Insertion and deletion (indel), a common form of genetic variation, has been shown to cause or contribute to human genetic diseases and cancer. With the advance of next-generation sequencing technology, many indel calling tools have been developed; however, evaluation and comparison of these tools using large-scale real data are still scant. Here we evaluated seven popular and publicly available indel calling tools, GATK Unified Genotyper, VarScan, Pindel, SAMtools, Dindel, GTAK HaplotypeCaller, and Platypus, using 78 human genome low-coverage data from the 1000 Genomes project. Comparing indels called by these tools with a known set of indels, we found that Platypus outperforms other tools. In addition, a high percentage of known indels still remain undetected and the number of common indels called by all seven tools is very low. All these findings indicate the necessity of improving the existing tools or developing new algorithms to achieve reliable and consistent indel calling results.

There is growing evidence of abnormal epigenetic processes playing a role in the neurobiology of psychiatric disorders, although the precise nature of these anomalies remains largely unknown. To study neurobiological (including epigenetic) factors that influence emotionality, we use rats bred for distinct behavioral responses to novelty. Rats bred for low novelty response (low responder [LR]) exhibit high levels of anxiety- and depressive-like behavior compared with high novelty responder (HR) rats. Prior work revealed distinct limbic brain development in HR versus LR rats, including altered expression of genes involved in DNA methylation. This led us to hypothesize that DNA methylation differences in the developing brain drive the disparate HR/LR neurobehavioral phenotypes. Here we report altered DNA methylation markers (altered DNA methyltransferase protein levels and increased global DNA methylation levels) in the early postnatal amygdala of LR versus HR male rats. Next-generation sequencing methylome profiling identified numerous differentially methylated regions across the genome in the early postnatal HR/LR amygdala. We also contrasted methylation profiles of male HRs and LRs with a control rat strain that displays an intermediate behavioral phenotype relative to the HR/LR extremes; this revealed that the LR amygdalar methylome was abnormal, with the HR profile more closely resembling that of the control group. Finally, through two methylation manipulations in early life, we found that decreasing DNA methylation in the developing male and female amygdala improves adult anxiety- and depression-like behavior. These findings suggest that inborn DNA methylation differences play important roles in shaping brain development and lifelong emotional behavior.SIGNIFICANCE STATEMENTEpigenetic changes are biological mechanisms that regulate the expression and function of genes throughout the brain and body. DNA methylation, one type of epigenetic mechanism, is known to be altered in brains of psychiatric patients, which suggests a role for DNA methylation in the pathogenesis of psychiatric disorders, such as depression and anxiety. The present study examines brains of rats that display high versus low levels of anxiety- and depression-like behavior to investigate how neural DNA methylation levels differ in these animals and how such differences shape their emotional behavioral differences. Studying how epigenetic processes affect emotional behavior may improve our understanding of the neurobiology of psychiatric disorders and lead to improved treatments.

Embryonic stem cells (ESCs) consist of a population of self-renewing cells displaying extensive phenotypic and functional heterogeneity. Research towards the understanding of the epigenetic mechanisms underlying the heterogeneity among ESCs is still in its initial stage. Key issues, such as how to identify cell-subset specifically methylated loci and how to interpret the biological meanings of methylation variations remain largely unexplored. To fill in the research gap, we implemented a computational pipeline to analyze single-cell methylome and to perform an integrative analysis with single-cell transcriptome data. According to the origins of variation in DNA methylation, we determined the genomic loci associated with allelic-specific methylation or asymmetric DNA methylation, and explored a beta mixture model to infer the genomic loci exhibiting cell-subset specific methylation (CSM). We observed that the putative CSM loci in ESCs are significantly enriched in CpG island (CGI) shelves and regions with histone marks for promoter and enhancer, and the genes hosting putative CSM loci show wide-ranging expression among ESCs. More interestingly, the putative CSM loci may be clustered into co-methylated modules enriching the binding motifs of distinct sets of transcription factors. Taken together, our study provided a novel tool to explore single-cell methylome and transcriptome to reveal the underlying transcriptional regulatory networks associated with epigenetic heterogeneity of ESCs. DNA methylation is an epigenetic mark with covalent modification that occurs directly on genetic material. In vertebrates, the most common form of DNA methylation is 5-methylcytosine (5-mC) at which a methyl group (CH3) is attached to the cytosine nucleotide, especially in the context of CpG dinucleotide. DNA methylation has important regulatory roles in a broad range of biological processes and diseases, such as embryonic stem cells (ESCs) differentiation and development. ESC populations can be strikingly heterogeneous in DNA methylation. Emerging single-cell methods for capturing DNA methylation are being developed with the exciting potential to investigate the DNA methylation feature within complex and heterogeneous tissues. In this study, we implemented a computational pipeline to infer cell-subset specific methylation of ESCs from single-cell methylome. Through integrative analyses with transcription factor binding and single-cell transcriptome, we explored the underlying regulatory mechanisms associated with methylation heterogeneity in ESCs to interpret the biological functional relevance of methylation variations.

We consider an infinite-allele Markov branching process (IAMBP). Our main focus is the frequency spectrum of this process, that is, the proportion of alleles having a given number of copies at a specified time point. We derive the variance of the frequency spectrum, which is useful for interval estimation and hypothesis testing for process parameters. In addition, for a class of special IAMBP with birth and death offspring distribution, we show that the mean of its limiting frequency spectrum has an explicit form in terms of the hypergeometric function. We also derive an asymptotic expression for convergence rate to the limit. Simulations are used to illustrate the results for the birth and death process.

With the advance of next generation sequencing (NGS) technologies, a large number of insertion and deletion (indel) variants have been identified in human populations. Despite much research into variant calling, it has been found that a non-negligible proportion of the identified indel variants might be false positives due to sequencing errors, artifacts caused by ambiguous alignments, and annotation errors. In this paper, we examine indel redundancy in dbSNP, one of the central databases for indel variants, and develop a standalone computational pipeline, dubbed Vindel, to detect redundant indels. The pipeline first applies indel position information to form candidate redundant groups, then performs indel mutations to the reference genome to generate corresponding indel variant substrings. Finally the indel variant substrings in the same candidate redundant groups are compared in a pairwise fashion to identify redundant indels. We applied our pipeline to check for redundancy in the human indels in dbSNP. Our pipeline identified approximately 8% redundancy in insertion type indels, 12% in deletion type indels, and overall 10% for insertions and deletions combined. These numbers are largely consistent across all human autosomes. We also investigated indel size distribution and adjacent indel distance distribution for a better understanding of the mechanisms generating indel variants. Vindel, a simple yet effective computational pipeline, can be used to check whether a set of indels are redundant with respect to those already in the database of interest such as NCBI’s dbSNP. Of the approximately 5.9 million indels we examined, nearly 0.6 million are redundant, revealing a serious limitation in the current indel annotation. Statistics results prove the consistency of the pipeline on indel redundancy detection for all 22 chromosomes. Apart from the standalone Vindel pipeline, the indel redundancy check algorithm is also implemented in the web server http://bioinformatics.cs.vt.edu/zhanglab/indelRedundant.php .