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Storato D, Comin M. K2Mem: Discovering Discriminative K-mers From Sequencing Data for Metagenomic Reads Classification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:220-229. [PMID: 34606462 DOI: 10.1109/tcbb.2021.3117406] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The major problem when analyzing a metagenomic sample is to taxonomically annotate its reads to identify the species they contain. Most of the methods currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of recall (the actual number of classified reads) the performances fall at around 50%. One of the reasons is the fact that the sequences in a sample can be very different from the corresponding reference genome, e.g., viral genomes are highly mutated. To address this issue, in this paper we study the problem of metagenomic reads classification by improving the reference k-mers library with novel discriminative k-mers from the input sequencing reads. We evaluated the performance in different conditions against several other tools and the results showed an improved F-measure, especially when close reference genomes are not available. Availability: https://github.com.
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Shibuya Y, Comin M. Indexing k-mers in linear space for quality value compression. J Bioinform Comput Biol 2019; 17:1940011. [DOI: 10.1142/s0219720019400110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Many bioinformatics tools heavily rely on [Formula: see text]-mer dictionaries to describe the composition of sequences and allow for faster reference-free algorithms or look-ups. Unfortunately, naive [Formula: see text]-mer dictionaries are very memory-inefficient, requiring very large amount of storage space to save each [Formula: see text]-mer. This problem is generally worsened by the necessity of an index for fast queries. In this work, we discuss how to build an indexed linear reference containing a set of input [Formula: see text]-mers and its application to the compression of quality scores in FASTQ files. Most of the entropies of sequencing data lie in the quality scores, and thus they are difficult to compress. Here, we present an application to improve the compressibility of quality values while preserving the information for SNP calling. We show how a dictionary of significant [Formula: see text]-mers, obtained from SNP databases and multiple genomes, can be indexed in linear space and used to improve the compression of quality value. Availability: The software is freely available at https://github.com/yhhshb/yalff .
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Affiliation(s)
- Yoshihiro Shibuya
- Department of Information Engineering, University of Padua, via Gradenigo 6B, Padua, Italy
- Laboratoire d’Informatique Gaspard-Monge (LIGM), University Paris-Est Marne-la-Vallée, Bâtiment Copernic - 5, bd Descartes, Champs sur Marne, France
| | - Matteo Comin
- Department of Information Engineering, University of Padua, via Gradenigo 6B, Padua, Italy
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Shibuya Y, Comin M. Better quality score compression through sequence-based quality smoothing. BMC Bioinformatics 2019; 20:302. [PMID: 31757199 PMCID: PMC6873394 DOI: 10.1186/s12859-019-2883-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 05/07/2019] [Indexed: 11/10/2022] Open
Abstract
MOTIVATION Current NGS techniques are becoming exponentially cheaper. As a result, there is an exponential growth of genomic data unfortunately not followed by an exponential growth of storage, leading to the necessity of compression. Most of the entropy of NGS data lies in the quality values associated to each read. Those values are often more diversified than necessary. Because of that, many tools such as Quartz or GeneCodeq, try to change (smooth) quality scores in order to improve compressibility without altering the important information they carry for downstream analysis like SNP calling. RESULTS We use the FM-Index, a type of compressed suffix array, to reduce the storage requirements of a dictionary of k-mers and an effective smoothing algorithm to maintain high precision for SNP calling pipelines, while reducing quality scores entropy. We present YALFF (Yet Another Lossy Fastq Filter), a tool for quality scores compression by smoothing leading to improved compressibility of FASTQ files. The succinct k-mers dictionary allows YALFF to run on consumer computers with only 5.7 GB of available free RAM. YALFF smoothing algorithm can improve genotyping accuracy while using less resources. AVAILABILITY https://github.com/yhhshb/yalff.
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Affiliation(s)
- Yoshihiro Shibuya
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy
- Laboratoire d’Informatique Gaspard-Monge (LIGM), University Paris-Est Marne-la-Vallée, Bâtiment Copernic - 5, bd Descartes, Champs sur Marne, France
| | - Matteo Comin
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy
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Qian J, Comin M. MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage. BMC Bioinformatics 2019; 20:367. [PMID: 31757198 PMCID: PMC6873667 DOI: 10.1186/s12859-019-2904-4] [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: 04/30/2019] [Accepted: 05/15/2019] [Indexed: 11/30/2022] Open
Abstract
Motivation Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths. Results In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT. Electronic supplementary material The online version of this article (10.1186/s12859-019-2904-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jia Qian
- Department of Information Engineering, University of Padova, Via Giovanni Gradenigo 6, Padova, Italy
| | - Matteo Comin
- Department of Information Engineering, University of Padova, Via Giovanni Gradenigo 6, Padova, Italy.
