Biology subjects

Noble, W. S.

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

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Matrix prior for data transfer between single cell data types in latent Dirichlet allocation

Single cell ATAC-seq (scATAC-seq) enables the mapping of regulatory elements in fine-grained cell types. Despite this advance, analysis of the resulting data is challenging, and large scale scATAC-seq data are difficult to obtain and expensive to generate. This motivates a method to leverage information from previously generated large scale scATAC-seq or scRNA-seq data to guide our analysis of new scATAC-seq datasets. We analyze scATAC-seq data using latent Dirichlet allocation (LDA), a Bayesian algorithm that was developed to model text corpora, summarizing documents as mixtures of topics defined based on the words that distinguish the documents. When applied to scATAC-seq, LDA treats cells as documents and their accessible sites as words, identifying "topics" based on the cell type-specific accessible sites in those cells. Previous work used uniform symmetric priors in LDA, but we hypothesized that nonuniform matrix priors generated from LDA models trained on existing data sets may enable improved detection of cell types in new data sets, especially if they have relatively few cells. In this work, we test this hypothesis in scATAC-seq data from whole C. elegans nematodes and SHARE-seq data from mouse skin cells. We show that nonsymmetric matrix priors for LDA improve our ability to capture cell type information from small scATAC-seq datasets.

bioinformatics↗

bioRxiv2022

The Crux toolkit for analysis of bottom-up tandem mass spectrometry proteomics data

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

bioinformatics↗

bioRxiv2022

Efficient indexing of peptides for database search using Tide

The first step in the analysis of protein tandem mass spectrometry data typically involves searching the observed spectra against a protein database. During database search, the search engine must digest the proteins in the database into peptides, subject to digestion rules that are under user control. The choice of these digestion parameters, as well as selection of post-translational modifications (PTMs), can dramatically affect the size of the search space and hence the statistical power of the search. The Tide search engine separates the creation of the peptide index from the database search step, thereby saving time by allowing a peptide index to be reused in multiple searches. Here we describe an improved implementation of the indexing component of Tide that consumes around four times less resources (CPU and RAM) than the previous version and can generate arbitrarily large peptide databases, limited by only the amount of available disk space. We use this improved implementation to explore the relationship between database size and the parameters controlling digestion and PTMs, as well as database size and statistical power. Our results can help guide practitioners in proper selection of these important parameters.

bioinformatics↗

bioRxiv2022

The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles

Functional genomics experiments are invaluable for understanding mechanisms of gene regulation. However, comprehensively performing all such experiments, even across a fixed set of sample and assay types, is often infeasible in practice. A promising alternative to performing experiments exhaustively is to, instead, perform a core set of experiments and subsequently use machine learning methods to impute the remaining experiments. However, questions remain as to the quality of the imputations, the best approaches for performing imputations, and even what performance measures meaningfully evaluate performance of such models. In this work, we address these questions by comprehensively analyzing imputations from 23 imputation models submitted to the ENCODE Imputation Challenge. We find that measuring the quality of imputations is significantly more challenging than reported in the literature, and is confounded by three factors: major distributional shifts that arise because of differences in data collection and processing over time, the amount of available data per cell type, and redundancy among performance measures. Our systematic analyses suggest several steps that are necessary, but also simple, for fairly evaluating the performance of such models, as well as promising directions for more robust research in this area.

bioinformatics↗

bioRxiv2022

Detecting more peptides from bottom-up mass spectrometry data via peptide-level target-decoy competition

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrums best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here we first highlight and empirically augment a little-known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at a 1% FDR threshold relative to the PSM-only method.

bioinformatics↗

bioRxiv2022

Linking cells across single-cell modalities by synergistic matching of neighborhood structure

