DNA methylation is an important epigenetic mechanism for phenotypic diversification in all forms of life. We previously described remarkable cell-to-cell heterogeneity in epigenetic pattern within a clonal population of Streptococcus pneumoniae, a leading human pathogen. We here report that the epigenetic diversity is caused by extensive DNA inversions among hsdS A, hsdS B, and hsdS C, three methyltransferase hsdS genes in the Spn556II type-I restriction modification (R-M) locus. Because hsdS A encodes the sequence recognition subunit of this type-I R-M DNA methyltransferase, these site-specific recombinations generate pneumococcal cells with variable HsdSA alleles and thereby diverse genome methylation patterns. Most importantly, the DNA methylation pattern specified by the HsdSA1 allele leads to the formation of opaque colonies, whereas the pneumococci lacking HsdSA1 produce transparent colonies. Furthermore, this HsdSA1-dependent phase variation requires intact DNA methylase activity encoded by hsdM in the Spn556II (renamed colony opacity determinant or cod) locus. Thus, the DNA inversion-driven ON/OFF switch of the hsdS A1 allele in the cod locus and resulting epigenetic switch dictate the phase variation between the opaque and transparent phenotypes. Phase variation has been well documented for its importance in pneumococcal carriage and invasive infection, but its molecular basis remains unclear. Our work has discovered a novel epigenetic cause for this significant pathobiology phenomenon in S. pneumoniae. Lastly, our findings broadly represents a significant advancement in our understanding of bacterial R-M systems and their potential in shaping epigenetic and phenotypic diversity of the prokaryotic organisms because similar site-specific recombination systems widely exist in many archaeal and bacterial species.
Supplementary data are available at Bioinformatics online.
Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed.Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations.Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.
High-throughput single-cell RNA-seq (scRNA-seq) data contains excess zero values, including those of genes not expressed in the cell, and those produced due to dropout events. Existing imputation methods do not distinguish these two types of zeros. We present a modest imputation method scRecover to only impute the dropout zeros. It estimates the zero dropout probability of each gene in each cell, and predicts the number of truly expressed genes in the cell. scRecover is combined with other imputation methods like scImpute, SAVER and MAGIC to fulfil the imputation. Downsampling experiments show that it recovers dropout zeros with higher accuracy and avoids overimputing true zero values. Experiments on real data illustrate scRecover improves downstream analysis and visualization.
There are excessive zero values in single-cell RNA-seq (scRNA-seq) data. Some of them are real zeros of non-expressed genes, while the others are the so-called "dropout" zeros caused by the low mRNA capture efficiency of tiny amounts of mRNAs in single cells. These two types of zeros should be distinguished in differential expression (DE) analysis and other types of analyses of scRNA-seq data. We proposed a new method DEsingle for DE analysis in scRNA-seq data by employing the Zero-Inflated Negative Binomial (ZINB) model. We proved that DEsingle could estimate the percentage of real zeros and dropout zeros by modelling the mRNA capture procedure. According to this model, DEsingle can distinguish three types of differential expression between two groups of single cells, with regard to differences in expression status, in expression abundances, and in both.We validated the performance of the method on simulation data and applied it on real scRNA-seq data of human preimplantation embryonic cells of different days of embryo development. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different functions.
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