Biology subjects

Noble, W. S.

Publications and source records attributed to Noble, W. S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Programmatic design and editing of cis-regulatory elements

The development of modern genome editing and DNA synthesis has enabled researchers to edit DNA sequences with high precision but has left unsolved the problem of designing these edits. We introduce Ledidi, a computational method that rephrases the discrete design task of choosing which edits to make as an easily solvable continuous optimization problem. Ledidi can use any pre-trained deep learning model to guide the optimization, yielding an edited sequence that exhibits the desired outcome while explicitly minimizing the number of edits used. When applied in dozens of settings, we find that Ledidis designs can precisely control transcription factor binding, chromatin accessibility, transcription, and enhancer activity in silico. By using several deep learning models simultaneously, we design cell type-specific enhancers and experimentally validate them in cellulo. Finally, we introduce the concept of an "affinity catalog", where the design task is repeated multiple times across continuous variants of the design target. We demonstrate how these catalogs can be used to interpret deep learning models and the impact of starting template sequences, and also to design regulatory elements that control transcriptional dosage while maintaining cell type-specificity.

bioinformatics↗

bioRxiv2025

Fast and memory efficient searching of large-scale mass spectrometry data using Tide

Over the past 30 years, software for searching tandem mass spectrometry data against a protein database has improved dramatically in speed and statistical power. However, existing tools can still struggle to analyze truly massive datasets, when either the number of spectra or the number of proteins being analyzed grows too large. Here we describe enhancements to the Tide search engine that allow it to handle datasets containing >10 million spectra and databases containing >7 billion peptides on commodity hardware. We demonstrate that the new Tide architecture is around 2-7 times faster than the previous version and is now comparable to MSFragger and Sage in speed while requiring much less memory. Tide is open source and is publicly available as pre-compiled binaries for Windows, Linux and Mac.

bioinformatics↗

bioRxiv2025

CryoNeRF: reconstruction of homogeneous and heterogeneous cryo-EM structures using neural radiance field

Cryogenic electron microscopy (cryo-EM) has become a widely used technique for determining the 3D structures of proteins. However, cryo-EM datasets often exhibit heterogeneity, with protein particle images from multiple conformations or compositional states. Here we propose CryoNeRF, a novel neural radiance fields (NeRF)-based cryo-EM reconstruction framework operating directly in Euclidean 3D space. CryoNeRF introduces a multi-resolution hash encoding and heterogeneity-aware cryo-EM encoder to model cryo-EM heterogenity. Extensive experiments demonstrate the stability and superior performance of CryoNeRF in both homogeneous and heterogeneous settings. On homogeneous datasets, CryoNeRF achieves 15.8% improvement over previous state-of-the-art methods. On both simulated and experimental heterogeneous datasets, CryoNeRF demonstrates exceptional capability in handling both conformational and compositional variations, which is consistent with previous experimental discoveries. Notably, CryoNeRF successfully distinguishes assembly states that even only account for 2% particles of the dataset in cases of compositional heterogeneity.

bioinformatics↗

bioRxiv2024

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species

Nuclear DNA is organized into a compact three-dimensional (3D) structure that impacts critical cellular processes. High-throughput chromosome conformation capture (Hi-C) is the most widely used method for measuring 3D genome architecture, while linear epigenomic assays, such as ATAC-seq, DNase-seq, and ChIP-seq, are extensively employed to characterize epigenomic regulation. However, the integrative analysis of chromatin interactions and associated epigenomic regulation remains challenging due to the pairwise nature of Hi-C data, mismatched resolution between Hi-C and epigenomic assays, and inconsistencies among analysis tools. Here we propose HiCFoundation, a Hi-C-based foundation model for integrative analysis linking chromatin structure to downstream regulatory function. HiCFoundation is trained from hundreds of Hi-C assays encompassing 118 million contact matrix submatrices. The model achieves state-of-the-art performance on multiple types of 3D genome analysis, including reproducibility analysis, resolution enhancement, and loop detection. We further demonstrate the models generalizability through genome architecture analysis of 316 species. Notably, by enhancing low-coverage experimental Hi-C data, HiCFoundation reveals genome-wide loop loss during differentiation of hematopoietic stem and progenitor cells (HSPCs) to neutrophils. Additionally, HiCFoundation is able to predict multiple types of epigenomic activity from Hi-C input and further interprets the link between Hi-C input and epigenomic output to reveal the relationship between chromatin conformation and genome function. Finally, HiCFoundation can analyze single-cell Hi-C data, shedding light on genome structure at single-cell resolution. HiCFoundation thus provides a unified, e"cient, generalizable, and interpretable foundation for genome architecture, single-cell and multi-omics analysis across species, paving the path for systematically studying genome 3D architecture and its regulatory mechanisms.

