A comparative study of k-spectrum-based error correction methods for next-generation sequencing data analysis

Redefining “Gold Standard”: Ultra-Sensitive Characterization of Commercial DNA Standards with Duplex Sequencing

The advent of DNA and RNA sequencing has revolutionized the study of genomics and molecular biology. Next generation sequencing (NGS) technologies like Illumina, Ion Torrent, SOLiD sequencing etc. have brought about a quick and cheap way to sequence genomes. Recently, third generation sequencing (TGS) technologies like PacBio and Oxford Nanopore Technology (ONT) have also been developed. Different technologies use different underlying methods for sequencing and are prone to different error rates. Though many tools exist for error correction of sequencing data from NGS and TGS methods, no standard method is available yet to evaluate the accuracy and effectiveness of these error-correction tools. In this study, we present a Software Package for Error Correction Tool Assessment on nuCLEic acid sequences (SPECTACLE) providing comprehensive algorithms to evaluate error-correction methods for DNA and RNA sequencing, for NGS and TGS platforms. We also present a compilation of sequencing datasets for Illumina, PacBio and ONT platforms that present challenging scenarios for error-correction tools. Using these datasets and SPECTACLE, we evaluate the performance of 23 different error-correction tools and present unique and helpful insights into their strengths and weaknesses. We hope that our methodology will standardize the evaluation of DNA and RNA error-correction tools in the future.

Library Preparation Is the Major Factor Affecting Differences in Results of Immunoglobulin Gene Rearrangements Detection on Two Major Next-Generation Sequencing Platforms

Introduction: Next-generation sequencing (NGS) has become a critical diagnostic assay to identify pathologic SNVs, CNVs, and gene rearrangements. However, it has not been routinely used to assess expression levels of target genes that could indicate patient populations responsive to therapeutics. This is largely due to the absence of reliable bioinformatics tools for the assessment of expression levels within RNA assays. We have developed a novel within-sample distribution-based method that assesses the relative extremity of expression for individual genes. Our predominate focus has been on assessing the expression of NTRK1, NTRK2, NTRK3, ROS1, and ALK, however this method can be readily applied to other genes and NGS platforms. Methods: Within the kinase domain, the expression of a targeted region is represented by the number of unique deduplicated reads for NGS studies and normalized probe expression values (based on spike-in controls) for NanoString studies. A Poisson distribution is used to represent primer expression with the parameters estimated via maximum likelihood. The interquartile range (IQR) of the entirety of the sample's read counts is calculated and only those that do not exceed the third quartile bound by more than 150% of the IQR are considered during parameter estimation. A probability is then assigned to each of the primers based on their read counts. A geometric mean of the individual primer probabilities represents the probability value for the entire gene. Expression values are reported as -log10 (p-value) and cutoffs of 6 and 1 were used to call significant expression for NGS and NanoString platforms, respectively. Results: The NGS-based approach correctly identified significant expression in all gene rearrangement positive cell lines (n = 11). In cell lines not harboring gene rearrangements, the NGS and NanoString platforms showed 100% concordance in calling significant expression (n = 12). In a gene rearrangement negative FFPE cohort, concordance between NGS and NanoString platforms was 97%-99% for target genes (n = 102). ROS1 and ALK were most commonly found to be significantly expressed in FFPE samples (5% and 2%). Conclusions: We have developed a statistical-based approach to detecting expression levels for RNA-based NGS assays. This can be applied to cohort studies to not only identify clinical samples that may benefit from targeted kinase inhibitor therapeutics but also correlate with predicted outcome of disease. Citation Format: Robert Shoemaker, Aaron Berlin, Amy Diliberto, Heather Ely, Marissa Chen, Danielle Murphy, Jason Christiansen, Vince Reuter, Abel Licon. A novel, statistical-based method to determine RNA expression by next-generation sequencing in clinical FFPE samples. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5274.

Next-generation sequencing (NGS) or high-throughput sequencing platforms refers to different modern sequencing technologies that allow scientist to sequence millions to billions of reads (base pairs) in a single, much more quicker and cheaper cost than the previously used Sanger (first generation) sequencing method. Next-generation sequencing platforms classified as second generation sequencing platforms which require polymerase chain reaction (PCR) amplification and third generation sequencing platforms which do not require PCR amplification for sequence. The appearance of next generation sequencing technologies has revolutionized the genome sequencing of organisms including farm animals. These platforms enable scientists to access latest and detailed information about genetic markers that are responsible for economically important traits. Identification of candidate genes responsible for these traits in different species can bring down the overall cost of livestock breeding by improving productivity and disease resistance. Even though the output and error rate of third generation sequencing platforms (Pacific Biosystems and Nanopore) remain to be improved, they offered long read length relatively with cheaper sequencing cost and easy sample preparation. Oxfored NanoporeMinION devices are the most portable third generation DNA sequencer available in the size of a small cell phone that can be plugged into the USB of a laptop.   Key words: Oxford nanopore, single nucleotide polymorphisms, third-generation.

