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Lightbody G, Haberland V, Browne F, Taggart L, Zheng H, Parkes E, Blayney JK. Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application. Brief Bioinform 2019; 20:1795-1811. [PMID: 30084865 PMCID: PMC6917217 DOI: 10.1093/bib/bby051] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 05/01/2018] [Indexed: 12/28/2022] Open
Abstract
There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.
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Affiliation(s)
- Gaye Lightbody
- School of Computing, Ulster University, Newtownabbey, UK
| | - Valeriia Haberland
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Fiona Browne
- School of Computing, Ulster University, Newtownabbey, UK
| | | | - Huiru Zheng
- School of Computing, Ulster University, Newtownabbey, UK
| | - Eileen Parkes
- Centre for Cancer Research & Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK
| | - Jaine K Blayney
- Centre for Cancer Research & Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK
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The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences. Viruses 2019; 11:v11050394. [PMID: 31035503 PMCID: PMC6563281 DOI: 10.3390/v11050394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 04/19/2019] [Accepted: 04/22/2019] [Indexed: 01/07/2023] Open
Abstract
Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data.
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Manekar SC, Sathe SR. Estimating the k-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art. Curr Genomics 2019; 20:2-15. [PMID: 31015787 PMCID: PMC6446480 DOI: 10.2174/1389202919666181026101326] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/05/2018] [Accepted: 10/24/2018] [Indexed: 12/24/2022] Open
Abstract
Background In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years. Objective In this article, we present a comparative assessment of the different k-mer frequency estima-tion programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits. Methods Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods. Results The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie. Conclusion The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appro-priate method. Such results analysis also help researchers to discover remaining open research ques-tions, effective combinations of existing techniques and possible avenues for future research.
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Affiliation(s)
- Swati C Manekar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Shailesh R Sathe
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
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Manekar SC, Sathe SR. A benchmark study of k-mer counting methods for high-throughput sequencing. Gigascience 2018; 7:5140149. [PMID: 30346548 PMCID: PMC6280066 DOI: 10.1093/gigascience/giy125] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 10/16/2018] [Indexed: 11/25/2022] Open
Abstract
The rapid development of high-throughput sequencing technologies means that hundreds of gigabytes of sequencing data can be produced in a single study. Many bioinformatics tools require counts of substrings of length k in DNA/RNA sequencing reads obtained for applications such as genome and transcriptome assembly, error correction, multiple sequence alignment, and repeat detection. Recently, several techniques have been developed to count k-mers in large sequencing datasets, with a trade-off between the time and memory required to perform this function. We assessed several k-mer counting programs and evaluated their relative performance, primarily on the basis of runtime and memory usage. We also considered additional parameters such as disk usage, accuracy, parallelism, the impact of compressed input, performance in terms of counting large k values and the scalability of the application to larger datasets.We make specific recommendations for the setup of a current state-of-the-art program and suggestions for further development.
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Affiliation(s)
- Swati C Manekar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur 440 010, India
| | - Shailesh R Sathe
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur 440 010, India
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Lee B, Min H, Yoon S. MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment. Bioinformatics 2018; 37:1562-1570. [PMID: 29474530 DOI: 10.1093/bioinformatics/bty096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Revised: 02/09/2018] [Accepted: 02/18/2018] [Indexed: 11/13/2022] Open
Abstract
Abstract
Motivation
Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. A typical metagenomic sequencing produces a large amount of data (often in the order of terabytes or more), and computational tools are indispensable for efficient processing. In particular, error correction in metagenomics is crucial for accurate and robust genetic cataloging of microbial communities. However, many existing error-correction tools take a prohibitively long time and often bottleneck the whole analysis pipeline.
Results
To overcome this computational hurdle, we analyzed and exploited the data-level parallelism that exists in the error-correction procedure and proposed a tool named MUGAN that exploits both multi-core central processing units and multiple graphics processing units for co-processing. According to the experimental results, our approach reduced not only the time demand for denoising amplicons from approximately 59 h to only 46 min, but also the overestimation of the number of OTUs, estimating 6.7 times less species-level OTUs than the baseline. In addition, our approach provides web-based intuitive visualization of results. Given its efficiency and convenience, we anticipate that our approach would greatly facilitate denoising efforts in metagenomics studies.
