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Zhang J, Schneller NM, Field MA, Chan CX, Miller DJ, Strugnell JM, Riginos C, Bay L, Cooke I. Chromosomal inversions harbour excess mutational load in the coral, Acropora kenti, on the Great Barrier Reef. Mol Ecol 2024; 33:e17468. [PMID: 39046252 DOI: 10.1111/mec.17468] [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: 04/17/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/25/2024]
Abstract
The future survival of coral reefs in the Anthropocene depends on the capacity of corals to adapt as oceans warm and extreme weather events become more frequent. Targeted interventions designed to assist evolutionary processes in corals require a comprehensive understanding of the distribution and structure of standing variation, however, efforts to map genomic variation in corals have so far focussed almost exclusively on SNPs, overlooking structural variants that have been shown to drive adaptive processes in other taxa. Here, we show that the reef-building coral, Acropora kenti, harbours at least five large, highly polymorphic structural variants, all of which exhibit signatures of strongly suppressed recombination in heterokaryotypes, a feature commonly associated with chromosomal inversions. Based on their high minor allele frequency, uniform distribution across habitats and elevated genetic load, we propose that these inversions in A. kenti are likely to be under balancing selection. An excess of SNPs with high impact on protein-coding genes within these loci elevates their importance both as potential targets for adaptive selection and as contributors to genetic decline if coral populations become fragmented or inbred in future.
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Affiliation(s)
- Jia Zhang
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, Queensland, Australia
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Nadja M Schneller
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, Queensland, Australia
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Matt A Field
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, Queensland, Australia
- Immunogenomics Lab, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Cheong Xin Chan
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia
| | - David J Miller
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, Queensland, Australia
| | - Jan M Strugnell
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, Queensland, Australia
- Centre for Sustainable Tropical Fisheries and Aquaculture, James Cook University, Townsville, Queensland, Australia
- College of Science and Engineering, James Cook University, Townsville, Queensland, Australia
| | - Cynthia Riginos
- School of the Environment, The University of Queensland, Brisbane, Queensland, Australia
- Australian Institute of Marine Science, Townsville, Queensland, Australia
| | - Line Bay
- Australian Institute of Marine Science, Townsville, Queensland, Australia
| | - Ira Cooke
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, Queensland, Australia
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Chantzi N, Mareboina M, Konnaris MA, Montgomery A, Patsakis M, Mouratidis I, Georgakopoulos-Soares I. The determinants of the rarity of nucleic and peptide short sequences in nature. NAR Genom Bioinform 2024; 6:lqae029. [PMID: 38584871 PMCID: PMC10993293 DOI: 10.1093/nargab/lqae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 04/09/2024] Open
Abstract
The prevalence of nucleic and peptide short sequences across organismal genomes and proteomes has not been thoroughly investigated. We examined 45 785 reference genomes and 21 871 reference proteomes, spanning archaea, bacteria, eukaryotes and viruses to calculate the rarity of short sequences in them. To capture this, we developed a metric of the rarity of each sequence in nature, the rarity index. We find that the frequency of certain dipeptides in rare oligopeptide sequences is hundreds of times lower than expected, which is not the case for any dinucleotides. We also generate predictive regression models that infer the rarity of nucleic and proteomic sequences across nature or within each domain of life and viruses separately. When examining each of the three domains of life and viruses separately, the R² performance of the model predicting rarity for 5-mer peptides from mono- and dipeptides ranged between 0.814 and 0.932. A separate model predicting rarity for 10-mer oligonucleotides from mono- and dinucleotides achieved R² performance between 0.408 and 0.606. Our results indicate that the mono- and dinucleotide composition of nucleic sequences and the mono- and dipeptide composition of peptide sequences can explain a significant proportion of the variance in their frequencies in nature.
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Affiliation(s)
- Nikol Chantzi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Manvita Mareboina
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Maxwell A Konnaris
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Department of Statistics, Penn State University, University Park, PA, 16802, USA
- Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA
| | - Austin Montgomery
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Michail Patsakis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Ioannis Mouratidis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
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Liu G, Chen X, Luan Y, Li D. VirusPredictor: XGBoost-based software to predict virus-related sequences in human data. Bioinformatics 2024; 40:btae192. [PMID: 38597887 PMCID: PMC11052659 DOI: 10.1093/bioinformatics/btae192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/29/2024] [Accepted: 04/08/2024] [Indexed: 04/11/2024] Open
Abstract
MOTIVATION Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data. RESULTS We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, i.e. 0.76, 0.93, and 0.98 for 150-350 (Illumina short reads), 850-950 (Sanger sequencing data), and 2000-5000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to >0.98 when query sequences increased from 150-350 to >850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g. ∼1000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients' unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions. AVAILABILITY AND IMPLEMENTATION www.dllab.org/software/VirusPredictor.html.