Next-generation sequencing is a powerful approach to detect genetic mutations with which cancer diagnosis and treatment can be tailored to the individual patient in the era of personalized and precision medicine. Ion Torrent Systems Ion Proton and Illumina NextSeq are 2 major targeted sequencing platforms; however, not much work has been done to compare these platforms' performance for mutation detection in formalin-fixed paraffin-embedded (FFPE) materials. We benchmarked the performance by using a collection of FFPE samples from 23 patients with different cancers for NextSeq and Ion Proton platforms. We report analysis of sequencing in terms of average coverage depth, read length, and variant detection. Sequencing results by NextSeq and Ion Proton displayed near perfect coverage behavior (>99%) on target region. We analyzed the ability to call variants from each platform and found that Ion Proton sequencing can identify 89% of single nucleotide variants (SNVs) whose mutant allele frequency (MAF) is greater than or equal to 5% detected by the NextSeq pipeline in common analytical regions. The correlation coefficient of MAF for those common SNVs was 1.0046 (R2 = 0.973) between the 2 platforms. To call lower mutant frequency (5%-10%) mutations for NextSeq sequencing, coverage depth should be improved. The concordance of small insertions and deletions between these 2 pipelines was up to 100%. The 2 sequencing pipelines evaluated were able to generate usable sequence and had high concordance. They are proper for mutation detection in clinical application.

Leveraging The Old With The New: Exploring and Integrating Historic Microarray Studies With Next Generation Sequencing For Multiple Myeloma

Analysing next-generation sequencing (NGS) data for copy number variations (CNVs) detection is a relatively new and challenging field, with no accepted standard protocols or quality control measures so far. There are by now several algorithms developed for each of the four broad methods for CNV detection using NGS, namely the depth of coverage (DOC), read-pair, split-read and assembly-based methods. However, because of the complexity of the genome and the short read lengths from NGS technology, there are still many challenges associated with the analysis of NGS data for CNVs, no matter which method or algorithm is used. In this review, we describe and discuss areas of potential biases in CNV detection for each of the four methods. In particular, we focus on issues pertaining to (i) mappability, (ii) GC-content bias, (iii) quality control measures of reads and (iv) difficulty in identifying duplications. To gain insights to some of the issues discussed, we also download real data from the 1000 Genomes Project and analyse its DOC data. We show examples of how reads in repeated regions can affect CNV detection, demonstrate current GC-correction algorithms, investigate sensitivity of DOC algorithm before and after quality control of reads and discuss reasons for which duplications are harder to detect than deletions.

Next Generation Sequencing (NGS) technologies produce large quantities of short length reads with higher error rates. Erroneous reads that cannot be aligned, are either ignored during de-novo sequencing, or must be suitably corrected. Such reads pose problems for mapping as well, since it is difficult to distinguish errors from true variants. Methods for detection and correction of errors typically rely on frequencies of substrings of the reads. Suffix trees are often utilized for this purpose, since they can be used to index and count the frequencies of substrings of all lengths. Existing suffix-tree based methods detect errors by identifying statistically under-represented branches (suffixes) and fix them. However, they do not refer back to the reads to put the correction in context. Since an error in a single read manifests itself at multiple nodes of a suffix tree, a read-driven approach that relies on its multiple manifestations is expected to perform better. Based on this observation, we develop an algorithm, PLURIBUS, which reconciles corrections suggested by multiple manifestations of an error using a voting scheme. We compare the accuracy of PLURIBUS in detecting and correcting errors against existing error correction techniques using simulated sequencing data. We also assess the impact of error correction on the performance of sequence assembly. Our results show that PLURIBUS corrects errors with improved precision and enables the assembler to generate longer contigs, particularly when the genome is longer, or coverage is lower. PLURIBUS is freely available at http://compbio.case.edu/pluribus/.