A wide variety of experimental methods are available to characterize different properties of single cells in a complex biosample. However, because these measurement techniques are typically destructive, researchers are often presented with complementary measurements from disjoint subsets of cells, providing a fragmented view of the cells biological processes. This creates a need for computational tools capable of integrating disjoint multi-omics data. Because different measurements typically do not share any features, the problem requires the integration to be done in unsupervised fashion. Recently, several methods have been proposed that project the cell measurements into a common latent space and attempt to align the corresponding low-dimensional manifolds. In this study we present an approach, Synmatch, which produces a direct matching of the cells between modalities by exploiting information about neighborhood structure in each modality. Synmatch relies on the intuition that cells which are close in one measurement space should be close in the other as well. This allows us to formulate the matching problem as a constrained supermodular optimization problem over neighborhood structures that can be solved efficiently. We show that our approach successfully matches cells in small real multi-omics datasets and performs favorably when compared to recently published state-of-the-art methods. Further, we demonstrate that Synmatch is capable of scaling to large datasets of thousands of cells. The Synmatch code and data used in this manuscript are available at https://github.com/orgs/Noble-Lab/synmatch

genomics↗

bioRxiv2022

A benchmark of Hi-C scaffolders using reference genomes and de novo assemblies

BackgroundStudying a new species using high-throughput sequencing requires a high-quality reference genome. However, assembling chromosome length sequences remains challenging. Recent advances in chromatin conformation capture (Hi-C) have provided a new approach to scaffolding genome assemblies, and the last ten years have seen a proliferation of such methods. However, to our knowledge no comprehensive benchmarking of Hi-C scaffolders has been conducted to date. ResultsThrough a literature review we identified the most popular Hi-C scaffolders - Lachesis, HiRise, 3d-dna, SALSA, and AllHiC. We tested their ability to scaffold four well studied genomes - S. cerevisiae, L. tarentolae, A. thaliana, and H. sapiens. Scaffolders were tasked with both scaffolding fragmented versions of the reference genome as well as de novo assemblies derived from long read datasets. We found that all scaffolders can exceed 80% accuracy under ideal circumstances but that their performance quickly deteriorates under more challenging conditions. Surprisingly, many scaffolders also showed poor performance on the best assemblies, where contigs are near chromosome length. Overall, we found that HiRise and Lachesis offer the best performance on average across all conditions. ConclusionsWe compare the performance of five Hi-C scaffolders using multiple reference species under both ideal and real-life conditions, thereby illuminating their strengths and weaknesses.

genomics↗

bioRxiv2022

Predicting A/B compartments from histone modifications using deep learning

Genomes fold into organizational units in the 3D space that can influence critical biological functions. In particular, the organization of chromatin into A and B compartments segregates its active regions from inactive regions. Compartments, evident in Hi-C contact matrices, have been used to describe cell-type specific changes in the A/B organization. However, obtaining Hi-C data for all cell and tissue types of interest is prohibitively expensive, which has limited the widespread consideration of compartment status. We present a prediction tool called Compartment prediction using Recurrent Neural Network (CoRNN) that models the relationship between the compartmental organization of the genome and histone modification enrichment. Our model predicts A/B compartments, in a cross-cell type setting, with an average area under the ROC curve of 90.9%. Our cell type-specific compartment predictions show high overlap with known functional elements. We investigate our predictions by systematically removing combinations of histone marks and find that H3K27ac and H3K36me3 are the most predictive marks. We then perform a detailed analysis of loci where compartment status cannot be accurately predicted from these marks. These regions represent chromatin with ambiguous compartmental status, likely due to variations in status within the population of cells. These ambiguous loci also show highly variable compartmental status between biological replicates in the same GM12878 cell type. Finally, we demonstrate the generalizability of our model by predicting compartments in independent tissue samples. Our software and trained model are publicly available at https://github.com/rsinghlab/CoRNN.

bioinformatics↗

bioRxiv2022

Dynamic chromatin organization and regulatory interactions in human endothelial cell differentiation

