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Huang W, Markantonis JE, Yin C, Pozdol JR, Briley KP, Fallon JT. Local Genomic Surveillance of Invasive Streptococcus pyogenes in Eastern North Carolina (ENC) in 2022-2023. Int J Mol Sci 2024; 25:8179. [PMID: 39125755 PMCID: PMC11311789 DOI: 10.3390/ijms25158179] [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: 04/24/2024] [Revised: 07/13/2024] [Accepted: 07/15/2024] [Indexed: 08/12/2024] Open
Abstract
The recent increase in Group A Streptococcus (GAS) incidences in several countries across Europe and some areas of the Unites States (U.S.) has raised concerns. To understand GAS diversity and prevalence, we conducted a local genomic surveillance in Eastern North Carolina (ENC) in 2022-2023 with 95 isolates and compared its results to those of the existing national genomic surveillance in the U.S. in 2015-2021 with 13,064 isolates. We observed their epidemiological changes before and during the COVID-19 pandemic and detected a unique sub-lineage in ENC among the most common invasive GAS strain, ST28/emm1. We further discovered a multiple-copy insertion sequence, ISLgar5, in ST399/emm77 and its single-copy variants in some other GAS strains. We discovered ISLgar5 was linked to a Tn5801-like tetM-carrying integrative and conjugative element, and its copy number was associated with an ermT-carrying pRW35-like plasmid. The dynamic insertions of ISLgar5 may play a vital role in genome fitness and adaptation, driving GAS evolution relevant to antimicrobial resistance and potentially GAS virulence.
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Affiliation(s)
- Weihua Huang
- Department of Pathology and Laboratory Medicine, Brody School of Medicine, Eastern Carolina University, Greenville, NC 27834, USA; (J.E.M.); (C.Y.); (J.R.P.); (K.P.B.); (J.T.F.)
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2
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Lemieux JE, Huang W, Hill N, Cerar T, Freimark L, Hernandez S, Luban M, Maraspin V, Bogovič P, Ogrinc K, Ruzič-Sabljič E, Lapierre P, Lasek-Nesselquist E, Singh N, Iyer R, Liveris D, Reed KD, Leong JM, Branda JA, Steere AC, Wormser GP, Strle F, Sabeti PC, Schwartz I, Strle K. Whole genome sequencing of human Borrelia burgdorferi isolates reveals linked blocks of accessory genome elements located on plasmids and associated with human dissemination. PLoS Pathog 2023; 19:e1011243. [PMID: 37651316 PMCID: PMC10470944 DOI: 10.1371/journal.ppat.1011243] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/13/2023] [Indexed: 09/02/2023] Open
Abstract
Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 B. burgdorferi (Bb) isolates derived from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bb isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bb isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ~900 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination in humans and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, have increased rates of dissemination in humans. OspC type A strains possess a unique set of strongly linked genetic elements including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. These features of OspC type A strains reflect a broader paradigm across Bb isolates, in which near-clonal genotypes are defined by strain-specific clusters of linked genetic elements, particularly those encoding surface-exposed lipoproteins. These clusters of genes are maintained by strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.
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Affiliation(s)
- Jacob E. Lemieux
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Weihua Huang
- New York Medical College, Valhalla, New York, United States of America
- East Carolina University, Greenville, North Carolina, United States of America
| | - Nathan Hill
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Tjasa Cerar
- University of Ljubljana, Ljubljana, Slovenia
| | - Lisa Freimark
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Sergio Hernandez
- Wadsworth Center, New York State Department of Health, Albany, New York, United States of America
| | - Matteo Luban
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Vera Maraspin
- University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Petra Bogovič
- University Medical Center Ljubljana, Ljubljana, Slovenia
| | | | | | - Pascal Lapierre
- Wadsworth Center, New York State Department of Health, Albany, New York, United States of America
| | - Erica Lasek-Nesselquist
- Wadsworth Center, New York State Department of Health, Albany, New York, United States of America
| | - Navjot Singh
- Wadsworth Center, New York State Department of Health, Albany, New York, United States of America
| | - Radha Iyer
- New York Medical College, Valhalla, New York, United States of America
| | - Dionysios Liveris
- New York Medical College, Valhalla, New York, United States of America
| | - Kurt D. Reed
- University of Wisconsin, Madison, Wisconsin, United States of America
| | - John M. Leong
- Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - John A. Branda
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Allen C. Steere
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Gary P. Wormser
- New York Medical College, Valhalla, New York, United States of America
| | - Franc Strle
- University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Pardis C. Sabeti
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Harvard University, Cambridge, Massachusetts, United States of America
- Harvard T.H.Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Ira Schwartz
- New York Medical College, Valhalla, New York, United States of America
| | - Klemen Strle
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Wadsworth Center, New York State Department of Health, Albany, New York, United States of America
- Tufts University School of Medicine, Boston, Massachusetts, United States of America
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3
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Ponsero AJ, Miller M, Hurwitz BL. Comparison of k-mer-based de novo comparative metagenomic tools and approaches. MICROBIOME RESEARCH REPORTS 2023; 2:27. [PMID: 38058765 PMCID: PMC10696585 DOI: 10.20517/mrr.2023.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/28/2023] [Accepted: 07/12/2023] [Indexed: 12/08/2023]
Abstract
Aim: Comparative metagenomic analysis requires measuring a pairwise similarity between metagenomes in the dataset. Reference-based methods that compute a beta-diversity distance between two metagenomes are highly dependent on the quality and completeness of the reference database, and their application on less studied microbiota can be challenging. On the other hand, de-novo comparative metagenomic methods only rely on the sequence composition of metagenomes to compare datasets. While each one of these approaches has its strengths and limitations, their comparison is currently limited. Methods: We developed sets of simulated short-reads metagenomes to (1) compare k-mer-based and taxonomy-based distances and evaluate the impact of technical and biological variables on these metrics and (2) evaluate the effect of k-mer sketching and filtering. We used a real-world metagenomic dataset to provide an overview of the currently available tools for de novo metagenomic comparative analysis. Results: Using simulated metagenomes of known composition and controlled error rate, we showed that k-mer-based distance metrics were well correlated to the taxonomic distance metric for quantitative Beta-diversity metrics, but the correlation was low for presence/absence distances. The community complexity in terms of taxa richness and the sequencing depth significantly affected the quality of the k-mer-based distances, while the impact of low amounts of sequence contamination and sequencing error was limited. Finally, we benchmarked currently available de-novo comparative metagenomic tools and compared their output on two datasets of fecal metagenomes and showed that most k-mer-based tools were able to recapitulate the data structure observed using taxonomic approaches. Conclusion: This study expands our understanding of the strength and limitations of k-mer-based de novo comparative metagenomic approaches and aims to provide concrete guidelines for researchers interested in applying these approaches to their metagenomic datasets.
