QC-Chain: Fast and Holistic Quality Control Method for Next-Generation Sequencing Data

Next-generation sequencing (NGS) technologies have been widely used in life sciences. However, several kinds of sequencing artifacts, including low-quality reads and contaminating reads, were found to be quite common in raw sequencing data, which compromise downstream analysis. Therefore, quality control (QC) is essential for raw NGS data. However, although a few NGS data quality control tools are publicly available, there are two limitations: First, the processing speed could not cope with the rapid increase of large data volume. Second, with respect to removing the contaminating reads, none of them could identify contaminating sources de novo, and they rely heavily on prior information of the contaminating species, which is usually not available in advance. Here we report QC-Chain, a fast, accurate and holistic NGS data quality-control method. The tool synergeticly comprised of user-friendly tools for (1) quality assessment and trimming of raw reads using Parallel-QC, a fast read processing tool; (2) identification, quantification and filtration of unknown contamination to get high-quality clean reads. It was optimized based on parallel computation, so the processing speed is significantly higher than other QC methods. Experiments on simulated and real NGS data have shown that reads with low sequencing quality could be identified and filtered. Possible contaminating sources could be identified and quantified de novo, accurately and quickly. Comparison between raw reads and processed reads also showed that subsequent analyses (genome assembly, gene prediction, gene annotation, etc.) results based on processed reads improved significantly in completeness and accuracy. As regard to processing speed, QC-Chain achieves 7–8 time speed-up based on parallel computation as compared to traditional methods. Therefore, QC-Chain is a fast and useful quality control tool for read quality process and de novo contamination filtration of NGS reads, which could significantly facilitate downstream analysis. QC-Chain is publicly available at: http://www.computationalbioenergy.org/qc-chain.html.     

NGS QC Toolkit: a toolkit for quality control of next generation sequencing data

Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.     

Evaluating the fidelity of de novo short read metagenomic assembly using simulated data

A frequent step in metagenomic data analysis comprises the assembly of the sequenced reads. Many assembly tools have been published in the last years targeting data coming from next-generation sequencing (NGS) technologies but these assemblers have not been designed for or tested in multi-genome scenarios that characterize metagenomic studies. Here we provide a critical assessment of current de novo short reads assembly tools in multi-genome scenarios using complex simulated metagenomic data. With this approach we tested the fidelity of different assemblers in metagenomic studies demonstrating that even under the simplest compositions the number of chimeric contigs involving different species is noticeable. We further showed that the assembly process reduces the accuracy of the functional classification of the metagenomic data and that these errors can be overcome raising the coverage of the studied metagenome. The results presented here highlight the particular difficulties that de novo genome assemblers face in multi-genome scenarios demonstrating that these difficulties, that often compromise the functional classification of the analyzed data, can be overcome with a high sequencing effort.     

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.     

BIGpre: a quality assessment package for next-generation sequencing data

The emergence of next-generation sequencing (NGS) technologies has significantly improved sequencing throughput and reduced costs. However, the short read length, duplicate reads and massive volume of data make the data processing much more difficult and complicated than the first-generation sequencing technology. Although there are some software packages developed to assess the data quality, those packages either are not easily available to users or require bioinformatics skills and computer resources. Moreover, almost all the quality assessment software currently available didn’t taken into account the sequencing errors when dealing with the duplicate assessment in NGS data. Here, we present a new user-friendly quality assessment software package called BIGpre, which works for both Illumina and 454 platforms. BIGpre contains all the functions of other quality assessment software, such as the correlation between forward and reverse reads, read GC-content distribution, and base Ns quality. More importantly, BIGpre incorporates associated programs to detect and remove duplicate reads after taking sequencing errors into account and trimming low quality reads from raw data as well. BIGpre is primarily written in Perl and integrates graphical capability from the statistics package R. This package produces both tabular and graphical summaries of data quality for sequencing datasets from Illumina and 454 platforms. Processing hundreds of millions reads within minutes, this package provides immediate diagnostic information for user to manipulate sequencing data for downstream analyses. BIGpre is freely available at http://bigpre.sourceforge.net/.     