BackgroundVascular endothelial cells are a mesoderm-derived lineage with many essential functions, including angiogenesis and coagulation. However, the gene regulatory mechanisms that underpin endothelial specialization are largely unknown, as are the roles of 3D chromatin organization in regulating endothelial cell transcription. MethodsTo investigate the relationships between 3D chromatin organization and gene expression in endothelial cell differentiation, we induced endothelial cell differentiation from human pluripotent stem cells and performed Hi-C and RNA-seq assays at specific timepoints in differentiation. ResultsOur analyses reveal that long-range intrachromosomal contacts increase over the course of endothelial cell differentiation, as do genomic compartment transitions between active and inactive states. These compartmental states are tightly associated with endothelial transcription. Dynamic topologically associating domain (TAD) boundaries strengthen and converge on an endothelial cell state, and nascent TAD boundaries are linked to the expression of genes that support endothelial cell specification. Relatedly, chromatin pairwise point interactions (DNA loops) increase in frequency during differentiation and are linked to the expression of genes with essential roles in vascular biology, including MECOM, TFPI, and KDR. To identify forms of regulation specific to endothelial cell differentiation, we compared the functional chromatin dynamics of endothelial cells with those of developing cardiomyocytes. Cardiomyocytes exhibit greater long-range cis interactions than endothelial cells, whereas endothelial cells have increased local intra-TAD interactions and much more abundant pairwise point interactions. ConclusionsGenome topology changes dynamically during endothelial differentiation, including acquisition of long-range cis interactions and new TAD boundaries, interconversion of hetero- and euchromatin, and formation of DNA loops. These chromatin dynamics guide transcription in the development of endothelial cells and promote the divergence of endothelial cells from related cell types such as cardiomyocytes.

developmental biology↗

bioRxiv2022

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion, liquid chromatography, and in the mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or over-estimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of MS/MS experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments, and can be used with labeled or label-free approaches, and with data-independent or data-dependent acquisition.

bioinformatics↗

bioRxiv2022

Proteome-wide identification of amino acid substitutions deleterious for protein function

DNA sequencing has led to the discovery of millions of mutations that change the encoded protein sequences, but the impact of nearly all of these mutations on protein function is unknown. We addressed this scarcity of functional data by developing Miro, a proteomic technology that uses mistranslation to introduce amino acid substitutions and biochemical assays to quantify functional differences of thousands of protein variants by mass spectrometry. We apply this technology to the proteome of yeast to reveal amino acid substitutions that impact protein structure, ligand binding, protein-protein interactions, protein post-translational modifications, and protein thermal stability. Adapting Miro to human cells will provide a means to efficiently accelerate our mechanistic interpretation of genomic mutations to predict disease risk.

genomics↗

bioRxiv2022

Edison: measuring scaffolding accuracy with edit distance

MotivationThe quality of reference genomes critically affects analyses of next generation sequencing experiments. During the construction of the reference genome, contigs are organized into their underlying chromosomes in the scaffolding step. Historically, the quality of scaffolding software has been difficult to evaluate in a systematic and quantitative fashion. To this end, we identified genomic edit distance as a compelling method for evaluating the quality of a scaffold. ResultsWe present Edison, a Python implementation of the Double Cut and Join (DCJ) edit distance algorithm. Edison calculates the overall accuracy of a given scaffold using a reference genome and also provides scores for characterizing different aspects of the scaffolding accuracy, including grouping, ordering, and orientation. All metrics are calculated on a length-weighted basis, which rewards the correct placement of longer contigs over shorter ones. By creating 1000 random assemblies of the S. cerevisiae genome, we show that our scaffolding accuracy provides a more reliable metric than the commonly used metric, N50. Edison can be used to benchmark new scaffolding algorithms, providing insights into the strengths and weaknesses of each approach. Availability and ImplementationEdison is available under an MIT license at https://github.com/Noble-Lab/edison.

genomics↗

bioRxiv2022

LSMMD-MA: Scaling multimodal data integration for single-cell genomics data analysis

MotivationModality matching in single-cell omics data analysis--i.e., matching cells across data sets collected using different types of genomic assays--has become an important problem, because unifying perspectives across different technologies holds the promise of yielding biological and clinical discoveries. However, single-cell dataset sizes can now reach hundreds of thousands to millions of cells, which remains out of reach for most multi-modal computational methods. ResultsWe propose LSMMD-MA, a large-scale Python implementation of the MMD-MA method for multimodal data integration. In LSMMD-MA we reformulate the MMD-MA optimization problem using linear algebra and solve it with KeOps, a CUDA framework for symbolic matrix computation in Python. We show that LSMMD-MA scales to a million cells in each modality, two orders of magnitude greater than existing implementations. AvailabilityLSMMD-MA is freely available at https://github.com/google-research/large_scale_mmdma Contactlpapaxanthos@google.com

