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卢 雨. Pseudo-Time Analysis of Single-Cell Transcriptome Data Based on Natural Language Processing. Biophysics (Nagoya-shi) 2022. [DOI: 10.12677/biphy.2022.102004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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2
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Tay AP, Hosking B, Hosking C, Bauer DC, Wilson LO. INSIDER: alignment-free detection of foreign DNA sequences. Comput Struct Biotechnol J 2021; 19:3810-3816. [PMID: 34285780 PMCID: PMC8273350 DOI: 10.1016/j.csbj.2021.06.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 11/21/2022] Open
Abstract
External DNA sequences can be inserted into an organism's genome either through natural processes such as gene transfer, or through targeted genome engineering strategies. Being able to robustly identify such foreign DNA is a crucial capability for health and biosecurity applications, such as anti-microbial resistance (AMR) detection or monitoring gene drives. This capability does not exist for poorly characterised host genomes or with limited information about the integrated sequence. To address this, we developed the INserted Sequence Information DEtectoR (INSIDER). INSIDER analyses whole genome sequencing data and identifies segments of potentially foreign origin by their significant shift in k-mer signatures. We demonstrate the power of INSIDER to separate integrated DNA sequences from normal genomic sequences on a synthetic dataset simulating the insertion of a CRISPR-Cas gene drive into wild-type yeast. As a proof-of-concept, we use INSIDER to detect the exact AMR plasmid in whole genome sequencing data from a Citrobacter freundii patient isolate. INSIDER streamlines the process of identifying integrated DNA in poorly characterised wild species or when the insert is of unknown origin, thus enhancing the monitoring of emerging biosecurity threats.
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Affiliation(s)
- Aidan P. Tay
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Sydney, Australia
- Applied BioSciences, Faculty of Science and Engineering, Macquarie University, New South Wales, Sydney, Australia
| | - Brendan Hosking
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Sydney, Australia
| | - Cameron Hosking
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Sydney, Australia
| | - Denis C. Bauer
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Sydney, Australia
- Department of Biomedical Sciences, Macquarie University, New South Wales, Sydney, Australia
- Applied BioSciences, Faculty of Science and Engineering, Macquarie University, New South Wales, Sydney, Australia
| | - Laurence O.W. Wilson
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Sydney, Australia
- Applied BioSciences, Faculty of Science and Engineering, Macquarie University, New South Wales, Sydney, Australia
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Abstract
The advent of comparative genomics in the late 1990s led to the discovery of extensive lateral gene transfer in prokaryotes. The resulting debate over whether life as a whole is best represented as a tree or a network has since given way to a general consensus in which trees and networks co-exist rather than stand in opposition. Embracing this consensus allows us to move beyond the question of which is true or false. The future of the tree of life debate lies in asking what trees and networks can, and should, do for science.
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Affiliation(s)
- Cédric Blais
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, NS, Canada; Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS, Canada.
| | - John M Archibald
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, NS, Canada; Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS, Canada.
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Bernard G, Chan CX, Chan YB, Chua XY, Cong Y, Hogan JM, Maetschke SR, Ragan MA. Alignment-free inference of hierarchical and reticulate phylogenomic relationships. Brief Bioinform 2019; 20:426-435. [PMID: 28673025 PMCID: PMC6433738 DOI: 10.1093/bib/bbx067] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 05/04/2017] [Indexed: 11/22/2022] Open
Abstract
We are amidst an ongoing flood of sequence data arising from the application of high-throughput technologies, and a concomitant fundamental revision in our understanding of how genomes evolve individually and within the biosphere. Workflows for phylogenomic inference must accommodate data that are not only much larger than before, but often more error prone and perhaps misassembled, or not assembled in the first place. Moreover, genomes of microbes, viruses and plasmids evolve not only by tree-like descent with modification but also by incorporating stretches of exogenous DNA. Thus, next-generation phylogenomics must address computational scalability while rethinking the nature of orthogroups, the alignment of multiple sequences and the inference and comparison of trees. New phylogenomic workflows have begun to take shape based on so-called alignment-free (AF) approaches. Here, we review the conceptual foundations of AF phylogenetics for the hierarchical (vertical) and reticulate (lateral) components of genome evolution, focusing on methods based on k-mers. We reflect on what seems to be successful, and on where further development is needed.
