Suffix-Tree Based Error Correction of NGS Reads Using Multiple Manifestations of an Error

BackgroundInnumerable opportunities for new genomic research have been stimulated by advancement in high-throughput next-generation sequencing (NGS). However, the pitfall of NGS data abundance is the complication of distinction between true biological variants and sequence error alterations during downstream analysis. Many error correction methods have been developed to correct erroneous NGS reads before further analysis, but independent evaluation of the impact of such dataset features as read length, genome size, and coverage depth on their performance is lacking. This comparative study aims to investigate the strength and weakness as well as limitations of some newest k-spectrum-based methods and to provide recommendations for users in selecting suitable methods with respect to specific NGS datasets.MethodsSix k-spectrum-based methods, i.e., Reptile, Musket, Bless, Bloocoo, Lighter, and Trowel, were compared using six simulated sets of paired-end Illumina sequencing data. These NGS datasets varied in coverage depth (10× to 120×), read length (36 to 100 bp), and genome size (4.6 to 143 MB). Error Correction Evaluation Toolkit (ECET) was employed to derive a suite of metrics (i.e., true positives, false positive, false negative, recall, precision, gain, and F-score) for assessing the correction quality of each method.ResultsResults from computational experiments indicate that Musket had the best overall performance across the spectra of examined variants reflected in the six datasets. The lowest accuracy of Musket (F-score = 0.81) occurred to a dataset with a medium read length (56 bp), a medium coverage (50×), and a small-sized genome (5.4 MB). The other five methods underperformed (F-score < 0.80) and/or failed to process one or more datasets.ConclusionsThis study demonstrates that various factors such as coverage depth, read length, and genome size may influence performance of individual k-spectrum-based error correction methods. Thus, efforts have to be paid in choosing appropriate methods for error correction of specific NGS datasets. Based on our comparative study, we recommend Musket as the top choice because of its consistently superior performance across all six testing datasets. Further extensive studies are warranted to assess these methods using experimental datasets generated by NGS platforms (e.g., 454, SOLiD, and Ion Torrent) under more diversified parameter settings (k-mer values and edit distances) and to compare them against other non-k-spectrum-based classes of error correction methods.

Soft errors in General Purpose Graphics Processing Units (GPGPUs) result in data or control flow errors. Error detection and correction methods for data and control flow errors are orthogonal, and these methods incur separate area, power, and performance overheads. This paper proposes a low-overhead predictive control flow error detection method called GPGPU Predictive Detector (GPD), which leverages data flow error detection and correction methods to detect control flow errors. GPD is non-intrusive to application software and transparent to users. GPD is built on earlier work on data flow error detection and correction methods DDSR and TREFU. In GPD, DDSR and TREFU combined architecture protects all non-control flow instructions. The control flow error is detected by calculating the address of the instruction that succeeds the control flow instruction in advance and comparing it with the actual address it accesses. The effectiveness of GPD has been shown through a set of ISPASS-2009 and RODINIA benchmarks. Relative to a non-fault-tolerant GPGPU architecture, GPD has a performance overhead of 5% and average and peak power overheads of 4% and 3%, respectively. We prove through induction that the GPD provides fault coverage against GPGPU control flow and data flow errors.

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

In this paper, two approaches are evaluated using the Full Error Detection and Correction (FEDC) method for a pipelined structure. The approaches are referred to as Full Duplication with Comparison (FDC) and Concurrent Checking with Parity Prediction (CCPP). Aforementioned approaches are focused on the borderline cases of FEDC method which implement Error Detection Circuit (EDC) in two manners for the purpose of protection of combinational logic to address the soft errors of unspecified duration. The FDC approach implements a full duplication of the combinational circuit, as the most complex and expensive implementation of the FEDC method, and the CCPP approach implements only the parity prediction bit, being the simplest and cheapest technique, for soft error detection. Both approaches are capable of detecting soft errors in the combinational logic, with single faults being injected into the design. On the one hand, the FDC approach managed to detect and correct all injected faults while the CCPP approach could not detect multiple faults created at the output of combinational circuit. On the other hand, the FDC approach leads to higher power consumption and area increase compared to the CCPP approach.