Availability and implementation
http://data.snu.ac.kr/pub/mugan
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Byunghan Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea
| | - Hyeyoung Min
- College of Pharmacy, Chung-Ang University, Seoul 06974, Korea
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
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From next-generation resequencing reads to a high-quality variant data set. Heredity (Edinb) 2016; 118:111-124. [PMID: 27759079 DOI: 10.1038/hdy.2016.102] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 09/03/2016] [Accepted: 09/06/2016] [Indexed: 12/11/2022] Open
Abstract
Sequencing has revolutionized biology by permitting the analysis of genomic variation at an unprecedented resolution. High-throughput sequencing is fast and inexpensive, making it accessible for a wide range of research topics. However, the produced data contain subtle but complex types of errors, biases and uncertainties that impose several statistical and computational challenges to the reliable detection of variants. To tap the full potential of high-throughput sequencing, a thorough understanding of the data produced as well as the available methodologies is required. Here, I review several commonly used methods for generating and processing next-generation resequencing data, discuss the influence of errors and biases together with their resulting implications for downstream analyses and provide general guidelines and recommendations for producing high-quality single-nucleotide polymorphism data sets from raw reads by highlighting several sophisticated reference-based methods representing the current state of the art.
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The A, C, G, and T of Genome Assembly. BIOMED RESEARCH INTERNATIONAL 2016; 2016:6329217. [PMID: 27247941 PMCID: PMC4877455 DOI: 10.1155/2016/6329217] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Accepted: 12/22/2015] [Indexed: 11/18/2022]
Abstract
Genome assembly in its two decades of history has produced significant research, in terms of both biotechnology and computational biology. This contribution delineates sequencing platforms and their characteristics, examines key steps involved in filtering and processing raw data, explains assembly frameworks, and discusses quality statistics for the assessment of the assembled sequence. Furthermore, the paper explores recent Ubuntu-based software environments oriented towards genome assembly as well as some avenues for future research.
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Alic AS, Tomas A, Medina I, Blanquer I. MuffinEc: Error correction for de Novo assembly via greedy partitioning and sequence alignment. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2015.09.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Alic AS, Ruzafa D, Dopazo J, Blanquer I. Objective review of de novostand-alone error correction methods for NGS data. WILEY INTERDISCIPLINARY REVIEWS: COMPUTATIONAL MOLECULAR SCIENCE 2016. [DOI: 10.1002/wcms.1239] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Andy S. Alic
- Institute of Instrumentation for Molecular Imaging (I3M); Universitat Politècnica de València; València Spain
| | - David Ruzafa
- Departamento de Quìmica Fìsica e Instituto de Biotecnologìa, Facultad de Ciencias; Universidad de Granada; Granada Spain
| | - Joaquin Dopazo
- Department of Computational Genomics; Príncipe Felipe Research Centre (CIPF); Valencia Spain
- CIBER de Enfermedades Raras (CIBERER); Valencia Spain
- Functional Genomics Node (INB) at CIPF; Valencia Spain
| | - Ignacio Blanquer
- Institute of Instrumentation for Molecular Imaging (I3M); Universitat Politècnica de València; València Spain
- Biomedical Imaging Research Group GIBI 2; Polytechnic University Hospital La Fe; Valencia Spain
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Laehnemann D, Borkhardt A, McHardy AC. Denoising DNA deep sequencing data-high-throughput sequencing errors and their correction. Brief Bioinform 2016; 17:154-79. [PMID: 26026159 PMCID: PMC4719071 DOI: 10.1093/bib/bbv029] [Citation(s) in RCA: 177] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 04/09/2015] [Indexed: 12/23/2022] Open
Abstract
Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here.
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Song L, Florea L, Langmead B. Lighter: fast and memory-efficient sequencing error correction without counting. Genome Biol 2015; 15:509. [PMID: 25398208 PMCID: PMC4248469 DOI: 10.1186/s13059-014-0509-9] [Citation(s) in RCA: 144] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Indexed: 02/02/2023] Open
Abstract
Lighter is a fast, memory-efficient tool for correcting sequencing errors. Lighter avoids counting k-mers. Instead, it uses a pair of Bloom filters, one holding a sample of the input k-mers and the other holding k-mers likely to be correct. As long as the sampling fraction is adjusted in inverse proportion to the depth of sequencing, Bloom filter size can be held constant while maintaining near-constant accuracy. Lighter is parallelized, uses no secondary storage, and is both faster and more memory-efficient than competing approaches while achieving comparable accuracy.
