundreds of thousands of new splice junctions that globally define lemur gene structures and reveal over 85,000 primate splice junctions missing in mice. We systematically explore the lemur immune system, comparing the global expression profiles of key immune genes in health and disease, and molecular mapping of immune cell development, trafficking, and their local and global activation to infection. We characterize primate/lemur-specific physiology and disease including molecular features of the immune program, of lemur adipocytes that exhibit dramatic seasonal rhythms, and of metastatic endometrial cancer that resembles the human malignancy. We identify and describe the expression patterns of over 400 primate genes missing in mice, many with similar expression patterns in human and lemur and some implicated in human disease. Finally, we provide an experimental framework for reverse genetic analysis by identifying naturally-occurring nonsense (null) mutations in three primate genes missing in mice and analyzing their transcriptional phenotypes. This work establishes mouse lemur as a tractable primate model organism for genetic and molecular analysis, and it prioritizes primate genes, splice junctions, physiology, and disease for future study.

genetics↗

bioRxiv2022

A statistical, reference-free algorithm subsumes myriad problems in genome science and enables novel discovery

Todays genomics workflows typically require alignment to a reference sequence, which limits discovery. We introduce a new unifying paradigm, SPLASH (Statistically Primary aLignment Agnostic Sequence Homing), an approach that directly analyzes raw sequencing data to detect a signature of regulation: sample-specific sequence variation. The approach, which includes a new statistical test, is computationally efficient and can be run at scale. SPLASH unifies detection of myriad forms of sequence variation. We demonstrate that SPLASH identifies complex mutation patterns in SARS-CoV-2 strains, discovers regulated RNA isoforms at the single cell level, documents the vast sequence diversity of adaptive immune receptors, and uncovers biology in non-model organisms undocumented in their reference genomes: geographic and seasonal variation and diatom association in eelgrass, an oceanic plant impacted by climate change, and tissue-specific transcripts in octopus. SPLASH is a new unifying approach to genomic analysis that enables an expansive scope of discovery without metadata or references. One-sentence summarySPLASH is a unifying, statistically driven approach to biological discovery from raw sequencing data, bypassing alignment.

genomics↗

bioRxiv2022

DIVE: a reference-free statistical approach to diversity-generating and mobile genetic element discovery

Diversity-generating and mobile genetic elements are paramount to microbial and viral evolution and result in evolutionary leaps conferring novel phenotypes, such as antimicrobial resistance. State-of-the-art algorithms to detect these elements have many limitations, including reliance on reference genomes, assemblers, and heuristics, resulting in computational bottlenecks and limiting the scope of biological discoveries. Here we introduce DIVE, a new reference-free approach to overcome these limitations using information contained in sequencing reads alone. We show that DIVE has improved detection power compared to existing reference-based methods using simulations and real data. We use DIVE to rediscover and characterize the activity of known and novel elements and generate new biological hypotheses about the mobilome. Using DIVE we rediscover CRISPR and identify novel repeats, and we discover unannotated genetic hyper-variability hotspots in Escherichia coli and Vibrio cholerae. Building on DIVE, we develop a reference-free framework capable of de novo discovery of mobile genetic elements, not currently available to our knowledge, and we use it to rediscover the known transposons in Mycobacterium tuberculosis, the causative agent of tuberculosis.

bioinformatics↗

bioRxiv2022

Activated interstitial macrophages are a predominant target of viral takeover and focus of inflammation in COVID-19 initiation in human lung

Early stages of deadly respiratory diseases such as COVID-19 have been challenging to elucidate due to lack of an experimental system that recapitulates the cellular and structural complexity of the human lung while allowing precise control over disease initiation and systematic interrogation of molecular events at cellular resolution. Here we show healthy human lung slices cultured ex vivo can be productively infected with SARS-CoV-2, and the cellular tropism of the virus and its distinct and dynamic effects on host cell gene expression can be determined by single cell RNA sequencing and reconstruction of "infection pseudotime" for individual lung cell types. This revealed that the prominent SARS-CoV-2 target is a population of activated interstitial macrophages (IMs), which as infection proceeds accumulate thousands of viral RNA molecules per cell, comprising up to 60% of the cellular transcriptome and including canonical and novel subgenomic RNAs. During viral takeover of IMs, there is cell-autonomous induction of a pro-fibrotic program (TGFB1, SPP1), and an inflammatory program characterized by the early interferon response, chemokines (CCL2, 7, 8, 13, CXCL10) and cytokines (IL6, IL10), along with destruction of cellular architecture and formation of dense viral genomic RNA bodies revealed by super-resolution microscopy. In contrast, alveolar macrophages (AMs) showed neither viral takeover nor induction of a substantial inflammatory response, although both purified AMs and IMs supported production of infectious virions. Spike-dependent viral entry into AMs was neutralized by blockade of ACE2 or Sialoadhesin/CD169, whereas IM entry was neutralized only by DC-SIGN/CD209 blockade. These results provide a molecular characterization of the initiation of COVID-19 in human lung tissue, identify activated IMs as a prominent site of viral takeover and focus of inflammation and fibrosis, and suggest therapeutic targeting of the DC-SIGN/CD209 entry mechanism to prevent IM infection, destruction and early pathology in COVID-19 pneumonia. Our approach can be generalized to define the initiation program and evaluate therapeutics for any human lung infection at cellular resolution.

genomics↗

bioRxiv2021

ReadZS detects developmentally regulated RNA processing programs in single cell RNA-seq and defines subpopulations independent of gene expression

RNA processing (RNAP), including splicing and alternative polyadenylation, is crucial to gene function and regulation, but methods to detect RNAP from single-cell RNA sequencing data are limited by reliance on pre-existing annotations, peak-calling heuristics, and collapsing measurements by cell type. We introduce ReadZS, the first annotation-free statistical approach to identify regulated RNAP in single cells. ReadZS discovers cell type-specific RNAP in the human lung and conserved, developmentally regulated RNAP in mammalian spermatogenesis - including global 3 UTR shortening in human spermatogenesis. ReadZS also discovers global 3 UTR lengthening in Arabidopsis root development, highlighting the usefulness of this method in under-annotated transcriptomes.

bioinformatics↗