We consider methods of error detection and/or error correction in software and hardware of a distributed system computing values of numerical functions. These methods are based on software and hardware redundancy for the computation of additional check functions. The check functions are easily derived for any given multiplicity of errors. The redundancy does not depend on the number of processors in the original system and depends only on the multiplicity of errors. We describe methods for the construction of optimal checks, required software and hardware redundancy, and implementation of the corresponding error detecting/correcting procedures by a distributed system.

Post Transplantation Measurable Residual Disease (MRD) Monitoring Using Next-Generation Sequencing Is Highly Predictive for Relapseafter Allogeneic Stem Cell Transplantation

In this work we present a novel fault-tolerant circuits design method. It combines time and area redundancy to achieve error-correction abilities similar to a triple-modular redundancy (TMR) and the area-overhead close to a duplex system. New logic gates design allowing a complete stuck-at fault testability will be presented. Our method allows to test combinational parts of the circuit using a universal short-duration offline test. The offline-testable module with an online-checker allows to compose a fault-tolerant system with the mentioned properties. This system will be denoted as a time-extended duplex scheme. In this scheme the offline test is sufficiently short to allow error correction during the computation (paused pipeline). The presented method adopts some principles from dual-rail logic and asynchronous circuits design.

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.

Offset, gain and time mismatch errors exist in the channels of the time-interleaved analog-to-digital converter (TIADC), which severely affect the signal-to-noise and distortion ratio (SNDR), spurious-free dynamic range (SFDR) and effective number of bits (ENOB). Here, an error detection and correction method of TIADC based on genetic algorithm (GA) optimization is presented. The algorithm based on sine fitting is used for the first estimation and rough correction of the input signal, and GA is used for the second detection and fine correction of channel mismatch errors on the signal after frequency domain filtering. Experimental results show that the SNDR of the TIADC system is enhanced to 92.24 dB, the SFDR is enhanced to 102.05 dB, the ENOB is enhanced to 15.03 bits and the spectrum spurs are reduced. This method provides a new idea for error detection and correction of high-resolution TIADC systems with more than 16 bits.

Sensitive detection of internal tandem duplication (ITD) mutation of FLT3 is very important in acute myeloid leukemia. To increase detection sensitivity of FLT3-ITD, we developed new detection algorithm using next generation sequencing (NGS) data. We validated results using nested polymerase chain reaction (PCR) methods. We compared results of NGS data, nested PCR and conventional PCR methods. First, using whole exome sequencing data of 83 AML patients, we applied calling algorithm for FLT3-ITD. Briefly, to detect ITDs with NGS data, the reads are aligned to a reference sequence (UCSC hg19), with BWA which is a read aligner allowing soft-clipping. Some reads can be an indication of the occurrence of ITD and BWA aligns those reads as soft-clipped. Second, we deigned two types of primer for Nested PCR. The first primer was targeted wildly for between exon14 and exon15 of FLT3 gene. Nested PCR primer was deigned to target previously reported regions which are frequently occurred ITD mutation. PCR reactions of two steps were performed using the PCR primers sequentially. In these 83 patients, FLT3-ITD was positive only in 7 patients when tested by conventional PCR methods. When NGS detection method was applied, this resulted in positive FLT3-ITD in 11 patients (11/83, 13%). When validation was performed using nested PCR, FLT3-ITD was confirmed in all of 11 patients. Nested PCR detected additional 4 patient positive for FLT3-ITD in this population. For 68 patients, FLT3-ITD was negative by both NGS and nested PCR method. Overall, NGS method improved sensitivity of FLT3-ITD detection by 57% in this population. And the concordance rate of NGS method and nested PCR was 95.2% (79/83). Then we investigated clinical significance of sensitive FLT3-ITD detection. For this, we performed nested PCR and conventional PCR at the same time in 238 AML patients to detect FLT3-ITD. Positive rate for FLT3-ITD was 20% (48/238) and 10% (24/238) by nested PCR and conventional PCR respectively. When survival analysis was performed, among patients with negative FLT3-ITD result by conventional PCR, patients who showed positive for FLT3-ITD by nested PCR had shorter overall survival compared to those who showed negative for FLT3-ITD by nested PCR. (p = 0.03). This implies that sensitive FLT3-ITD detection using nested PCR is clinically meaningful. Diagnosis of FLT3-ITD is very important genetic factor, leading a therapeutic direction for AML patient. Here we report that we have developed alternative more sensitive detection methods for FLT3-ITD based on nested PCR and NGS. Sensitive detection of FLT3-ITD was clinically meaningful, suggesting that these methods should be incorporated in a future clinical practice. Also, we want to note that, NGS method is capable of quantifying FLT3-ITD size and amount in AML patients. Citation Format: Daeyoon Kim, Yoojin Hong, Youngil Koh, Sung-Soo Yoon, Choong-Hyun Sun, Kwang-Sung Ahn, Seungmook Lee, Hongseok Yun, Suyeon Lee. Improved sensitive detection method of FLT3 (FMS-like tyrosine kinase) internal tandem duplication (ITD) mutation using next-generation sequencing technology and nested PCR. [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 3186.