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Affiliation(s)
- Alise Jany Ponsero
- Human Microbiome Research Program, University of Helsinki, Helsinki 00290, Finland
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA
- BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA
| | - Matthew Miller
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA
| | - Bonnie Louise Hurwitz
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA
- BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA
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4
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Lemieux JE, Huang W, Hill N, Cerar T, Freimark L, Hernandez S, Luban M, Maraspin V, Bogovic P, Ogrinc K, Ruzic-Sabljic E, Lapierre P, Lasek-Nesselquist E, Singh N, Iyer R, Liveris D, Reed KD, Leong JM, Branda JA, Steere AC, Wormser GP, Strle F, Sabeti PC, Schwartz I, Strle K. Whole genome sequencing of Borrelia burgdorferi isolates reveals linked clusters of plasmid-borne accessory genome elements associated with virulence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.530159. [PMID: 36909473 PMCID: PMC10002713 DOI: 10.1101/2023.02.26.530159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 patient-derived B. burgdorferi sensu stricto ( Bbss ) isolates from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bbss isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bbss isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ∼800 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, are associated with increased rates of dissemination. OspC type A strains possess a unique constellation of strongly linked genetic changes including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. The patterns of OspC type A strains typify a broader paradigm across Bbss isolates, in which genetic structure is defined by correlated groups of strain-variable genes located predominantly on plasmids, particularly for expression of surface-exposed lipoproteins. These clusters of genes are inherited in blocks through strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.
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Affiliation(s)
- Jacob E Lemieux
- Massachusetts General Hospital, Harvard Medical School
- Broad Institute of MIT and Harvard
| | - Weihua Huang
- New York Medical College
- East Carolina University
| | - Nathan Hill
- Massachusetts General Hospital, Harvard Medical School
- Broad Institute of MIT and Harvard
| | | | | | | | - Matteo Luban
- Massachusetts General Hospital, Harvard Medical School
- Broad Institute of MIT and Harvard
| | | | | | | | | | | | | | | | | | | | | | - John M Leong
- Tufts University, Department of Molecular Biology and Microbiology
| | - John A Branda
- Massachusetts General Hospital, Harvard Medical School
| | | | | | | | - Pardis C Sabeti
- Massachusetts General Hospital, Harvard Medical School
- Broad Institute of MIT and Harvard
- Harvard University
- Harvard T.H.Chan School of Public Health
| | | | - Klemen Strle
- Massachusetts General Hospital, Harvard Medical School
- Wadsworth Center
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5
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Tang R, Yu Z, Li J. KINN: An alignment-free accurate phylogeny reconstruction method based on inner distance distributions of k-mer pairs in biological sequences. Mol Phylogenet Evol 2023; 179:107662. [PMID: 36375789 DOI: 10.1016/j.ympev.2022.107662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/10/2022] [Accepted: 11/02/2022] [Indexed: 11/13/2022]
Abstract
Alignment-based methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational complexity. Alignment-free methods for sequence comparison and phylogeny inference have attracted a great deal of attention in recent years. Here, we explore an alignment-free approach that uses inner distance distributions of k-mer pairs in biological sequences for phylogeny inference. For every sequence in a dataset, our method transforms the sequence into a numeric feature vector consisting of features each representing a specific k-mer pair's contribution to the characterization of the sequentiality uniqueness of the sequence. This newly defined k-mer pair's contribution is an integration of the reverse Kullback-Leibler divergence, pseudo mode and the classic entropy of an inner distance distribution of the k-mer pair in the sequence. Our method has been tested on datasets of complete genome sequences, complete protein sequences, and gene sequences of rRNA of various lengths. Our method achieves the best performance in comparison with state-of-the-art alignment-free methods as measured by the Robinson-Foulds distance between the reference and the constructed phylogeny trees.
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Affiliation(s)
- Runbin Tang
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, China; School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Zuguo Yu
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, China.
| | - Jinyan Li
- Data Science Institute, University of Technology Sydney, Ultimo, NSW 2007, Australia.
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6
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Bohnsack KS, Kaden M, Abel J, Villmann T. Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:119-135. [PMID: 34990369 DOI: 10.1109/tcbb.2022.3140873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning community is aware of the necessity to rigorously distinguish data transformation from data comparison and adopt reasonable combinations thereof, this awareness is often lacking in the field of comparative sequence analysis. With realization of the disadvantages of alignments for sequence comparison, some typical applications use more and more so-called alignment-free approaches. In light of this development, we present a conceptual framework for alignment-free sequence comparison, which highlights the delineation of: 1) the sequence data transformation comprising of adequate mathematical sequence coding and feature generation, from 2) the subsequent (dis-)similarity evaluation of the transformed data by means of problem-specific but mathematically consistent proximity measures. We consider coding to be an information-loss free data transformation in order to get an appropriate representation, whereas feature generation is inevitably information-lossy with the intention to extract just the task-relevant information. This distinction sheds light on the plethora of methods available and assists in identifying suitable methods in machine learning and data analysis to compare the sequences under these premises.
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7
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Chen J, Zhao Z, Shu Q, Cai G. Feature extraction based on microstate sequences for EEG-based emotion recognition. Front Psychol 2022; 13:1065196. [PMID: 36619090 PMCID: PMC9816384 DOI: 10.3389/fpsyg.2022.1065196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022] Open
Abstract
Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D 2 ∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.
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Affiliation(s)
- Jing Chen
- School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China,Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Zexian Zhao
- Department of Neurology, Zhejiang Hospital, Hangzhou, China
| | - Qinfen Shu
- Department of Neurology, Zhejiang Hospital, Hangzhou, China,*Correspondence: Qinfen Shu,
| | - Guolong Cai
- Department of Critical Care Medicine, Zhejiang Hospital, Hangzhou, China,Guolong Cai,
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8
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The first long-read nuclear genome assembly of Oryza australiensis, a wild rice from northern Australia. Sci Rep 2022; 12:10823. [PMID: 35752642 PMCID: PMC9233661 DOI: 10.1038/s41598-022-14893-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Oryza australiensis is a wild rice native to monsoonal northern Australia. The International Oryza Map Alignment Project emphasises its significance as the sole representative of the EE genome clade. Assembly of the O. australiensis genome has previously been challenging due to its high Long Terminal Repeat (LTR) retrotransposon (RT) content. Oxford Nanopore long reads were combined with Illumina short reads to generate a high-quality ~ 858 Mbp genome assembly within 850 contigs with 46× long read coverage. Reference-guided scaffolding increased genome contiguity, placing 88.2% of contigs into 12 pseudomolecules. After alignment to the Oryza sativa cv. Nipponbare genome, we observed several structural variations. PacBio Iso-Seq data were generated for five distinct tissues to improve the functional annotation of 34,587 protein-coding genes and 42,329 transcripts. We also report SNV numbers for three additional O. australiensis genotypes based on Illumina re-sequencing. Although genetic similarity reflected geographical separation, the density of SNVs also correlated with our previous report on variations in salinity tolerance. This genome re-confirms the genetic remoteness of the O. australiensis lineage within the O. officinalis genome complex. Assembly of a high-quality genome for O. australiensis provides an important resource for the discovery of critical genes involved in development and stress tolerance.
