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Rádai Z, Váradi A, Takács P, Nagy NA, Schmitt N, Prépost E, Kardos G, Laczkó L. An overlooked phenomenon: complex interactions of potential error sources on the quality of bacterial de novo genome assemblies. BMC Genomics 2024; 25:45. [PMID: 38195441 PMCID: PMC10777565 DOI: 10.1186/s12864-023-09910-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 12/15/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Parameters adversely affecting the contiguity and accuracy of the assemblies from Illumina next-generation sequencing (NGS) are well described. However, past studies generally focused on their additive effects, overlooking their potential interactions possibly exacerbating one another's effects in a multiplicative manner. To investigate whether or not they act interactively on de novo genome assembly quality, we simulated sequencing data for 13 bacterial reference genomes, with varying levels of error rate, sequencing depth, PCR and optical duplicate ratios. RESULTS We assessed the quality of assemblies from the simulated sequencing data with a number of contiguity and accuracy metrics, which we used to quantify both additive and multiplicative effects of the four parameters. We found that the tested parameters are engaged in complex interactions, exerting multiplicative, rather than additive, effects on assembly quality. Also, the ratio of non-repeated regions and GC% of the original genomes can shape how the four parameters affect assembly quality. CONCLUSIONS We provide a framework for consideration in future studies using de novo genome assembly of bacterial genomes, e.g. in choosing the optimal sequencing depth, balancing between its positive effect on contiguity and negative effect on accuracy due to its interaction with error rate. Furthermore, the properties of the genomes to be sequenced also should be taken into account, as they might influence the effects of error sources themselves.
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Affiliation(s)
- Zoltán Rádai
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary.
- Department of Dermatology, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany.
| | - Alex Váradi
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Péter Takács
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary
- Department of Health Informatics, Institute of Health Sciences, Faculty of Health, University of Debrecen, Debrecen, Hungary
| | - Nikoletta Andrea Nagy
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary
- Department of Evolutionary Zoology, ELKH-DE Behavioural Ecology Research Group, University of Debrecen, Debrecen, Hungary
- Department of Evolutionary Zoology and Human Biology, University of Debrecen, Debrecen, Hungary
| | - Nicholas Schmitt
- Department of Dermatology, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | - Eszter Prépost
- Department of Health Industry, University of Debrecen, Debrecen, Hungary
| | - Gábor Kardos
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary
- Department of Gerontology, Faculty of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - Levente Laczkó
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary
- ELKH-DE Conservation Biology Research Group, Debrecen, Hungary
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Yan L, Yin Z, Zhang H, Zhao Z, Wang M, Müller A, Kallenborn F, Wichmann A, Wei Y, Niu B, Schmidt B, Liu W. RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data. Methods 2023; 216:39-50. [PMID: 37330158 DOI: 10.1016/j.ymeth.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/26/2023] [Accepted: 06/12/2023] [Indexed: 06/19/2023] Open
Abstract
Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems. RabbitQCPlus uses vectorization, memory copy reduction, parallel (de)compression, and optimized data structures to achieve substantial performance gains. It is 1.1 to 5.4 times faster when performing basic quality control operations compared to state-of-the-art applications yet requires fewer compute resources. Moreover, RabbitQCPlus is at least 4 times faster than other applications when processing gzip-compressed FASTQ files and 1.3 times faster with the error correction module turned on. Furthermore, it takes less than 4 minutes to process 280 GB of plain FASTQ sequencing data, while other applications take at least 22 minutes on a 48-core server when enabling the per-read over-representation analysis. C++ sources are available at https://github.com/RabbitBio/RabbitQCPlus.
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Affiliation(s)
- Lifeng Yan
- School of Software, Shandong University, Jinan, China
| | - Zekun Yin
- School of Software, Shandong University, Jinan, China.
| | - Hao Zhang
- School of Software, Shandong University, Jinan, China
| | - Zhan Zhao
- School of Software, Shandong University, Jinan, China
| | - Mingkai Wang
- School of Software, Shandong University, Jinan, China
| | - André Müller
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Felix Kallenborn
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Alexander Wichmann
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Yanjie Wei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Beifang Niu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
| | - Bertil Schmidt
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Weiguo Liu
- School of Software, Shandong University, Jinan, China
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K-Mer Spectrum-Based Error Correction Algorithm for Next-Generation Sequencing Data. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8077664. [PMID: 35875730 PMCID: PMC9303089 DOI: 10.1155/2022/8077664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/13/2022] [Indexed: 11/26/2022]
Abstract
In the mid-1970s, the first-generation sequencing technique (Sanger) was created. It used Advanced BioSystems sequencing devices and Beckman's GeXP genetic testing technology. The second-generation sequencing (2GS) technique arrived just several years after the first human genome was published in 2003. 2GS devices are very quicker than Sanger sequencing equipment, with considerably cheaper manufacturing costs and far higher throughput in the form of short reads. The third-generation sequencing (3GS) method, initially introduced in 2005, offers further reduced manufacturing costs and higher throughput. Even though sequencing technique has result generations, it is error-prone due to a large number of reads. The study of this massive amount of data will aid in the decoding of life secrets, the detection of infections, the development of improved crops, and the improvement of life quality, among other things. This is a challenging task, which is complicated not just by a large number of reads and by the occurrence of sequencing mistakes. As a result, error correction is a crucial duty in data processing; it entails identifying and correcting read errors. Various k-spectrum-based error correction algorithms' performance can be influenced by a variety of characteristics like coverage depth, read length, and genome size, as demonstrated in this work. As a result, time and effort must be put into selecting acceptable approaches for error correction of certain NGS data.
