Biology subjects

Libbrecht, M. W.

Publications and source records attributed to Libbrecht, M. W..

15 recordsLinked to original sources

bioRxiv2025

Pan-cell type continuous chromatin state annotation of all IHEC epigenomes

The International Human Epigenome Consortium has generated thousands of epigenomic datasets that mea-sure various biochemical activities in the genome, including transcription factor binding, histone modification, and DNA accessibility. Currently, the predominant methods for integrating these datasets to annotate regu-latory elements are segmentation and genome annotation (SAGA) algorithms. The majority of annotations by these methods are cell type-specific. However, as the number of profiled cell types has grown into the thousands, using thousands of cell type-specific chromatin state annotations proves undesirable for many applications. Here, we present a pan-cell type annotation that summarizes all IHEC epigenomes using the recently-developed method, epigenome-ssm.

genomics↗

bioRxiv2025

CANDI: self-supervised, confidence-aware denoising imputation of genomic data

Large-scale epigenomic datasets such as histone modifications and DNA accessibility have greatly advanced our understanding of genomic function. However, these measurements often suffer from noise, batch effects and irreproducibility. Epigenome imputation has emerged as a promising solution to these challenges. These methods integrate patterns across experiments, cell types, and genomic loci to predict the results of experiments, yielding predictions that often surpass observed data in quality. Thus, researchers increasingly leverage imputation for denoising data prior to downstream analysis. However, existing methods for imputation-based denoising have significant limitations. Here, we propose CANDI (Confidence-Aware Neural Denoising Imputer), a method for epigenome imputation that (1) predicts raw counts and handles experiment-specific covariates such as sequencing depth, (2) can (optionally) incorporate information from a low-quality existing experiment when predicting a target without retraining, and (3) outputs a calibrated measure of uncertainty. This approach is enabled using a Transformer model with self-supervised learning (SSL) training.

genomics↗

bioRxiv2023

Integrative chromatin state annotation of 234 human ENCODE4 cell types using Segway reveals disease drivers

Towards the goal of identifying functional elements in the human genome, the fourth and final phase of the ENCODE consortium has newly profiled hundreds of human tissues using sequencing-based measurements of genomic activity such as ChIP-seq measures of transcription factor binding and histone modification. Chromatin state annotations created by segmentation and genome annotation (SAGA) methods such as Segway have emerged as the predominant integrative summary of such epigenomic data sets. Here, we present the ENCODE4 catalog of Segway annotations, a set of sample-specific genome-wide Segway chromatin state annotations for 234 ENCODE human biosamples inferred from 1,794 functional genomics experiments. We define an updated vocabulary of chromatin state terms that includes patterns of activity present only in a subset of samples or identified only with rarely-performed assays. We show that these ENCODE4 Segway annotations accurately capture both general and cell-type-specific regulatory patterns, and do so with substantially improved sensitivity relative to prior large-scale chromatin annotation sets. This catalog facilitates the downstream discovery of regulatory mechanisms which underlie diseases and traits identified by genome-wide association studies.

genomics↗

bioRxiv2023

Robust chromatin state annotation

BackgroundSegmentation and genome annotations (SAGA) methods such as ChromHMM and Segway are widely to annotate chromatin states in the genome. These algorithms take as input a collection of genomics datasets, partition the genome, and assign a label to each segment such that positions with the same label have similar patterns in the input data. SAGA methods output an human-interpretable summary of the genome by labeling every genomic position with its annotated activity such as Enhancer, Transcribed, etc. Chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of SAGA state assignments. ResultsTowards the goal of producing robust chromatin state annotations, we performed a comprehensive evaluation of the reproducibility of SAGA methods. We show that SAGA annotations exhibit a large degree of disagreement, even when run with the same method on replicated data sets. This finding suggests that there is significant risk to using SAGA chromatin state annotations. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to SAGA annotations. This r-value is assigned to each genomic bin of a SAGA annotation and represents the probability that the label of this bin will be reproduced in a replicated experiment. This process is analogous to irreproducible discovery rate (IDR) analysis that is commonly used for ChIP-seq peak calling and related tasks. Thus SAGAconf allows a researcher to select only the reliable parts of a SAGA annotation for use in downstream analyses. SAGAconf r-values provide accurate confidence estimates of SAGA annotations, allowing researchers to filter out unreliable elements and remove doubt in those that stand up to this scrutiny.

