Quality Of Next-generation Sequencing Data Research Articles

Chromatin conformation and histone modification profiling across human kidney anatomic regions

The three major anatomic regions of the human kidney include the cortex, medulla and papilla, with different functions and vulnerabilities to kidney diseases. Epigenetic mechanisms underlying these anatomic structures are incompletely understood. Here, we performed chromatin conformation capture with Hi-C and histone modification H3K4me3/H3K27me3 Cleavage Under Targets and Release Using Nuclease (CUT&RUN) sequencing on the kidney cortex, medulla and papilla dissected from one individual donor. Nuclear suspensions were generated from each region and split subjected to paired Hi-C and CUT&RUN sequencing. We evaluated the quality of next-generation sequencing data, Hi-C chromatin contact matrices and CUT&RUN peak calling. H3K4me3 and H3K27me3 histone modifications represent active and repressive gene transcription, respectively, and differences in chromatin conformation between kidney regions can be analyzed with this dataset. All raw and processed data files are publicly available, allowing researchers to survey the epigenetic landscape across regional human kidney anatomy.

Utility of next-generation sequencing for the etiological diagnosis of Orientia tsutsugamushi infection

BackgroundScrub typhus, an acute febrile disease caused by Orientia tsutsugamushi, is transmitted to humans through infected chigger mites. We present a case of scrub typhus in a previously healthy man from Shandong Province diagnosed using next-generation sequencing (NGS) and PCR and review recent literature on NGS for scrub typhus diagnosis. MethodsNGS was utilized for testing whole blood collected on admission. Confirmatory testing was done by detecting IgM and IgG antibodies to Orientia in acute and convalescent sera by ELISA. Orientia 47-kDa protein gene TaqMan and standard PCR of the 56-kDa protein gene and Sanger sequencing were performed on eschar scab DNA. ResultsThe NGS diagnosis was confirmed by 47-kDa protein gene TaqMan and sequencing of a fragment of the O. tsutsugamushi 56-kDa protein gene from the eschar scab. Analysis of this sequence and the NGS data indicated O. tsutsugamushi strain Cheeloo2020 is a novel genotype. Mapping of the NGS data against the O. tsutsugamushi Gilliam strain genome sequence identified 304 reads with high similarity. ConclusionsNGS is not only useful for multiplex diagnosis of scrub typhus, but also provides insight into the genetic diversity of O. tsutsugamushi. The common failure to submit sequences to databases makes it difficult to determine the minimal quantity and quality of NGS data being used for the positive identification of Orientia DNA in clinical specimens.

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

Intra-Exon Motif Correlations as a Proxy Measure for Mean Per-Tile Sequence Quality Data in RNA-Seq.

Given the wide variability in the quality of next-generation sequencing data submitted to public repositories, it is essential to identify methods that can perform quality control on these data sets when additional quality control data, such as mean tile data, are missing from public repositories. In this study, we present evidence that correlating counts of reads corresponding to pairs of motifs separated over specific distances on individual exons can be used as a proxy mean tile data in the data sets we analyzed and hence could be used when mean tile data are not available. As test data sets we use the Homo sapiens in vitro transcribed (IVT) data set, and a Drosophila melanogaster data set comprising wild and mutant types. We find that a FastQC analysis of the available parts of these data sets demonstrates that the per-tile sequencing quality is good for all the data sets apart from the mutant-type data where the mutant-r3 data are worse than the mutant-r2 data. Correspondingly, intra-exon motif correlations are reasonably large for all data sets except this latter case where the mutant-r2 correlations are low and the mutant-r3 correlations close to zero. We propose that these extremely low correlations are indicative of bias of technical origin, such as flowcell errors. In addition to this, the intra-exon motif correlations as a function of both guanosine-cytosine (GC) content parameters are somewhat higher and less dependent on the GC content parameters in the IVT-Plasmids messenger RNA (mRNA) selection free RNA-Seq sample (control) than in the other RNA-Seq samples that did undergo mRNA selection: both ribosomal depletion (IVT-Only) and PolyA selection (IVT-PolyA, wild type, and mutant).

