Biology subjects

Sauerwald, N.

Publications and source records attributed to Sauerwald, N..

7 recordsLinked to original sources

bioRxiv2025

Variant-resolved prediction of context-specific isoform variation with a graph-based attention model

In eukaryotes, most genes produce multiple transcript isoforms that diversify the transcriptome and proteome, serving as a key mechanism of functional regulation. Genetic variation can disrupt the RNA processing signals that shape isoform structure and abundance, yet modeling these effects at full-length isoform resolution remains challenging due to the complexity of transcript regulation. Here, we introduce Otari, an attention-based graph neural network framework trained on the human genomic sequence and long-read transcriptomes across 30 tissue types and brain regions. Otari predicts tissue-specific differential isoform abundance by integrating sequence-derived epigenetic and post-transcriptional signals, enabling isoform-resolved variant effect interpretation. Applied to large-scale variant datasets including an autism cohort, Otari uncovers patterns of isoform dysregulation undetectable at the gene level, such as variant-driven perturbations in isoform abundance and microexon usage implicated in autism pathophysiology. We provide Otari as a resource for powering isoform-level analyses across tissues at scale.

bioinformatics↗

bioRxiv2021

Capturing the complexity of topologically associating domains through multi-feature optimization

The three-dimensional structure of human chromosomes is tied to gene regulation and replication timing, but there is still a lack of consensus on the computational and biological definitions for chromosomal substructures such as topologically associating domains (TADs). TADs are described and identified by various computational properties leading to different TAD sets with varying compatibility with biological properties such as boundary occupancy of structural proteins. We unify many of these computational and biological targets into one algorithmic framework that jointly maximizes several computational TAD definitions and optimizes TAD selection for a quantifiable biological property. Using this framework, we explore the variability of TAD sets optimized for six different desirable properties of TAD sets: high occupancy of CTCF, RAD21, and H3K36me3 at boundaries, reproducibility between replicates, high intra- vs inter-TAD difference in contact frequencies, and many CTCF binding sites at boundaries. The compatibility of these biological targets varies by cell type, and our results suggest that these properties are better reflected as subpopulations or families of TADs rather than a singular TAD set fitting all TAD definitions and properties. We explore the properties that produce similar TAD sets (reproducibility and inter- vs intra-TAD difference, for example) and those that lead to very different TADs (such as CTCF binding sites and inter- vs intra-TAD contact frequency difference).

genomics↗

bioRxiv2019

dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images. The spot detection algorithm uses the a trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli.

cell biology↗

bioRxiv2019

A statistical method for identifying consistently important features across samples

In many applications, a consistently high measurement across many samples can indicate particularly meaningful or useful information for quality control or biological interpretation. Identification of these strong features among many others can be challenging especially when the samples cannot be expected to have the same distribution or range of values. We present a general method called conserved feature discovery (CFD) for identifying features with consistently strong signals across multiple conditions or samples. Given any real-valued data, CFD requires no parameters, makes no assumptions on the shape of the underlying sample distributions, and is robust to differences across these distributions.We show that with high probability CFD identifies all true positives and no false positives under certain assumptions on the median and variance distributions of the feature measurements. Using simulated data, we show that CFD is tolerant to a small percentage of poor quality samples and robust to false positives. Applying CFD to RNA sequencing data from the Human Body Map project and GTEx, we identify housekeeping genes as highly expressed genes across tissue types and compare to housekeeping gene lists from previous methods. CFD is consistent between the Human Body Map and GTEx data sets, and identifies lists of genes enriched for basic cellular processes as expected. The framework can be easily adapted for many data types and desired feature properties. AvailabilityCode for CFD and scripts to reproduce the figures and analysis in this work are available at https://github.com/Kingsford-Group/cfd. Supplementary informationSupplementary data are available at https://github.com/Kingsford-Group/cfd.

genomics↗

bioRxiv2019

Topological data analysis reveals principles of chromosome structure throughout cellular differentiation

Three-dimensional chromosome structure has a significant influence in many diverse genomic processes and has recently been shown to relate to cellular differentiation. Many methods for describing the chromosomal architecture focus on specific substructures such as topologically-associating domains (TADs) or compartments, but we are still missing a global view of all geometric features of chromosomes. Topological data analysis (TDA) is a mathematically well-founded set of methods to derive robust information about the structure and topology of data sets, making it well-suited to better understand the key features of chromosome structure. By applying TDA to the study of chromosome structure through differentiation across three cell lines, we provide insight into principles of chromosome folding generally, and observe structural changes across lineages. We identify both global and local differences in chromosome topology through differentiation, identifying trends consistent across human cell lines. AvailabilityScripts to reproduce the results from this study can be found at https://github.com/Kingsford-Group/hictda Contactcarlk@cs.cmu.edu

genomics↗

bioRxiv2018

Analysis of the structural variability of topologically associated domains as revealed by Hi-C

Three-dimensional chromosome structure plays an integral role in gene expression and regulation, replication timing, and other cellular processes. Topologically associating domains (TADs), one of the building blocks of chromosome structure, are genomic regions with higher contact frequencies within the region than outside the region. A central question is the degree to which TADs are conserved or vary between conditions. We analyze a set of 137 Hi-C samples from 9 different studies under 3 measures in order to quantify the effects of various sources of biological and experimental variation. We observe significant variation in TAD sets between both non-replicate and replicate samples, and show that this variability does not seem to come from genetic sequence differences. The effects of experimental protocol differences are also measured, demonstrating that samples can have protocol-specific structural changes, but that TADs are generally robust to lab-specific differences. This study represents a systematic quantification of the key factors influencing comparisons of chromosome structure.

genomics↗

bioRxiv2016

Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings

Understanding the three-dimensional (3D) architecture of the chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to have a glimpse into the 3D genome organization and identify topologically associated domains (TADs), but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (> 50 megabases) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to the task of modeling chromatin dynamics using Hi-C contact data. We show that the GNM can identify structural dynamics at multiple scales: it can quantify the fluctuations in the positions of gene loci, find large genomic compartments and smaller TADs that undergo en-bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with DNase-seq and ATAC-seq measurements on accessibility, the previously identified A and B compartments of chromatin structure, and pairs of interacting loci identified by ChIA-PET. We describe a method to use the GNM to identify novel cross-correlated distal domains (CCDDs) representing regions of long-range dynamic coupling and show that CCDDs are often associated with increased gene coexpression using a large-scale analysis of 212 expression experiments. Together, these results show that GNM provides a mathematically well-founded unified framework for assessing chromatin dynamics and the structural basis of genome-wide observations.

genomics↗