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Marçais G, Yorke JA, Zimin A. QuorUM: An Error Corrector for Illumina Reads. PLoS One 2015; 10:e0130821. [PMID: 26083032 PMCID: PMC4471408 DOI: 10.1371/journal.pone.0130821] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Accepted: 05/26/2015] [Indexed: 11/18/2022] Open
Abstract
Motivation Illumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 × coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous k-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term “error correction” to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous k-mers in the output reads and preserving the most true k-mers, and we introduce a composite statistic π that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available. Results We produce trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. We compared QuorUM against several published error correctors and found that it is the best performer in most metrics we use. QuorUM is efficiently implemented making use of current multi-core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core). We also demonstrate that a third-party assembler (SOAPdenovo) benefits significantly from using QuorUM error-corrected reads. QuorUM error corrected reads result in a factor of 1.1 to 4 improvement in N50 contig size compared to using the original reads with SOAPdenovo for the data sets investigated. Availability QuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu. Contact gmarcais@umd.edu.
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Affiliation(s)
- Guillaume Marçais
- IPST, University of Maryland, College Park, MD, USA
- * E-mail: (AZ), (GM)
| | | | - Aleksey Zimin
- IPST, University of Maryland, College Park, MD, USA
- * E-mail: (AZ), (GM)
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Molnar M, Ilie L. Correcting Illumina data. Brief Bioinform 2014; 16:588-99. [PMID: 25183248 DOI: 10.1093/bib/bbu029] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Accepted: 08/02/2014] [Indexed: 11/12/2022] Open
Abstract
Next-generation sequencing technologies revolutionized the ways in which genetic information is obtained and have opened the door for many essential applications in biomedical sciences. Hundreds of gigabytes of data are being produced, and all applications are affected by the errors in the data. Many programs have been designed to correct these errors, most of them targeting the data produced by the dominant technology of Illumina. We present a thorough comparison of these programs. Both HiSeq and MiSeq types of Illumina data are analyzed, and correcting performance is evaluated as the gain in depth and breadth of coverage, as given by correct reads and k-mers. Time and memory requirements, scalability and parallelism are considered as well. Practical guidelines are provided for the effective use of these tools. We also evaluate the efficiency of the current state-of-the-art programs for correcting Illumina data and provide research directions for further improvement.
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Heo Y, Wu XL, Chen D, Ma J, Hwu WM. BLESS: bloom filter-based error correction solution for high-throughput sequencing reads. ACTA ACUST UNITED AC 2014; 30:1354-62. [PMID: 24451628 DOI: 10.1093/bioinformatics/btu030] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Rapid advances in next-generation sequencing (NGS) technology have led to exponential increase in the amount of genomic information. However, NGS reads contain far more errors than data from traditional sequencing methods, and downstream genomic analysis results can be improved by correcting the errors. Unfortunately, all the previous error correction methods required a large amount of memory, making it unsuitable to process reads from large genomes with commodity computers. RESULTS We present a novel algorithm that produces accurate correction results with much less memory compared with previous solutions. The algorithm, named BLoom-filter-based Error correction Solution for high-throughput Sequencing reads (BLESS), uses a single minimum-sized Bloom filter, and is also able to tolerate a higher false-positive rate, thus allowing us to correct errors with a 40× memory usage reduction on average compared with previous methods. Meanwhile, BLESS can extend reads like DNA assemblers to correct errors at the end of reads. Evaluations using real and simulated reads showed that BLESS could generate more accurate results than existing solutions. After errors were corrected using BLESS, 69% of initially unaligned reads could be aligned correctly. Additionally, de novo assembly results became 50% longer with 66% fewer assembly errors. AVAILABILITY AND IMPLEMENTATION Freely available at http://sourceforge.net/p/bless-ec CONTACT dchen@illinois.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yun Heo
- Department of Electrical and Computer Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Abstract
MOTIVATION High-throughput next-generation sequencing technologies enable increasingly fast and affordable sequencing of genomes and transcriptomes, with a broad range of applications. The quality of the sequencing data is crucial for all applications. A significant portion of the data produced contains errors, and ever more efficient error correction programs are needed. RESULTS We propose RACER (Rapid and Accurate Correction of Errors in Reads), a new software program for correcting errors in sequencing data. RACER has better error-correcting performance than existing programs, is faster and requires less memory. To support our claims, we performed extensive comparison with the existing leading programs on a variety of real datasets. AVAILABILITY RACER is freely available for non-commercial use at www.csd.uwo.ca/∼ilie/RACER/.
