Biology subjects

Jessa, S.

Publications and source records attributed to Jessa, S..

7 recordsLinked to original sources

bioRxiv2025

Sensitive, direct detection of non-coding off-target base editor unwinding and editing in primary cells

Base editors create precise nucleotide changes in DNA, but their off-target activity remains challenging to quantify. Here, we develop and deploy a direct, in cellulo sequencing assay that simultaneously measures both Cas9-mediated unwinding and deaminase editing of genomic DNA (beCasKAS). Our strategy nominates >460-fold more potential off-target sites than other methods by enriching for Cas9-dependent R-loops immediately preceding editing. Using beCasKAS in primary human T-cells, we observe that mRNA-encoded ABE8e and PAMless ABE8e-SpRY base editors have distinct off-target profiles that can be mitigated by optimizing mRNA dose. Finally, we combine beCasKAS with base-resolution deep learning models to risk-stratify off-target edits by their likelihood of epigenetic dysregulation. Collectively, beCasKAS offers a sensitive and facile tool to optimize the balance between base editor on- and off-target activity.

genomics↗

bioRxiv2025

Dissecting regulatory syntax in human development with scalable multiomics and deep learning

Transcription factors (TFs) establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility, and regulating gene expression. Mapping accessible chromatin offers critical insights into transcriptional control, but available datasets for human development are restricted to bulk tissue, single organs, or single modalities. Here, we present the Human Development Multiomic Atlas (HDMA), a single-cell atlas of chromatin accessibility and gene expression from 817,740 fetal cells across 12 organs, spanning 203 cell types and over 1 million candidate cis-regulatory elements, many of which exhibit organ-specific in vivo enhancer activity. Deep learning models trained to predict accessibility from local DNA sequence unravel a comprehensive lexicon of motifs which influence accessibility, including composite motifs exhibiting distinct syntactic constraints predicted to mediate TF cooperativity. We identify "hard" syntactic rules requiring precise motif spacing and orientation, "soft" rules allowing flexible motif arrangements, and ubiquitous motifs that inhibit accessibility. Model-based interpretation of genetic variants reveals that disruption of motifs with positive and negative effects is associated with concordant effects on gene expression. Our work delineates how motif syntax governs cell type-specific chromatin accessibility and provides a foundational resource for decoding cis-regulatory logic and interpreting genetic variation during human development.

genomics↗

bioRxiv2025

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart

Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, rare variants exert more cell-type-shared regulatory effects, with selective pressures particularly targeting variants affecting fetal brain neurons. To prioritize de novo mutations with extreme regulatory effects, we developed FLARE, a context-specific functional genomic model of constraint. FLARE outperformed other methods in prioritizing case mutations from autism-affected families near syndromic autism-associated genes; for example, identifying mutation outliers near CNTNAP2 that would be missed by alternative approaches. Overall, our findings demonstrate the potential of integrating single-cell maps with population genetics and deep learning-based variant effect prediction to elucidate mechanisms of development and disease-ultimately, supporting the notion that genetic contributions to neurodevelopmental disorders are predominantly rare.

genomics↗

bioRxiv2022

vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework

MotivationGenotyping error can impact downstream SNP-based analyses. Simulating various modes and and levels of error can help investigators better understand potential biases caused by miscalled genotypes. ResultsWe have developed and validated vcferr, a tool to probabilistically simulate genotyping error and missigness in VCF files. We demonstrate how vcferr could be used to address a research question by introducing varying levels of error of different type into a sample in a simulated pedigree, and assessed how kinship analysis degrades as a function of kind and type of error. Software Availabilityvcferr is available for installation via PyPi (https://pypi.org/project/vcferr/) or conda (https://anaconda.org/bioconda/vcferr). The software is released under the MIT license with source code available on GitHub (https://github.com/signaturescience/vcferr).

bioinformatics↗

bioRxiv2021

skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation

MotivationSNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream processing and manipulation. A dedicated software package that consistently and intuitively implements this analysis functionality is needed. ResultsHere we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. AvailabilityThe skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater.

bioinformatics↗

bioRxiv2017

Genome-wide discovery of somatic coding and regulatory variants in Diffuse Large B-cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is an aggressive cancer originating from mature B-cells. Many known driver mutations are over-represented in one of its two molecular subgroups, knowledge of which has aided in the development of therapeutics that target these features. The heterogeneity of DLBCL determined through prior genomic analysis suggests an incomplete understanding of its molecular aetiology, with a limited diversity of genetic events having thus far been attributed to the activated B-cell (ABC) subgroup. Through an integrative genomic analysis we uncovered genes and non-coding loci that are commonly mutated in DLBCL including putative regulatory sequences. We implicate recurrent mutations in the 3UTR of NFKBIZ as a novel mechanism of oncogene deregulation and found small amplifications associated with over-expression of FC-{gamma} receptor genes. These results inform on mechanisms of NF-{kappa}B pathway activation in ABC DLBCL and may reveal a high-risk population of patients that might not benefit from standard therapeutics.

genomics↗

bioRxiv2016

Enhancing Knowledge Discovery from Cancer Genomics Data with Galaxy

We present a collection of Galaxy tools representing many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We implemented methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability on cloud-based infrastructure and commodity hardware. Some tools represents extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate several of these to perform some standard data integration and visualization tasks are demonstrated on a cohort of 96 diffuse large B-cell lymphomas, enabling the discovery of multiple candidate lymphoma-related genes that have not been reported previously.

genomics↗