Stochastic Modeling of Biophysical Responses to Perturbation DOI Creative Commons
Tara Chari, Gennady Gorin, Lior Pachter

et al.

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

Published: July 6, 2024

Abstract Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation the cell across multiple biological entities or modalities, from chromatin mRNA information to protein production spatial morphology. This presents an unprecedented opportunity unravel processes DNA RNA regulation direct fate determination disease response. Most methods designed analyzing large-scale perturbation data focus on observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as bursting splicing dynamics, can underlie these complex noisy observations. In this analysis, we demonstrate a stochastic biophysical modeling approach interpreting high-throughout enables deeper ‘how’ behind molecular measurements. Our takes advantage modalities already present produced with current technologies, nascent mature measurements, illuminate dynamics induced by perturbation, predict kinetic behaviors new settings, uncover novel populations cells distinct responses perturbation.

Language: Английский

Biophysically interpretable inference of cell types from multimodal sequencing data DOI
Tara Chari, Gennady Gorin, Lior Pachter

et al.

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(9), P. 677 - 689

Published: Sept. 20, 2024

Language: Английский

Citations

6
Forseti: a mechanistic and predictive model of the splicing status of scRNA-seq reads DOI Creative Commons
Dongze He, Yuan Gao, Spencer Skylar Chan

et al.

Bioinformatics, Journal Year: 2024, Volume and Issue: 40(Supplement_1), P. i297 - i306

Published: April 12, 2024

Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty even the seemingly straightforward task of elucidating status molecules from which sequenced fragments are drawn. This arises, part, limited read length positional biases, substantially reduce specificity fragments. As a result, many reads ambiguous because lack definitive evidence. We therefore need methods that can recover which, turn, lead more accuracy confidence downstream analyses.

Language: Английский

Citations

3
Brooklyn plots to identify co-expression dysregulation in single cell sequencing DOI Creative Commons
Arun H. Patil, Matthew N. McCall, Marc K. Halushka

et al.

NAR Genomics and Bioinformatics, Journal Year: 2024, Volume and Issue: 6(1)

Published: Jan. 5, 2024

Abstract Altered open chromatin regions, impacting gene expression, is a feature of some human disorders. We discovered it possible to detect global changes in genomically-related adjacent co-expression within single cell RNA sequencing (scRNA-seq) data. built software package generate and test non-randomness using ‘Brooklyn plots’ identify the percent genes significantly co-expressed from same chromosome ∼10 MB intervals across genome. These plots establish an expected low baseline scRNA-seq most types, but, as seen dilated cardiomyopathy cardiomyocytes, altered patterns appear. may relate larger regions transcriptional bursting, observable cell, but not bulk datasets.

Language: Английский

Citations

0
scCensus: Off-target scRNA-seq reads reveal meaningful biology DOI Creative Commons
Dongze He, Stephen M. Mount, Rob Patro

et al.

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

Published: Jan. 31, 2024

Single-cell RNA-sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity. Although scRNA-seq reads from most prevalent and popular tagged-end protocols are expected to arise the 3

Language: Английский

Citations

0
Stochastic Modeling of Biophysical Responses to Perturbation DOI Creative Commons
Tara Chari, Gennady Gorin, Lior Pachter

et al.

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

Published: July 6, 2024

Abstract Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation the cell across multiple biological entities or modalities, from chromatin mRNA information to protein production spatial morphology. This presents an unprecedented opportunity unravel processes DNA RNA regulation direct fate determination disease response. Most methods designed analyzing large-scale perturbation data focus on observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as bursting splicing dynamics, can underlie these complex noisy observations. In this analysis, we demonstrate a stochastic biophysical modeling approach interpreting high-throughout enables deeper ‘how’ behind molecular measurements. Our takes advantage modalities already present produced with current technologies, nascent mature measurements, illuminate dynamics induced by perturbation, predict kinetic behaviors new settings, uncover novel populations cells distinct responses perturbation.

Language: Английский

Citations

0