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9
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Sung I, Lee S, Pak M, Shin Y, Kim S. AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning. BMC Bioinformatics 2022; 23:149. [PMID: 35468739 PMCID: PMC9036508 DOI: 10.1186/s12859-022-04679-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e., the number of patients. However, we are yet to understand the spread at the level of individual patients. As of March 2021, COVID-19 is wide-spread all over the world with new genetic variants. One important question is to track the early spreading patterns of COVID-19 until the virus has got spread all over the world. RESULTS In this work, we proposed AutoCoV, a deep learning method with multiple loss object, that can track the early spread of COVID-19 in terms of spatial and temporal patterns until the disease is fully spread over the world in July 2020. Performances in learning spatial or temporal patterns were measured with two clustering measures and one classification measure. For annotated SARS-CoV-2 sequences from the National Center for Biotechnology Information (NCBI), AutoCoV outperformed seven baseline methods in our experiments for learning either spatial or temporal patterns. For spatial patterns, AutoCoV had at least 1.7-fold higher clustering performances and an F1 score of 88.1%. For temporal patterns, AutoCoV had at least 1.6-fold higher clustering performances and an F1 score of 76.1%. Furthermore, AutoCoV demonstrated the robustness of the embedding space with an independent dataset, Global Initiative for Sharing All Influenza Data (GISAID). CONCLUSIONS In summary, AutoCoV learns geographic and temporal spreading patterns successfully in experiments on NCBI and GISAID datasets and is the first of its kind that learns virus spreading patterns from the genome sequences, to the best of our knowledge. We expect that this type of embedding method will be helpful in characterizing fast-evolving pandemics.
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Affiliation(s)
- Inyoung Sung
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Seoul, Republic of Korea
| | - Minwoo Pak
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Yunyol Shin
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea
- AIGENDRUG Co., Ltd., Seoul, Republic of Korea
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10
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Nunn A, Can SN, Otto C, Fasold M, Díez Rodríguez B, Fernández-Pozo N, Rensing SA, Stadler P, Langenberger D. EpiDiverse Toolkit: a pipeline suite for the analysis of bisulfite sequencing data in ecological plant epigenetics. NAR Genom Bioinform 2021; 3:lqab106. [PMID: 34805989 PMCID: PMC8598301 DOI: 10.1093/nargab/lqab106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 09/29/2021] [Accepted: 10/22/2021] [Indexed: 12/23/2022] Open
Abstract
The expanding scope and scale of next generation sequencing experiments in ecological plant epigenetics brings new challenges for computational analysis. Existing tools built for model data may not address the needs of users looking to apply these techniques to non-model species, particularly on a population or community level. Here we present a toolkit suitable for plant ecologists working with whole genome bisulfite sequencing; it includes pipelines for mapping, the calling of methylation values and differential methylation between groups, epigenome-wide association studies, and a novel implementation for both variant calling and discriminating between genetic and epigenetic variation.
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Affiliation(s)
- Adam Nunn
- ecSeq Bioinformatics GmbH, Leipzig 04103, Germany
- Institute for Computer Science, University of Leipzig, Leipzig 04107, Germany
| | - Sultan Nilay Can
- Deparment of Biology, University of Marburg, Marburg 35043, Germany
| | | | - Mario Fasold
- ecSeq Bioinformatics GmbH, Leipzig 04103, Germany
| | | | - Noé Fernández-Pozo
- Deparment of Biology, University of Marburg, Marburg 35043, Germany
- Institute for Mediterranean and Subtropical Horticulture, Málaga 29010, Spain
| | - Stefan A Rensing
- Deparment of Biology, University of Marburg, Marburg 35043, Germany
- Center for Biological Signaling Studies (BIOSS), University of Freiburg, Freiburg 79104, Germany
- Center for Synthetic Microbiology (SYNMIKRO), University of Marburg, Marburg 35043, Germany
| | - Peter F Stadler
- Institute for Computer Science, University of Leipzig, Leipzig 04107, Germany
- Interdisciplinary Center for Bioinformatics; German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; Competence Center for Scalable Data Services and Solutions; Leipzig Research Center for Civilization Diseases; and Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig 04109, Germany
- Max Planck Institute for Mathematics in the Sciences, Leipzig 04103, Germany
- Institute for Theoretical Chemistry, University of Vienna, Vienna 1090, Austria
- Facultad de Ciencias, Universidad National de Colombia, Sede Bogotá, Colombia
- Santa Fe Institute, Santa Fe, USA
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11
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Wu YQ, Yu ZG, Tang RB, Han GS, Anh VV. An Information-Entropy Position-Weighted K-Mer Relative Measure for Whole Genome Phylogeny Reconstruction. Front Genet 2021; 12:766496. [PMID: 34745231 PMCID: PMC8568955 DOI: 10.3389/fgene.2021.766496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 09/29/2021] [Indexed: 11/30/2022] Open
Abstract
Alignment methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational costs in handling time and space complexity. On the other hand, alignment-free methods incur low computational costs and have recently gained popularity in the field of bioinformatics. Here we propose a new alignment-free method for phylogenetic tree reconstruction based on whole genome sequences. A key component is a measure called information-entropy position-weighted k-mer relative measure (IEPWRMkmer), which combines the position-weighted measure of k-mers proposed by our group and the information entropy of frequency of k-mers. The Manhattan distance is used to calculate the pairwise distance between species. Finally, we use the Neighbor-Joining method to construct the phylogenetic tree. To evaluate the performance of this method, we perform phylogenetic analysis on two datasets used by other researchers. The results demonstrate that the IEPWRMkmer method is efficient and reliable. The source codes of our method are provided at https://github.com/ wuyaoqun37/IEPWRMkmer.