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Affiliation(s)
- Guangchen Liu
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont 05405, United States
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
- School of Mathematics and Statistics, Ludong University, Yantai, Shandong 264025, China
| | - Xun Chen
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont 05405, United States
| | - Yihui Luan
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Dawei Li
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont 05405, United States
- Department of Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, Texas 79430, United States
- ICanCME Research Network, Sainte-Justine University Hospital Research Center, Montreal, Quebec H3T 1C5, Canada
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Ruperao P, Gandham P, Odeny DA, Mayes S, Selvanayagam S, Thirunavukkarasu N, Das RR, Srikanda M, Gandhi H, Habyarimana E, Manyasa E, Nebie B, Deshpande SP, Rathore A. Exploring the sorghum race level diversity utilizing 272 sorghum accessions genomic resources. FRONTIERS IN PLANT SCIENCE 2023; 14:1143512. [PMID: 37008459 PMCID: PMC10063887 DOI: 10.3389/fpls.2023.1143512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 02/22/2023] [Indexed: 06/19/2023]
Abstract
Due to evolutionary divergence, sorghum race populations exhibit significant genetic and morphological variation. A k-mer-based sorghum race sequence comparison identified the conserved k-mers of all 272 accessions from sorghum and the race-specific genetic signatures identified the gene variability in 10,321 genes (PAVs). To understand sorghum race structure, diversity and domestication, a deep learning-based variant calling approach was employed in a set of genotypic data derived from a diverse panel of 272 sorghum accessions. The data resulted in 1.7 million high-quality genome-wide SNPs and identified selective signature (both positive and negative) regions through a genome-wide scan with different (iHS and XP-EHH) statistical methods. We discovered 2,370 genes associated with selection signatures including 179 selective sweep regions distributed over 10 chromosomes. Co-localization of these regions undergoing selective pressure with previously reported QTLs and genes revealed that the signatures of selection could be related to the domestication of important agronomic traits such as biomass and plant height. The developed k-mer signatures will be useful in the future to identify the sorghum race and for trait and SNP markers for assisting in plant breeding programs.
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Affiliation(s)
- Pradeep Ruperao
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Prasad Gandham
- School of Plant, Environmental and Soil Sciences, Louisiana State University Agricultural Center, LA, United States
| | - Damaris A. Odeny
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Sean Mayes
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Nepolean Thirunavukkarasu
- Genomics and Molecular Breeding Lab, Indian Council of Agricultural Research (ICAR) - Indian Institute of Millets Research, Hyderabad, India
| | - Roma R. Das
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Manasa Srikanda
- Department of Statistics, Osmania University, Hyderabad, India
| | - Harish Gandhi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Ephrem Habyarimana
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Eric Manyasa
- Sorghum Breeding Program, International Crops Research Institute for the Semi-Arid Tropics, Nairobi, Kenya
| | - Baloua Nebie
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | | | - Abhishek Rathore
- Excellence in Breeding, International Maize and Wheat Improvement Center (CIMMYT), Hyderabad, India
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5
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de Souza LC, Azevedo KS, de Souza JG, Barbosa RDM, Fernandes MAC. New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning. BMC Bioinformatics 2023; 24:92. [PMID: 36906520 PMCID: PMC10007673 DOI: 10.1186/s12859-023-05188-1] [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: 10/11/2022] [Accepted: 02/15/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. RESULTS In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. CONCLUSIONS The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.
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Affiliation(s)
- Luísa C. de Souza
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
| | - Karolayne S. Azevedo
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
| | - Jackson G. de Souza
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
| | - Raquel de M. Barbosa
- Department of Pharmacy and Pharmaceutical Technology, University of Granada, Granada, Spain
| | - Marcelo A. C. Fernandes
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
- Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
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6
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Satoh S, Tanaka R, Yokono M, Endoh D, Yabuki T, Tanaka A. Phylogeny analysis of whole protein-coding genes in metagenomic data detected an environmental gradient for the microbiota. PLoS One 2023; 18:e0281288. [PMID: 36730456 PMCID: PMC9894459 DOI: 10.1371/journal.pone.0281288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Environmental factors affect the growth of microorganisms and therefore alter the composition of microbiota. Correlative analysis of the relationship between metagenomic composition and the environmental gradient can help elucidate key environmental factors and establishment principles for microbial communities. However, a reasonable method to quantitatively compare whole metagenomic data and identify the primary environmental factors for the establishment of microbiota has not been reported so far. In this study, we developed a method to compare whole proteomes deduced from metagenomic shotgun sequencing data, and quantitatively display their phylogenetic relationships as metagenomic trees. We called this method Metagenomic Phylogeny by Average Sequence Similarity (MPASS). We also compared one of the metagenomic trees with dendrograms of environmental factors using a comparison tool for phylogenetic trees. The MPASS method correctly constructed metagenomic trees of simulated metagenomes and soil and water samples. The topology of the metagenomic tree of samples from the Kirishima hot springs area in Japan was highly similarity to that of the dendrograms based on previously reported environmental factors for this area. The topology of the metagenomic tree also reflected the dynamics of microbiota at the taxonomic and functional levels. Our results strongly suggest that MPASS can successfully classify metagenomic shotgun sequencing data based on the similarity of whole protein-coding sequences, and will be useful for the identification of principal environmental factors for the establishment of microbial communities. Custom Perl script for the MPASS pipeline is available at https://github.com/s0sat/MPASS.