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Bernard G, Greenfield P, Ragan MA, Chan CX. k-mer Similarity, Networks of Microbial Genomes, and Taxonomic Rank. mSystems 2018; 3:e00257-18. [PMID: 30505941 PMCID: PMC6247013 DOI: 10.1128/msystems.00257-18] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 11/02/2018] [Indexed: 01/27/2023] Open
Abstract
Microbial genomes have been shaped by parent-to-offspring (vertical) descent and lateral genetic transfer. These processes can be distinguished by alignment-based inference and comparison of phylogenetic trees for individual gene families, but this approach is not scalable to whole-genome sequences, and a tree-like structure does not adequately capture how these processes impact microbial physiology. Here we adopted alignment-free approaches based on k-mer statistics to infer phylogenomic networks involving 2,783 completely sequenced bacterial and archaeal genomes and compared the contributions of rRNA, protein-coding, and plasmid sequences to these networks. Our results show that the phylogenomic signal arising from ribosomal RNAs is strong and extends broadly across all taxa, whereas that from plasmids is strong but restricted to closely related groups, particularly Proteobacteria. However, the signal from the other chromosomal regions is restricted in breadth. We show that mean k-mer similarity can correlate with taxonomic rank. We also link the implicated k-mers to genome annotation (thus, functions) and define core k-mers (thus, core functions) in specific phyletic groups. Highly conserved functions in most phyla include amino acid metabolism and transport as well as energy production and conversion. Intracellular trafficking and secretion are the most prominent core functions among Spirochaetes, whereas energy production and conversion are not highly conserved among the largely parasitic or commensal Tenericutes. These observations suggest that differential conservation of functions relates to niche specialization and evolutionary diversification of microbes. Our results demonstrate that k-mer approaches can be used to efficiently identify phylogenomic signals and conserved core functions at the multigenome scale. IMPORTANCE Genome evolution of microbes involves parent-to-offspring descent, and lateral genetic transfer that convolutes the phylogenomic signal. This study investigated phylogenomic signals among thousands of microbial genomes based on short subsequences without using multiple-sequence alignment. The signal from ribosomal RNAs is strong across all taxa, and the signal of plasmids is strong only in closely related groups, particularly Proteobacteria. However, the signal from other chromosomal regions (∼99% of the genomes) is remarkably restricted in breadth. The similarity of subsequences is found to correlate with taxonomic rank and informs on conserved and differential core functions relative to niche specialization and evolutionary diversification of microbes. These results provide a comprehensive, alignment-free view of microbial genome evolution as a network, beyond a tree-like structure.
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Affiliation(s)
- Guillaume Bernard
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Paul Greenfield
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), North Ryde, NSW, Australia
| | - Mark A. Ragan
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Cheong Xin Chan
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
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Ren J, Bai X, Lu YY, Tang K, Wang Y, Reinert G, Sun F. Alignment-Free Sequence Analysis and Applications. Annu Rev Biomed Data Sci 2018; 1:93-114. [PMID: 31828235 PMCID: PMC6905628 DOI: 10.1146/annurev-biodatasci-080917-013431] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Genome and metagenome comparisons based on large amounts of next generation sequencing (NGS) data pose significant challenges for alignment-based approaches due to the huge data size and the relatively short length of the reads. Alignment-free approaches based on the counts of word patterns in NGS data do not depend on the complete genome and are generally computationally efficient. Thus, they contribute significantly to genome and metagenome comparison. Recently, novel statistical approaches have been developed for the comparison of both long and shotgun sequences. These approaches have been applied to many problems including the comparison of gene regulatory regions, genome sequences, metagenomes, binning contigs in metagenomic data, identification of virus-host interactions, and detection of horizontal gene transfers. We provide an updated review of these applications and other related developments of word-count based approaches for alignment-free sequence analysis.
