Towards functional maps of non-coding variants in cancer DOI Creative Commons
Yihan Wang, Gary C. Hon

Frontiers in Genome Editing, Год журнала: 2024, Номер 6

Опубликована: Окт. 31, 2024

Large scale cancer genomic studies in patients have unveiled millions of non-coding variants. While a handful been shown to drive development, the vast majority unknown function. This review describes challenges functionally annotating variants and understanding how they contribute cancer. We summarize recently developed high-throughput technologies address these challenges. Finally, we outline future prospects for genetics help catalyze personalized therapy.

Язык: Английский

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants DOI Creative Commons
Anusri Pampari, Anna Shcherbina, Evgeny Z. Kvon

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 25, 2024

Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (TF) binding at context-specific cREs remain elusive. We introduce ChromBPNet, a deep learning DNA model base-resolution profiles detects, learns deconvolves assay-specific enzyme biases from regulatory determinants accessibility, enabling robust discovery compact TF motif lexicons, cooperative precision footprints assays sequencing depths. Extensive benchmarks show despite its lightweight design, is competitive much larger contemporary models predicting variant effects on pioneer reporter activity cell ancestry, while providing interpretation disrupted syntax. ChromBPNet also helps prioritize interpret influence complex traits rare diseases, thereby powerful lens to decode variation.

Язык: Английский

Процитировано

8
High-throughput screening of human genetic variants by pooled prime editing DOI Creative Commons
Michael Herger, Christina M. Kajba, Megan Buckley

и другие.

Cell Genomics, Год журнала: 2025, Номер unknown, С. 100814 - 100814

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
Dissecting the cis-regulatory syntax of transcription initiation with deep learning DOI Creative Commons
Kelly Cochran,

Melody Yin,

Anika Mantripragada

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 1, 2024

Despite extensive characterization of mammalian Pol II transcription, the DNA sequence determinants transcription initiation at a third human promoters and most enhancers remain poorly understood. We trained interpreted neural network called ProCapNet that accurately models base-resolution profiles from PRO-cap experiments using local sequence. learns motifs with distinct effects on rates TSS positioning uncovers context-specific cryptic initiator elements intertwined within other TF motifs. annotates predictive in nearly all actively transcribed regulatory across multiple cell-lines, revealing shared

Язык: Английский

Процитировано

5
A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening DOI Creative Commons
Ann Cirincione, Danny Simpson, Weihao Yan

и другие.

Nature Methods, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 19, 2024

Prime editing installs precise edits into the genome with minimal unwanted byproducts, but low and variable efficiencies have complicated application of approach to high-throughput functional genomics. Here we assembled a prime platform capable high-efficiency substitution suitable for interrogation small genetic variants. We benchmarked this pooled, loss-of-function screening using library ~240,000 engineered guide RNAs (epegRNAs) targeting ~17,000 codons 1–3 bp substitutions. Comparing abundance these epegRNAs across screen samples identified negative selection phenotypes 7,996 nonsense mutations targeted 1,149 essential genes synonymous that disrupted splice site motifs at 3′ exon boundaries. Rigorous evaluation codon-matched controls demonstrated were highly specific intended edit. Altogether, established multiplexed, characterization variants simple readouts. This work establishes (up tens thousands) phenotypes.

Язык: Английский

Процитировано

4
Deconstructing cancer with precision genome editing DOI Creative Commons

Grace A. Johnson,

Samuel I. Gould, Francisco J. Sánchez‐Rivera

и другие.

Biochemical Society Transactions, Год журнала: 2024, Номер 52(2), С. 803 - 819

Опубликована: Апрель 17, 2024

Recent advances in genome editing technologies are allowing investigators to engineer and study cancer-associated mutations their endogenous genetic contexts with high precision efficiency. Of these, base prime quickly becoming gold-standards the field due versatility scalability. Here, we review merits limitations of these technologies, application modern cancer research, speculate how could be integrated address future directions field.