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James BT, Luczak BB, Girgis HZ. MeShClust: an intelligent tool for clustering DNA sequences. Nucleic Acids Res 2019; 46:e83. [PMID: 29718317 PMCID: PMC6101578 DOI: 10.1093/nar/gky315] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 04/13/2018] [Indexed: 11/13/2022] Open
Abstract
Sequence clustering is a fundamental step in analyzing DNA sequences. Widely-used software tools for sequence clustering utilize greedy approaches that are not guaranteed to produce the best results. These tools are sensitive to one parameter that determines the similarity among sequences in a cluster. Often times, a biologist may not know the exact sequence similarity. Therefore, clusters produced by these tools do not likely match the real clusters comprising the data if the provided parameter is inaccurate. To overcome this limitation, we adapted the mean shift algorithm, an unsupervised machine-learning algorithm, which has been used successfully thousands of times in fields such as image processing and computer vision. The theory behind the mean shift algorithm, unlike the greedy approaches, guarantees convergence to the modes, e.g. cluster centers. Here we describe the first application of the mean shift algorithm to clustering DNA sequences. MeShClust is one of few applications of the mean shift algorithm in bioinformatics. Further, we applied supervised machine learning to predict the identity score produced by global alignment using alignment-free methods. We demonstrate MeShClust's ability to cluster DNA sequences with high accuracy even when the sequence similarity parameter provided by the user is not very accurate.
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Affiliation(s)
- Benjamin T James
- Bioinformatics Toolsmith Laboratory, Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA.,Mathematics Department, University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA
| | - Brian B Luczak
- Bioinformatics Toolsmith Laboratory, Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA.,Mathematics Department, University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA
| | - Hani Z Girgis
- Bioinformatics Toolsmith Laboratory, Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA
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Abstract
Background Spaced-seeds, i.e. patterns in which some fixed positions are allowed to be wild-cards, play a crucial role in several bioinformatics applications involving substrings counting and indexing, by often providing better sensitivity with respect to k-mers based approaches. K-mers based approaches are usually fast, being based on efficient hashing and indexing that exploits the large overlap between consecutive k-mers. Spaced-seeds hashing is not as straightforward, and it is usually computed from scratch for each position in the input sequence. Recently, the FSH (Fast Spaced seed Hashing) approach was proposed to improve the time required for computation of the spaced seed hashing of DNA sequences with a speed-up of about 1.5 with respect to standard hashing computation. Results In this work we propose a novel algorithm, Fast Indexing for Spaced seed Hashing (FISH), based on the indexing of small blocks that can be combined to obtain the hashing of spaced-seeds of any length. The method exploits the fast computation of the hashing of runs of consecutive 1 in the spaced seeds, that basically correspond to k-mer of the length of the run. Conclusions We run several experiments, on NGS data from simulated and synthetic metagenomic experiments, to assess the time required for the computation of the hashing for each position in each read with respect to several spaced seeds. In our experiments, FISH can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.9x to 6.03x, depending on the structure of the spaced seeds. Electronic supplementary material The online version of this article (10.1186/s12859-018-2415-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Samuele Girotto
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy
| | - Matteo Comin
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy.
| | - Cinzia Pizzi
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy.
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Ryšavý P, Železný F. Estimating sequence similarity from read sets for clustering next-generation sequencing data. Data Min Knowl Discov 2018. [DOI: 10.1007/s10618-018-0584-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Girotto S, Comin M, Pizzi C. FSH: fast spaced seed hashing exploiting adjacent hashes. Algorithms Mol Biol 2018; 13:8. [PMID: 29588651 PMCID: PMC5863468 DOI: 10.1186/s13015-018-0125-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 03/12/2018] [Indexed: 01/27/2023] Open
Abstract
Background Patterns with wildcards in specified positions, namely spaced seeds, are increasingly used instead of k-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity. Many of these applications require to compute the hashing of each position in the input sequences with respect to the given spaced seed, or to multiple spaced seeds. While the hashing of k-mers can be rapidly computed by exploiting the large overlap between consecutive k-mers, spaced seeds hashing is usually computed from scratch for each position in the input sequence, thus resulting in slower processing. Results The method proposed in this paper, fast spaced-seed hashing (FSH), exploits the similarity of the hash values of spaced seeds computed at adjacent positions in the input sequence. In our experiments we compute the hash for each positions of metagenomics reads from several datasets, with respect to different spaced seeds. We also propose a generalized version of the algorithm for the simultaneous computation of multiple spaced seeds hashing. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6\documentclass[12pt]{minimal}
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\begin{document}$$\times$$\end{document}×, depending on the structure of the spaced seed. Conclusions Spaced seed hashing is a routine task for several bioinformatics application. FSH allows to perform this task efficiently and raise the question of whether other hashing can be exploited to further improve the speed up. This has the potential of major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient. Availability The software FSH is freely available for academic use at: https://bitbucket.org/samu661/fsh/overview.