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Affiliation(s)
- Soichirou Satoh
- Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Japan
- Faculty of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Japan
- * E-mail:
| | - Rei Tanaka
- Faculty of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Japan
| | - Makio Yokono
- Division of Environmental Photobiology, National Institute for Basic Biology, Okazaki, Japan
| | - Daiji Endoh
- Department of Radiation Biology, School of Veterinary Medicine, Rakuno Gakuen University, Ebetsu, Japan
| | - Tetsuo Yabuki
- General Education Department, Hokusei Gakuen University, Sapporo, Japan
| | - Ayumi Tanaka
- Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan
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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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Chen J, Yang L, Li L, Goodison S, Sun Y. Alignment-free comparison of metagenomics sequences via approximate string matching. BIOINFORMATICS ADVANCES 2022; 2:vbac077. [PMID: 36388153 PMCID: PMC9645238 DOI: 10.1093/bioadv/vbac077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/16/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022]
Abstract
Summary Quantifying pairwise sequence similarities is a key step in metagenomics studies. Alignment-free methods provide a computationally efficient alternative to alignment-based methods for large-scale sequence analysis. Several neural network-based methods have recently been developed for this purpose. However, existing methods do not perform well on sequences of varying lengths and are sensitive to the presence of insertions and deletions. In this article, we describe the development of a new method, referred to as AsMac that addresses the aforementioned issues. We proposed a novel neural network structure for approximate string matching for the extraction of pertinent information from biological sequences and developed an efficient gradient computation algorithm for training the constructed neural network. We performed a large-scale benchmark study using real-world data that demonstrated the effectiveness and potential utility of the proposed method. Availability and implementation The open-source software for the proposed method and trained neural-network models for some commonly used metagenomics marker genes were developed and are freely available at www.acsu.buffalo.edu/~yijunsun/lab/AsMac.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Lu Li
- Department of Oral Biology, University at Buffalo, Buffalo, NY 14215, USA
| | - Steve Goodison
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Yijun Sun
- To whom correspondence should be addressed.
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Coombes CE, Coombes KR, Fareed N. Sequences of Events from the Electronic Medical Record and the Onset of Infection. Chem Biodivers 2022; 19:e202200657. [PMID: 36216587 DOI: 10.1002/cbdv.202200657] [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: 07/13/2022] [Accepted: 09/15/2022] [Indexed: 11/06/2022]
Abstract
We present a novel model of time-series analysis to learn from electronic health record (EHR) data when infection occurred in the intensive care unit (ICU) by translating methods from proteomics and Bayesian statistics. Using 48,536 patients hospitalized in an ICU, we describe each hospital course as an 'alphabet' of 23 physician actions ('events') in temporal order. We analyze these as k-mers of length 3-12 events and apply a Bayesian model of (cumulative) relative risk (RR). The log2-transformed RR (median=0.248, mean=0.226) supported the conclusion that the events selected were individually associated with increased risk of infection. Selecting from all possible cutoffs of maximum gain (MG), MG>0.0244 predicts administration of antibiotics with PPV 82.0 %, NPV 44.4 %, and AUC 0.706. Our approach holds value for retrospective analysis of other clinical syndromes for which time-of-onset is critical to analysis but poorly marked in EHRs, including delirium and decompensation.