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Affiliation(s)
- Lucian Ilie
- Department of Computer Science, University of Western Ontario, N6A 5B7 London, ON, Canada
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Abstract
The extremely high error rates reported by Keegan et al. in ‘A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE’ (PLoS Comput Biol 2012;8:e1002541) for many next-generation sequencing datasets prompted us to re-examine their results. Our analysis reveals that the presence of conserved artificial sequences, e.g. Illumina adapters, and other naturally occurring sequence motifs accounts for most of the reported errors. We conclude that DRISEE reports inflated levels of sequencing error, particularly for Illumina data. Tools offered for evaluating large datasets need scrupulous review before they are implemented.
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Liu Y, Schröder J, Schmidt B. Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data. ACTA ACUST UNITED AC 2012. [PMID: 23202746 DOI: 10.1093/bioinformatics/bts690] [Citation(s) in RCA: 175] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
MOTIVATION The imperfect sequence data produced by next-generation sequencing technologies have motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures. RESULTS In this article, we present Musket, an efficient multistage k-mer-based corrector for Illumina short-read data. We use the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared with all other evaluated correctors as well as a highly competitive overall execution time. AVAILABILITY Musket is available at http://musket.sourceforge.net.
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Affiliation(s)
- Yongchao Liu
- Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz 55099, Germany.
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Scaling metagenome sequence assembly with probabilistic de Bruijn graphs. Proc Natl Acad Sci U S A 2012; 109:13272-7. [PMID: 22847406 DOI: 10.1073/pnas.1121464109] [Citation(s) in RCA: 186] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for de novo assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory. We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for de novo assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly.
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Harvey MJ, De Fabritiis G. A survey of computational molecular science using graphics processing units. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012. [DOI: 10.1002/wcms.1101] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Bryant DW, Priest HD, Mockler TC. Detection and quantification of alternative splicing variants using RNA-seq. Methods Mol Biol 2012; 883:97-110. [PMID: 22589127 DOI: 10.1007/978-1-61779-839-9_7] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Next-generation sequencing has enabled genome-wide studies of alternative pre-mRNA splicing, allowing for empirical determination, characterization, and quantification of the expressed RNAs in a sample in toto. As a result, RNA sequencing (RNA-seq) has shown tremendous power to drive biological discoveries. At the same time, RNA-seq has created novel challenges that necessitate the development of increasingly sophisticated computational approaches and bioinformatic tools. In addition to the analysis of massive datasets, these tools also need to facilitate questions and analytical approaches driven by such rich data. HTS and RNA-seq are still in a stage of very rapid evolution and are, therefore, only introduced in general terms. This chapter mainly focuses on the methods for discovery, detection, and quantification of alternatively spliced transcript variants.
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Medvedev P, Scott E, Kakaradov B, Pevzner P. Error correction of high-throughput sequencing datasets with non-uniform coverage. Bioinformatics 2011; 27:i137-41. [PMID: 21685062 PMCID: PMC3117386 DOI: 10.1093/bioinformatics/btr208] [Citation(s) in RCA: 85] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open. RESULTS In this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data. AVAILABILITY http://www.cs.toronto.edu/~pashadag. CONTACT pmedvedev@cs.ucsd.edu.
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Affiliation(s)
- Paul Medvedev
- Department of Computer Science and Engineering, University of California, San Diego, CA, USA.