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Affiliation(s)
- Yao-Qun Wu
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan, China.,Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, China
| | - Zu-Guo Yu
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan, China
| | - Run-Bin Tang
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan, China
| | - Guo-Sheng Han
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan, China
| | - Vo V Anh
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, Australia
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12
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Shenton M, Kobayashi M, Terashima S, Ohyanagi H, Copetti D, Hernández-Hernández T, Zhang J, Ohmido N, Fujita M, Toyoda A, Ikawa H, Fujiyama A, Furuumi H, Miyabayashi T, Kubo T, Kudrna D, Wing R, Yano K, Nonomura KI, Sato Y, Kurata N. Evolution and Diversity of the Wild Rice Oryza officinalis Complex, across Continents, Genome Types, and Ploidy Levels. Genome Biol Evol 2021; 12:413-428. [PMID: 32125373 PMCID: PMC7531200 DOI: 10.1093/gbe/evaa037] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2020] [Indexed: 12/15/2022] Open
Abstract
The Oryza officinalis complex is the largest species group in
Oryza, with more than nine species from four continents, and is a
tertiary gene pool that can be exploited in breeding programs for the improvement of
cultivated rice. Most diploid and tetraploid members of this group have a C genome. Using
a new reference C genome for the diploid species O. officinalis, and
draft genomes for two other C genome diploid species Oryza eichingeri and
Oryza rhizomatis, we examine the influence of transposable elements on
genome structure and provide a detailed phylogeny and evolutionary history of the
Oryza C genomes. The O. officinalis genome is 1.6
times larger than the A genome of cultivated Oryza sativa, mostly due to
proliferation of Gypsy type long-terminal repeat transposable elements,
but overall syntenic relationships are maintained with other Oryza
genomes (A, B, and F). Draft genome assemblies of the two other C genome diploid species,
Oryza eichingeri and Oryza rhizomatis, and short-read
resequencing of a series of other C genome species and accessions reveal that after the
divergence of the C genome progenitor, there was still a substantial degree of variation
within the C genome species through proliferation and loss of both DNA and long-terminal
repeat transposable elements. We provide a detailed phylogeny and evolutionary history of
the Oryza C genomes and a genomic resource for the exploitation of the
Oryza tertiary gene pool.
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Affiliation(s)
| | | | | | - Hajime Ohyanagi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Dario Copetti
- Arizona Genomics Institute, BIO5 Institute and School of Plant Sciences, University of Arizona.,T.T. Chang Genetic Resources Center, International Rice Research Institute, Los Baños, Philippines
| | | | - Jianwei Zhang
- Arizona Genomics Institute, BIO5 Institute and School of Plant Sciences, University of Arizona
| | - Nobuko Ohmido
- Division of the Living Environment, Kobe University, Japan
| | | | | | | | | | | | | | - Takahiko Kubo
- National Institute of Genetics, Mishima, Japan.,Faculty of Agriculture, Kyushu University, Fukuoka, Japan
| | - David Kudrna
- Arizona Genomics Institute, BIO5 Institute and School of Plant Sciences, University of Arizona
| | - Rod Wing
- Arizona Genomics Institute, BIO5 Institute and School of Plant Sciences, University of Arizona.,T.T. Chang Genetic Resources Center, International Rice Research Institute, Los Baños, Philippines.,Biological and Environment Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Kentaro Yano
- School of Agriculture, Meiji University, Tokyo, Japan
| | | | - Yutaka Sato
- National Institute of Genetics, Mishima, Japan
| | - Nori Kurata
- National Institute of Genetics, Mishima, Japan
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13
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Pechlivanis N, Togkousidis A, Tsagiopoulou M, Sgardelis S, Kappas I, Psomopoulos F. A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data. Front Genet 2021; 12:618170. [PMID: 34122498 PMCID: PMC8194296 DOI: 10.3389/fgene.2021.618170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 05/04/2021] [Indexed: 11/13/2022] Open
Abstract
The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.
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Affiliation(s)
- Nikolaos Pechlivanis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anastasios Togkousidis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Maria Tsagiopoulou
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Stefanos Sgardelis
- Department of Ecology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ilias Kappas
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Fotis Psomopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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14
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The EpiDiverse Plant Epigenome-Wide Association Studies (EWAS) Pipeline. EPIGENOMES 2021; 5:epigenomes5020012. [PMID: 34968299 PMCID: PMC8594691 DOI: 10.3390/epigenomes5020012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 11/29/2022] Open
Abstract
Bisulfite sequencing is a widely used technique for determining DNA methylation and its relationship with epigenetics, genetics, and environmental parameters. Various techniques were implemented for epigenome-wide association studies (EWAS) to reveal meaningful associations; however, there are only very few plant studies available to date. Here, we developed the EpiDiverse EWAS pipeline and tested it using two plant datasets, from P. abies (Norway spruce) and Q. lobata (valley oak). Hence, we present an EWAS implementation tested for non-model plant species and describe its use.
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15
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Wang S, Ge S, Colijn C, Biller P, Wang L, Elliott LT. Estimating Genetic Similarity Matrices Using Phylogenies. J Comput Biol 2021; 28:587-600. [PMID: 33926225 PMCID: PMC8219189 DOI: 10.1089/cmb.2020.0375] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Genetic similarity is a measure of the genetic relatedness among individuals. The standard method for computing these matrices involves the inner product of observed genetic variants. Such an approach is inaccurate or impossible if genotypes are not available, or not densely sampled, or of poor quality (e.g., genetic analysis of extinct species). We provide a new method for computing genetic similarities among individuals using phylogenetic trees. Our method can supplement (or stand in for) computations based on genotypes. We provide simulations suggesting that the genetic similarity matrices computed from trees are consistent with those computed from genotypes. With our methods, quantitative analysis on genetic traits and analysis of heritability and coheritability can be conducted directly using genetic similarity matrices and so in the absence of genotype data, or under uncertainty in the phylogenetic tree. We use simulation studies to demonstrate the advantages of our method, and we provide applications to data.
