Biology subjects

Bonneau, R.

Publications and source records attributed to Bonneau, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2020

CRISPR-Decryptr reveals cis-regulatory elements from noncoding perturbation screens

Clustered Regularly Interspace Short Palindromic Repeats (CRISPR)-Cas9 genome editing methods provide the tools necessary to examine phenotypic impacts of targeted perturbations in high-throughput screens. While these technologies have the potential to reveal functional elements with direct therapeutic applications, statistical techniques to analyze noncoding screen data remain limited. We present CRISPR-Decryptr, a computational tool for the analysis of CRISPR noncoding screens. Our method leverages experimental design: accounting for multiple conditions, controls, and replicates to infer the regulatory landscape of noncoding genomic regions. We validate our method on a variety of mutagenesis, CRISPR activation, and CRISPR interference screens, extracting new insights from previously published data.

bioinformatics↗

bioRxiv2020

NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity

Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to proteome and biological network functional annotation use sequence similarity to transfer knowledge between species. These similarity-based approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular or organismal context for meaningful function prediction. In order to supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, the majority of these methods are tied to a network for a single species, and many species lack biological networks. In this work, we integrate sequence and network information across multiple species by applying an IsoRank-derived network alignment algorithm to create a meta-network profile of the proteins of multiple species. We then use this integrated multispecies meta-network as input features to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and more diverse examples from multiple organisms, and consequently leads to significant improvements in function prediction performance. Further, we evaluate our approach in a setting in which an organisms PPI network is left out, using other organisms network information and sequence homology in order to make predictions for the left-out organism, to simulate cases in which a newly sequenced species has no network information available.

bioinformatics↗

bioRxiv2020

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWAKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods and serve as a foundation for future research. Code and example data for DEWAKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

bioinformatics↗

bioRxiv2019

Disentangling microbial associations from hidden environmental and technical factors via latent graphical models

Detecting community-wide statistical relationships from targeted amplicon-based and metagenomic profiling of microbes in their natural environment is an important step toward understanding the organization and function of these communities. We present a robust and computationally tractable latent graphical model inference scheme that allows simultaneous identification of parsimonious statistical relationships among microbial species and unobserved factors that influence the prevalence and variability of the abundance measurements. Our method comes with theoretical performance guarantees and is available within the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework ( SpiecEasi R-package). Using simulations, as well as a comprehensive collection of amplicon-based gut microbiome datasets, we illustrate the methods ability to jointly identify compositional biases, latent factors that correlate with observed technical covariates, and robust statistical microbial associations that replicate across different gut microbial data sets.

bioinformatics↗

bioRxiv2019

Revisiting microbe-metabolite interactions: doing better than random

I.Recently, Quinn and Erb et al [1] made the case that when used correctly, correlation and proportionality can outperform MMvec when identifying microbe-metabolite interactions. We revisit this comparison and show that the proposed correlation and proportionality are outperformed by MMvec on real data due to their inability to deal with sparsity commonly observed in microbiome and metabolome datasets.

bioinformatics↗

bioRxiv2019

Splotch: Robust estimation of aligned spatial temporal gene expression data.

Spatial genomics technologies enable new approaches to study how cells interact and function in intact multicellular environments but present a host of technical and computational challenges. Here we describe Splotch, a novel computational framework for the analysis of spatially resolved transcriptomics data. Splotch aligns transcriptomics data from multiple tissue sections and timepoints to generate improved posterior estimates of gene expression. We demonstrate alignment of a large corpus of single-cell RNA-seq data into an automatically generated spatial-temporal coordinate and study optimal design for spatial transcriptomics experiments.

bioinformatics↗

bioRxiv2019

Distinct polysaccharide growth profiles of human intestinal Prevotella copri isolates

The gut-dwelling Prevotella copri, the most prevalent Prevotella species in human gut, have been associated with diet as well as disease, but our understanding of their diversity and functional roles remains rudimentary, as studies have been limited to 16S and metagenomic surveys and experiments using a single type strain. Here, we describe the genomic diversity of 83 P. copri isolates from 11 donors. We demonstrate that genomically distinct isolates, which can be categorized into different P. copri complex clades, utilize defined sets of plant polysaccharides that can be predicted in part from their genomes and from metagenomic data. This study thus reveals both genomic and functional differences in polysaccharide utilization between several novel human intestinal P. copri strains.

microbiology↗

bioRxiv2019

Designing Peptides on a Quantum Computer

Although a wide variety of quantum computers are currently being developed, actual computational results have been largely restricted to contrived, artificial tasks. Finding ways to apply quantum computers to useful, real-world computational tasks remains an active research area. Here we describe our mapping of the protein design problem to the D-Wave quantum annealer. We present a system whereby Rosetta, a state-of-the-art protein design software suite, interfaces with the D-Wave quantum processing unit to find amino acid side chain identities and conformations to stabilize a fixed protein backbone. Our approach, which we call the QPacker, uses a large side-chain rotamer library and the full Rosetta energy function, and in no way reduces the design task to a simpler format. We demonstrate that quantum annealer-based design can be applied to complex real-world design tasks, producing designed molecules comparable to those produced by widely adopted classical design approaches. We also show through large-scale classical folding simulations that the results produced on the quantum annealer can inform wet-lab experiments. For design tasks that scale exponentially on classical computers, the QPacker achieves nearly constant runtime performance over the range of problem sizes that could be tested. We anticipate better than classical performance scaling as quantum computers mature.