MutAid: Sanger and NGS Based Integrated Pipeline for Mutation Identification,Validation and Annotation in Human Molecular Genetics

Traditional Sanger sequencing as well as Next-Generation Sequencing have been used for the identification of disease causing mutations in human molecular research. The majority of currently available tools are developed for research and explorative purposes and often do not provide a complete, efficient, one-stop solution. As the focus of currently developed tools is mainly on NGS data analysis, no integrative solution for the analysis of Sanger data is provided and consequently a one-stop solution to analyze reads from both sequencing platforms is not available. We have therefore developed a new pipeline called MutAid to analyze and interpret raw sequencing data produced by Sanger or several NGS sequencing platforms. It performs format conversion, base calling, quality trimming, filtering, read mapping, variant calling, variant annotation and analysis of Sanger and NGS data under a single platform. It is capable of analyzing reads from multiple patients in a single run to create a list of potential disease causing base substitutions as well as insertions and deletions. MutAid has been developed for expert and non-expert users and supports four sequencing platforms including Sanger, Illumina, 454 and Ion Torrent. Furthermore, for NGS data analysis, five read mappers including BWA, TMAP, Bowtie, Bowtie2 and GSNAP and four variant callers including GATK-HaplotypeCaller, SAMTOOLS, Freebayes and VarScan2 pipelines are supported. MutAid is freely available at https://sourceforge.net/projects/mutaid.     

Metagenomic analysis of historical herbarium specimens reveals a postmortem microbial community

Advances in DNA extraction and next‐generation sequencing have made a vast number of historical herbarium specimens available for genomic investigation. These specimens contain not only genomic information from the individual plants themselves, but also from associated microorganisms such as bacteria and fungi. These microorganisms may have colonized the living plant (e.g., pathogens or host‐associated commensal taxa) or may result from postmortem colonization that may include decomposition processes or contamination during sample handling. Here we characterize the metagenomic profile from shotgun sequencing data from herbarium specimens of two widespread plant species (Ambrosia artemisiifolia and Arabidopsis thaliana) collected up to 180 years ago. We used blast searching in combination with megan and were able to infer the metagenomic community even from the oldest herbarium sample. Through comparison with contemporary plant collections, we identify three microbial species that are nearly exclusive to herbarium specimens, including the fungus Alternaria alternata, which can comprise up to 7% of the total sequencing reads. This species probably colonizes the herbarium specimens during preparation for mounting or during storage. By removing the probable contaminating taxa, we observe a temporal shift in the metagenomic composition of the invasive weed Am. artemisiifolia. Our findings demonstrate that it is generally possible to use herbarium specimens for metagenomic analyses, but that the results should be treated with caution, as some of the identified species may be herbarium contaminants rather than representing the natural metagenomic community of the host plant.     

The effects of read length,quality and quantity on microsatellite discovery and primer development: from Illumina to PacBio

The advent of next‐generation sequencing (NGS) technologies has transformed the way microsatellites are isolated for ecological and evolutionary investigations. Recent attempts to employ NGS for microsatellite discovery have used the 454, Illumina, and Ion Torrent platforms, but other methods including single‐molecule real‐time DNA sequencing (Pacific Biosciences or PacBio) remain viable alternatives. We outline a workflow from sequence quality control to microsatellite marker validation in three plant species using PacBio circular consensus sequencing (CCS). We then evaluate the performance of PacBio CCS in comparison with other NGS platforms for microsatellite isolation, through simulations that focus on variations in read length, read quantity and sequencing error rate. Although quality control of CCS reads reduced microsatellite yield by around 50%, hundreds of microsatellite loci that are expected to have improved conversion efficiency to functional markers were retrieved for each species. The simulations quantitatively validate the advantages of long reads and emphasize the detrimental effects of sequencing errors on NGS‐enabled microsatellite development. In view of the continuing improvement in read length on NGS platforms, sequence quality and the corresponding strategies of quality control will become the primary factors to consider for effective microsatellite isolation. Among current options, PacBio CCS may be optimal for rapid, small‐scale microsatellite development due to its flexibility in scaling sequencing effort, while platforms such as Illumina MiSeq will provide cost‐efficient solutions for multispecies microsatellite projects.     

Performance evaluation method for read mapping tool in clinical panel sequencing

In addition to the rapid advancement in Next-Generation Sequencing (NGS) technology, clinical panel sequencing is being used increasingly in clinical studies and tests. However, tools that are used in NGS data analysis have not been comparatively evaluated in performance for panel sequencing. This study aimed to evaluate the tools used in the alignment process, the first procedure in bioinformatics analysis, by comparing tools that have been widely used with ones that have been introduced recently. With the accumulated panel sequencing data, detected variant lists were cataloged and inserted into simulated reads produced from the reference genome (h19). The amount of unmapped reads and misaligned reads, mapping quality distribution, and runtime were measured as standards for comparison. As the most widely used tools, Bowtie2 and BWA–MEM each showed explicit performance with AUC of 0.9984 and 0.9970 respectively. Kart, maintaining superior runtime and less number of misaligned read, also similarly possessed high level of AUC (0.9723). Such selection and optimization method of tools appropriate for panel sequencing can be utilized for fields requiring error minimization, such as clinical application and liquid biopsy studies.     

