基因组二代测序数据的自动化分析流程

二代测序技术的发展对测序数据的处理分析提出了很高的要求。目前二代测序数据分析软件很多, 但是绝大多数软件仅能完成单一的分析功能(例如：仅进行序列比对或变异读取或功能注释等), 如何能正确高效地选择整合这些软件已成为迫切需求。文章设计了一套基于perl语言和SGE资源管理的自动化处理流程来分析Illumina平台基因组测序数据。该流程以测序原始序列数据作为输入, 调用业界标准的数据处理软件(如：BWA, Samtools, GATK, ANNOVAR等), 最终生成带有相应功能注释、便于研究者进一步分析的变异位点列表。该流程通过自动化并行脚本控制流程的高效运行, 一站式输出分析结果和报告, 简化了数据分析过程中的人工操作, 大大提高了运行效率。用户只需填写配置文件或使用图形界面输入即可完成全部操作。该工作为广大研究者分析二代测序数据提供了便利的途径。     

The Quantification of Representative Sequences pipeline for amplicon sequencing: case study on within‐population ITS1 sequence variation in a microparasite infecting Daphnia

Next generation sequencing (NGS) platforms are replacing traditional molecular biology protocols like cloning and Sanger sequencing. However, accuracy of NGS platforms has rarely been measured when quantifying relative frequencies of genotypes or taxa within populations. Here we developed a new bioinformatic pipeline (QRS) that pools similar sequence variants and estimates their frequencies in NGS data sets from populations or communities. We tested whether the estimated frequency of representative sequences, generated by 454 amplicon sequencing, differs significantly from that obtained by Sanger sequencing of cloned PCR products. This was performed by analysing sequence variation of the highly variable first internal transcribed spacer (ITS1) of the ichthyosporean Caullerya mesnili, a microparasite of cladocerans of the genus Daphnia. This analysis also serves as a case example of the usage of this pipeline to study within‐population variation. Additionally, a public Illumina data set was used to validate the pipeline on community‐level data. Overall, there was a good correspondence in absolute frequencies of C. mesnili ITS1 sequences obtained from Sanger and 454 platforms. Furthermore, analyses of molecular variance (amova ) revealed that population structure of C. mesnili differs across lakes and years independently of the sequencing platform. Our results support not only the usefulness of amplicon sequencing data for studies of within‐population structure but also the successful application of the QRS pipeline on Illumina‐generated data. The QRS pipeline is freely available together with its documentation under GNU Public Licence version 3 at http://code.google.com/p/quantification-representative-sequences .     

SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.     

Windows下16S rRNA基因扩增子测序数据分析的简易流程

微生物组数据分析需要掌握Linux系统操作,这对缺乏计算机知识的生物研究人员是一个很大的障碍。为此我们设计了一套在Windows的Linux子系统(WSL)下分析16S rRNA基因扩增子高通量测序数据的简易流程。本流程整合常用的开源软件VSEARCH与QIIME等,能对16S rRNA测序数据进行质量控制、OTU聚类、多样性分析及结果可视化呈现。以唾液微生物组分析为例,详细介绍从原始数据到多样性统计分析过程的参数和命令,及结果解读。教学实践证明,此流程易于学习,并有助于掌握微生物组的基本概念与方法。利用Windows系统最新的WSL功能,本流程方便Windows用户使用大量在Linux上运行的生物信息工具,有助于促进微生物组研究的发展。流程的安装程序与测序数据可从网址(http://www. ligene. cn/win16s/)免费下载使用。     

A tool kit for quantifying eukaryotic rRNA gene sequences from human microbiome samples

ABSTRACT: Eukaryotic microorganisms are important but understudied components of the human microbiome. Here we present a pipeline for analysis of deep sequencing data on single cell eukaryotes. We designed a new 18S rRNA gene specific PCR primer set and compared a published rRNA gene internal transcribed spacer (ITS) gene primer set. Amplicons were tested against 24 specimens from defined eukaryotes and eight well-characterized human stool samples. A software pipeline (https://sourceforge.net/projects/brocc/) was developed for taxonomic attribution, validated against simulated data, and tested on pyrosequence data. This study provides a well-characterized tool kit for sequence-based enumeration of eukaryotic organisms in human microbiome samples.     

VSQual: a visual system to assist DNA sequencing quality control

A lack of pliant software tools that support small- to medium-scale DNA sequencing efforts is a major hindrance for recording and using laboratory workflow information to monitor the overall quality of data production. Here we describe VSQual, a set of Perl programs intended to provide simple and powerful tools to check several quality features of the sequencing data generated by automated DNA sequencing machines. The core program of VSQual is a flexible Perl-based pipeline, designed to be accessible and useful for both programmers and non-programmers. This pipeline directs the processing steps and can be easily customized for laboratory needs. Basically, the raw DNA sequencing trace files are processed by Phred and Cross_match, then the outputs are parsed, reformatted into Web-based graphical reports, and added to a Web site structure. The result is a set of real time sequencing reports easily accessible and understood by common laboratory people. These reports facilitate the monitoring of DNA sequencing as well as the management of laboratory workflow, significantly reducing operational costs and ensuring high quality and scientifically reliable results.     

