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.     

Web Resources for Metagenomics Studies

The development of next-generation sequencing(NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.     

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench ("wet bench") and data analyses conducted using bioinformatics pipelines ("dry bench"). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible "tag" PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.     

The impact of next-generation sequencing on genomics

This article reviews basic concepts,general applications,and the potential impact of next-generation sequencing(NGS)technologies on genomics,with particular reference to currently available and possible future platforms and bioinformatics.NGS technologies have demonstrated the capacity to sequence DNA at unprecedented speed,thereby enabling previously unimaginable scientific achievements and novel biological applications.But,the massive data produced by NGS also presents a significant challenge for data storage,analyses,and management solutions.Advanced bioinformatic tools are essential for the successful application of NGS technology.As evidenced throughout this review,NGS technologies will have a striking impact on genomic research and the entire biological field.With its ability to tackle the unsolved challenges unconquered by previous genomic technologies,NGS is likely to unravel the complexity of the human genome in terms of genetic variations,some of which may be confined to susceptible loci for some common human conditions.The impact of NGS technologies on genomics will be far reaching and likely change the field for years to come.     

An Integrated Approach for Analyzing Clinical Genomic Variant Data from Next-Generation Sequencing

Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource’s iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease.     

Intra‐individual polymorphism in chloroplasts from NGS data: where does it come from and how to handle it?

Next‐generation sequencing allows access to a large quantity of genomic data. In plants, several studies used whole chloroplast genome sequences for inferring phylogeography or phylogeny. Even though the chloroplast is a haploid organelle, NGS plastome data identified a nonnegligible number of intra‐individual polymorphic SNPs. Such observations could have several causes such as sequencing errors, the presence of heteroplasmy or transfer of chloroplast sequences in the nuclear and mitochondrial genomes. The occurrence of allelic diversity has practical important impacts on the identification of diversity, the analysis of the chloroplast data and beyond that, significant evolutionary questions. In this study, we show that the observed intra‐individual polymorphism of chloroplast sequence data is probably the result of plastid DNA transferred into the mitochondrial and/or the nuclear genomes. We further assess nine different bioinformatics pipelines’ error rates for SNP and genotypes calling using SNPs identified in Sanger sequencing. Specific pipelines are adequate to deal with this issue, optimizing both specificity and sensitivity. Our results will allow a proper use of whole chloroplast NGS sequence and will allow a better handling of NGS chloroplast sequence diversity.     

Next generation molecular diagnosis of mitochondrial disorders

Mitochondrial disorders are by far the most genetically heterogeneous group of diseases, involving two genomes, the 16.6 kb mitochondrial genome and ~ 1500 genes encoded in the nuclear genome. For maternally inherited mitochondrial DNA disorders, a complete molecular diagnosis requires several different methods for the detection and quantification of mtDNA point mutations and large deletions. For mitochondrial disorders caused by autosomal recessive, dominant, and X-linked nuclear genes, the diagnosis has relied on clinical, biochemical, and molecular studies to point to a group of candidate genes followed by stepwise Sanger sequencing of the candidate genes one-by-one. The development of Next Generation Sequencing (NGS) has revolutionized the diagnostic approach. Using massively parallel sequencing (MPS) analysis of the entire mitochondrial genome, mtDNA point mutations and deletions can be detected and quantified in one single step. The NGS approach also allows simultaneous analyses of a group of genes or the whole exome, thus, the mutations in causative gene(s) can be identified in one-step. New approaches make genetic analyses much faster and more efficient. Huge amounts of sequencing data produced by the new technologies brought new challenges to bioinformatics, analytical pipelines, and interpretation of numerous novel variants. This article reviews the clinical utility of next generation sequencing for the molecular diagnoses of complex dual genome mitochondrial disorders.     

Discovering and sequencing new plant viral genomes by next‐generation sequencing: description of a practical pipeline

Small‐scale sequencing has improved substantially in recent decades, culminating in the development of next‐generation sequencing (NGS) technologies. Modern NGS methods have helped the discovery of many new plant viruses. Nevertheless, there is still a need to establish solid assembly pipelines targeting small genomes characterised by low identities to known viral sequences. Here, we describe and discuss the fundamental steps required for discovering and sequencing new plant viral genomes by NGS. A practical pipeline and standard alternative tools used in NGS analysis are presented.     