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Sharma A, Jain P, Mahgoub A, Zhou Z, Mahadik K, Chaterji S. Lerna: transformer architectures for configuring error correction tools for short- and long-read genome sequencing. BMC Bioinformatics 2022; 23:25. [PMID: 34991450 PMCID: PMC8734100 DOI: 10.1186/s12859-021-04547-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 12/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluating the performance (i.e., Alignment-rate or Gain) of a given tool usually relies on a reference genome, but quality reference genomes are not always available. We introduce Lerna for the automated configuration of k-mer-based EC tools. Lerna first creates a language model (LM) of the uncorrected genomic reads, and then, based on this LM, calculates a metric called the perplexity metric to evaluate the corrected reads for different parameter choices. Next, it finds the one that produces the highest alignment rate without using a reference genome. The fundamental intuition of our approach is that the perplexity metric is inversely correlated with the quality of the assembly after error correction. Therefore, Lerna leverages the perplexity metric for automated tuning of k-mer sizes without needing a reference genome. RESULTS First, we show that the best k-mer value can vary for different datasets, even for the same EC tool. This motivates our design that automates k-mer size selection without using a reference genome. Second, we show the gains of our LM using its component attention-based transformers. We show the model's estimation of the perplexity metric before and after error correction. The lower the perplexity after correction, the better the k-mer size. We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality. We validate our approach on both short and long reads. Additionally, we show that our attention-based models have significant runtime improvement for the entire pipeline-18[Formula: see text] faster than previous works, due to parallelizing the attention mechanism and the use of JIT compilation for GPU inferencing. CONCLUSION Lerna improves de novo genome assembly by optimizing EC tools. Our code is made available in a public repository at: https://github.com/icanforce/lerna-genomics .
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Affiliation(s)
| | - Pranjal Jain
- Indian Institute of Technology Bombay, Mumbai, India
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Warren RL, Coombe L, Mohamadi H, Zhang J, Jaquish B, Isabel N, Jones SJM, Bousquet J, Bohlmann J, Birol I. ntEdit: scalable genome sequence polishing. Bioinformatics 2020; 35:4430-4432. [PMID: 31095290 PMCID: PMC6821332 DOI: 10.1093/bioinformatics/btz400] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 03/04/2019] [Accepted: 05/07/2019] [Indexed: 02/05/2023] Open
Abstract
Motivation In the modern genomics era, genome sequence assemblies are routine practice. However, depending on the methodology, resulting drafts may contain considerable base errors. Although utilities exist for genome base polishing, they work best with high read coverage and do not scale well. We developed ntEdit, a Bloom filter-based genome sequence editing utility that scales to large mammalian and conifer genomes. Results We first tested ntEdit and the state-of-the-art assembly improvement tools GATK, Pilon and Racon on controlled Escherichia coli and Caenorhabditis elegans sequence data. Generally, ntEdit performs well at low sequence depths (<20×), fixing the majority (>97%) of base substitutions and indels, and its performance is largely constant with increased coverage. In all experiments conducted using a single CPU, the ntEdit pipeline executed in <14 s and <3 m, on average, on E.coli and C.elegans, respectively. We performed similar benchmarks on a sub-20× coverage human genome sequence dataset, inspecting accuracy and resource usage in editing chromosomes 1 and 21, and whole genome. ntEdit scaled linearly, executing in 30–40 m on those sequences. We show how ntEdit ran in <2 h 20 m to improve upon long and linked read human genome assemblies of NA12878, using high-coverage (54×) Illumina sequence data from the same individual, fixing frame shifts in coding sequences. We also generated 17-fold coverage spruce sequence data from haploid sequence sources (seed megagametophyte), and used it to edit our pseudo haploid assemblies of the 20 Gb interior and white spruce genomes in <4 and <5 h, respectively, making roughly 50M edits at a (substitution+indel) rate of 0.0024. Availability and implementation https://github.com/bcgsc/ntedit Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- René L Warren
- Genome Sciences Centre, BC Cancer, Vancouver, Canada
| | - Lauren Coombe
- Genome Sciences Centre, BC Cancer, Vancouver, Canada
| | | | - Jessica Zhang
- Genome Sciences Centre, BC Cancer, Vancouver, Canada
| | - Barry Jaquish
- BC Ministry of Forests, Lands, and Natural Resource Operations, Victoria, Canada
| | - Nathalie Isabel
- Laurentian Forestry Centre, Natural Resources Canada, Québec, Canada
| | | | - Jean Bousquet
- Canada Research Chair in Forest Genomics, Université Laval, Québec, Canada
| | - Joerg Bohlmann
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
| | - Inanç Birol
- Genome Sciences Centre, BC Cancer, Vancouver, Canada
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Limasset A, Flot JF, Peterlongo P. Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs. Bioinformatics 2019; 36:1374-1381. [DOI: 10.1093/bioinformatics/btz102] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 01/07/2019] [Accepted: 02/18/2019] [Indexed: 12/25/2022] Open
Abstract
Abstract
Motivation
Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large datasets or consider reads as mere suites of k-mers, without taking into account their full-length sequence information.