A Large-Scale and Serverless Computational Approach for Improving Quality of NGS Data Supporting Big Multi-Omics Data Analyses.

Various types of analyses performed over multi-omics data are driven today by next-generation sequencing (NGS) techniques that produce large volumes of DNA/RNA sequences. Although many tools allow for parallel processing of NGS data in a Big Data distributed environment, they do not facilitate the improvement of the quality of NGS data for a large scale in a simple declarative manner. Meanwhile, large sequencing projects and routine DNA/RNA sequencing associated with molecular profiling of diseases for personalized treatment require both good quality data and appropriate infrastructure for efficient storing and processing of the data. To solve the problems, we adapt the concept of Data Lake for storing and processing big NGS data. We also propose a dedicated library that allows cleaning the DNA/RNA sequences obtained with single-read and paired-end sequencing techniques. To accommodate the growth of NGS data, our solution is largely scalable on the Cloud and may rapidly and flexibly adjust to the amount of data that should be processed. Moreover, to simplify the utilization of the data cleaning methods and implementation of other phases of data analysis workflows, our library extends the declarative U-SQL query language providing a set of capabilities for data extraction, processing, and storing. The results of our experiments prove that the whole solution supports requirements for ample storage and highly parallel, scalable processing that accompanies NGS-based multi-omics data analyses.

Abstract 2281: Impact of DNA contaminants on next-generation sequencing data quality

Abstract Nucleic acid purification and clean-up are critical steps for genomics applications, including next-generation sequencing (NGS). Variability in the quality of sample extraction methods can result in differential removal of inhibitors, or reaction contaminants, that diminish NGS experimental data quality. To maximize sample recovery and create more consistent results, efficient removal of inhibitors or contaminants is critical during nucleic acid isolation and purification. In this study, we examine the effect of three common tissue-specific nucleic acid contaminants: (1) collagen, (2) myoglobin, and (3) melanin, on NGS library prep and sequencing metrics. These contaminants are present in skin and muscle tissue and are known to cause inhibition of PCR reactions. In addition, we will extract DNA from both of these samples types and evaluate the performance of two commonly used DNA extraction technologies: (1) magnetic bead-based clean-up; and (2) silica-based columns, for their ability to remove contaminants from DNA extractions and the differential effect of these extraction methods on downstream NGS metrics. These findings demonstrate the importance of the choice of extraction chemistry on NGS metrics and read quality. We also demonstrate that magnetic bead-based- protocols offer a superior reliable, rapid, automatable method to produce high quality DNA and remove contaminants. Citation Format: Amy Dubinsky, Lauren Saunders. Impact of DNA contaminants on next-generation sequencing data quality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2281.

Abstract 2280: A comprehensive sample tracking and data processing workflow for next generation sequencing

Abstract The successful application of Next Generation Sequencing (NGS) to drug discovery requires systems to manage and document each step of the sequencing process from sample receipt through data generation and data processing. We combined BenchlingTM, a solution for tracking NGS lab processes, with FONDA (Framework Of Next generation sequencing Data Analysis) an internally developed data processing platform, to support multiple types of NGS data generation and processing. Benchling combines a digital notebook and a laboratory information management system (LIMS). The system documents and automates steps in the NGS process including: sample registration, nucleic acid extraction, library construction, flow cell construction, sequencer sample sheet generation and BCL2FASTQ conversion. This enables wet lab scientists to easily retrieve an appropriate protocol for each sample and sequencing library type. We connected our sequencers to Benchling in order to monitor each sequencing run and to keep track of the quality of NGS data. In addition, it generates “analysis ready sample sheet” (contains project and study information, location of FASTQ, sample species and library type) and uploads it into designated S3 buckets for data processing. Benchling dashboards provide overviews of NGS sample preparation, data generation and quality control. In summary, Benchling interconnects the original sample, the labels, the barcodes, the cDNA/DNA, the library, and all the QC results. We process NGS data using pipelines implemented in FONDA on a dockerized Amazon Web Services cloud platform. Analyses can be configured automatically from information exported by Benchling or launched manually. After data processing is completed, output files such as gene expression counts or variant calls are deposited into project-specific folders, ready for secondary analysis. In the current FONDA version (as of Nov 2020), we have developed pipelines for single cell multi-omics (CITE-seq and single-cell immune profiling) and bulk RNA-seq. The modular design of FONDA facilitates the development, the updating, and the extension of pipelines to new sequencing technologies. In summary, Benchling and FONDA enable high quality sample and NGS data flows from the lab for target identification, understanding mechanism of action, patient stratification and biomarker discovery. Availability and implementation: FONDA is implemented in Java and released under the Apache License 2.0. FONDA can be downloaded from GitHub at https://github.com/epam/fonda. Citation Format: Chandra Sekhar Pedamallu, Joon Sang Lee, Shu Yan, Adalis Maisonet, Aleksandr Sidoruk, Tengui Chen, Yulia Kamyshova, Mariia Zueva, Mark Magid, Quan Wan, Jeffrey Thompson, Valerie Zebrouck, Immanuel Gadaczek, Mikhail Alperovich, Brian McNatt, Alexei Protopopov, Donald Jackson, Jack Pollard. A comprehensive sample tracking and data processing workflow for next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2280.