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Affiliation(s)
- Shijia Wang
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Shufei Ge
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Priscila Biller
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Liangliang Wang
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada
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16
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Lee S, Lee T, Noh YK, Kim S. Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG Islands. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1174-1183. [PMID: 31494555 DOI: 10.1109/tcbb.2019.2938949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
MOTIVATION Existing k-mer based string kernel methods have been successfully used for sequence comparison. However, existing kernel methods have limitations for comparative and evolutionary comparisons of genomes due to the sensitiveness to over-represented k-mers and variable sequence lengths. RESULTS In this study, we propose a novel ranked k-spectrum string (RKSS) kernel. 1) RKSS kernel utilizes common k-mer sets across species, named landmarks, that can be used for comparing multiple genomes. 2) Based on the landmarks, we can use ranks of k-mers, rather than frequencies, that can produce more robust distances between genomes. To show the power of RKSS kernel, we conducted two experiments using 10 mammalian species with exon, intron, and CpG island sequences. RKSS kernel reconstructed more consistent evolutionary trees than the k-spectrum string kernel. In the subsequent experiment, for each sequence, kernel distance was calculated from 30 landmarks representing exon, intron, and CpG island sequences of 10 genomes. Based on kernel distances, concordance tests were performed and the result suggested that more information is conserved in CpG islands across species than in introns. In conclusion, our analysis suggests that the relational order, exon CpG island intron, in terms of evolutionary information contents.
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17
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Miller ME, Nazareno ES, Rottschaefer SM, Riddle J, Dos Santos Pereira D, Li F, Nguyen-Phuc H, Henningsen EC, Persoons A, Saunders DGO, Stukenbrock E, Dodds PN, Kianian SF, Figueroa M. Increased virulence of Puccinia coronata f. sp.avenae populations through allele frequency changes at multiple putative Avr loci. PLoS Genet 2020; 16:e1009291. [PMID: 33370783 PMCID: PMC7793281 DOI: 10.1371/journal.pgen.1009291] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 01/08/2021] [Accepted: 12/04/2020] [Indexed: 12/17/2022] Open
Abstract
Pathogen populations are expected to evolve virulence traits in response to resistance deployed in agricultural settings. However, few temporal datasets have been available to characterize this process at the population level. Here, we examined two temporally separated populations of Puccinia coronata f. sp. avenae (Pca), which causes crown rust disease in oat (Avena sativa) sampled from 1990 to 2015. We show that a substantial increase in virulence occurred from 1990 to 2015 and this was associated with a genetic differentiation between populations detected by genome-wide sequencing. We found strong evidence for genetic recombination in these populations, showing the importance of the alternate host in generating genotypic variation through sexual reproduction. However, asexual expansion of some clonal lineages was also observed within years. Genome-wide association analysis identified seven Avr loci associated with virulence towards fifteen Pc resistance genes in oat and suggests that some groups of Pc genes recognize the same pathogen effectors. The temporal shift in virulence patterns in the Pca populations between 1990 and 2015 is associated with changes in allele frequency in these genomic regions. Nucleotide diversity patterns at a single Avr locus corresponding to Pc38, Pc39, Pc55, Pc63, Pc70, and Pc71 showed evidence of a selective sweep associated with the shift to virulence towards these resistance genes in all 2015 collected isolates.
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Affiliation(s)
- Marisa E. Miller
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Eric S. Nazareno
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Susan M. Rottschaefer
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Jakob Riddle
- USDA-ARS Cereal Disease Laboratory, St. Paul, Minnesota, United States of America
| | - Danilo Dos Santos Pereira
- Environmental Genomics Group, Max Planck Institute for Evolutionary Biology, Plon, Germany
- Christian-Albrechts University of Kiel, Kiel Germany
| | - Feng Li
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Hoa Nguyen-Phuc
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Eva C. Henningsen
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, United States of America
| | - Antoine Persoons
- INRA/Universite de Lorraine Interactions Abres/Microorganismes, Champenoux, France
| | | | - Eva Stukenbrock
- Environmental Genomics Group, Max Planck Institute for Evolutionary Biology, Plon, Germany
- Christian-Albrechts University of Kiel, Kiel Germany
| | - Peter N. Dodds
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, Canberra, Australia
| | - Shahryar F. Kianian
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, United States of America
- USDA-ARS Cereal Disease Laboratory, St. Paul, Minnesota, United States of America
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, Canberra, Australia
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18
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Chanda P, Costa E, Hu J, Sukumar S, Van Hemert J, Walia R. Information Theory in Computational Biology: Where We Stand Today. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E627. [PMID: 33286399 PMCID: PMC7517167 DOI: 10.3390/e22060627] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/31/2020] [Accepted: 06/03/2020] [Indexed: 12/30/2022]
Abstract
"A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.
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Affiliation(s)
- Pritam Chanda
- Corteva Agriscience™, Indianapolis, IN 46268, USA
- Computer and Information Science, Indiana University-Purdue University, Indianapolis, IN 46202, USA
| | - Eduardo Costa
- Corteva Agriscience™, Mogi Mirim, Sao Paulo 13801-540, Brazil
| | - Jie Hu
- Corteva Agriscience™, Indianapolis, IN 46268, USA
| | | | | | - Rasna Walia
- Corteva Agriscience™, Johnston, IA 50131, USA
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19
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Elworth RAL, Wang Q, Kota PK, Barberan CJ, Coleman B, Balaji A, Gupta G, Baraniuk RG, Shrivastava A, Treangen T. To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics. Nucleic Acids Res 2020; 48:5217-5234. [PMID: 32338745 PMCID: PMC7261164 DOI: 10.1093/nar/gkaa265] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/20/2020] [Accepted: 04/04/2020] [Indexed: 02/01/2023] Open
Abstract
As computational biologists continue to be inundated by ever increasing amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of sequence archives has remained a challenge. In recent years, the accelerated pace of genomic data availability has been accompanied by the application of a wide array of highly efficient approaches from other fields to the field of metagenomics. For instance, sketching algorithms such as MinHash have seen a rapid and widespread adoption. These techniques handle increasingly large datasets with minimal sacrifices in quality for tasks such as sequence similarity calculations. Here, we briefly review the fundamentals of the most impactful probabilistic and signal processing algorithms. We also highlight more recent advances to augment previous reviews in these areas that have taken a broader approach. We then explore the application of these techniques to metagenomics, discuss their pros and cons, and speculate on their future directions.