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Qian J, Marchiori D, Comin M. Fast and Sensitive Classification of Short Metagenomic Reads with SKraken. BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES 2018. [DOI: 10.1007/978-3-319-94806-5_12] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Zielezinski A, Vinga S, Almeida J, Karlowski WM. Alignment-free sequence comparison: benefits, applications, and tools. Genome Biol 2017; 18:186. [PMID: 28974235 PMCID: PMC5627421 DOI: 10.1186/s13059-017-1319-7] [Citation(s) in RCA: 244] [Impact Index Per Article: 34.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Alignment-free sequence analyses have been applied to problems ranging from whole-genome phylogeny to the classification of protein families, identification of horizontally transferred genes, and detection of recombined sequences. The strength of these methods makes them particularly useful for next-generation sequencing data processing and analysis. However, many researchers are unclear about how these methods work, how they compare to alignment-based methods, and what their potential is for use for their research. We address these questions and provide a guide to the currently available alignment-free sequence analysis tools.
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Affiliation(s)
- Andrzej Zielezinski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University in Poznan, Umultowska 89, 61-614, Poznan, Poland
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Jonas Almeida
- Stony Brook University (SUNY), 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Wojciech M Karlowski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University in Poznan, Umultowska 89, 61-614, Poznan, Poland.
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Girotto S, Pizzi C, Comin M. MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures. Bioinformatics 2016; 32:i567-i575. [DOI: 10.1093/bioinformatics/btw466] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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Comin M, Schimd M. Fast comparison of genomic and meta-genomic reads with alignment-free measures based on quality values. BMC Med Genomics 2016; 9 Suppl 1:36. [PMID: 27535823 PMCID: PMC4989896 DOI: 10.1186/s12920-016-0193-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background Sequencing technologies are generating enormous amounts of read data, however assembly of genomes and metagenomes remain among the most challenging tasks. In this paper we study the comparison of genomes and metagenomes only based on read data, using word counts statistics called alignment-free thus not requiring reference genomes or assemblies. Quality scores produced by sequencing platforms are fundamental for various analyses, moreover future-generation sequencing platforms, will produce longer reads but with error rate around 15 %. In this context it will be fundamental to exploit quality values information within the framework of alignment-free measures. Results In this paper we present a family of alignment-free measures, called dq-type, that are based on k-mer counts and quality values. These statistics can be used to compare genomes and metagenomes based on their read sets. Results show that the evolutionary relationship of genomes can be reconstructed based on the direct comparison of theirs reads sets. Conclusion The use of quality values on average improves the classification accuracy, and its contribution increases when the reads are more noisy. Also the comparison of metagenomic microbial communities can be performed efficiently. Similar metagenomes are quickly detected, just by processing their read data, without the need of costly alignments.
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Affiliation(s)
- Matteo Comin
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, Italy.
| | - Michele Schimd
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padova, Italy
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Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populations. BMC Bioinformatics 2016; 17:292. [PMID: 27465705 PMCID: PMC4963998 DOI: 10.1186/s12859-016-1159-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 07/21/2016] [Indexed: 11/24/2022] Open
Abstract
Background Next generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut microflora, combined with the sustained exponential growth in sequencing capacity, has led to the challenge of identifying microbial organisms by DNA sequence. We sought to validate a Scalable Metagenomics Alignment Research Tool (SMART), a novel searching heuristic for shotgun metagenomics sequencing results. Results After retrieving all genomic DNA sequences from the NCBI GenBank, over 1 × 1011 base pairs of 3.3 × 106 sequences from 9.25 × 105 species were indexed using 4 base pair hashtable shards. A MapReduce searching strategy was used to distribute the search workload in a computing cluster environment. In addition, a one base pair permutation algorithm was used to account for single nucleotide polymorphisms and sequencing errors. Simulated datasets used to evaluate Kraken, a similar metagenomics classification tool, were used to measure and compare precision and accuracy. Finally using a same set of training sequences we compared Kraken, CLARK, and SMART within the same computing environment. Utilizing 12 computational nodes, we completed the classification of all datasets in under 10 min each using exact matching with an average throughput of over 1.95 × 106 reads classified per minute. With permutation matching, we achieved sensitivity greater than 83 % and precision greater than 94 % with simulated datasets at the species classification level. We demonstrated the application of this technique applied to conjunctival and gut microbiome metagenomics sequencing results. In our head to head comparison, SMART and CLARK had similar accuracy gains over Kraken at the species classification level, but SMART required approximately half the amount of RAM of CLARK. Conclusions SMART is the first scalable, efficient, and rapid metagenomics classification algorithm capable of matching against all the species and sequences present in the NCBI GenBank and allows for a single step classification of microorganisms as well as large plant, mammalian, or invertebrate genomes from which the metagenomic sample may have been derived.