genomics↗

bioRxiv2022

Integrative chromatin domain annotation throughgraph embedding of Hi-C data

MotivationThe organization of the genome into domains plays a central role in gene expression and other cellular activities. Researchers identify genomic domains mainly through two views: 1D functional assays such as ChIP-seq, and chromatin conformation assays such as Hi-C. Fully understanding domains requires integrative modeling that combines these two views. However, the predominant form of integrative modeling uses segmentation and genome annotation (SAGA) along with the rigid assumption that loci in contact are more likely to share the same domain type, which is not necessarily true for epigenomic domain types and genome-wide chromatin interactions. ResultsHere, we present an integrative approach that annotates domains using both 1D functional genomic signals and Hi-C measurements of genome-wide 3D interactions without the use of a pairwise prior. We do so by using a graph embedding to learn structural features corresponding to each genomic region, then inputting learned structural features along with functional genomic signals to a SAGA algorithm. We show that our domain types recapitulate well-known subcompartments with an additional granularity that distinguishes a combination of the spatial and functional states of the genomic regions. In particular, we identified a division of the previously-identified A2 subcompartment such that the divided domain types have significantly varying expression levels. Availabilityhttps://github.com/nedashokraneh/IChDA Contactmaxwl@sfu.ca Supplementary information

genomics↗

bioRxiv2021

VSS-Hi-C: Variance-stabilized signals for chromatin 3D contacts

MotivationThe genome-wide chromosome conformation capture assay Hi-C is widely used to study chromatin 3D structures and their functional implications. Read counts from Hi-C indicate the strength of chromatin contact between each pair of genomic loci. These read counts are heteroskedastic: that is, a difference between the interaction frequency of 0 and 100 is much more significant than a difference between the interaction frequency of 1000 and 1100. This property impedes visualization and downstream analysis because it violates the Gaussian variable assumption of many computational tools. Thus heuristic transformations aimed at stabilizing the variance of signals like the shifted-log transformation are typically applied to data before its visualization and inputting to models with Gaussian assumption. However, such heuristic transformations cannot fully stabilize the variance because of their restrictive assumptions about the mean-variance relationship in the data. ResultsHere we present VSS-Hi-C, a data-driven variance stabilization method for Hi-C data. We show that VSS-Hi-C signals have a unit variance improving visualization of Hi-C, for example in heatmap contact maps. VSS-Hi-C signals also improve the performance of subcompartment callers relying on Gaussian observations. VSS-Hi-C is implemented as an R package and can be used for variance stabilization of different genomic and epigenomic data types with two replicates available. Availabilityhttps://github.com/nedashokraneh/vssHiC Contactmaxwl@sfu.ca

bioinformatics↗

bioRxiv2021

Hi-C-LSTM: Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation

Despite the availability of chromatin conformation capture experiments, understanding the relationship between regulatory elements and conformation remains a challenge. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory (LSTM) neural network model. We find that these representations contain all the information needed to recreate the original Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.

genomics↗

bioRxiv2021

SigTools: Exploratory Visualization for Genomic Signals

MotivationWith the advancement of sequencing technologies, genomic data sets are constantly being expanded by high volumes of different data types. One recently introduced data type in genomic science is genomic signals, which are usually short-read coverage measurements over the genome. An example of genomic signals is Epigenomic marks which are utilized to locate functional and nonfunctional elements in genome annotation studies. To understand and evaluate the results of such studies, one needs to understand and analyze the characteristics of the input data. ResultsSigTools is an R-based genomic signals visualization package developed with two objectives: 1) to facilitate genomic signals exploration in order to uncover insights for later model training, refinement, and development by including distribution and autocorrelation plots. 2) to enable genomic signals interpretation by including correlation, and aggregation plots. Moreover, Sigtools also provides text-based descriptive statistics of the given signals which can be practical when developing and evaluating learning models. We also include results from 2 case studies. The first examines several previously studied genomic signals called histone modifications. This use case demonstrates how SigTools can be beneficial for satisfying scientists curiosity in exploring and establishing recognized datasets. The second use case examines a dataset of novel chromatin state features which are novel genomic signals generated by a learning model. This use case demonstrates how SigTools can assist in exploring the characteristics and behavior of novel signals towards their interpretation. In addition, our corresponding web application, SigTools-Shiny, extends the accessibility scope of these modules to people who are more comfortable working with graphical user interfaces instead of command-line tools. AvailabilitySigTools source code, installation guide, and manual is available on http://github.com/shohre73. Contactshohre_masoumi@sfu.ca

bioinformatics↗

bioRxiv2021

Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

The availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were celltype specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-celltype representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions and evolutionary conservation. These representations outperform existing methods in a majority of cell-types, while yielding smoother representations along the genomic axis due to their sequential nature.

bioinformatics↗

bioRxiv2020

Variance-stabilized units for sequencing-based genomic signals

MotivationA sequencing-based genomic assay such as ChIP-seq outputs a real-valued signal for each position in the genome that measures the strength of activity at that position. Most genomic signals lack the property of variance stabilization. That is, a difference between 100 and 200 reads usually has a very different statistical importance from a difference between 1,100 and 1,200 reads. A statistical model such as a negative binomial distribution can account for this pattern, but learning these models is computationally challenging. Therefore, many applications--including imputation and segmentation and genome annotation (SAGA)--instead use Gaussian models and use a transformation such as log or inverse hyperbolic sine (asinh) to stabilize variance. ResultsWe show here that existing transformations do not fully stabilize variance in genomic data sets. To solve this issue, we propose VSS, a method that produces variance-stabilized signals for sequencingbased genomic signals. VSS learns the empirical relationship between the mean and variance of a given signal data set and produces transformed signals that normalize for this dependence. We show that VSS successfully stabilizes variance and that doing so improves downstream applications such as SAGA. VSS will eliminate the need for downstream methods to implement complex mean-variance relationship models, and will enable genomic signals to be easily understood by eye. Contactmaxwl@sfu.ca. Availabilityhttps://github.com/faezeh-bayat/Variance-stabilized-units-for-sequencing-based-genomic-signals.