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Affiliation(s)
| | - Qi Wang
- Systems, Synthetic, and Physical Biology (SSPB) Graduate Program, Houston, TX 77005, USA
| | - Pavan K Kota
- Department of Bioengineering, Houston, TX 77005, USA
| | - C J Barberan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Benjamin Coleman
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Advait Balaji
- Department of Computer Science, Houston, TX 77005, USA
| | - Gaurav Gupta
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Richard G Baraniuk
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Anshumali Shrivastava
- Department of Computer Science, Houston, TX 77005, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Todd J Treangen
- Department of Computer Science, Houston, TX 77005, USA
- Systems, Synthetic, and Physical Biology (SSPB) Graduate Program, Houston, TX 77005, USA
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20
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Positional Correlation Natural Vector: A Novel Method for Genome Comparison. Int J Mol Sci 2020; 21:ijms21113859. [PMID: 32485813 PMCID: PMC7312176 DOI: 10.3390/ijms21113859] [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: 04/13/2020] [Revised: 05/17/2020] [Accepted: 05/26/2020] [Indexed: 12/17/2022] Open
Abstract
Advances in sequencing technology have made large amounts of biological data available. Evolutionary analysis of data such as DNA sequences is highly important in biological studies. As alignment methods are ineffective for analyzing large-scale data due to their inherently high costs, alignment-free methods have recently attracted attention in the field of bioinformatics. In this paper, we introduce a new positional correlation natural vector (PCNV) method that involves converting a DNA sequence into an 18-dimensional numerical feature vector. Using frequency and position correlation to represent the nucleotide distribution, it is possible to obtain a PCNV for a DNA sequence. This new numerical vector design uses six suitable features to characterize the correlation among nucleotide positions in sequences. PCNV is also very easy to compute and can be used for rapid genome comparison. To test our novel method, we performed phylogenetic analysis with several viral and bacterial genome datasets with PCNV. For comparison, an alignment-based method, Bayesian inference, and two alignment-free methods, feature frequency profile and natural vector, were performed using the same datasets. We found that the PCNV technique is fast and accurate when used for phylogenetic analysis and classification of viruses and bacteria.
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21
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Röhling S, Linne A, Schellhorn J, Hosseini M, Dencker T, Morgenstern B. The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances. PLoS One 2020; 15:e0228070. [PMID: 32040534 PMCID: PMC7010260 DOI: 10.1371/journal.pone.0228070] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 01/08/2020] [Indexed: 12/14/2022] Open
Abstract
We study the number Nk of length-k word matches between pairs of evolutionarily related DNA sequences, as a function of k. We show that the Jukes-Cantor distance between two genome sequences-i.e. the number of substitutions per site that occurred since they evolved from their last common ancestor-can be estimated from the slope of a function F that depends on Nk and that is affine-linear within a certain range of k. Integers kmin and kmax can be calculated depending on the length of the input sequences, such that the slope of F in the relevant range can be estimated from the values F(kmin) and F(kmax). This approach can be generalized to so-called Spaced-word Matches (SpaM), where mismatches are allowed at positions specified by a user-defined binary pattern. Based on these theoretical results, we implemented a prototype software program for alignment-free sequence comparison called Slope-SpaM. Test runs on real and simulated sequence data show that Slope-SpaM can accurately estimate phylogenetic distances for distances up to around 0.5 substitutions per position. The statistical stability of our results is improved if spaced words are used instead of contiguous words. Unlike previous alignment-free methods that are based on the number of (spaced) word matches, Slope-SpaM produces accurate results, even if sequences share only local homologies.
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Affiliation(s)
- Sophie Röhling
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Alexander Linne
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Jendrik Schellhorn
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | | | - Thomas Dencker
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Burkhard Morgenstern
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Göttingen, Germany
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22
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Integrated Genome-Wide Analysis of an Isogenic Pair of Pseudomonas aeruginosa Clinical Isolates with Differential Antimicrobial Resistance to Ceftolozane/Tazobactam, Ceftazidime/Avibactam, and Piperacillin/Tazobactam. Int J Mol Sci 2020; 21:ijms21031026. [PMID: 32033143 PMCID: PMC7037351 DOI: 10.3390/ijms21031026] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 01/27/2020] [Accepted: 02/02/2020] [Indexed: 01/10/2023] Open
Abstract
Multidrug-resistant (MDR) Pseudomonas aeruginosa is one of the main causes of morbidity and mortality in hospitalized patients and the leading cause of nosocomial infections. We investigated, here, two MDR P. aeruginosa clinical isolates from a hospitalized patient with differential antimicrobial resistance to ceftazidime/avibactam (CZA), ceftolozane/tazobactam (C/T), and piperacillin/tazobactam (P/T). Their assembled complete genomes revealed they belonged to ST235, a widespread MDR clone; and were isogenic with only a single nucleotide variant, causing G183D mutation in AmpC β-lactamase, responsible for a phenotypic change from susceptible to resistant to CZA and C/T. Further epigenomic profiling uncovered two conserved DNA methylation motifs targeted by two distinct putative methyltransferase-containing restriction-modification systems, respectively; more intriguingly, there was a significant difference between the paired isolates in the pattern of genomic DNA methylation and modifications. Moreover, genome-wide gene expression profiling demonstrated the inheritable genomic methylation and modification induced 14 genes being differentially regulated, of which only toxR (downregulated), a regulatory transcription factor, had its promoter region differentially methylate and modified. Since highly expressed opdQ encodes an OprD porin family protein, therefore, we proposed an epigenetic regulation of opdQ expression pertinent to the phenotypic change of P. aeruginosa from resistant to susceptible to P/T. The disclosed epigenetic mechanism controlling phenotypic antimicrobial resistance deserves further experimental investigation.
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23
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Martín-Vide C, Vega-Rodríguez MA, Wheeler T. PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals. ALGORITHMS FOR COMPUTATIONAL BIOLOGY 2020. [PMCID: PMC7197062 DOI: 10.1007/978-3-030-42266-0_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In this paper we study the problem of clustering bacterial isolates into epidemiologically related groups from next-generation sequencing data. Existing methods for this problem mainly use a single genotyping signal, and either use a distance-based method with a pre-specified number of clusters, or a phylogenetic tree-based method with a pre-specified threshold. We propose PathOGiST, an algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds. PathOGiST uses different genotypic signals, clusters the isolates based on these individual signals with correlation clustering, and combines the clusterings based on the individual signals through consensus clustering. We implemented and tested PathOGiST on three different bacterial pathogens - Escherichia coli, Yersinia pseudotuberculosis, and Mycobacterium tuberculosis - and we conclude by discussing further avenues to explore.
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24
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Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage. BMC Bioinformatics 2019; 20:638. [PMID: 31842735 PMCID: PMC6916211 DOI: 10.1186/s12859-019-3205-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In many fields of biomedical research, it is important to estimate phylogenetic distances between taxa based on low-coverage sequencing reads. Major applications are, for example, phylogeny reconstruction, species identification from small sequencing samples, or bacterial strain typing in medical diagnostics. RESULTS We adapted our previously developed software program Filtered Spaced-Word Matches (FSWM) for alignment-free phylogeny reconstruction to take unassembled reads as input; we call this implementation Read-SpaM. CONCLUSIONS Test runs on simulated reads from semi-artificial and real-world bacterial genomes show that our approach can estimate phylogenetic distances with high accuracy, even for large evolutionary distances and for very low sequencing coverage.