A comparison of next-generation sequencing analysis methods for cancer xenograft samples

The application of next-generation sequencing (NGS) technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft (PDX) models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.     

The Role of Quality Control in Targeted Next-generation Sequencing Library Preparation

Next-generation sequencing (NGS) is getting routinely used in the diagnosis of hereditary diseases, such as human cardiomyopathies. Hence, it is of utter importance to secure high quality sequencing data, enabling the identification of disease-relevant mutations or the conclusion of neg-ative test results. During the process of sample preparation, each protocol for target enrichment library preparation has its own requirements for quality control (QC); however, there is little evi-dence on the actual impact of these guidelines on resulting data quality. In this study, we analyzed the impact of QC during the diverse library preparation steps of Agilent SureSelect XT target enrichment and Illumina sequencing. We quantified the parameters for a cohort of around 600 sam-ples, which include starting amount of DNA, amount of sheared DNA, smallest and largest frag-ment size of the starting DNA; amount of DNA after the pre-PCR, and smallest and largest fragment size of the resulting DNA;as well as the amount of the final library, the corresponding smallest and largest fragment size, and the number of detected variants. Intriguingly, there is a high tolerance for variations in all QC steps, meaning that within the boundaries proposed in the current study, a considerable variance at each step of QC can be well tolerated without compromising NGS quality.     

MetaSee: An Interactive and Extendable Visualization Toolbox for Metagenomic Sample Analysis and Comparison

The NGS (next generation sequencing)-based metagenomic data analysis is becoming the mainstream for the study of microbial communities. Faced with a large amount of data in metagenomic research, effective data visualization is important for scientists to effectively explore, interpret and manipulate such rich information. The visualization of the metagenomic data, especially multi-sample data, is one of the most critical challenges. The different data sample sources, sequencing approaches and heterogeneous data formats make robust and seamless data visualization difficult. Moreover, researchers have different focuses on metagenomic studies: taxonomical or functional, sample-centric or genome-centric, single sample or multiple samples, etc. However, current efforts in metagenomic data visualization cannot fulfill all of these needs, and it is extremely hard to organize all of these visualization effects in a systematic manner. An extendable, interactive visualization tool would be the method of choice to fulfill all of these visualization needs. In this paper, we have present MetaSee, an extendable toolbox that facilitates the interactive visualization of metagenomic samples of interests. The main components of MetaSee include: (I) a core visualization engine that is composed of different views for comparison of multiple samples: Global view, Phylogenetic view, Sample view and Taxa view, as well as link-out for more in-depth analysis; (II) front-end user interface with real metagenomic models that connect to the above core visualization engine and (III) open-source portal for the development of plug-ins for MetaSee. This integrative visualization tool not only provides the visualization effects, but also enables researchers to perform in-depth analysis of the metagenomic samples of interests. Moreover, its open-source portal allows for the design of plug-ins for MetaSee, which would facilitate the development of any additional visualization effects.     

Comparison of Hepatocellular Carcinoma miRNA Expression Profiling as Evaluated by Next Generation Sequencing and Microarray

MicroRNA (miRNA) expression profiling has proven useful in diagnosing and understanding the development and progression of several diseases. Microarray is the standard method for analyzing miRNA expression profiles; however, it has several disadvantages, including its limited detection of miRNAs. In recent years, advances in genome sequencing have led to the development of next-generation sequencing (NGS) technologies, which significantly advance genome sequencing speed and discovery. In this study, we compared the expression profiles obtained by next generation sequencing (NGS) with the profiles created using microarray to assess if NGS could produce a more accurate and complete miRNA profile. Total RNA from 14 hepatocellular carcinoma tumors (HCC) and 6 matched non-tumor control tissues were sequenced with Illumina MiSeq 50-bp single-end reads. Micro RNA expression profiles were estimated using miRDeep2 software. As a comparison, miRNA expression profiles for 11 out of 14 HCCs were also established by microarray (Agilent human microRNA microarray). The average total sequencing exceeded 2.2 million reads per sample and of those reads, approximately 57% mapped to the human genome. The average correlation for miRNA expression between microarray and NGS and subtraction were 0.613 and 0.587, respectively, while miRNA expression between technical replicates was 0.976. The diagnostic accuracy of HCC, p-value, and AUC were 90.0%, 7.22×10⁻⁴, and 0.92, respectively. In summary, NGS created an miRNA expression profile that was reproducible and comparable to that produced by microarray. Moreover, NGS discovered novel miRNAs that were otherwise undetectable by microarray. We believe that miRNA expression profiling by NGS can be a useful diagnostic tool applicable to multiple fields of medicine.     