bioengineering↗

bioRxiv2019

Inference of bacterial small RNA regulatory networks and integration with transcription factor driven regulatory networks

Small non-coding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect messenger RNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional datasets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines. We comprehensively assess the accuracy of inferred sRNA regulons using available experimental data. We uncover 30 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Our findings expand the role of sRNAs in the regulation of chemotaxis, oxidation-reduction processes, galactose intake, and generation of pyruvate. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa and Bacillus subtilis. Overall, our strategy generates novel insights into the functional implications of sRNA regulation in multiple bacterial species.\n\nIMPORTANCEIndividual bacterial genomes can have dozens of small non-coding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false positives. Due to its data-driven nature, our method emerges as an ideal strategy to identify biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

systems biology↗

bioRxiv2019

The Prevotella copri complex comprises four distinct clades that are underrepresented in Westernised populations

Prevotella copri is a common inhabitant of the human gut. Interest in P. copri has gathered pace due to conflicting reports on whether it is beneficial or detrimental to health. In a cross-continent meta-analysis exploiting >6,500 available metagenomes supported by new isolate sequencing and recovery of high-quality genomes from metagenomes, we obtained >1,000 P. copri genomes. This 100-fold increase over existing isolate genomes allowed the genetic and global population structure of P. copri to be explored at an unprecedented depth. We demonstrate P. copri is not a monotypic species, but encompasses four distinct clades (>10% inter-clade vs. <4% intra-clade average single nucleotide variants) for which we propose the name P. copri complex, comprising clades A, B, C and D. We show the complex is near ubiquitous in non-Westernised populations (95.4% versus 29.6% in Westernised populations), where all four clades are typically co-present within an individual (61.6% of the cases), in contrast to Westernised populations (4.6%). Genomic analysis of the complex reveals substantial and complementary functional diversity, including the potential for utilisation of complex carbohydrates, suggestive that multi-generational dietary modifications may be a driver for the reduced P. copri prevalence in Westernised populations. Analysis of ancient stool microbiomes highlights a similar pattern of P. copri presence consistent with modern non-Westernised populations, allowing us to estimate the time of clade delineation to pre-date human migratory waves out of Africa. Our analysis reveals P. copri to be far more diverse than previously appreciated and this diversity appears to be underrepresented in Western-lifestyle populations.

microbiology↗

bioRxiv2019

Integrative protein modeling in RosettaNMR from sparse paramagnetic restraints

Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals hold great potential to study larger proteins and complexes. Additionally, computational methods designed to work with sparse data add atomic detail that is missing in the experimental restraints, allowing application to systems that are difficult to investigate. While specific frameworks in the Rosetta macromolecular modeling suite support the use of certain NMR restraint types, use of all commonly measured restraint types together is precluded. Here, we introduce a comprehensive framework into Rosetta that reconciles CS-Rosetta, PCS-Rosetta and RosettaNMR into a single framework, that, in addition to backbone chemical shifts and nuclear Overhauser effect distance restraints, leverages NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements, measured at multiple tagging sites. We further showcase the generality of RosettaNMR for various modeling challenges and benchmark it on 28 structure prediction cases, eight symmetric assemblies, two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models ([≤] 2[A]) in 50% of the cases.\n\nSignificance StatementComputational methods such as Rosetta can assist NMR structure determination by employing efficient conformational search algorithms alongside physically realistic energy functions to model protein structure from sparse experimental data. We have developed a framework in Rosetta that leverages paramagnetic NMR data in addition to chemical shift and nuclear Overhauser effect restraints and extends RosettaNMR calculations to the prediction of symmetric assemblies, protein-protein and protein-ligand complexes. RosettaNMR generated high-accuracy models ([≤] 2[A]) in 50% of cases for a benchmark set of 28 monomeric and eight symmetric proteins and predicted protein-protein and protein-ligand interfaces with up to 1[A] accuracy. The method expands Rosettas rich toolbox for integrative data-driven modeling and promises to be broadly useful in structural biology.

biophysics↗

bioRxiv2019

NSD2 overexpression drives clustered chromatin and transcriptional changes in insulated domains

CTCF and cohesin play a key role in organizing chromatin into TAD structures. Disruption of a single CTCF binding site is sufficient to change chromosomal interactions leading to alterations in chromatin modifications and gene regulation. However, the extent to which alterations in chromatin modifications can disrupt 3D chromosome organization leading to transcriptional changes is unknown. In multiple myeloma a 4;14 translocation induces overexpression of the histone methyltransferase, NSD2 resulting in expansion of H3K36me2 and shrinkage of antagonistic H3K27me3 domains. Using isogenic cell lines producing high and low levels of NSD2, we find oncogene activation is linked to alterations in H3K27ac and CTCF within H3K36me2 enriched chromatin. A linear regression model reveals that changes in both CTCF and/or H3K27ac significantly increase the probability that a gene sharing the same insulated domain will be differentially expressed. These results identify a bidirectional relationship between 2D chromatin and 3D genome organization in gene regulation.