Results
We propose a new method to correct short reads using de Bruijn graphs and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond.
Availability and implementation
The implementation is open source, available at http://github.com/Malfoy/BCOOL under the Affero GPL license and as a Bioconda package.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Antoine Limasset
- Evolutionary Biology & Ecology, Université Libre de Bruxelles (ULB), Bruxelles, Belgium
| | - Jean-François Flot
- Evolutionary Biology & Ecology, Université Libre de Bruxelles (ULB), Bruxelles, Belgium
- Interuniversity Institute of Bioinformatics in Brussels – (IB) 2, Brussels, Belgium
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Single-Molecule Sequencing: Towards Clinical Applications. Trends Biotechnol 2019; 37:72-85. [DOI: 10.1016/j.tibtech.2018.07.013] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 07/16/2018] [Accepted: 07/18/2018] [Indexed: 12/31/2022]
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Paracchini V, Petrillo M, Reiting R, Angers-Loustau A, Wahler D, Stolz A, Schönig B, Matthies A, Bendiek J, Meinel DM, Pecoraro S, Busch U, Patak A, Kreysa J, Grohmann L. Molecular characterization of an unauthorized genetically modified Bacillus subtilis production strain identified in a vitamin B 2 feed additive. Food Chem 2017; 230:681-689. [PMID: 28407967 PMCID: PMC5399532 DOI: 10.1016/j.foodchem.2017.03.042] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 03/08/2017] [Accepted: 03/08/2017] [Indexed: 12/17/2022]
Abstract
Genetically modified Bacillus subtilis identified in a vitamin B2 product. Whole genome sequencing runs are performed for characterization of the isolated strain. Complex modifications of the genome are identified. Four putative recombinant plasmids are characterized. Real-time PCR methods are developed and available for testing vitamin B2 products.
Many food and feed additives result from fermentation of genetically modified (GM) microorganisms. For vitamin B2 (riboflavin), GM Bacillus subtilis production strains have been developed and are often used. The presence of neither the GM strain nor its recombinant DNA is allowed for fermentation products placed on the EU market as food or feed additive. A vitamin B2 product (80% feed grade) imported from China was analysed. Viable B. subtilis cells were identified and DNAs of two bacterial isolates (LHL and LGL) were subjected to three whole genome sequencing (WGS) runs with different devices (MiSeq, 454 or HiSeq system). WGS data revealed the integration of a chloramphenicol resistance gene, the deletion of the endogenous riboflavin (rib) operon and presence of four putative plasmids harbouring rib operons. Event- and construct-specific real-time PCR methods for detection of the GM strain and its putative plasmids in food and feed products have been developed.
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Affiliation(s)
| | - Mauro Petrillo
- European Commission, Joint Research Centre, Ispra, Italy
| | - Ralf Reiting
- Hessian State Laboratory Kassel (LHL), Kassel, Germany
| | | | - Daniela Wahler
- Federal Office of Consumer Protection and Food Safety (BVL), Genetic Engineering Department, Berlin, Germany
| | - Andrea Stolz
- Federal Office of Consumer Protection and Food Safety (BVL), Genetic Engineering Department, Berlin, Germany
| | - Birgit Schönig
- Federal Office of Consumer Protection and Food Safety (BVL), Genetic Engineering Department, Berlin, Germany
| | - Anastasia Matthies
- Federal Office of Consumer Protection and Food Safety (BVL), Genetic Engineering Department, Berlin, Germany
| | - Joachim Bendiek
- Federal Office of Consumer Protection and Food Safety (BVL), Genetic Engineering Department, Berlin, Germany
| | - Dominik M Meinel
- Bavarian Health and Food Safety Authority (LGL), Oberschleissheim, Germany
| | - Sven Pecoraro
- Bavarian Health and Food Safety Authority (LGL), Oberschleissheim, Germany
| | - Ulrich Busch
- Bavarian Health and Food Safety Authority (LGL), Oberschleissheim, Germany
| | - Alex Patak
- European Commission, Joint Research Centre, Ispra, Italy
| | - Joachim Kreysa
- European Commission, Joint Research Centre, Ispra, Italy
| | - Lutz Grohmann
- Federal Office of Consumer Protection and Food Safety (BVL), Genetic Engineering Department, Berlin, Germany.
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Milicchio F, Prosperi M. Efficient data structures for mobile de novo genome assembly by third-generation sequencing. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.procs.2017.06.115] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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