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Liu Y, Schmidt B, Maskell DL. Parallelized short read assembly of large genomes using de Bruijn graphs. BMC Bioinformatics 2011; 12:354. [PMID: 21867511 PMCID: PMC3167803 DOI: 10.1186/1471-2105-12-354] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2011] [Accepted: 08/25/2011] [Indexed: 11/12/2022] Open
Abstract
Background Next-generation sequencing technologies have given rise to the explosive increase in DNA sequencing throughput, and have promoted the recent development of de novo short read assemblers. However, existing assemblers require high execution times and a large amount of compute resources to assemble large genomes from quantities of short reads. Results We present PASHA, a parallelized short read assembler using de Bruijn graphs, which takes advantage of hybrid computing architectures consisting of both shared-memory multi-core CPUs and distributed-memory compute clusters to gain efficiency and scalability. Evaluation using three small-scale real paired-end datasets shows that PASHA is able to produce more contiguous high-quality assemblies in shorter time compared to three leading assemblers: Velvet, ABySS and SOAPdenovo. PASHA's scalability for large genome datasets is demonstrated with human genome assembly. Compared to ABySS, PASHA achieves competitive assembly quality with faster execution speed on the same compute resources, yielding an NG50 contig size of 503 with the longest correct contig size of 18,252, and an NG50 scaffold size of 2,294. Moreover, the human assembly is completed in about 21 hours with only modest compute resources. Conclusions Developing parallel assemblers for large genomes has been garnering significant research efforts due to the explosive size growth of high-throughput short read datasets. By employing hybrid parallelism consisting of multi-threading on multi-core CPUs and message passing on compute clusters, PASHA is able to assemble the human genome with high quality and in reasonable time using modest compute resources.
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Affiliation(s)
- Yongchao Liu
- School of Computer Engineering, Nanyang Technological University, Singapore.
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Melsted P, Pritchard JK. Efficient counting of k-mers in DNA sequences using a bloom filter. BMC Bioinformatics 2011; 12:333. [PMID: 21831268 PMCID: PMC3166945 DOI: 10.1186/1471-2105-12-333] [Citation(s) in RCA: 116] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2011] [Accepted: 08/10/2011] [Indexed: 12/17/2022] Open
Abstract
Background Counting k-mers (substrings of length k in DNA sequence data) is an essential component of many methods in bioinformatics, including for genome and transcriptome assembly, for metagenomic sequencing, and for error correction of sequence reads. Although simple in principle, counting k-mers in large modern sequence data sets can easily overwhelm the memory capacity of standard computers. In current data sets, a large fraction-often more than 50%-of the storage capacity may be spent on storing k-mers that contain sequencing errors and which are typically observed only a single time in the data. These singleton k-mers are uninformative for many algorithms without some kind of error correction. Results We present a new method that identifies all the k-mers that occur more than once in a DNA sequence data set. Our method does this using a Bloom filter, a probabilistic data structure that stores all the observed k-mers implicitly in memory with greatly reduced memory requirements. We then make a second sweep through the data to provide exact counts of all nonunique k-mers. For example data sets, we report up to 50% savings in memory usage compared to current software, with modest costs in computational speed. This approach may reduce memory requirements for any algorithm that starts by counting k-mers in sequence data with errors. Conclusions A reference implementation for this methodology, BFCounter, is written in C++ and is GPL licensed. It is available for free download at http://pritch.bsd.uchicago.edu/bfcounter.html
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Affiliation(s)
- Páll Melsted
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.
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Liu Y, Schmidt B, Maskell DL. DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI. BMC Bioinformatics 2011; 12:85. [PMID: 21447171 PMCID: PMC3072957 DOI: 10.1186/1471-2105-12-85] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2010] [Accepted: 03/29/2011] [Indexed: 01/25/2023] Open
Abstract
Background Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads) at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets. Results We present DecGPU, the first parallel and distributed error correction algorithm for high-throughput short reads (HTSRs) using a hybrid combination of CUDA and MPI parallel programming models. DecGPU provides CPU-based and GPU-based versions, where the CPU-based version employs coarse-grained and fine-grained parallelism using the MPI and OpenMP parallel programming models, and the GPU-based version takes advantage of the CUDA and MPI parallel programming models and employs a hybrid CPU+GPU computing model to maximize the performance by overlapping the CPU and GPU computation. The distributed feature of our algorithm makes it feasible and flexible for the error correction of large-scale HTSR datasets. Using simulated and real datasets, our algorithm demonstrates superior performance, in terms of error correction quality and execution speed, to the existing error correction algorithms. Furthermore, when combined with Velvet and ABySS, the resulting DecGPU-Velvet and DecGPU-ABySS assemblers demonstrate the potential of our algorithm to improve de novo assembly quality for de-Bruijn-graph-based assemblers. Conclusions DecGPU is publicly available open-source software, written in CUDA C++ and MPI. The experimental results suggest that DecGPU is an effective and feasible error correction algorithm to tackle the flood of short reads produced by next-generation sequencing technologies.
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Affiliation(s)
- Yongchao Liu
- School of Computer Engineering, Nanyang Technological University, 639798, Singapore.