genomics↗

bioRxiv2019

A comprehensive map of the dendritic cell transcriptional network engaged upon innate sensing of HIV

Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly understood. During infection with HIV-1, human dendritic cells (DCs) can detect the virus through an innate sensing pathway leading to antiviral interferon and DC maturation. Here, we developed an iterative experimental and computational approach to map the innate response circuitry during HIV-1 infection. By integrating genome-wide chromatin accessibility with expression kinetics, we inferred a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observed that an interferon response is required, yet insufficient to drive DC maturation, and identified PRDM1 and RARA as essential regulators of the interferon response and DC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the DC response to infection.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/579920_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (60K):\norg.highwire.dtl.DTLVardef@f57059org.highwire.dtl.DTLVardef@1a2f811org.highwire.dtl.DTLVardef@4c2908org.highwire.dtl.DTLVardef@19e1e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2019

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.

genomics↗

bioRxiv2019

Characterizing the epigenetic landscape of cellular populations from bulk and single-cell ATAC-seq information.

Given its ability to map chromatin accessibility with single base pair resolution, ATAC-seq has become a leading technology to probe the epigenomic landscape of single and aggregated cells. Understanding ATAC-seq data presents distinct analysis challenges, compared to RNA-seq technologies, because of the relative sparseness of the data produced and the interaction of complex noise with multiple chromatin structure scales. Methods commonly used to analyze chromatin accessibility datasets are adapted from algorithms designed to process different experimental technologies, disregarding the statistical and biological differences intrinsic to the ATAC-seq technology. Here, we present a Bayesian statistical approach, termed ChromA, to analyze ATAC-seq data. ChromA annotates the cellular epigenetic landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. Our method can analyze single cell ATAC-seq data, improving cell type identification and correcting many of the biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on several biological systems, including mouse and human immune cells and find it effective at recovering accessible chromatin, establishing ChromA as a top preforming general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs.

bioinformatics↗

bioRxiv2019

High-density spatial transcriptomics arrays for in situ tissue profiling

Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays either lack global transcriptome information or are not at the single-cell level. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are then randomly deposited in individual wells on a slide. The barcoded beads are decoded and coupled to a specific spatial address. We then capture and spatially in situ label RNA from the same histological tissue sections placed on the bead array slide. HDST recovers hundreds of thousands of transcript-coupled barcodes per experiment at 2 m resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to 2D spatial analysis of tissues at high resolution.

genomics↗

bioRxiv2018

Transcriptional regulatory networks that promote and restrict identities and functions of intestinal innate lymphoid cells

Innate lymphoid cells (ILCs) can be subdivided into several distinct cytokine-secreting lineages that promote tissue homeostasis and immune defense but also contribute to inflammatory diseases. Accumulating evidence suggests that ILCs, similarly to other immune populations, are capable of phenotypic and functional plasticity in response to infectious or environmental stimuli. Yet the transcriptional circuits that control ILC identity and function are largely unknown. Here we integrate gene expression and chromatin accessibility data to infer transcriptional regulatory networks within intestinal type 1, 2, and 3 ILCs. We predict the \"core\" sets of transcription-factor (TF) regulators driving each ILC subset identity, among which only a few TFs were previously known. To assist in the interpretation of these networks, TFs were organized into cooperative clusters, or modules that control gene programs with distinct functions. The ILC network reveals extensive alternative-lineage-gene repression, whose regulation may explain reported plasticity between ILC subsets. We validate new roles for c-MAF and BCL6 as regulators affecting the type 1 and type 3 ILC lineages. Manipulation of TF pathways identified here might provide a novel means to selectively regulate ILC effector functions to alleviate inflammatory disease or enhance host tolerance to pathogenic microbes or noxious stimuli. Our results will enable further exploration of ILC biology, while our network approach will be broadly applicable to identifying key cell state regulators in other in vivo cell populations.

immunology↗

bioRxiv2018

Normalization methods for microbial abundance data strongly affect correlation estimates

Consistent estimation of associations in microbial genomic survey count data is fundamental to microbiome research. Technical limitations, including compositionality, low sample sizes, and technical variability, obstruct standard application of association measures and require data normalization prior to estimating associations. Here, we investigate the interplay between data normalization and microbial association estimation by a comprehensive analysis of statistical consistency. Leveraging the large sample size of the American Gut Project (AGP), we assess the consistency of the two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis work flows and log-ratio transformations. We show that shrinkage estimation, a standard technique in high-dimensional statistics, can universally improve the quality of association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as examples of exploratory data analysis, we show that variance-stabilizing and log-ratio approaches provide for the most consistent estimation of taxonomic and structural coherence. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

bioinformatics↗