bioinformatics↗

bioRxiv2024

Improved detection of differentially abundant proteins through FDR-control of peptide-identity-propagation

The goal of proteomics is to identify and quantify peptides and proteins within a biological sample. Almost all algorithms for the identification of peptides in LC-MS/MS data employ two steps: peptide/spectrum matching and peptide-identity-propagation (PIP), also known as match-between-runs. PIP was originally envisioned as a backup method to overcome measurement stochasticity. However, current PIP implementations can routinely account for up to 40% of all results, with that proportion rising as high as 75% in single-cell proteomics. Unlike peptide identities derived through peptide/spectrum matches, for which error estimation has been strictly enforced for decades, peptide identities derived through PIP have not historically been subject to statistical evaluation. As an indispensable component of label free quantification, PIP needs a simple and statistically rigorous method for estimating its error rates. Although several tools claim to control the false discovery rate (FDR) of PIP, these claims cannot be validated as there is currently no accepted method to assess the accuracy of the stated FDR. We present a method for FDR control of PIP, called PIP-ECHO, and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different benchmark datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that only PIP-ECHO can accurately control the FDR of PIP at 1% across all datasets, including single cell. When analyzing spike-in datasets where different known amounts of yeast or E. coli peptides are added to a constant background of human peptides, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis, yielding 53% more differentially abundant proteins than MaxQuant and 146% more than IonQuant.

bioinformatics↗

bioRxiv2024

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used proteomics technique for profiling the sequence diversity of circulating antibodies (Ab-seq), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal antibodies with known protein sequences in 70 different combinations of concentration and oligoclonality, both with and without polyclonal serum IgG background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 LC-MS/MS runs. We quantified the dependence of MS-based Ab-seq identification on antibody sequence, concentration, protease, background signal diversity, and bioinformatics setups. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced antibody identification. De novo peptide sequencing showed similar performance to database-dependent methods for higher antibody concentrations, but de novo antibody reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based antibody profiling.

immunology↗

bioRxiv2024

Nascent transcript O-MAP reveals the molecular architecture of a single-locus subnuclear compartment built by RBM20 and the TTN RNA

Eukaryotic nuclei adopt a highly compartmentalized architecture that influences nearly all genomic processes. Understanding how this architecture impacts gene expression has been hindered by a lack of tools for elucidating the molecular interactions at individual genomic loci. Here, we adapt oligonucleotide-mediated proximity-interactome mapping (O-MAP) to biochemically characterize discrete, micron-scale nuclear neighborhoods. By targeting O-MAP to introns within the TTN pre-mRNA, we systematically map the chromatin loci, RNAs, and proteins within a muscle-specific RNA factory organized around the TTN locus. This reveals an unanticipated compartmental architecture that organizes cis- and trans-interacting chromosomal domains, including a hub of transcriptionally silenced chromatin. The factory also recruits dozens of unique RNA-binding and chromatin-scaffolding factors, including QKI and SAFB, along with their target transcripts. Loss of the cardiac-specific splicing factor RBM20--a master regulator of TTN splicing that is mutated in dilated cardiomyopathy--remodels nearly every facet of this architecture. This establishes O-MAP as a pioneering method for probing single-locus, microcompartment-level interactions that are opaque to conventional tools. Our findings suggest new mechanisms by which coding genes can "moonlight" in nuclear-architectural roles.

genomics↗

bioRxiv2024

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

bioinformatics↗

bioRxiv2024

Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle:High-Resolution Sampling Every 4 Hours Over 22 Days

