Dataset Information

Bayesian inference of origin firing time distributions, origin interference and licencing probabilities from Next Generation Sequencing data.

ABSTRACT: DNA replication is a stochastic process with replication forks emanating from multiple replication origins. The origins must be licenced in G1, and the replisome activated at licenced origins in order to generate bi-directional replication forks in S-phase. Differential firing times lead to origin interference, where a replication fork from an origin can replicate through and inactivate neighbouring origins (origin obscuring). We developed a Bayesian algorithm to characterize origin firing statistics from Okazaki fragment (OF) sequencing data. Our algorithm infers the distributions of firing times and the licencing probabilities for three consecutive origins. We demonstrate that our algorithm can distinguish partial origin licencing and origin obscuring in OF sequencing data from Saccharomyces cerevisiae and human cell types. We used our method to analyse the decreased origin efficiency under loss of Rat1 activity in S. cerevisiae, demonstrating that both reduced licencing and increased obscuring contribute. Moreover, we show that robust analysis is possible using only local data (across three neighbouring origins), and analysis of the whole chromosome is not required. Our algorithm utilizes an approximate likelihood and a reversible jump sampling technique, a methodology that can be extended to analysis of other mechanistic processes measurable through Next Generation Sequencing data.

SUBMITTER: Bazarova A

PROVIDER: S-EPMC6412128 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Bayesian inference of origin firing time distributions, origin interference and licencing probabilities from Next Generation Sequencing data.

Bazarova Alina A Nieduszynski Conrad A CA Akerman Ildem I Burroughs Nigel J NJ

Nucleic acids research 20190301 5

DNA replication is a stochastic process with replication forks emanating from multiple replication origins. The origins must be licenced in G1, and the replisome activated at licenced origins in order to generate bi-directional replication forks in S-phase. Differential firing times lead to origin interference, where a replication fork from an origin can replicate through and inactivate neighbouring origins (origin obscuring). We developed a Bayesian algorithm to characterize origin firing stati ...[more]

PMID: 30859196

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Project description:Motivation:Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project. Results:To allow inference on population structure using PCA in these situations, we provide an approach that is based on using sequencing reads directly without calling genotypes. Our approach is to adjust the data from different sequencing groups to have the same read depth and error rate so that PCA does not generate spurious components representing sequencing quality. To accomplish this, we have developed a subsampling procedure to match the depth distributions in different sequencing groups, and a read-flipping procedure to match the error rates. We average over subsamples and read flips to minimize loss of information. We demonstrate the utility of our approach using two datasets from 1000 Genomes, and further evaluate it using simulation studies. Availability and implementation:TASER-PC software is publicly available at http://web1.sph.emory.edu/users/yhu30/software.html. Contact:yijuan.hu@emory.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Bayesian inference of origin firing time distributions, origin interference and licencing probabilities from Next Generation Sequencing data.

Publications

Bayesian inference of origin firing time distributions, origin interference and licencing probabilities from Next Generation Sequencing data.

Similar Datasets

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