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Comin M, Antonello M. On the comparison of regulatory sequences with multiple resolution Entropic Profiles. BMC Bioinformatics 2016; 17:130. [PMID: 26987840 PMCID: PMC4797186 DOI: 10.1186/s12859-016-0980-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 03/06/2016] [Indexed: 11/28/2022] Open
Abstract
Background Enhancers are stretches of DNA (100–1000 bp) that play a major role in development gene expression, evolution and disease. It has been recently shown that in high-level eukaryotes enhancers rarely work alone, instead they collaborate by forming clusters of cis-regulatory modules (CRMs). Although the binding of transcription factors is sequence-specific, the identification of functionally similar enhancers is very difficult and it cannot be carried out with traditional alignment-based techniques. Results The use of fast similarity measures, like alignment-free measures, to detect related regulatory sequences is crucial to understand functional correlation between two enhancers. In this paper we study the use of alignment-free measures for the classification of CRMs. However, alignment-free measures are generally tied to a fixed resolution k. Here we propose an alignment-free statistic, called \documentclass[12pt]{minimal}
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\begin{document}$EP^{*}_{2}$\end{document}EP2∗, that is based on multiple resolution patterns derived from the Entropic Profiles (EPs). The Entropic Profile is a function of the genomic location that captures the importance of that region with respect to the whole genome. As a byproduct we provide a formula to compute the exact variance of variable length word counts, a result that can be of general interest also in other applications. Conclusions We evaluate several alignment-free statistics on simulated data and real mouse ChIP-seq sequences. The new statistic, \documentclass[12pt]{minimal}
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\begin{document}$EP^{*}_{2}$\end{document}EP2∗, is highly successful in discriminating functionally related enhancers and, in almost all experiments, it outperforms fixed-resolution methods. We implemented the new alignment-free measures, as well as traditional ones, in a software called EP-sim that is freely available: http://www.dei.unipd.it/~ciompin/main/EP-sim.html.
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Affiliation(s)
- Matteo Comin
- Department of Information Engineering, University of Padova, Padova, Italy.
| | - Morris Antonello
- Department of Information Engineering, University of Padova, Padova, Italy
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Smits SL, Bodewes R, Ruiz-González A, Baumgärtner W, Koopmans MP, Osterhaus ADME, Schürch AC. Recovering full-length viral genomes from metagenomes. Front Microbiol 2015; 6:1069. [PMID: 26483782 PMCID: PMC4589665 DOI: 10.3389/fmicb.2015.01069] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 09/17/2015] [Indexed: 12/17/2022] Open
Abstract
Infectious disease metagenomics is driven by the question: “what is causing the disease?” in contrast to classical metagenome studies which are guided by “what is out there?” In case of a novel virus, a first step to eventually establishing etiology can be to recover a full-length viral genome from a metagenomic sample. However, retrieval of a full-length genome of a divergent virus is technically challenging and can be time-consuming and costly. Here we discuss different assembly and fragment linkage strategies such as iterative assembly, motif searches, k-mer frequency profiling, coverage profile binning, and other strategies used to recover genomes of potential viral pathogens in a timely and cost-effective manner.
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Affiliation(s)
- Saskia L Smits
- Department of Viroscience, Erasmus Medical Center Rotterdam, Netherlands
| | - Rogier Bodewes
- Department of Viroscience, Erasmus Medical Center Rotterdam, Netherlands
| | - Aritz Ruiz-González
- Department of Zoology and Animal Cell Biology, University of the Basque Country (UPV/EHU) Vitoria-Gasteiz, Spain ; Systematics, Biogeography and Population Dynamics Research Group, Lascaray Research Center, University of the Basque Country (UPV/EHU) Vitoria-Gasteiz, Spain ; Conservation Genetics Laboratory, National Institute for Environmental Protection and Research Bologna, Italy
| | - Wolfgang Baumgärtner
- Department of Pathology, University of Veterinary Medicine Hannover Hannover, Germany
| | - Marion P Koopmans
- Department of Viroscience, Erasmus Medical Center Rotterdam, Netherlands ; Centre for Infectious Diseases Research, Diagnostics and Screening, National Institute for Public Health and the Environment Bilthoven, Netherlands
| | - Albert D M E Osterhaus
- Department of Viroscience, Erasmus Medical Center Rotterdam, Netherlands ; Center for Infection Medicine and Zoonoses Research Hannover, Germany
| | - Anita C Schürch
- Department of Viroscience, Erasmus Medical Center Rotterdam, Netherlands
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