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25
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Roddy AC, Jurek-Loughrey A, Souza J, Gilmore A, O’Reilly PG, Stupnikov A, Gonzalez de Castro D, Prise KM, Salto-Tellez M, McArt DG. NUQA: Estimating Cancer Spatial and Temporal Heterogeneity and Evolution through Alignment-Free Methods. Mol Biol Evol 2019; 36:2883-2889. [PMID: 31424551 PMCID: PMC6878956 DOI: 10.1093/molbev/msz182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Longitudinal next-generation sequencing of cancer patient samples has enhanced our understanding of the evolution and progression of various cancers. As a result, and due to our increasing knowledge of heterogeneity, such sampling is becoming increasingly common in research and clinical trial sample collections. Traditionally, the evolutionary analysis of these cohorts involves the use of an aligner followed by subsequent stringent downstream analyses. However, this can lead to large levels of information loss due to the vast mutational landscape that characterizes tumor samples. Here, we propose an alignment-free approach for sequence comparison-a well-established approach in a range of biological applications including typical phylogenetic classification. Such methods could be used to compare information collated in raw sequence files to allow an unsupervised assessment of the evolutionary trajectory of patient genomic profiles. In order to highlight this utility in cancer research we have applied our alignment-free approach using a previously established metric, Jensen-Shannon divergence, and a metric novel to this area, Hellinger distance, to two longitudinal cancer patient cohorts in glioma and clear cell renal cell carcinoma using our software, NUQA. We hypothesize that this approach has the potential to reveal novel information about the heterogeneity and evolutionary trajectory of spatiotemporal tumor samples, potentially revealing early events in tumorigenesis and the origins of metastases and recurrences. Key words: alignment-free, Hellinger distance, exome-seq, evolution, phylogenetics, longitudinal.
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Affiliation(s)
- Aideen C Roddy
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Anna Jurek-Loughrey
- School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, United Kingdom
| | - Jose Souza
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Alan Gilmore
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Paul G O’Reilly
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Alexey Stupnikov
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD
| | - David Gonzalez de Castro
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Kevin M Prise
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Manuel Salto-Tellez
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Darragh G McArt
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
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Murray KD, Janes JK, Jones A, Bothwell HM, Andrew RL, Borevitz JO. Landscape drivers of genomic diversity and divergence in woodland Eucalyptus. Mol Ecol 2019; 28:5232-5247. [PMID: 31647597 PMCID: PMC7065176 DOI: 10.1111/mec.15287] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 10/23/2019] [Indexed: 01/03/2023]
Abstract
Spatial genetic patterns are influenced by numerous factors, and they can vary even among coexisting, closely related species due to differences in dispersal and selection. Eucalyptus (L'Héritier 1789; the "eucalypts") are foundation tree species that provide essential habitat and modulate ecosystem services throughout Australia. Here we present a study of landscape genomic variation in two woodland eucalypt species, using whole-genome sequencing of 388 individuals of Eucalyptus albens and Eucalyptus sideroxylon. We found exceptionally high genetic diversity (π ≈ 0.05) and low genome-wide, interspecific differentiation (FST = 0.15) and intraspecific differentiation between localities (FST ≈ 0.01-0.02). We found no support for strong, discrete population structure, but found substantial support for isolation by geographic distance (IBD) in both species. Using generalized dissimilarity modelling, we identified additional isolation by environment (IBE). Eucalyptus albens showed moderate IBD, and environmental variables have a small but significant amount of additional predictive power (i.e. IBE). Eucalyptus sideroxylon showed much stronger IBD and moderate IBE. These results highlight the vast adaptive potential of these species and set the stage for testing evolutionary hypotheses of interspecific adaptive differentiation across environments.
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Affiliation(s)
| | - Jasmine K Janes
- University of New EnglandArmidaleNSWAustralia
- Vancouver Island University,NanaimoBCCanada
| | - Ashley Jones
- Australian National UniversityCanberraACTAustralia
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27
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Huang W, Wang G, Yin C, Chen D, Dhand A, Chanza M, Dimitrova N, Fallon JT. Optimizing a Whole-Genome Sequencing Data Processing Pipeline for Precision Surveillance of Health Care-Associated Infections. Microorganisms 2019; 7:microorganisms7100388. [PMID: 31554234 PMCID: PMC6843764 DOI: 10.3390/microorganisms7100388] [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: 08/14/2019] [Revised: 09/04/2019] [Accepted: 09/20/2019] [Indexed: 11/16/2022] Open
Abstract
The surveillance of health care-associated infection (HAI) is an essential element of the infection control program. While whole-genome sequencing (WGS) has widely been adopted for genomic surveillance, its data processing remains to be improved. Here, we propose a three-level data processing pipeline for the precision genomic surveillance of microorganisms without prior knowledge: species identification, multi-locus sequence typing (MLST), and sub-MLST clustering. The former two are closely connected to what have widely been used in current clinical microbiology laboratories, whereas the latter one provides significantly improved resolution and accuracy in genomic surveillance. Comparing to a broadly used reference-dependent alignment/mapping method and an annotation-dependent pan-/core-genome analysis, we implemented our reference- and annotation-independent, k-mer-based, simplified workflow to a collection of Acinetobacter and Enterococcus clinical isolates for tests. By taking both single nucleotide variants and genomic structural changes into account, the optimized k-mer-based pipeline demonstrated a global view of bacterial population structure in a rapid manner and discriminated the relatedness between bacterial isolates in more detail and precision. The newly developed WGS data processing pipeline would facilitate WGS application to the precision genomic surveillance of HAI. In addition, the results from such a WGS-based analysis would be useful for the precision laboratory diagnosis of infectious microorganisms.
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Affiliation(s)
- Weihua Huang
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA.
| | - Guiqing Wang
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA.
- Department of Pathology and Clinical Laboratories, Westchester Medical Center, Valhalla, NY 10595, USA.
| | - Changhong Yin
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA.
| | - Donald Chen
- Department of Medicine, New York Medical College, Valhalla, NY 10595, USA.
- Department of Infection Prevention and Control, Westchester Medical Center, Valhalla, NY 10595, USA.
| | - Abhay Dhand
- Department of Medicine, New York Medical College, Valhalla, NY 10595, USA.
| | - Melissa Chanza
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA.
| | | | - John T Fallon
- Department of Pathology, New York Medical College, Valhalla, NY 10595, USA.
- Department of Pathology and Clinical Laboratories, Westchester Medical Center, Valhalla, NY 10595, USA.