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Zhang W, Chen J, Yang Y, Tang Y, Shang J, Shen B. A practical comparison of de novo genome assembly software tools for next-generation sequencing technologies. PLoS One 2011; 6:e17915. [PMID: 21423806 PMCID: PMC3056720 DOI: 10.1371/journal.pone.0017915] [Citation(s) in RCA: 164] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2010] [Accepted: 02/15/2011] [Indexed: 12/20/2022] Open
Abstract
The advent of next-generation sequencing technologies is accompanied with the development of many whole-genome sequence assembly methods and software, especially for de novo fragment assembly. Due to the poor knowledge about the applicability and performance of these software tools, choosing a befitting assembler becomes a tough task. Here, we provide the information of adaptivity for each program, then above all, compare the performance of eight distinct tools against eight groups of simulated datasets from Solexa sequencing platform. Considering the computational time, maximum random access memory (RAM) occupancy, assembly accuracy and integrity, our study indicate that string-based assemblers, overlap-layout-consensus (OLC) assemblers are well-suited for very short reads and longer reads of small genomes respectively. For large datasets of more than hundred millions of short reads, De Bruijn graph-based assemblers would be more appropriate. In terms of software implementation, string-based assemblers are superior to graph-based ones, of which SOAPdenovo is complex for the creation of configuration file. Our comparison study will assist researchers in selecting a well-suited assembler and offer essential information for the improvement of existing assemblers or the developing of novel assemblers.
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Affiliation(s)
- Wenyu Zhang
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Jiajia Chen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Yang Yang
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Yifei Tang
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Jing Shang
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
- * E-mail:
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Zhao Z, Yin J, Zhan Y, Xiong W, Li Y, Liu F. PSAEC: An Improved Algorithm for Short Read Error Correction Using Partial Suffix Arrays. FRONTIERS IN ALGORITHMICS AND ALGORITHMIC ASPECTS IN INFORMATION AND MANAGEMENT 2011. [DOI: 10.1007/978-3-642-21204-8_25] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Zhao Z, Yin J, Li Y, Xiong W, Zhan Y. An Efficient Hybrid Approach to Correcting Errors in Short Reads. LECTURE NOTES IN COMPUTER SCIENCE 2011. [DOI: 10.1007/978-3-642-22589-5_19] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Kelley DR, Schatz MC, Salzberg SL. Quake: quality-aware detection and correction of sequencing errors. Genome Biol 2010; 11:R116. [PMID: 21114842 PMCID: PMC3156955 DOI: 10.1186/gb-2010-11-11-r116] [Citation(s) in RCA: 369] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2010] [Revised: 10/20/2010] [Accepted: 11/29/2010] [Indexed: 12/20/2022] Open
Abstract
We introduce Quake, a program to detect and correct errors in DNA sequencing reads. Using a maximum likelihood approach incorporating quality values and nucleotide specific miscall rates, Quake achieves the highest accuracy on realistically simulated reads. We further demonstrate substantial improvements in de novo assembly and SNP detection after using Quake. Quake can be used for any size project, including more than one billion human reads, and is freely available as open source software from http://www.cbcb.umd.edu/software/quake.
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Affiliation(s)
- David R Kelley
- Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, and Department of Computer Science, University of Maryland, College Park, MD 20742, USA
| | - Michael C Schatz
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Steven L Salzberg
- Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, and Department of Computer Science, University of Maryland, College Park, MD 20742, USA
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Ilie L, Fazayeli F, Ilie S. HiTEC: accurate error correction in high-throughput sequencing data. Bioinformatics 2010; 27:295-302. [PMID: 21115437 DOI: 10.1093/bioinformatics/btq653] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION High-throughput sequencing technologies produce very large amounts of data and sequencing errors constitute one of the major problems in analyzing such data. Current algorithms for correcting these errors are not very accurate and do not automatically adapt to the given data. RESULTS We present HiTEC, an algorithm that provides a highly accurate, robust and fully automated method to correct reads produced by high-throughput sequencing methods. Our approach provides significantly higher accuracy than previous methods. It is time and space efficient and works very well for all read lengths, genome sizes and coverage levels. AVAILABILITY The source code of HiTEC is freely available at www.csd.uwo.ca/~ilie/HiTEC/.
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Affiliation(s)
- Lucian Ilie
- Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada.
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