Comparison of alignment software for genome-wide bisulphite sequence data

Recent advances in next generation sequencing (NGS) technology now provide the opportunity to rapidly interrogate the methylation status of the genome. However, there are challenges in handling and interpretation of the methylation sequence data because of its large volume and the consequences of bisulphite modification. We sequenced reduced representation human genomes on the Illumina platform and efficiently mapped and visualized the data with different pipelines and software packages. We examined three pipelines for aligning bisulphite converted sequencing reads and compared their performance. We also comment on pre-processing and quality control of Illumina data. This comparison highlights differences in methods for NGS data processing and provides guidance to advance sequence-based methylation data analysis for molecular biologists.     

Application of next-generation sequencing for the identification of herbal products

Conventional Sanger Sequencing for authentication of herbal products is difficult since they are mixture of herbs with fragmented DNA. Next-generation sequencing (NGS) techniques give massive parallelization of sequencing reaction to generate multiple reads with various read length, thus different components in herbal products with fragmented DNA can be identified. NGS is especially suitable for animal derived products with the lack of effective markers for chemical analysis. Currently, second generation sequencing such as Illumina Sequencing and Ion Torrent Sequencing, and third generation sequencing such as PacBio Sequencing and Nanopore Sequencing are representative NGS platforms. The constructed library is first sequenced to obtain a pool of genomic data, followed by bioinformatics analysis and comparison with DNA database. NGS also facilitates the determination of contaminant which is essential for quality control regulation in Good Manufacturing Practice (GMP) factory. In this article, we provide an overview on NGS, summarize the cases on the use of NGS to identify herbal products, discuss the key technological challenges and provide perspectives on future directions for authentication and quality control of herbal products.     

Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis,Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology

This review discusses the current testing methodologies for COVID-19 diagnosis and explores next-generation sequencing (NGS) technology for the detection of SARS-CoV-2 and monitoring phylogenetic evolution in the current COVID-19 pandemic. The review addresses the development, fundamentals, assay quality control and bioinformatics processing of the NGS data. This article provides a comprehensive review of the obstacles and opportunities facing the application of NGS technologies for the diagnosis, surveillance, and study of SARS-CoV-2 and other infectious diseases. Further, we have contemplated the opportunities and challenges inherent in the adoption of NGS technology as a diagnostic test with real-world examples of its utility in the fight against COVID-19.     

Sequencing our way towards understanding global eukaryotic biodiversity

Microscopic eukaryotes are abundant, diverse and fill critical ecological roles across every ecosystem on Earth, yet there is a well-recognized gap in understanding of their global biodiversity. Fundamental advances in DNA sequencing and bioinformatics now allow accurate en masse biodiversity assessments of microscopic eukaryotes from environmental samples. Despite a promising outlook, the field of eukaryotic marker gene surveys faces significant challenges: how to generate data that are most useful to the community, especially in the face of evolving sequencing technologies and bioinformatics pipelines, and how to incorporate an expanding number of target genes.     

环境微生物宏基因组学研究中的生物信息学方法

高通量测序技术的发展促进了组学技术在环境微生物研究中的广泛应用,而宏基因组学是目前最为关键和成熟的组学方法。生物信息学在微生物宏基因组学研究中具有至关重要的作用。它贯穿于宏基因组学的数据收集和存储、数据处理和分析等各个阶段,既是宏基因组学推广的最大瓶颈,也是目前宏基因组学研究发展的关键所在。本文主要介绍和归纳了目前在高通量宏基因组测序中常用的生物信息学分析平台及其重要的信息分析工具。未来几年之内,测序成本的下降和测序深度的增加将进一步增大宏基因组学研究在数据存储、数据处理和数据挖掘层面的难度,因此相应生物信息学技术与方法的研究和发展也势在必行。近期内我们应该首先加强基础性分析和存储平台的建设以方便普通环境微生物研究者使用,同时针对目前生物信息分析的瓶颈步骤和关键任务重点突破,逐步发展。     