Next generation sequencing (NGS) technologies have enabled cheap, large-scale, and high-throughput production of short DNA sequence reads and thereby have promoted the explosive growth of data volume. Unfortunately, the produced reads are short and prone to contain errors that are incurred during sequencing cycles. Both large data volume and sequencing errors have complicated the mapping of NGS reads onto the reference genome and have motivated the development of various aligners for very short reads, typically less than 100 base pairs (bps) in length. As read length continues to increase, propelled by advances in NGS technologies, these longer reads tend to have higher sequencing error rates and more true mutations (including substitutions, insertions, or deletions) to the genome. Such new characteristics make inefficient the aligners, which are optimized for very short reads and support only ungapped alignments or gapped alignments with very limited number of gaps (typically one gap), and thereby call for new aligners with fully gapped alignment supported. In this chapter, we present the CUSHAW software suite for NGS read alignment, which is open-source and consists of three individual aligners: CUSHAW, CUSHAW2, and CUSHAW3. This suite offers parallel and efficient NGS read alignments to large genomes, such as the human genome, by harnessing multi-core CPUs or compute unified device architecture (CUDA)-enabled graphics processing units (GPUs). Moreover, it has the capability to align both base-space and color-space reads and is consistently shown to be one of the best alignment tools through our performance evaluations.

In this paper, we propose an original method for agreement error detection and correction that we apply to Arabic. The detection is based on a global syntactic analysis of the sentence. It consists of an extended syntagmatic analysis that can first locate the syntagm boundaries and, second, regroup all the sentence components that have an agreement relationship. The correction is based on a multicriteria analysis aiming to rank the correction alternatives in order to choose the best one. This method has the advantage of reducing the dominated alternatives and ranking the remaining ones according to different evaluation criteria.

An End-to-End Method for Chinese Spelling Error Detection and Correction

BackgroundIn recent years, huge improvements have been made in the context of sequencing genomic data under what is called Next Generation Sequencing (NGS). However, the DNA reads generated by current NGS platforms are not free of errors, which can affect the quality of downstream analysis. Although error correction can be performed as a preprocessing step to overcome this issue, it usually requires long computational times to analyze those large datasets generated nowadays through NGS. Therefore, new software capable of scaling out on a cluster of nodes with high performance is of great importance.ResultsIn this paper, we present SparkEC, a parallel tool capable of fixing those errors produced during the sequencing process. For this purpose, the algorithms proposed by the CloudEC tool, which is already proved to perform accurate corrections, have been analyzed and optimized to improve their performance by relying on the Apache Spark framework together with the introduction of other enhancements such as the usage of memory-efficient data structures and the avoidance of any input preprocessing. The experimental results have shown significant improvements in the computational times of SparkEC when compared to CloudEC for all the representative datasets and scenarios under evaluation, providing an average and maximum speedups of 4.9times and 11.9times, respectively, over its counterpart.ConclusionAs error correction can take excessive computational time, SparkEC provides a scalable solution for correcting large datasets. Due to its distributed implementation, SparkEC speed can increase with respect to the number of nodes in a cluster. Furthermore, the software is freely available under GPLv3 license and is compatible with different operating systems (Linux, Windows and macOS).

Next generation sequencing (NGS) revolutionized genomic data generation by enabling high-throughput parallel sequencing, making large scale genomic data analysis a crucial task. To improve NGS data quality, we developed an efficient algorithm that uses a flexible read decomposition method to improve accuracy of error correction. We further proposed a statistical framework to differentiate infrequently observed sub reads from sequencing errors in the prevalence of genomic repeats. To enable the analysis of microbial organism composition in environmental samples, we developed a parallel solution for metagenomic sequence clustering integrating sketching, quasi-clique enumeration and MapReduce techniques.