bioinformatics↗

bioRxiv2022

De novo mass spectrometry peptide sequencing with a transformer model

Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex biological samples and is thus the primary technology driving the growth of the field of proteomics. A key outstanding challenge in this field involves identifying the sequence of amino acids--the peptide--responsible for generating each observed spectrum, without making use of prior knowledge in the form of a peptide sequence database. Although various machine learning methods have been developed to address this de novo sequencing problem, challenges that arise when modeling tandem mass spectra have led to complex models that combine multiple neural networks and post-processing steps. We propose a simple yet powerful method for de novo peptide sequencing, Casanovo, that uses a transformer framework to map directly from a sequence of observed peaks (a mass spectrum) to a sequence of amino acids (a peptide). Our experiments show that Casanovo achieves state-of-the-art performance on a benchmark dataset using a standard cross-species evaluation framework which involves testing with spectra with never-before-seen peptide labels. Casanovo not only achieves superior performance but does so at a fraction of the model complexity and inference time required by other methods.

bioinformatics↗

bioRxiv2022

Group-walk, a rigorous approach to separate FDR analysis by TDC

Target-decoy competition (TDC) is a commonly used method for false discovery rate (FDR) control in the analysis of tandem mass spectrometry data. This type of competitionbased FDR control has recently gained significant popularity in other fields after Barber and Candes laid its theoretical foundation in a more general setting that included the feature selection problem. In both cases, the competition is based on a head-to-head comparison between an (observed) target score and a corresponding decoy (knockoff) score. However, the effectiveness of TDC depends on whether the data is homogeneous, which is often not the case: in many settings, the data consists of groups with different score profiles or different proportions of true nulls. In such cases, applying TDC while ignoring the group structure often yields imbalanced lists of discoveries, where some groups might include relatively many false discoveries and other groups include relatively very few. On the other hand, as we show, the alternative approach of applying TDC separately to each group does not rigorously control the FDR. We developed Group-walk, a procedure that controls the FDR in the target-decoy / knockoff setting while taking into account a given group structure. Group-walk is derived from the recently developed AdaPT -- a general framework for controlling the FDR with sideinformation. We show using simulated and real datasets that when the data naturally divides into groups with different characteristics Group-walk can deliver consistent power gains that in some cases are substantial. These groupings include the precursor charge state (4% more discovered peptides at 1% FDR threshold), the peptide length (3.6% increase) and the mass difference due to modifications (26% increase). Group-walk is available at https://cran.r-project.org/web/packages/groupwalk/index.html

bioinformatics↗

bioRxiv2022

MS1Connect: a mass spectrometry run similarity measure

Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols. We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though this data is not used by MS1Connect. The MS1Connect software will be made available upon acceptance at https://github.com/bmx8177/MS1Connect.

bioinformatics↗

bioRxiv2021

Epiphany: predicting Hi-C contact maps from 1D epigenomic signals

Recent deep learning models that predict the Hi-C contact map from DNA sequence achieve promising accuracy but cannot generalize to new cell types and indeed do not capture cell-type-specific differences among training cell types. We propose Epiphany, a neural network to predict cell-type-specific Hi-C contact maps from five epigenomic tracks that are already available in hundreds of cell types and tissues: DNase I hypersensitive sites and ChIP-seq for CTCF, H3K27ac, H3K27me3, and H3K4me3. Epiphany uses 1D convolutional layers to learn local representations from the input tracks, a bidirectional long short-term memory (Bi-LSTM) layers to capture long term dependencies along the epigenome, as well as a generative adversarial network (GAN) architecture to encourage contact map realism. To improve the usability of predicted contact matrices, we trained and evaluated models using multiple normalization and matrix balancing techniques including KR, ICE, and HiC-DC+ Z-score and observed-over-expected count ratio. Epiphany is trained with a combination of MSE and adversarial (i.a., a GAN) loss to enhance its ability to produce realistic Hi-C contact maps for downstream analysis. Epiphany shows robust performance and generalization to held-out chromosomes within and across cell types and species, and its predicted contact matrices yield accurate TAD and significant interaction calls. At inference time, Epiphany can be used to study the contribution of specific epigenomic peaks to 3D architecture and to predict the structural changes caused by perturbations of epigenomic signals.

genomics↗

bioRxiv2021

A flexible workflow for building spectral libraries from narrow window data independent acquisition mass spectrometry data

Advances in library-based methods for peptide detection from data independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

bioinformatics↗