Progress of analytical tools and techniques for human gut microbiome research

Massive DNA sequencing studies have expanded our insights and understanding of the ecological and functional characteristics of the gut microbiome. Advanced sequencing technologies allow us to understand the close association of the gut microbiome with human health and critical illnesses. In the future, analyses of the gut microbiome will provide key information associating with human individual health, which will help provide personalized health care for diseases. Numerous molecular biological analysis tools have been rapidly developed and employed for the gut microbiome researches; however, methodological differences among researchers lead to inconsistent data, limiting extensive share of data. It is therefore very essential to standardize the current methodologies and establish appropriate pipelines for human gut microbiome research. Herein, we review the methods and procedures currently available for studying the human gut microbiome, including fecal sample collection, metagenomic DNA extraction, massive DNA sequencing, and data analyses with bioinformatics. We believe that this review will contribute to the progress of gut microbiome research in the clinical and practical aspects of human health.     

amplisas: a web server for multilocus genotyping using next‐generation amplicon sequencing data

Next‐generation sequencing (NGS) technologies are revolutionizing the fields of biology and medicine as powerful tools for amplicon sequencing (AS). Using combinations of primers and barcodes, it is possible to sequence targeted genomic regions with deep coverage for hundreds, even thousands, of individuals in a single experiment. This is extremely valuable for the genotyping of gene families in which locus‐specific primers are often difficult to design, such as the major histocompatibility complex (MHC). The utility of AS is, however, limited by the high intrinsic sequencing error rates of NGS technologies and other sources of error such as polymerase amplification or chimera formation. Correcting these errors requires extensive bioinformatic post‐processing of NGS data. Amplicon Sequence Assignment (amplisas ) is a tool that performs analysis of AS results in a simple and efficient way, while offering customization options for advanced users. amplisas is designed as a three‐step pipeline consisting of (i) read demultiplexing, (ii) unique sequence clustering and (iii) erroneous sequence filtering. Allele sequences and frequencies are retrieved in excel spreadsheet format, making them easy to interpret. amplisas performance has been successfully benchmarked against previously published genotyped MHC data sets obtained with various NGS technologies.     

新一代高通量RNA测序数据的处理与分析

随着新一代高通量DNA测序技术的快速发展,RNA测序(RNA-seq)已成为基因表达和转录组分析新的重要手段．RNA-seq技术产生的海量数据为生物信息学带来了新的机遇和挑战．有效地对测序数据进行针对性的生物信息学处理和分析,成为RNA-seq技术能否在科学探索中发挥重大作用的关键．以新一代Illumina/Solexa测序平台所产生的数据为例,在扼要介绍高通量RNA-seq测序流程的基础上,对RNA-seq数据处理和分析的方法和现有软件做一个较为全面的综述,并对其中有待进一步研究的问题进行展望．     

Pipeliner: software to evaluate the performance of bioinformatics pipelines for next‐generation resequencing

The choice of technology and bioinformatics approach is critical in obtaining accurate and reliable information from next‐generation sequencing (NGS) experiments. An increasing number of software and methodological guidelines are being published, but deciding upon which approach and experimental design to use can depend on the particularities of the species and on the aims of the study. This leaves researchers unable to produce informed decisions on these central questions. To address these issues, we developed pipeliner – a tool to evaluate, by simulation, the performance of NGS pipelines in resequencing studies. Pipeliner provides a graphical interface allowing the users to write and test their own bioinformatics pipelines with publicly available or custom software. It computes a number of statistics summarizing the performance in SNP calling, including the recovery, sensitivity and false discovery rate for heterozygous and homozygous SNP genotypes. Pipeliner can be used to answer many practical questions, for example, for a limited amount of NGS effort, how many more reliable SNPs can be detected by doubling coverage and halving sample size or what is the false discovery rate provided by different SNP calling algorithms and options. Pipeliner thus allows researchers to carefully plan their study's sampling design and compare the suitability of alternative bioinformatics approaches for their specific study systems. Pipeliner is written in C++ and is freely available from http://github.com/brunonevado/Pipeliner .