bioinformatics↗

bioRxiv2020

Semi-supervised segmentation and genome annotation

Segmentation and genome annotation methods automatically discover joint signal patterns in whole genome datasets. Previously, researchers trained these algorithms in a fully unsupervised way, with no prior knowledge of the functions of particular regions. Adding information provided by expert-created annotations to supervise training could improve the annotations created by these methods. We implemented semi-supervised learning using virtual evidence in the annotation method Segway. Additionally, we defined a positionally tolerant precision and recall metric for scoring genome annotations based on the proximity of each annotation feature to the truth set. We demonstrate semi-supervised Segways ability to learn patterns corresponding to provided transcription start sites on a specified supervision label, and subsequently recover other transcription start sites in unseen data on the same supervision label.

bioinformatics↗

bioRxiv2018

Continuous chromatin state feature annotation of the human epigenome

Semi-automated genome annotation (SAGA) methods are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity (such as ChIP-seq measurements of histone modification and transcription factor binding), and output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm, that uses a Kalman filter state space model to efficiently annotate the genome with chromatin state features. We show that chromatin state features from epigenome-ssm are more useful for several downstream applications than both continuous and discrete alternatives, including their ability to identify expressed genes and enhancers. Therefore, we expect that these continuous chromatin state features will be valuable reference annotations to be used in visualization and downstream analysis.

bioinformatics↗

bioRxiv2017

Segway 2.0: Gaussian mixture models and minibatch training

SummarySegway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters.\n\nAvailability and ImplementationSegway and its source code are freely available for download at https://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802940) and datasets (https://doi.org/10.5281/zenodo.802907) for this papers analysis.\n\nContactmichael.hoffman@utoronto.ca

bioinformatics↗

bioRxiv2017

PREDICTD: PaRallel Epigenomics Data Imputation With Cloud-based Tensor Decomposition

The Encyclopedia of DNA Elements (ENCODE) and the Roadmap Epigenomics Project have produced thousands of data sets mapping the epigenome in hundreds of cell types. However, the number of cell types remains too great to comprehensively map given current time and financial constraints. We present a method, PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD), to address this issue by computationally imputing missing experiments in collections of epigenomics experiments. PREDICTD leverages an intuitive and natural model called \"tensor decomposition\" to impute many experiments simultaneously. Compared with the current state-of-the-art method, ChromImpute, PREDICTD produces lower overall mean squared error, and combining methods yields further improvement. We show that PREDICTD data can be used to investigate enhancer biology at non-coding human accelerated regions. PREDICTD provides reference imputed data sets and open-source software for investigating new cell types, and demonstrates the utility of tensor decomposition and cloud computing, two technologies increasingly applicable in bioinformatics.

bioinformatics↗

bioRxiv2016

A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types

Semi-automated genome annotation methods such as Segway enable understanding of chromatin activity. Here we present chromatin state annotations of 164 human cell types using 1,615 genomics data sets. To produce these annotations, we developed a fully-automated annotation strategy in which we train separate unsupervised annotation models on each cell type and use a machine learning classifier to automate the state interpretation step. Using these annotations, we developed a measure of the importance of each genomic position called the \"conservation-associated activity score,\" which we use to aggregate information across cell types into a multi-cell type view. The aggregated conservation-associated activity score provides a measure of importance directly attributable to a specific activity in a specific set of cell types. In contrast to evolutionary conservation, this measure is not biased to detect only elements shared with related species. Using the conservation-associated activity score, we combined all our annotations into a single, cell type-agnostic encyclopedia that catalogs all human transcriptional and regulatory elements, enabling easy and intuitive interpretation of the effect of genome variants on phenotype, such as in disease-associated, evolutionarily conserved or positively selected loci. These resources, including cell type-specific annotations, encyclopedia, and a visualization server, are available at http://noble.gs.washington.edu/proj/encyclopedia.\n\nAuthor SummaryGenome annotation algorithms are an effective class of tools for understanding the function of the genome. These algorithms take as input a set of genome-wide measurements about the activity at each base pair in a given tissue, such as where a given protein is binding or how accessible the DNA is to being read by a protein. The genome is then partitioned and each segment is assigned a label such that positions with the same label exhibit similar patterns in the input data. Such annotations are widely used for many applications, such as to understand the mechanism of impact of a given genetic variant. Here we present, to our knowledge, the most comprehensive set of genome annotations created so far, encompassing 164 human cell types and including 1,615 genomics data sets. These comprehensive annotations are made possible by a strategy that automates the previous interpretation step. Furthermore, we present several methodological innovations that make these genome annotations more useful.

genomics↗