Язык: Английский

Процитировано

3
Deep-learning prediction of gene expression from personal genomes DOI Creative Commons
Shiron Drusinsky, Sean Whalen, Katherine S. Pollard

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 27, 2024

Models that predict RNA levels from DNA sequences show tremendous promise for decoding tissue-specific gene regulatory mechanisms, revealing the genetic architecture of traits, and interpreting noncoding variation. Existing methods take two different approaches: 1) associating expression with linear combinations common variants (training across individuals on single genes), or 2) learning genome-wide sequence-to-expression rules neural networks loci using a reference genome). Since limitations both strategies have been highlighted recently, we sought to combine sequence context provided by deep information cross-individual training. We utilized fine-tuning develop Performer, model accuracy approaching cis-heritability most genes. Performer prioritizes allele frequency spectrum disrupt motifs, fall in annotated elements, functional evidence modulating expression. While obstacles remain personalized prediction, our findings establish as viable strategy.

Язык: Английский

Процитировано

3
Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion DOI Creative Commons
Anirban Sarkar, Ziqi Tang,

Chris Zhao

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 24, 2024

Engineering regulatory DNA sequences with precise activity levels in specific cell types hold immense potential for medicine and biotechnology. However, the vast combinatorial space of possible complex grammars governing gene regulation have proven challenging existing approaches. Supervised deep learning models that score proposed by local search algorithms ignore global structure functional sequence space. While diffusion-based generative shown promise these distributions, their application to has been limited. Evaluating quality generated also remains due a lack unified framework characterizes key properties DNA. Here we introduce Discrete Diffusion (D3), conditionally sampling targeted levels. We develop comprehensive suite evaluation metrics assess similarity, composition sequences. Through benchmarking on three high-quality genomics datasets spanning human promoters fly enhancers, demonstrate D3 outperforms methods capturing diversity cis-regulatory generating more accurately reflect genomic Furthermore, show D3-generated can effectively augment supervised improve predictive performance, even data-limited scenarios.

Язык: Английский

Процитировано

2
Understanding variants of unknown significance: the computational frontier DOI Creative Commons
Xi Fu, Raúl Rabadán

The Oncologist, Год журнала: 2024, Номер unknown

Опубликована: Июнь 7, 2024

Abstract The rapid advancement of sequencing technologies has led to the identification numerous mutations in cancer genomes, many which are variants unknown significance (VUS). Computational models increasingly being used predict functional impact these mutations, both coding and noncoding regions. Integration with emerging genomic datasets will refine our understanding mutation effects guide clinical decision making. Future advancements modeling protein interactions transcriptional regulation further enhance ability interpret VUS. Periodic incorporation developments into VUS reclassification practice potential significantly improve personalized care.

Язык: Английский

Процитировано

2
Massively parallel approaches for characterizing noncoding functional variation in human evolution DOI
Stephen Rong,

Elise Root,

Steven K. Reilly

и другие.

Current Opinion in Genetics & Development, Год журнала: 2024, Номер 88, С. 102256 - 102256

Опубликована: Авг. 31, 2024

Язык: Английский

Процитировано

2
Molecular convergence of risk variants for congenital heart defects leveraging a regulatory map of the human fetal heart DOI Creative Commons
X. Rosa,

Stephanie D. Conley,

Michael Kosicki

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 21, 2024

Abstract Congenital heart defects (CHD) arise in part due to inherited genetic variants that alter genes and noncoding regulatory elements the human genome. These are thought act during fetal development influence formation of different structures. However, identifying genes, pathways, cell types mediate these effects has been challenging immense diversity involved as well superimposed complexities interpreting sequences. As such, understanding molecular functions both coding remains paramount our fundamental cardiac CHD. Here, we created a gene regulation map healthy across developmental time, applied it interpret associated with CHD quantitative traits. We collected single-cell multiomic data from 734,000 single cells sampled 41 hearts spanning post-conception weeks 6 22, enabling construction maps 90 states, including rare populations conduction cells. Through an unbiased analysis all types, find common valve traits converge affect valvular interstitial (VICs). VICs enriched for high expression known previously identified through mapping variants. Eight other similar linked diseases or via enhancers VICs. In addition, certain impact activities highly specific particular subanatomic structures heart, illuminating how such can aspects structure function. Together, results implicate new enhancers, etiology CHD, identify convergence on VICs, suggest more expansive view instrumental risk beyond working cardiomyocyte. This will provide foundational resource development, disease, discovering targets cell-type therapies.

Язык: Английский

Процитировано

2