Modeling performance of sample collection sites using whole exome sequencing metrics.

Although next-generation sequencing assays are routinely carried out using samples from cancer trials, the sequencing data are not always of the required quality. There is a need to evaluate the performance of tissue collection sites and provide feedback about the quality of next-generation sequencing data. This study used a modeling approach based on whole exome sequencing quality control (QC) metrics to evaluate the relative performance of sites participating in the Bristol Myers Squibb Immuno-Oncology clinical trials sample collection. We identified several events for the sample swap. Overall, most sites performed well and few showed poor performance. These findings can increase awareness of sample failure and improve the quality of samples.

Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data.

While next generation sequencing (NGS) has become the technology of choice for clinical diagnostics, most genetic laboratories still use Sanger sequencing for orthogonal confirmation of NGS results. Previous studies have shown that when the quality of NGS data is high, most calls are indicated by Sanger sequencing, making confirmation redundant. We aimed at establishing a set of criteria that make it possible to distinguish NGS calls that need orthogonal confirmation from those that do not would significantly decrease the amount of work necessary to reach a diagnosis. A data set of 7976 NGS calls confirmed as true or false positive by Sanger sequencing was used to train and test different machine learning (ML) approaches. By varying the size and class balance of the training dataset, we measured the performance of the different algorithms to determine the conditions under which ML is a valid approach for confirming NGS calls in a diagnostic environment. Our results indicate that machine learning is a valid approach to find variant calls that need more investigation, but in order to reach the high accuracy required in a clinical environment, the training data set must include enough observations and these observations must be well-balanced between true/false positive NGS calls. Our results show that it is possible to integrate the diagnostic NGS validation workflow with a machine learning approach to reduce the number of Sanger confirmations of high- quality NGS calls, reducing the time and costs of diagnosis.

NGSReadsTreatment \u2013 A Cuckoo Filter-based Tool for Removing Duplicate Reads in NGS Data

The Next-Generation Sequencing (NGS) platforms provide a major approach to obtaining millions of short reads from samples. NGS has been used in a wide range of analyses, such as for determining genome sequences, analyzing evolutionary processes, identifying gene expression and resolving metagenomic analyses. Usually, the quality of NGS data impacts the final study conclusions. Moreover, quality assessment is generally considered the first step in data analyses to ensure the use of only reliable reads for further studies. In NGS platforms, the presence of duplicated reads (redundancy) that are usually introduced during library sequencing is a major issue. These might have a serious impact on research application, as redundancies in reads can lead to difficulties in subsequent analysis (e.g., de novo genome assembly). Herein, we present NGSReadsTreatment, a computational tool for the removal of duplicated reads in paired-end or single-end datasets. NGSReadsTreatment can handle reads from any platform with the same or different sequence lengths. Using the probabilistic structure Cuckoo Filter, the redundant reads are identified and removed by comparing the reads with themselves. Thus, no prerequisite is required beyond the set of reads. NGSReadsTreatment was compared with other redundancy removal tools in analyzing different sets of reads. The results demonstrated that NGSReadsTreatment was better than the other tools in both the amount of redundancies removed and the use of computational memory for all analyses performed. Available in https://sourceforge.net/projects/ngsreadstreatment/.

