Fine Mapping Regulatory Variants by Characterizing Native CpG Methylation with Nanopore Long Read Sequencing DOI Creative Commons
Yijun Tian, Shannon K. McDonnell,

Lang Wu

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 28, 2024

5-methylcytosine (5mC) is the most common chemical modification occurring on CpG sites across human genome. Bisulfite conversion combined with short-read whole genome sequencing can capture and quantify at single nucleotide resolution. However, PCR amplification process could lead to duplicative methylation patterns introduce 5mC detection bias. Additionally, limited read length also restricts co-methylation analysis between distant sites. The bisulfite presents a significant challenge for detecting variant-specific due destruction of allele information in reads. To address these issues, we sought characterize profiling nanopore long-read sequencing, aiming demonstrate its potential long-range native call intact retained. In this regard, first analyzed demo data adaptive sampling run targeting all islands. We applied linkage disequilibrium (LD) R

Language: Английский

MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays DOI Creative Commons
Alan F. Rubin,

Jeremy Stone,

Aisha Haley Bianchi

et al.

Genome biology, Journal Year: 2025, Volume and Issue: 26(1)

Published: Jan. 21, 2025

Abstract Multiplexed assays of variant effect (MAVEs) are a critical tool for researchers and clinicians to understand genetic variants. Here we describe the 2024 update MaveDB ( https://www.mavedb.org/ ) with four key improvements MAVE community’s database record: more available data including over 7 million measurements, an improved model supporting such as saturation genome editing, new built-in exploration visualization tools, powerful APIs federation streamlined submission access. Together these changes support MaveDB’s role hub analysis dissemination MAVEs now into future.

Language: Английский

Citations

4
An evolving understanding of multiple causal variants underlying genetic association signals DOI
Erping Long, Jacob Williams, Haoyu Zhang

et al.

The American Journal of Human Genetics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

1
Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart DOI Creative Commons
Andrew R. Marderstein, Soumya Kundu, Evin M. Padhi

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

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

Language: Английский

Citations

1
Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria DOI Creative Commons
Timothy Bergquist, Sarah L. Stenton, Emily A.W. Nadeau

et al.

Published: Sept. 21, 2024

ABSTRACT Purpose We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations the reliable use of impact predictors provide evidence strength up Strong . A new generation using distinctive approaches have since been released, and these methods must be independently calibrated application. Method Using our local posterior probability-based calibration established data set ClinVar pathogenic benign variants, we determined provided by three (AlphaMissense, ESM1b, VARITY) scores meeting each strength. Results All reached level pathogenicity Moderate benignity, though sometimes few variants. Compared recommended tools, yielded at best only modest improvements in tradeoffs false positive predictions. Conclusion At thresholds, similar four (and comparable with functional assays some variants). This broadens scope application classification. Their offer promise future advancement field.

Language: Английский

Citations

5
dsDAP: An efficient method for high-abundance DNA-encoded library construction in mammalian cells DOI
Kaili Zhang, Yiping Wang,

S. S. Jiang

et al.

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: 298, P. 140089 - 140089

Published: Jan. 20, 2025

Language: Английский

Citations

0
Advancements in Gene Structure Prediction: Innovation and Prospects of Deep Learning Models Apply in Multi-species DOI Creative Commons
Tong Wang, H. J. Yang,

Ting Xu

et al.

Published: Jan. 25, 2025

In recent years, advancements in gene structure prediction have been significantly driven by the integration of deep learning technologies into bioinformatics. Transitioning from traditional thermodynamics and comparative genomics methods to modern learning-based models such as CDSBERT, DNABERT, RNA-FM, PlantRNA-FM accuracy generalization seen remarkable improvements. These models, leveraging genome sequence data along with secondary tertiary information, facilitated diverse applications studying functions across animals, plants, humans. They also hold substantial potential for multi-application early disease diagnosis, personalized treatment, genomic evolution research. This review combines learning, showcasing functional region annotation, protein-RNA interactions, cross-species analysis. It highlights their contributions animal, plant, human research while exploring future opportunities cancer mutation prediction, RNA vaccine design, CRISPR editing optimization. The emphasizes directions, model refinement, multimodal integration, global collaboration. By offering a concise overview forward-looking insights, this article aims provide foundational resource practical guidance advancing nucleic acid

Language: Английский

Citations

0
The association of single nucleotide polymorphisms in tumor necrosis factor 3 with susceptibility/resistance of Magallana gigas to Halomonas sp. 7T DOI Creative Commons
Hongmei Fan, Qiuju Peng, Tian Liu

et al.

Comparative Immunology Reports, Journal Year: 2025, Volume and Issue: unknown, P. 200208 - 200208

Published: Feb. 1, 2025

Language: Английский

Citations

0
Using individual barcodes to increase quantification power of massively parallel reporter assays DOI Creative Commons
Pia Keukeleire, Jonathan D. Rosen,

Angelina Göbel-Knapp

et al.

BMC Bioinformatics, Journal Year: 2025, Volume and Issue: 26(1)

Published: Feb. 13, 2025

Language: Английский

Citations

0
Non-local modeling of enhancer-promoter interactions, a correspondence on “LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions” DOI
M Beer

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: Feb. 27, 2025

Language: Английский

Citations

0
Genetic causes of obesity: mapping a path forward DOI Creative Commons
Ruth J. F. Loos

Trends in Molecular Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0