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Yapar, E.

Publications and source records attributed to Yapar, E..

3 recordsLinked to original sources

Landscape of microRNA and target expression variation and covariation in single mouse embryonic stem cells

MicroRNAs are small RNA molecules that can repress the expression of protein coding genes post-transcriptionally. Previous studies have shown that microRNAs can also have alternative functions including target noise buffering and co-expression, but these observations have been limited to a few microRNAs. Here we systematically study microRNA alternative functions in mouse embryonic stem cells, by genetically deleting Drosha - leading to global loss of microRNAs. We apply complementary single-cell RNA-seq methods to study the variation of the targets and the microRNAs themselves, and transcriptional inhibition to measure target half-lives. We find that microRNAs form four distinct co-expression groups across single cells. In particular the mir-290 and the mir-182 clusters are abundantly, variably and inversely expressed. Intriguingly, some cells have global biases towards specific miRNAs originating from either end of the hairpin precursor, suggesting the presence of unknown regulatory cofactors. We find that miRNAs generally increase variation and covariation of their targets at the RNA level, but we also find miRNAs such as miR-182 that appear to have opposite functions. In particular, miRNAs that are themselves variable in expression, such as miR-291a, are more likely to induce covariations. In summary, we apply genetic perturbation and multi-omics to give the first global picture of microRNA dynamics at the single cell level.

genomics↗

Pre-processing of paleogenomes: Mitigating reference bias and postmortem damage in ancient genome data

Ancient DNA analysis is subject to various technical challenges, including bias towards the reference allele ("reference bias"), postmortem damage (PMD) that confounds real variants, and limited coverage. Here, we conduct a systematic comparison of alternative approaches against reference bias and against PMD. To reduce reference bias, we either (a) mask variable sites before alignment or (b) align the data to a graph genome representing all variable sites. Compared to alignment to the linear reference genome, both masking and graph alignment effectively remove allelic bias when using simulated or real ancient human genome data, but only if sequencing data is available in FASTQ or unfiltered BAM format. Reference bias remains indelible in quality-filtered BAM files and in 1240K-capture data. We next study three approaches to overcome postmortem damage: (a) trimming, (b) rescaling base qualities, and (c) a new algorithm we present here, bamRefine, which masks only PMD-vulnerable polymorphic sites. We find that bamRefine is optimal in increasing the number of genotyped loci up to 20% compared to trimming and in improving accuracy compared to rescaling. We propose graph alignment coupled with bamRefine to minimise data loss and bias. We also urge the paleogenomics community to publish FASTQ files.

bioinformatics↗

Somatic copy number variant load in neurons of healthy controls and Alzheimer's disease patients

BackgroundThe possible role of somatic copy number variations (CNVs) in Alzheimers disease (AD) aetiology has been controversial. Although cytogenetic studies suggested increased CNV loads in AD brains, a recent single-cell whole-genome sequencing (scWGS) experiment, studying frontal cortex brain samples, found no such evidence. Here we readdressed this issue using lowcoverage scWGS on pyramidal neurons dissected using laser capture microdissection (LCM) across five brain regions: entorhinal cortex, temporal cortex, hippocampal CA1, hippocampal CA3, and the cerebellum. ResultsAmong reliably detected somatic CNVs identified in 1301 cells obtained from the brains of 13 AD patients and 7 healthy controls, deletions were more frequent compared to duplications. Interestingly, we observed slightly higher frequencies of CNV events in cells from AD compared to similar numbers of cells from controls (4.1% vs. 1.4%, or 0.9% vs. 0.7%, using different filtering approaches), although the differences were not statistically significant. We also observed that LCM-isolated cells show higher within-cell read depth variation compared to cells isolated with fluorescence activated cell sorting (FACS), which we argue may have both biological and technical causes. Furthermore, we found that LCM-isolated neurons in AD harbour slightly more read depth variability than neurons of controls, which might be related to the reported hyperploid profiles of some AD-affected neurons. We also propose a principal component analysis-based denoising approach that significantly reduces within-cell read depth variation in scWGS data. ConclusionsWe find slightly higher somatic CNV frequencies in the brains of AD patients, and higher sequencing coverage variability, although the effects measured do not reach statistical significance. The results call for improved experimental protocols to determine the possible role of CNVs in AD pathogenesis.

bioinformatics↗