Sequence deeper without sequencing more: Bayesian resolution of ambiguously mapped reads

Next-generation sequencing (NGS) has transformed molecular biology and contributed to many seminal insights into genomic regulation and function. Apart from whole-genome sequencing, an NGS workflow involves alignment of the sequencing reads to the genome of study, after which the resulting alignments can be used for downstream analyses. However, alignment is complicated by the repetitive sequences; many reads align to more than one genomic locus, with 15–30% of the genome not being uniquely mappable by short-read NGS. This problem is typically addressed by discarding reads that do not uniquely map to the genome, but this practice can lead to systematic distortion of the data. Previous studies that developed methods for handling ambiguously mapped reads were often of limited applicability or were computationally intensive, hindering their broader usage. In this work, we present SmartMap: an algorithm that augments industry-standard aligners to enable usage of ambiguously mapped reads by assigning weights to each alignment with Bayesian analysis of the read distribution and alignment quality. SmartMap is computationally efficient, utilizing far fewer weighting iterations than previously thought necessary to process alignments and, as such, analyzing more than a billion alignments of NGS reads in approximately one hour on a desktop PC. By applying SmartMap to peak-type NGS data, including MNase-seq, ChIP-seq, and ATAC-seq in three organisms, we can increase read depth by up to 53% and increase the mapped proportion of the genome by up to 18% compared to analyses utilizing only uniquely mapped reads. We further show that SmartMap enables the analysis of more than 140,000 repetitive elements that could not be analyzed by traditional ChIP-seq workflows, and we utilize this method to gain insight into the epigenetic regulation of different classes of repetitive elements. These data emphasize both the dangers of discarding ambiguously mapped reads and their power for driving biological discovery.     

Simultaneous virus identification and characterization of severe unexplained pneumonia cases using a metagenomics sequencing technique

Many viruses can cause respiratory diseases in humans. Although great advances have been achieved in methods of diagnosis, it remains challenging to identify pathogens in unexplained pneumonia (UP) cases. In this study, we applied next-generation sequencing (NGS) technology and a metagenomic approach to detect and characterize respiratory viruses in UP cases from Guizhou Province, China. A total of 33 oropharyngeal swabs were obtained from hospitalized UP patients and subjected to NGS. An unbiased metagenomic analysis pipeline identified 13 virus species in 16 samples. Human rhinovirus C was the virus most frequently detected and was identified in seven samples. Human measles virus, adenovirus B 55 and coxsackievirus A10 were also identified. Metagenomic sequencing also provided virus genomic sequences, which enabled genotype characterization and phylogenetic analysis. For cases of multiple infection, metagenomic sequencing afforded information regarding the quantity of each virus in the sample, which could be used to evaluate each viruses’ role in the disease. Our study highlights the potential of metagenomic sequencing for pathogen identification in UP cases.     

Application of Metagenomic Sequencing to Food Safety: Detection of Shiga Toxin-Producing Escherichia coli on Fresh Bagged Spinach

Culture-independent diagnostics reduce the reliance on traditional (and slower) culture-based methodologies. Here we capitalize on advances in next-generation sequencing (NGS) to apply this approach to food pathogen detection utilizing NGS as an analytical tool. In this study, spiking spinach with Shiga toxin-producing Escherichia coli (STEC) following an established FDA culture-based protocol was used in conjunction with shotgun metagenomic sequencing to determine the limits of detection, sensitivity, and specificity levels and to obtain information on the microbiology of the protocol. We show that an expected level of contamination (∼10 CFU/100 g) could be adequately detected (including key virulence determinants and strain-level specificity) within 8 h of enrichment at a sequencing depth of 10,000,000 reads. We also rationalize the relative benefit of static versus shaking culture conditions and the addition of selected antimicrobial agents, thereby validating the long-standing culture-based parameters behind such protocols. Moreover, the shotgun metagenomic approach was informative regarding the dynamics of microbial communities during the enrichment process, including initial surveys of the microbial loads associated with bagged spinach; the microbes found included key genera such as Pseudomonas, Pantoea, and Exiguobacterium. Collectively, our metagenomic study highlights and considers various parameters required for transitioning to such sequencing-based diagnostics for food safety and the potential to develop better enrichment processes in a high-throughput manner not previously possible. Future studies will investigate new species-specific DNA signature target regimens, rational design of medium components in concert with judicious use of additives, such as antibiotics, and alterations in the sample processing protocol to enhance detection.     

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.