SeqSQC: A Bioconductor Package for Evaluating the Sample Quality of Next-generation Sequencing Data

As next-generation sequencing (NGS) technology has become widely used to identify genetic causal variants for various diseases and traits, a number of packages for checking NGS data quality have sprung up in public domains. In addition to the quality of sequencing data, sample quality issues, such as gender mismatch, abnormal inbreeding coefficient, cryptic relatedness, and population outliers, can also have fundamental impact on downstream analysis. However, there is a lack of tools specialized in identifying problematic samples from NGS data, often due to the limitation of sample size and variant counts. We developed SeqSQC, a Bioconductor package, to automate and accelerate sample cleaning in NGS data of any scale. SeqSQC is designed for efficient data storage and access, and equipped with interactive plots for intuitive data visualization to expedite the identification of problematic samples. SeqSQC is available at http://bioconductor.org/packages/SeqSQC.

EasyQC: Tool with Interactive User Interface for Efficient Next-Generation Sequencing Data Quality Control.

The advent of next-generation sequencing (NGS) technologies has revolutionized the world of genomic research. Millions of sequences are generated in a short period of time and they provide intriguing insights to the researcher. Many NGS platforms have evolved over a period of time and their efficiency has been ever increasing. Still, primarily because of the chemistry, glitch in the sequencing machine and human handling errors, some artifacts tend to exist in the final sequence data set. These sequence errors have a profound impact on the downstream analyses and may provide misleading information. Hence, filtering of these erroneous reads has become inevitable and myriad of tools are available for this purpose. However, many of them are accessible as a command line interface that requires the user to enter each command manually. Here, we report EasyQC, a tool for NGS data quality control (QC) with a graphical user interface providing options to carry out trimming of NGS reads based on quality, length, homopolymer, and ambiguous bases. EasyQC also possesses features such as format converter, paired end merger, adapter trimmer, and a graph generator that generates quality distribution, length distribution, GC content, and base composition graphs. Comparison of raw and processed sequence data sets using EasyQC suggested significant increase in overall quality of the sequences. Testing of EasyQC using NGS data sets on a standalone desktop proved to be relatively faster. EasyQC is developed using PERL modules and can be executed in Windows and Linux platforms. With the various QC features, easy interface for end users, and cross-platform compatibility, EasyQC would be a valuable addition to the already existing tools facilitating better downstream analyses.

Next Generation Sequencing: Approach for Assessment and enrichment of raw data

Next-generation sequencing (NGS) is a non-sanger technique based on high-throughput capacity for determining the exact order of nucleotide in the given DNA & RNA molecule. Modern high-throughput sequencer’s produce millions of sequences read in a single run. The quality of these data are not perfect because they contains artefacts like poor quality reads, adapter contamination, wrong base calls and INDELs due to instrument failure or chemical process error. In this study, a procedure has been designed for the refinement and improvement of quality of NGS data for further biological processes.