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Affiliation(s)
- Jie Ren
- Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA
| | - Xin Bai
- Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China
| | - Yang Young Lu
- Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA
| | - Kujin Tang
- Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA
| | - Ying Wang
- Department of Automation, Xiamen University, Xiamen, Fujian, China
| | - Gesine Reinert
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Fengzhu Sun
- Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China
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Cipcigan F, Carrieri AP, Pyzer-Knapp EO, Krishna R, Hsiao YW, Winn M, Ryadnov MG, Edge C, Martyna G, Crain J. Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions. J Chem Phys 2018; 148:241744. [DOI: 10.1063/1.5027261] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Affiliation(s)
- Flaviu Cipcigan
- IBM Research UK, Hartree Centre, Daresbury WA4 4AD, United Kingdom
| | | | | | - Ritesh Krishna
- IBM Research UK, Hartree Centre, Daresbury WA4 4AD, United Kingdom
| | - Ya-Wen Hsiao
- STFC Daresbury Laboratories, Daresbury WA4 4AD, United Kingdom
| | - Martyn Winn
- STFC Daresbury Laboratories, Daresbury WA4 4AD, United Kingdom
| | - Maxim G. Ryadnov
- National Physical Laboratory, Hampton Road, Teddington, United Kingdom
| | - Colin Edge
- GSK Medicines Research Centre, Stevenage SG1 2NY, United Kingdom
| | - Glenn Martyna
- IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA
| | - Jason Crain
- IBM Research UK, Hartree Centre, Daresbury WA4 4AD, United Kingdom
- Maxwell Centre, University of Cambridge, Cambridge CB3 0HE, United Kingdom
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Tang K, Lu YY, Sun F. Background Adjusted Alignment-Free Dissimilarity Measures Improve the Detection of Horizontal Gene Transfer. Front Microbiol 2018; 9:711. [PMID: 29713314 PMCID: PMC5911508 DOI: 10.3389/fmicb.2018.00711] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 03/27/2018] [Indexed: 11/20/2022] Open
Abstract
Horizontal gene transfer (HGT) plays an important role in the evolution of microbial organisms including bacteria. Alignment-free methods based on single genome compositional information have been used to detect HGT. Currently, Manhattan and Euclidean distances based on tetranucleotide frequencies are the most commonly used alignment-free dissimilarity measures to detect HGT. By testing on simulated bacterial sequences and real data sets with known horizontal transferred genomic regions, we found that more advanced alignment-free dissimilarity measures such as CVTree and d2* that take into account the background Markov sequences can solve HGT detection problems with significantly improved performance. We also studied the influence of different factors such as evolutionary distance between host and donor sequences, size of sliding window, and host genome composition on the performances of alignment-free methods to detect HGT. Our study showed that alignment-free methods can predict HGT accurately when host and donor genomes are in different order levels. Among all methods, CVTree with word length of 3, d2* with word length 3, Markov order 1 and d2* with word length 4, Markov order 1 outperform others in terms of their highest F1-score and their robustness under the influence of different factors.
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Affiliation(s)
- Kujin Tang
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States
| | - Yang Young Lu
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States
| | - Fengzhu Sun
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States.,Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, China
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Cong Y, Chan YB, Phillips CA, Langston MA, Ragan MA. Robust Inference of Genetic Exchange Communities from Microbial Genomes Using TF-IDF. Front Microbiol 2017; 8:21. [PMID: 28154557 PMCID: PMC5243798 DOI: 10.3389/fmicb.2017.00021] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 01/04/2017] [Indexed: 11/13/2022] Open
Abstract
Bacteria and archaea can exchange genetic material across lineages through processes of lateral genetic transfer (LGT). Collectively, these exchange relationships can be modeled as a network and analyzed using concepts from graph theory. In particular, densely connected regions within an LGT network have been defined as genetic exchange communities (GECs). However, it has been problematic to construct networks in which edges solely represent LGT. Here we apply term frequency-inverse document frequency (TF-IDF), an alignment-free method originating from document analysis, to infer regions of lateral origin in bacterial genomes. We examine four empirical datasets of different size (number of genomes) and phyletic breadth, varying a key parameter (word length k) within bounds established in previous work. We map the inferred lateral regions to genes in recipient genomes, and construct networks in which the nodes are groups of genomes, and the edges natively represent LGT. We then extract maximum and maximal cliques (i.e., GECs) from these graphs, and identify nodes that belong to GECs across a wide range of k. Most surviving lateral transfer has happened within these GECs. Using Gene Ontology enrichment tests we demonstrate that biological processes associated with metabolism, regulation and transport are often over-represented among the genes affected by LGT within these communities. These enrichments are largely robust to change of k.
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Affiliation(s)
- Yingnan Cong
- Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, University of Queensland, St Lucia QLD, Australia
| | - Yao-Ban Chan
- School of Mathematics and Statistics, University of Melbourne, Parkville VIC, Australia
| | - Charles A Phillips
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville TN, USA
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville TN, USA
| | - Mark A Ragan
- Institute for Molecular Bioscience and ARC Centre of Excellence in Bioinformatics, University of Queensland, St Lucia QLD, Australia
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A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF. Sci Rep 2016; 6:30308. [PMID: 27453035 PMCID: PMC4958984 DOI: 10.1038/srep30308] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 07/04/2016] [Indexed: 11/09/2022] Open
Abstract
Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of k-mers in the sequences. If a set of contiguous k-mers appears sufficiently more frequently in another phyletic group than in its own, we infer that they have been transferred from the first group to the second. We performed rigorous tests of TF-IDF using simulated and empirical datasets. With the simulated data, we tested our method under different parameter settings for sequence length, substitution rate between and within groups and post-LGT, deletion rate, length of transferred region and k size, and found that we can detect LGT events with high precision and recall. Our method performs better than an established method, ALFY, which has high recall but low precision. Our method is efficient, with runtime increasing approximately linearly with sequence length.
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