In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of a Rhizosolenia sp. bloom, the initiation and complete bloom cycle of Chaetoceros socialis (8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22-1.2 {micro}m), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms.

ecology↗

bioRxiv2024

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC

Three-dimensional nuclear DNA architecture comprises well-studied intra-chromosomal (cis) folding and less characterized inter-chromosomal (trans) interfaces. Current predictive models of 3D genome folding can effectively infer pairwise cis-chromatin interactions from the primary DNA sequence but generally ignore trans contacts. There is an unmet need for robust models of trans-genome organization that provide insights into their underlying principles and functional relevance. We present TwinC, an interpretable convolutional neural network model that reliably predicts trans contacts measurable through proximity ligation-dependent (in situ and intact Hi-C) and independent (DNA SPRITE) genome-wide chromatin conformation assays.. TwinC uses a paired sequence design from replicate Hi-C experiments to learn single base pair relevance in trans interactions across two stretches of DNA. The method achieves high predictive accuracy (AUROC=0.80) on a cross-chromosomal test set from in situ and intact Hi-C experiments in heart tissue. Furthermore, we train TwinC using in situ Hi-C data from the widely used GM12878 cell line and validate its performance with orthogonal DNA SPRITE assay in the same cell type. Mechanistically, the neural network learns the importance of compartments, chromatin accessibility, clustered transcription factor binding and G-quadruplexes in forming trans contacts. In summary, TwinC models and interprets trans genome architecture, shedding light on this poorly understood aspect of gene regulation.

bioinformatics↗

bioRxiv2024

Predicting cell cycle stage from 3D single-cell nuclear-stained images

The cell cycle governs the proliferation, differentiation, and regeneration of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research spanning development, health, aging, and disease. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. To facilitate more efficient cell cycle annotation of microscopy data, we developed CellCycleNet, a machine learning (ML) workflow designed to simplify cell cycle staging with minimal experimenter intervention and cost. CellCycleNet accurately predicts cell cycle phase using only a fluorescent nuclear stain (DAPI) in fixed interphase cells. Using the Fucci2a cell cycle reporter system as ground truth, we collected two benchmarking image datasets and trained two ML models--a support vector machine (SVM) and a deep neural network--to classify nuclei as being in either the G1 or S/G2 phases of the cell cycle. Our results suggest that CellCycleNet outperforms state-of-the-art SVM models on each dataset individually. When trained on two image datasets simultaneously, CellCycleNet achieves the highest classification accuracy, with an improvement in AUROC of 0.08-0.09. The model also demonstrates excellent generalization across different microscopes, achieving an AUROC of 0.95. Overall, using features derived from 3D images, rather than 2D projections of those same images, significantly improves classification performance. We have released our image data, trained models, and software as a community resource.

cell biology↗

bioRxiv2024

Imputation of cancer proteomics data with a deep model that learns from many datasets

Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially expressed (DE) proteins and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in tandem mass tag (TMT) proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to TMT data from >1,000 cancer patient samples spanning ten cancer types from the Clinical Proteomics Tumor Atlas Consortium (CPTAC). Lupine outperforms the state of the art for TMT imputation, identifies more DE proteins than other methods, corrects for TMT batch effects, and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open source Python package.

bioinformatics↗

bioRxiv2024

A BLAST from the past: revisiting blastp's E-value

The Basic Local Alignment Search Tool, BLAST, is an indispensable tool for genomic research. BLAST established itself as the canonical tool for sequence similarity search in large part thanks to its meaningful statistical analysis. Specifically, BLAST reports the E-value of each reported alignment, which is defined as the expected number of optimal local alignments that will score at least as high as the observed alignment score, assuming that the query and the database sequences are randomly generated. Here we critically evaluate the E-values provided by the standard protein BLAST (blastp), showing that they can be at times significantly conservative while at others too liberal. We offer an alternative approach based on generating a small sample from the null distribution of random optimal alignments, and testing whether the observed alignment score is consistent with it. In contrast with blastp, our significance analysis seems valid, in the sense that it did not deliver inflated significance estimates in any of our extensive experiments. Moreover, although our method is slightly conservative, it is often significantly less so than the blastp E-value. Indeed, in cases where blastps analysis is valid (i.e., not too liberal), our approach seems to deliver a greater number of correct alignments. One advantage of our approach is that it works with any reasonable choice of substitution matrix and gap penalties, avoiding blastps limited options of matrices and penalties. In addition, we can formulate the problem using a canonical family-wise error rate control setup, thereby dispensing with E-values, which can at times be difficult to interpret.