P157 Evaluation of one lambda NXType™ next generation sequencing

Aim To evaluate the NXType™ (One Lambda, Inc.) HLA next generation sequencing (NGS) for accuracy and precision and to evaluate the accompanying Typestream™ analysis software. In addition, to asses NXType™ in terms of workflow, labor and the required technical skills. Methods Thirty-two genomic DNA samples that were previously typed by LABType Sequence Specific Oligonucleotide (SSO) (One Lambda, Inc.) including 11 samples typed by Sequence Specific primer (SSP) (Invitrogen, Inc.) were used in this study. These included 5 proficiency testing (PT) samples. The NXType™ NGS workflow protocol was followed strictly. The amplification was carried out using long range multiplex primer mixes for HLA class I and class II genes followed by library preparation, isothermal amplification, chip loading and sequencing. The sequencing data was analyzed by Typestream™ software. Repeat testing and analysis was performed on the 32 samples over two weeks period with PT samples repeated 3 times. Results NXType™ NGS typing showed 100% concordance and reproducibility with SSO/SSP for all HLA A, B, C, DRB1, DRB345, DQB1 & DPB1 loci. In addition NXType™ resolved common ambiguities in class I loci often seen with the SSP method. For HLA Class II, ambiguities were detected in 5 samples. These were DRB1∗09:01/21, DPB1∗13:01/107:01, DPB1∗17:01/124:01, DPB1∗104:01/131:01 and DPB1∗13:01/107:01/131:01. The entire run of the 32 samples can be completed from amplification to reporting within 5 routine working days. The Typestream™ software is user friendly and provides detailed information about chip loading, clonality, usable reads, quality of library, exon coverage and allele balance. Conclusions We concluded that the NXType™ NGS typing is accurate and reliable. However, it is labor intensive. NXType™ purification, size selection and isothermal amplification steps are very sensitive and directly impact the quality of NGS data. Automation of NXType™ workflow is needed to reduce technical errors, insure consistency and increase NGS data quality.

ORIO (Online Resource for Integrative Omics): a web-based platform for rapid integration of next generation sequencing data.

Established and emerging next generation sequencing (NGS)-based technologies allow for genome-wide interrogation of diverse biological processes. However, accessibility of NGS data remains a problem, and few user-friendly resources exist for integrative analysis of NGS data from different sources and experimental techniques. Here, we present Online Resource for Integrative Omics (ORIO; https://orio.niehs.nih.gov/), a web-based resource with an intuitive user interface for rapid analysis and integration of NGS data. To use ORIO, the user specifies NGS data of interest along with a list of genomic coordinates. Genomic coordinates may be biologically relevant features from a variety of sources, such as ChIP-seq peaks for a given protein or transcription start sites from known gene models. ORIO first iteratively finds read coverage values at each genomic feature for each NGS dataset. Data are then integrated using clustering-based approaches, giving hierarchical relationships across NGS datasets and separating individual genomic features into groups. In focusing its analysis on read coverage, ORIO makes limited assumptions about the analyzed data; this allows the tool to be applied across data from a variety of experiments and techniques. Results from analysis are presented in dynamic displays alongside user-controlled statistical tests, supporting rapid statistical validation of observed results. We emphasize the versatility of ORIO through diverse examples, ranging from NGS data quality control to characterization of enhancer regions and integration of gene expression information. Easily accessible on a public web server, we anticipate wide use of ORIO in genome-wide investigations by life scientists.

P082 The robust NGSgo workflow for high-resolution HLA typings also fully compatible with the Ion S5 platform

Aim Current technologies for HLA sequencing-based typing often show ambiguous results, most of which are caused by cis–trans allele combinations. Next-generation sequencing (NGS) technology however generates sequences from single DNA molecules, meaning that paternal and maternal alleles can be uniquely identified. Therefore, NGS technology has great potential in the application of HLA typing for diagnostic purposes. The NGSgo ® workflow that proved to be compatible on the Ion PGM platform was now validated on the Ion S5 platform. Here we present NGS data generated when processing pooled HLA loci on the Ion S5 platform using the fully automated Ion Chef system. Methods Amplification of 96 clinical samples was performed for 11 HLA loci with NGSgo-AmpX that enables amplification of whole class I genes, and the essential genomic regions for high-resolution typing of the class II genes. The amplicons generated were pooled per sample and processed in a library preparation that consisted of three steps. The fragmentation and end-repair were performed with the NGSgo-LibrX in a single step followed by the ligation of universal and 24 unique barcoded adapters using the NGSgo-IndX kit (GenDx). Finally, after pooling the libraries in one tube, a magnetic bead step removed all fragments ® software (GenDx). Results Here we will present the data output showing the NGS data quality generated. A preliminary dataset already showed a locus mappability of 89%, read depth of 4206, ambiguities only for 3rd or 4th field resolution (except for DRB1 with 2 ambiguities in the 2nd field due to differences outside of the amplicon) and 100% concordance of the NGS typing with the available pre-types. Conclusions The availability of several NGS workflows allows the end-user to choose the most preferred HLA typing NGS platform that fulfills the requirements of their laboratory for their specific applications. Here we will demonstrate that the NGSgo workflow developed for the Ion PGM is also compatible with the Ion S5 platform, making it a powerful method for high-throughput HLA typings by means of NGS. E. Rozemuller: Stock Shareholder; Company/Organization; GenDx. N. Westerink: Employee; Company/Organization; GenDx. M. Penning: Employee; Company/Organization; GenDx.

