A Simple Strategy for Reducing False Negatives in Calling Variants from Single-Cell Sequencing Data

Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in single-cell sequencing data analysis. Simulation results show that this method is highly reliable, with an error rate of 4.94×10^-5, which is orders of magnitude lower than the expected false negative rate (~34%) estimated from a single-cell exome dataset, though the method is limited by the low SNP density in the human genome. We applied this method to analyze the exome data of a few dozen single tumor cells generated in previous studies, and extracted cell specific mutation information for a small set of sites. Interestingly, we found that there are difficulties in using the classical clonal model of tumor cell growth to explain the mutation patterns observed in some tumor cells.     

Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes

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单细胞测序技术在干细胞领域的研究进展

细胞异质性是生物体内普遍存在的一种特性,这种特性容易受外界因素的影响,甚至是单一类型的细胞在生长环境发生改变时,其基因表达也可能出现变化并产生差异。干细胞是一类具有无限自我更新和分化潜能的特殊类型的细胞,在胚胎组织发育和成体组织的动态平衡中发挥了重要作用。单细胞测序为分析包括干细胞在内的细胞异质性提供了强有力的工具,这种技术可通过更加准确的方式剖析细胞异质性。该文综述了近年来发展起来的单细胞测序技术,包括单细胞分离、基因组扩增和测序分析,并讨论了它们在干细胞(包括多能干细胞、肿瘤干细胞和组织特异性干细胞)研究中的应用。     

Unravelling Intratumoral Heterogeneity through High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing

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ANGSD: Analysis of Next Generation Sequencing Data

Background

High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously.

Results

We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods.

Conclusions

The open source c/c++ program ANGSD is available at http://www.popgen.dk/angsd. The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0356-4) contains supplementary material, which is available to authorized users.     

单细胞测序技术在肝脏疾病的应用与展望

新兴的单细胞测序技术能够从单细胞水平揭示基因组、转录组和表观遗传学等分子水平发生的基因变异与表观修饰状态,也可用于鉴定新的细胞类型和表面标记物。这将帮助人们探明疾病发生时细胞基因、转录或表观修饰方面的变化,了解细胞之间的联系,以及深入理解肿瘤异质性。目前,单细胞测序技术已用于多种疾病的研究,其在肝脏疾病,包括肝硬化、肝癌中已有相关成果。于此,综述了单细胞测序技术在肝脏发育及肝病中的应用,讨论了肝脏疾病发生的内在机制以及该技术仍存在的问题,提出可能的解决方案,如发展三维单细胞测序技术将更能帮助人们深刻理解肝脏疾病发生机制。     

单细胞转录组测序技术在细胞分类中的应用

多细胞有机体的细胞类型多且复杂,细胞间普遍存在异质性。目前,单细胞转录组测序(single-cell RNA sequencing,scRNA-seq)技术是一项新兴的研究单个细胞转录水平的技术,其从数千个平行的细胞中生成转录谱,揭示个体细胞基因组的差异性表达,反映细胞间的异质性,从而鉴定出不同细胞类型,形成组织或器官的细胞图谱,在生物和临床医学等领域发挥重要作用。该文在对scRNA-seq测序平台进行阐述和比较的基础上,着重介绍其在神经系统和免疫系统细胞类型探索中的应用,并且总结scRNA-seq与空间转录组技术相结合的研究成果。     

A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data

High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.     

Estimating Single-Cell Lag Times via a Bayesian Scheme

Network models offer computationally efficient tools for estimating the variability of single-cell lag phases. Currently, optical methods for estimating the variability of single-cell lag phases use single-cell inocula and are technically challenging. A Bayesian network model incorporating small uncertain inocula addresses these limitations.     

WBSA: Web Service for Bisulfite Sequencing Data Analysis

Whole-Genome Bisulfite Sequencing (WGBS) and genome-wide Reduced Representation Bisulfite Sequencing (RRBS) are widely used to study DNA methylation. However, data analysis is complicated, lengthy, and hampered by a lack of seamless analytical pipelines. To address these issues, we developed a convenient, stable, and efficient web service called Web Service for Bisulfite Sequencing Data Analysis (WBSA) to analyze bisulfate sequencing data. WBSA focuses on not only CpG methylation, which is the most common biochemical modification in eukaryotic DNA, but also non-CG methylation, which have been observed in plants, iPS cells, oocytes, neurons and stem cells of human. WBSA comprises three main modules as follows: WGBS data analysis, RRBS data analysis, and differentially methylated region (DMR) identification. The WGBS and RRBS modules execute read mapping, methylation site identification, annotation, and advanced analysis, whereas the DMR module identifies actual DMRs and annotates their correlations to genes. WBSA can be accessed and used without charge either online or local version. WBSA also includes the executables of the Portable Batch System (PBS) and standalone versions that can be downloaded from the website together with the installation instructions. WBSA is available at no charge for academic users at http://wbsa.big.ac.cn.     

POSA: Perl Objects for DNA Sequencing Data Analysis

Background  

Capillary DNA sequencing machines allow the generation of vast amounts of data with little hands-on time. With this expansion of data generation, there is a growing need for automated data processing. Most available software solutions, however, still require user intervention or provide modules that need advanced informatics skills to allow implementation in pipelines.     

Single-Cell RNA Sequencing Maps Endothelial Metabolic Plasticity in Pathological Angiogenesis

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Clonal Architecture of Secondary Acute Myeloid Leukemia Defined by Single-Cell Sequencing

Next-generation sequencing has been used to infer the clonality of heterogeneous tumor samples. These analyses yield specific predictions—the population frequency of individual clones, their genetic composition, and their evolutionary relationships—which we set out to test by sequencing individual cells from three subjects diagnosed with secondary acute myeloid leukemia, each of whom had been previously characterized by whole genome sequencing of unfractionated tumor samples. Single-cell mutation profiling strongly supported the clonal architecture implied by the analysis of bulk material. In addition, it resolved the clonal assignment of single nucleotide variants that had been initially ambiguous and identified areas of previously unappreciated complexity. Accordingly, we find that many of the key assumptions underlying the analysis of tumor clonality by deep sequencing of unfractionated material are valid. Furthermore, we illustrate a single-cell sequencing strategy for interrogating the clonal relationships among known variants that is cost-effective, scalable, and adaptable to the analysis of both hematopoietic and solid tumors, or any heterogeneous population of cells.     

OTU Analysis Using Metagenomic Shotgun Sequencing Data

Because of technological limitations, the primer and amplification biases in targeted sequencing of 16S rRNA genes have veiled the true microbial diversity underlying environmental samples. However, the protocol of metagenomic shotgun sequencing provides 16S rRNA gene fragment data with natural immunity against the biases raised during priming and thus the potential of uncovering the true structure of microbial community by giving more accurate predictions of operational taxonomic units (OTUs). Nonetheless, the lack of statistically rigorous comparison between 16S rRNA gene fragments and other data types makes it difficult to interpret previously reported results using 16S rRNA gene fragments. Therefore, in the present work, we established a standard analysis pipeline that would help confirm if the differences in the data are true or are just due to potential technical bias. This pipeline is built by using simulated data to find optimal mapping and OTU prediction methods. The comparison between simulated datasets revealed a relationship between 16S rRNA gene fragments and full-length 16S rRNA sequences that a 16S rRNA gene fragment having a length >150 bp provides the same accuracy as a full-length 16S rRNA sequence using our proposed pipeline, which could serve as a good starting point for experimental design and making the comparison between 16S rRNA gene fragment-based and targeted 16S rRNA sequencing-based surveys possible.