Biology subjects

Masaki, N.

Publications and source records attributed to Masaki, N..

5 recordsLinked to original sources

bioRxiv2026

SPARK: Bayesian structure learning of enhancer-based gene regulatory networks from single-cell multiomics data and prior knowledge

Enhancer-based gene regulatory networks (eGRNs) linking enhancers with recruited transcription factors (TFs) and downstream target genes can help decipher complex regulatory mechanisms, such as the function of GWAS variants at enhancers. Molecular assays that profile gene expression and chromatin state jointly from the same cells (such as 10x Multiome) are a rich source of input data for eGRN reconstruction. However, these assays lack mechanistic evidence for enhancer-gene and TF-enhancer links, which can be contributed by prior knowledge from other assays and analyses. Here, we present SPARK, an eGRN reconstruction framework that formally combines single-cell multiome data with mechanistic prior knowledge. SPARK represents eGRNs as directed acyclic graphs (DAGs) and learns them using Bayesian inference, with a prior on the network structure constructed from heterogeneous sources of evidence. In a simulation study, we show that SPARK benefits from prior knowledge and outperforms existing DAG-based and regression-based algorithms. Finally, we use SPARK to study the regulatory circuitry of enhancers harboring immune disease GWAS variants in human primary monocytes, revealing known and novel regulatory links as candidates for experimental validation.

genomics↗

bioRxiv2025

Hidden Markov Models Detect Recombination and Ancestry of SARS-CoV-2

When individuals are co-infected with distinct SARS-CoV-2 lineages, homologous recombination can generate mosaic genomes carrying mutations from both parental lineages. A variety of methods exist to detect recombinant sequences and their parental lineages in surveillance-scale datasets comprised of millions of SARS-CoV-2 genomes. However, these methods often rely on user-specified parameters, such as the probability a recombination breakpoint occurs between adjacent positions on the query sequence. In this study, we devise a hidden Markov model that detects recombinant SARS-CoV-2 sequences and identifies their parental lineages within a test set of sequences. Our method does not depend on user-specified parameters and can accommodate de novo mutations on the query sequence that are not present in the predicted parental lineages. To achieve this, we use maximum likelihood to estimate parameters that characterize the transition and emission probabilities in our hidden Markov model. Applying our method to 440,307 SARS-CoV-2 sequences sampled in England between September 2020 and March 2024, we detect 7,619 recombinant sequences corresponding to 1.73% (95% CI: [1.69%, 1.77%]) of all sampled sequences. We observe a positive association between the proportion of query sequences detected as recombinant in each week and community SARS-CoV-2 prevalence. This is consistent with higher prevalence increasing the risk of co-infection by distinct lineages and promoting the emergence of recombinant sequences. Finally, we observe localized clusters of recombination breakpoints within spike and in intergenic regions.

genomics↗

bioRxiv2024

Mean gene conversion tract length in humans estimated to be 459 bp from UK Biobank sequence data

Non-crossover gene conversion is a type of meiotic recombination characterized by the non-reciprocal transfer of genetic material between homologous chromosomes. Gene conversions are thought to occur within relatively short tracts of DNA. However, the number of observable gene conversion tracts per study has so far been limited by the use of pedigree or sperm-typing data to detect gene conversion events. In this study, we propose a statistical method to model the length distribution of gene conversion tracts in humans, using nearly one million gene conversion tracts detected from the UK Biobank whole autosome data. To handle the large number of tracts, we designed a computationally efficient inferential framework. Our method further accounts for regional variation in marker density and heterozygosity across the genome, which can influence the observed length of gene conversion tracts. We allow for multiple candidate tract length distributions and select the best fitting distribution using the Akaike Information Criterion (AIC). Applying our method, we estimate that most tracts have a mean of 16.9 bp (95% CI: [16.4, 17.0]), and only a very small proportion of tracts have a much larger mean of 724.7 bp (95% CI: [720.1, 728.7]). We further estimate the proportion of gene conversion tracts with the larger mean to be 0.00525 (95% CI: [0.005, 0.00525]). After stratifying by crossover-hotspot overlap, we infer that tracts whose midpoints lie within crossover hotspots are, on average, longer than the remaining tracts.

genetics↗

bioRxiv2024

Simultaneous estimation of genotype error and uncalled deletion rates in whole genome sequence data

Genotype data include errors that may influence conclusions reached by downstream statistical analyses. Previous studies have estimated genotype error rates from discrepancies in human pedigree data, such as Mendelian inconsistent genotypes or apparent phase violations. However, uncalled deletions, which generally have not been accounted for in these studies, can lead to biased error rate estimates. In this study, we propose a genotype error model that considers both genotype errors and uncalled deletions when calculating the likelihood of the observed genotypes in parent-offspring trios. Using simulations, we show that when there are uncalled deletions, our model produces genotype error rate estimates that are less biased than estimates from a model that does not account for these deletions. We applied our model to SNVs in 77 sequenced White British parent-offspring trios in the UK Biobank. We use the Akaike information criterion to show that our model fits the data better than a model that does not account for uncalled deletions. We estimate the genotype error rate at SNVs with minor allele frequency > 0.001 in these data to be 3.2 x 10-4 (90% CI: [2.8 x 10-4, 6.2 x 10-4]). We estimate that 77% of the genotype errors at these markers are attributable to uncalled deletions (90% CI: [73%, 88%]). Author summaryA genotype error occurs when the genotype identified through molecular analysis does not match the actual genotype of the individual being analyzed. Because genotype errors can influence downstream statistical results, previous studies have attempted to estimate the rate of genotype errors in a study sample. However, uncalled deletions, which generally have not been accounted for in these studies, can lead to biased error rate estimates. In this study, we formulate a model adjusting for uncalled deletions when estimating genotype error rates. We show that when uncalled deletions are present, this model results in less biased estimates of genotype error rates compared to a model that does not adjust for uncalled deletions. We apply this model to SNVs in 77 sequenced White British parent-offspring trios in the UK Biobank and estimate the genotype error rate and the proportion of genotype errors that are attributable to uncalled deletions at SNVs with minor allele frequency > 0.001.

genetics↗

bioRxiv2020

Study of the Influence of Preprint in bioRχiv for Peer Review and Acceptance Time of PLOS ONE

This study examines the relation between acceptance times in preprint publishing and journal publishing. Specifically, we investigated the association between a papers posting time to bioR{chi}iv, a preprints server, and journal articles peer-review and acceptance time for PLOS ONE. So far, of the total papers published in 1,626 academic journals, the average publication rate of those posted in bioR{chi}iv is 40.67%. Meanwhile, PLOS ONE was the journal that published more papers. Analysis of peer-review and acceptance time of papers published in journals via preprints showed the time these papers are posted in relation to these intervals. The median of the peer-review and acceptance time of the journal submission date that was later than the date of first posting to bioR{chi}iv was 110.00 days, and in the reverse case, it was 139.50 days. Posting to the preprint server before journal submission shows a better order than vice versa. This study provides us a good understanding of the peer-review process. It also gives us good insights into optimizing this process, which would then facilitate paper publication and knowledge dissemination.

scientific communication and education↗