Comparison of Sample Preparation Methods Used for the Next-Generation Sequencing of Mycobacterium tuberculosis.

The advent and widespread application of next-generation sequencing (NGS) technologies to the study of microbial genomes has led to a substantial increase in the number of studies in which whole genome sequencing (WGS) is applied to the analysis of microbial genomic epidemiology. However, microorganisms such as Mycobacterium tuberculosis (MTB) present unique problems for sequencing and downstream analysis based on their unique physiology and the composition of their genomes. In this study, we compare the quality of sequence data generated using the Nextera and TruSeq isolate preparation kits for library construction prior to Illumina sequencing-by-synthesis. Our results confirm that MTB NGS data quality is highly dependent on the purity of the DNA sample submitted for sequencing and its guanine-cytosine content (or GC-content). Our data additionally demonstrate that the choice of library preparation method plays an important role in mitigating downstream sequencing quality issues. Importantly for MTB, the Illumina TruSeq library preparation kit produces more uniform data quality than the Nextera XT method, regardless of the quality of the input DNA. Furthermore, specific genomic sequence motifs are commonly missed by the Nextera XT method, as are regions of especially high GC-content relative to the rest of the MTB genome. As coverage bias is highly undesirable, this study illustrates the importance of appropriate protocol selection when performing NGS studies in order to ensure that sound inferences can be made regarding mycobacterial genomes.

Quality control of next-generation sequencing data without a reference.

Next-generation sequencing (NGS) technologies have dramatically expanded the breadth of genomics. Genome-scale data, once restricted to a small number of biomedical model organisms, can now be generated for virtually any species at remarkable speed and low cost. Yet non-model organisms often lack a suitable reference to map sequence reads against, making alignment-based quality control (QC) of NGS data more challenging than cases where a well-assembled genome is already available. Here we show that by generating a rapid, non-optimized draft assembly of raw reads, it is possible to obtain reliable and informative QC metrics, thus removing the need for a high quality reference. We use benchmark datasets generated from control samples across a range of genome sizes to illustrate that QC inferences made using draft assemblies are broadly equivalent to those made using a well-established reference, and describe QC tools routinely used in our production facility to assess the quality of NGS data from non-model organisms.

Effects of error-correction of heterozygous next-generation sequencing data.

BackgroundError correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes.ResultsQuake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers.Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly.ConclusionsThese findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO.

Live genomics for pathogen monitoring in public health.

Whole genome analysis based on next generation sequencing (NGS) now represents an affordable framework in public health systems. Robust analytical pipelines of genomic data provides in a short lapse of time (hours) information about taxonomy, comparative genomics (pan-genome) and single polymorphisms profiles. Pathogenic organisms of interest can be tracked at the genomic level, allowing monitoring at one-time several variables including: epidemiology, pathogenicity, resistance to antibiotics, virulence, persistence factors, mobile elements and adaptation features. Such information can be obtained not only at large spectra, but also at the “local” level, such as in the event of a recurrent or emergency outbreak. This paper reviews the state of the art in infection diagnostics in the context of modern NGS methodologies. We describe how actuation protocols in a public health environment will benefit from a “streaming approach” (pipeline). Such pipeline would include NGS data quality assessment, data mining for comparative analysis, searching differential genetic features, such as virulence, resistance persistence factors and mutation profiles (SNPs and InDels) and formatted “comprehensible” results. Such analytical protocols will enable a quick response to the needs of locally circumscribed outbreaks, providing information on the causes of resistance and genetic tracking elements for rapid detection, and monitoring actuations for present and future occurrences.