bioinformatics↗

bioRxiv2024

A haplotype-resolved view of human gene regulation

Diploid human cells contain two non-identical genomes, and differences in their regulation underlie human development and disease. We present Fiber-seq Inferred Regulatory Elements (FIRE) and show that FIRE provides a more comprehensive and quantitative snapshot of the accessible chromatin landscape across the 6 Gbp diploid human genome, overcoming previously unrecognized biases in existing regulatory element catalogs. FIRE enables comprehensive detection of haplotype-selective chromatin accessibility (HSCA), exposing novel imprinted elements lacking underlying parent-of-origin CpG methylation differences, and gene regulatory modules that permit genes to escape X chromosome inactivation. We uncover that the human leukocyte antigen (HLA) locus harbors the most HSCA in immune cells, where we resolve specific transcription factor (TF) binding events disrupted by disease-associated variants. Finally, we demonstrate that the regulatory landscape of a cell is littered with autosomal somatic chromatin epimutations that are propagated by clonal expansions to create mitotically stable and non-genetically deterministic chromatin alterations.

genomics↗

bioRxiv2024

A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data

A core computational challenge in the analysis of mass spectrometry data is the de novo sequencing problem, in which the generating amino acid sequence is inferred directly from an observed fragmentation spectrum without the use of a sequence database. Recently, deep learning models have made significant advances in de novo sequencing by learning from massive datasets of high-confidence labeled mass spectra. However, these methods are primarily designed for data-dependent acquisition (DDA) experiments. Over the past decade, the field of mass spectrometry has been moving toward using data-independent acquisition (DIA) protocols for the analysis of complex proteomic samples due to their superior specificity and reproducibility. Hence, we present a new de novo sequencing model called Cascadia, which uses a transformer architecture to handle the more complex data generated by DIA protocols. In comparisons with existing approaches for de novo sequencing of DIA data, Cascadia achieves state-of-the-art performance across a range of instruments and experimental protocols. Additionally, we demonstrate Cascadias ability to accurately discover de novo coding variants and peptides from the variable region of antibodies.

bioinformatics↗

bioRxiv2024

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a theoretical formulation of entrapment experiments that allows us to rigorously characterize the behavior of the various entrapment methods. We also propose a more powerful method for evaluating FDR control, and we employ that method, along with other existing techniques, to characterize a variety of popular search tools. We empirically validate our entrapment analysis in the fairly well-understood DDA setup before applying it in the DIA setup. We find that none of the DIA search tools consistently controls the FDR at the peptide level, and the tools struggle particularly with analysis of single cell datasets.

bioinformatics↗

bioRxiv2024

Accounting for digestion enzyme bias in Casanovo

A key parameter of any proteomics mass spectrometry experiment is the identity of the enzyme that is used to digest proteins in the sample into peptides. The Casanovo de novo sequencing model was trained using data that was generated with trypsin digestion; consequently, the model prefers to predict peptides that end with the amino acids "K" or "R." This bias is desirable when the Casanovo is used to analyze data that was also generated using trypsin but can be problematic if the data was generated using some other digestion enzyme. In this work, we modify Casanovo to take as input the identify of the digestion enzyme, alongside each observed spectrum. We then train Casanovo with data generated using several different restriction enzymes, and we demonstrate that the resulting model successfully learns to capture enzyme-specific behavior. However, we find, surprisingly, that this new model does not yield a significant improvement in sequencing accuracy relative to a model trained without the enzyme information but using the same training set. This observation may have important implications for future attempts to make use of experimental metadata in de novo sequencing models.

bioinformatics↗

bioRxiv2024

Predicting chromatin conformation contact maps

Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets--each represented as a 2D contact map-- spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.

bioinformatics↗