A beginners guide to SNP calling from high-throughput DNA-sequencing data

High-throughput DNA sequencing (HTS) is of increasing importance in the life sciences. One of its most prominent applications is the sequencing of whole genomes or targeted regions of the genome such as all exonic regions (i.e., the exome). Here, the objective is the identification of genetic variants such as single nucleotide polymorphisms (SNPs). The extraction of SNPs from the raw genetic sequences involves many processing steps and the application of a diverse set of tools. We review the essential building blocks for a pipeline that calls SNPs from raw HTS data. The pipeline includes quality control, mapping of short reads to the reference genome, visualization and post-processing of the alignment including base quality recalibration. The final steps of the pipeline include the SNP calling procedure along with filtering of SNP candidates. The steps of this pipeline are accompanied by an analysis of a publicly available whole-exome sequencing dataset. To this end, we employ several alignment programs and SNP calling routines for highlighting the fact that the choice of the tools significantly affects the final results.     

DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline for High-Throughput Analysis of Next-Generation Sequencing Data

High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at http://p.ddbj.nig.ac.jp/.     

PipeCraft: Flexible open‐source toolkit for bioinformatics analysis of custom high‐throughput amplicon sequencing data

High‐throughput sequencing methods have become a routine analysis tool in environmental sciences as well as in public and private sector. These methods provide vast amount of data, which need to be analysed in several steps. Although the bioinformatics may be applied using several public tools, many analytical pipelines allow too few options for the optimal analysis for more complicated or customized designs. Here, we introduce PipeCraft, a flexible and handy bioinformatics pipeline with a user‐friendly graphical interface that links several public tools for analysing amplicon sequencing data. Users are able to customize the pipeline by selecting the most suitable tools and options to process raw sequences from Illumina, Pacific Biosciences, Ion Torrent and Roche 454 sequencing platforms. We described the design and options of PipeCraft and evaluated its performance by analysing the data sets from three different sequencing platforms. We demonstrated that PipeCraft is able to process large data sets within 24 hr. The graphical user interface and the automated links between various bioinformatics tools enable easy customization of the workflow. All analytical steps and options are recorded in log files and are easily traceable.     

A novel assembly pipeline and functional annotations for targeted sequencing: A case study on the globally threatened Margaritiferidae (Bivalvia: Unionida)

The proliferation of genomic sequencing approaches has significantly impacted the field of phylogenetics. Target capture approaches provide a cost-effective, fast and easily applied strategy for phylogenetic inference of non-model organisms. However, several existing target capture processing pipelines are incapable of incorporating whole genome sequencing (WGS). Here, we develop a new pipeline for capture and de novo assembly of the targeted regions using whole genome re-sequencing reads. This new pipeline captured targeted loci accurately, and given its unbiased nature, can be used with any target capture probe set. Moreover, due to its low computational demand, this new pipeline may be ideal for users with limited resources and when high-coverage sequencing outputs are required. We demonstrate the utility of our approach by incorporating WGS data into the first comprehensive phylogenomic reconstruction of the freshwater mussel family Margaritiferidae. We also provide a catalogue of well-curated functional annotations of these previously uncharacterized freshwater mussel-specific target regions, representing a complementary tool for scrutinizing phylogenetic inferences while expanding future applications of the probe set.     

Detailed evaluation of cancer sequencing pipelines in different microenvironments and heterogeneity levels

The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.     

A structural metagenomics pipeline for examining the gut microbiome

Metagenomic sequencing data provide a rich resource from which to expand our understanding of differential protein functions involved in human health. Here, we outline a pipeline that combines microbial whole genome sequencing with protein structure data to yield a structural metagenomics-informed atlas of microbial enzyme families of interest. Visualizing metagenomics data through a structural lens facilitates downstream studies including targeted inhibition and probe-based proteomics to define at the molecular level how different enzyme orthologs impact in vivo function. Application of this pipeline to gut microbial enzymes like glucuronidases, TMA lyases, and bile salt hydrolases is expected to pinpoint their involvement in health and disease and may aid in the development of therapeutics that target specific enzymes within the microbiome.     

Mining new crystal protein genes from Bacillus thuringiensis on the basis of mixed plasmid-enriched genome sequencing and a computational pipeline

We have designed a high-throughput system for the identification of novel crystal protein genes (cry) from Bacillus thuringiensis strains. The system was developed with two goals: (i) to acquire the mixed plasmid-enriched genomic sequence of B. thuringiensis using next-generation sequencing biotechnology, and (ii) to identify cry genes with a computational pipeline (using BtToxin_scanner). In our pipeline method, we employed three different kinds of well-developed prediction methods, BLAST, hidden Markov model (HMM), and support vector machine (SVM), to predict the presence of Cry toxin genes. The pipeline proved to be fast (average speed, 1.02 Mb/min for proteins and open reading frames [ORFs] and 1.80 Mb/min for nucleotide sequences), sensitive (it detected 40% more protein toxin genes than a keyword extraction method using genomic sequences downloaded from GenBank), and highly specific. Twenty-one strains from our laboratory's collection were selected based on their plasmid pattern and/or crystal morphology. The plasmid-enriched genomic DNA was extracted from these strains and mixed for Illumina sequencing. The sequencing data were de novo assembled, and a total of 113 candidate cry sequences were identified using the computational pipeline. Twenty-seven candidate sequences were selected on the basis of their low level of sequence identity to known cry genes, and eight full-length genes were obtained with PCR. Finally, three new cry-type genes (primary ranks) and five cry holotypes, which were designated cry8Ac1, cry7Ha1, cry21Ca1, cry32Fa1, and cry21Da1 by the B. thuringiensis Toxin Nomenclature Committee, were identified. The system described here is both efficient and cost-effective and can greatly accelerate the discovery of novel cry genes.