BackgroundThe detection of pathogens in complex sample backgrounds has been revolutionized by wide access to next-generation sequencing (NGS) platforms. However, analytical methods to support NGS platforms are not as uniformly available. Pathosphere (found at Pathosphere.org) is a cloud - based open - sourced community tool that allows for communication, collaboration and sharing of NGS analytical tools and data amongst scientists working in academia, industry and government. The architecture allows for users to upload data and run available bioinformatics pipelines without the need for onsite processing hardware or technical support.ResultsThe pathogen detection capabilities hosted on Pathosphere were tested by analyzing pathogen-containing samples sequenced by NGS with both spiked human samples as well as human and zoonotic host backgrounds. Pathosphere analytical pipelines developed by Edgewood Chemical Biological Center (ECBC) identified spiked pathogens within a common sample analyzed by 454, Ion Torrent, and Illumina sequencing platforms. ECBC pipelines also correctly identified pathogens in human samples containing arenavirus in addition to animal samples containing flavivirus and coronavirus. These analytical methods were limited in the detection of sequences with limited homology to previous annotations within NCBI databases, such as parvovirus. Utilizing the pipeline-hosting adaptability of Pathosphere, the analytical suite was supplemented by analytical pipelines designed by the United States Army Medical Research Insititute of Infectious Diseases and Walter Reed Army Institute of Research (USAMRIID-WRAIR). These pipelines were implemented and detected parvovirus sequence in the sample that the ECBC iterative analysis previously failed to identify.ConclusionsBy accurately detecting pathogens in a variety of samples, this work demonstrates the utility of Pathosphere and provides a platform for utilizing, modifying and creating pipelines for a variety of NGS technologies developed to detect pathogens in complex sample backgrounds. These results serve as an exhibition for the existing pipelines and web-based interface of Pathosphere as well as the plug-in adaptability that allows for integration of newer NGS analytical software as it becomes available.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0840-5) contains supplementary material, which is available to authorized users.

Next generation sequencing and its application in deciphering head and neck cancer

External quality assessment (EQA) is a keystone element in the validation and implementation of next generation sequencing (NGS)-based HIV drug resistance testing (DRT). Software validation and evaluation is a critical element in NGS EQA programs. While the development, sharing, and adoption of wet lab protocols is coupled with the increasing access to NGS technology worldwide, rendering it easy to produce NGS data for HIV-DRT, bioinformatic data analysis remains a bottleneck for most of the diagnostic laboratories. Several computational tools have been made available, via free or commercial sources, to automate the conversion of raw NGS data into an actionable clinical report. Although different software platforms yield equivalent results when identical raw NGS datasets are analyzed for variations at higher abundance, discrepancies arise when variations at lower frequencies are considered. This implies that validation and performance assessment of the bioinformatics tools applied in NGS HIV-DRT is critical, and the origins of the observed discrepancies should be determined. Well-characterized reference NGS datasets with ground truth on the genotype composition at all examined loci and the exact frequencies of HIV variations they may harbor, so-called dry panels, would be essential in such cases. The strategic design and construction of such panels are challenging but imperative tasks in support of EQA programs for NGS-based HIV-DRT and the validation of relevant bioinformatics tools. Here, we present criteria that can guide the design of such dry panels, which were discussed in the Second International Winnipeg Symposium themed for EQA strategies for NGS HIVDR assays.

Next Generation Amplicon Sequencing of Immunoglobulin Heavy Chain Gene Rearrangaments for Minimal Residual Disease (MRD) Stratification in Childhood Acute Lymphoblastic Leukemia (ALL): A Comparison with Classical qPCR-Based Technique

Background: Primary Immunodeficiencies (PIDs) are a heterogeneous group of genetic immune disorders. While some PIDs can manifest with more than one phenotype, signs, and symptoms of various PIDs overlap considerably. Recently, novel defects in immune-related genes and additional variants in previously reported genes responsible for PIDs have been successfully identified by Next Generation Sequencing (NGS), allowing the recognition of a broad spectrum of disorders.Objective: To evaluate the strength and weakness of targeted NGS sequencing using custom-made Ion Torrent and Haloplex (Agilent) panels for diagnostics and research purposes.Methods: Five different panels including known and candidate genes were used to screen 105 patients with distinct PID features divided in three main PID categories: T cell defects, Humoral defects and Other PIDs. The Ion Torrent sequencing platform was used in 73 patients. Among these, 18 selected patients without a molecular diagnosis and 32 additional patients were analyzed by Haloplex enrichment technology.Results: The complementary use of the two custom-made targeted sequencing approaches allowed the identification of causative variants in 28.6% (n = 30) of patients. Twenty-two out of 73 (34.6%) patients were diagnosed by Ion Torrent. In this group 20 were included in the SCID/CID category. Eight out of 50 (16%) patients were diagnosed by Haloplex workflow. Ion Torrent method was highly successful for those cases with well-defined phenotypes for immunological and clinical presentation. The Haloplex approach was able to diagnose 4 SCID/CID patients and 4 additional patients with complex and extended phenotypes, embracing all three PID categories in which this approach was more efficient. Both technologies showed good gene coverage.Conclusions: NGS technology represents a powerful approach in the complex field of rare disorders but its different application should be weighted. A relatively small NGS target panel can be successfully applied for a robust diagnostic suspicion, while when the spectrum of clinical phenotypes overlaps more than one PID an in-depth NGS analysis is required, including also whole exome/genome sequencing to identify the causative gene.

Computational analysis of next generation sequencing data and its applications in clinical oncology