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28
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Evolutionary Insight into the Trypanosomatidae Using Alignment-Free Phylogenomics of the Kinetoplast. Pathogens 2019; 8:pathogens8030157. [PMID: 31540520 PMCID: PMC6789588 DOI: 10.3390/pathogens8030157] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/10/2019] [Accepted: 09/13/2019] [Indexed: 12/12/2022] Open
Abstract
Advancements in next-generation sequencing techniques have led to a substantial increase in the genomic information available for analyses in evolutionary biology. As such, this data requires the exponential growth in bioinformatic methods and expertise required to understand such vast quantities of genomic data. Alignment-free phylogenomics offer an alternative approach for large-scale analyses that may have the potential to address these challenges. The evolutionary relationships between various species within the trypanosomatid family, specifically members belonging to the genera Leishmania and Trypanosoma have been extensively studies over the last 30 years. However, there is a need for a more exhaustive analysis of the Trypanosomatidae, summarising the evolutionary patterns amongst the entire family of these important protists. The mitochondrial DNA of the trypanosomatids, better known as the kinetoplast, represents a valuable taxonomic marker given its unique presence across all kinetoplastid protozoans. The aim of this study was to validate the reliability and robustness of alignment-free approaches for phylogenomic analyses and its applicability to reconstruct the evolutionary relationships between the trypanosomatid family. In the present study, alignment-free analyses demonstrated the strength of these methods, particularly when dealing with large datasets compared to the traditional phylogenetic approaches. We present a maxicircle genome phylogeny of 46 species spanning the trypanosomatid family, demonstrating the superiority of the maxicircle for the analysis and taxonomic resolution of the Trypanosomatidae.
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29
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Zielezinski A, Girgis HZ, Bernard G, Leimeister CA, Tang K, Dencker T, Lau AK, Röhling S, Choi JJ, Waterman MS, Comin M, Kim SH, Vinga S, Almeida JS, Chan CX, James BT, Sun F, Morgenstern B, Karlowski WM. Benchmarking of alignment-free sequence comparison methods. Genome Biol 2019; 20:144. [PMID: 31345254 PMCID: PMC6659240 DOI: 10.1186/s13059-019-1755-7] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 07/03/2019] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment. RESULTS Here, we present a community resource (http://afproject.org) to establish standards for comparing alignment-free approaches across different areas of sequence-based research. We characterize 74 AF methods available in 24 software tools for five research applications, namely, protein sequence classification, gene tree inference, regulatory element detection, genome-based phylogenetic inference, and reconstruction of species trees under horizontal gene transfer and recombination events. CONCLUSION The interactive web service allows researchers to explore the performance of alignment-free tools relevant to their data types and analytical goals. It also allows method developers to assess their own algorithms and compare them with current state-of-the-art tools, accelerating the development of new, more accurate AF solutions.
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Affiliation(s)
- Andrzej Zielezinski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznańskiego 6, 61-614, Poznan, Poland
| | - Hani Z Girgis
- Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, USA
| | | | - Chris-Andre Leimeister
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Kujin Tang
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
| | - Thomas Dencker
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Anna Katharina Lau
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Sophie Röhling
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Jae Jin Choi
- Department of Chemistry, University of California, Berkeley, CA, 94720, USA
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Michael S Waterman
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China
| | - Matteo Comin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Sung-Hou Kim
- Department of Chemistry, University of California, Berkeley, CA, 94720, USA
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NIH/NCI), Bethesda, USA
| | - Cheong Xin Chan
- Institute for Molecular Bioscience, and School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Benjamin T James
- Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, USA
| | - Fengzhu Sun
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China
| | - Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Wojciech M Karlowski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznańskiego 6, 61-614, Poznan, Poland.
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30
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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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31
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Zhang C, Simpson RJ, Kim CM, Warthmann N, Delhaize E, Dolan L, Byrne ME, Wu Y, Ryan PR. Do longer root hairs improve phosphorus uptake? Testing the hypothesis with transgenic Brachypodium distachyon lines overexpressing endogenous RSL genes. THE NEW PHYTOLOGIST 2018; 217:1654-1666. [PMID: 29341123 DOI: 10.1111/nph.14980] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 11/04/2017] [Indexed: 05/14/2023]
Abstract
Mutants without root hairs show reduced inorganic orthophosphate (Pi) uptake and compromised growth on soils when Pi availability is restricted. What is less clear is whether root hairs that are longer than wild-type provide an additional benefit to phosphorus (P) nutrition. This was tested using transgenic Brachypodium lines with longer root hairs. The lines were transformed with the endogenous BdRSL2 and BdRSL3 genes using either a constitutive promoter or a root hair-specific promoter. Plants were grown for 32 d in soil amended with various Pi concentrations. Plant biomass and P uptake were measured and genotypes were compared on the basis of critical Pi values and P uptake per unit root length. Ectopic expression of RSL2 and RSL3 increased root hair length three-fold but decreased plant biomass. Constitutive expression of BdRSL2, but not expression of BdRSL3, consistently improved P nutrition as measured by lowering the critical Pi values and increasing Pi uptake per unit root length. Increasing root hair length through breeding or biotechnology can improve P uptake efficiency if the pleotropic effects on plant biomass are avoided. Long root hairs, alone, appear to be insufficient to improve Pi uptake and need to be combined with other traits to benefit P nutrition.
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Affiliation(s)
- Chunyan Zhang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
- CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Richard J Simpson
- CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Chul Min Kim
- Department of Plant Sciences, University of Oxford, Oxford, OX1 3RB, UK
| | - Norman Warthmann
- College of Medicine, Biology and Environment, Australian National University, Canberra, ACT, 2601, Australia
| | - Emmanuel Delhaize
- CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Liam Dolan
- Department of Plant Sciences, University of Oxford, Oxford, OX1 3RB, UK
| | - Mary E Byrne
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, 2006, Australia
| | - Yu Wu
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China
| | - Peter R Ryan
- CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia
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32
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Pandey P, Bender MA, Johnson R, Patro R. deBGR: an efficient and near-exact representation of the weighted de Bruijn graph. Bioinformatics 2017; 33:i133-i141. [PMID: 28881995 PMCID: PMC5870571 DOI: 10.1093/bioinformatics/btx261] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using 'long read' technologies like those offered by PacBio or Oxford Nanopore), efficient k -mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k -mer occurs, which is key in transcriptome assemblers. RESULTS We present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18-28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems. AVAILABILITY AND IMPLEMENTATION https://github.com/splatlab/debgr . CONTACT rob.patro@cs.stonybrook.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Prashant Pandey
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Michael A Bender
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Rob Johnson
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
- VMWare, Inc., Palo Alto, CA
| | - Rob Patro
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
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