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Affiliation(s)
- Caitlin E Coombes
- Department of Anesthesiology, Stanford University, 300 Pasteur Dr., Palo Alto, CA 94305, USA
| | - Kevin R Coombes
- Department of Population Health Sciences, Medical College of Georgia, 1420 Laney Walker Blvd, Augusta, GA 30912, USA
| | - Naleef Fareed
- Department of Biomedical Informatics, The Ohio State University College of Medicine, 370 W 9th Ave, Columbus, OH 43210, USA
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Swain MT, Vickers M. Interpreting alignment-free sequence comparison: what makes a score a good score? NAR Genom Bioinform 2022; 4:lqac062. [PMID: 36071721 PMCID: PMC9442500 DOI: 10.1093/nargab/lqac062] [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: 03/01/2022] [Revised: 07/01/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Alignment-free methods are alternatives to alignment-based methods when searching sequence data sets. The output from an alignment-free sequence comparison is a similarity score, the interpretation of which is not straightforward. We propose objective functions to interpret and calibrate outputs from alignment-free searches, noting that different objective functions are necessary for different biological contexts. This leads to advantages: visualising and comparing score distributions, including those from true positives, may be a relatively simple method to gain insight into the performance of different metrics. Using an empirical approach with both DNA and protein sequences, we characterise different similarity score distributions generated under different parameters. In particular, we demonstrate how sequence length can affect the scores. We show that scores of true positive sequence pairs may correlate significantly with their mean length; and even if the correlation is weak, the relative difference in length of the sequence pair may significantly reduce the effectiveness of alignment-free metrics. Importantly, we show how objective functions can be used with test data to accurately estimate the probability of true positives. This can significantly increase the utility of alignment-free approaches. Finally, we have developed a general-purpose software tool called KAST for use in high-throughput workflows on Linux clusters.
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Affiliation(s)
- Martin T Swain
- Department of Life Sciences, Aberystwyth University , Penglais, Aberystwyth, Ceredigion, SY23 3DA, UK
| | - Martin Vickers
- The John Innes Centre, Norwich Research Park , Norwich NR4 7UH, UK
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11
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Pevzner P, Vingron M, Reidys C, Sun F, Istrail S. Michael Waterman's Contributions to Computational Biology and Bioinformatics. J Comput Biol 2022; 29:601-615. [PMID: 35727100 DOI: 10.1089/cmb.2022.29066.pp] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
On the occasion of Dr. Michael Waterman's 80th birthday, we review his major contributions to the field of computational biology and bioinformatics including the famous Smith-Waterman algorithm for sequence alignment, the probability and statistics theory related to sequence alignment, algorithms for sequence assembly, the Lander-Waterman model for genome physical mapping, combinatorics and predictions of ribonucleic acid structures, word counting statistics in molecular sequences, alignment-free sequence comparison, and algorithms for haplotype block partition and tagSNP selection related to the International HapMap Project. His books Introduction to Computational Biology: Maps, Sequences and Genomes for graduate students and Computational Genome Analysis: An Introduction geared toward undergraduate students played key roles in computational biology and bioinformatics education. We also highlight his efforts of building the computational biology and bioinformatics community as the founding editor of the Journal of Computational Biology and a founding member of the International Conference on Research in Computational Molecular Biology (RECOMB).
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Affiliation(s)
- Pavel Pevzner
- Department of Computer Science and Engineering, University of California San Diego, San Diego, California, USA
| | - Martin Vingron
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Christian Reidys
- Department of Mathematics, Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, Virginia, USA
| | - Fengzhu Sun
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, USA
| | - Sorin Istrail
- Department of Computer Science, Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
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12
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Dougan KE, González-Pech RA, Stephens TG, Shah S, Chen Y, Ragan MA, Bhattacharya D, Chan CX. Genome-powered classification of microbial eukaryotes: focus on coral algal symbionts. Trends Microbiol 2022; 30:831-840. [DOI: 10.1016/j.tim.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 01/20/2022] [Accepted: 02/01/2022] [Indexed: 12/20/2022]
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13
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Blanca A, Harris RS, Koslicki D, Medvedev P. The Statistics of k-mers from a Sequence Undergoing a Simple Mutation Process Without Spurious Matches. J Comput Biol 2022; 29:155-168. [PMID: 35108101 DOI: 10.1089/cmb.2021.0431] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
k-mer-based methods are widely used in bioinformatics, but there are many gaps in our understanding of their statistical properties. Here, we consider the simple model where a sequence S (e.g., a genome or a read) undergoes a simple mutation process through which each nucleotide is mutated independently with some probability r, under the assumption that there are no spurious k-mer matches. How does this process affect the k-mers of S? We derive the expectation and variance of the number of mutated k-mers and of the number of islands (a maximal interval of mutated k-mers) and oceans (a maximal interval of nonmutated k-mers). We then derive hypothesis tests and confidence intervals (CIs) for r given an observed number of mutated k-mers, or, alternatively, given the Jaccard similarity (with or without MinHash). We demonstrate the usefulness of our results using a few select applications: obtaining a CI to supplement the Mash distance point estimate, filtering out reads during alignment by Minimap2, and rating long-read alignments to a de Bruijn graph by Jabba.
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Affiliation(s)
- Antonio Blanca
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Robert S Harris
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - David Koslicki
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.,Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, USA.,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Paul Medvedev
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA.,Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, USA
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14
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Cattaneo G, Ferraro Petrillo U, Giancarlo R, Palini F, Romualdi C. The power of word-frequency-based alignment-free functions: a comprehensive large-scale experimental analysis. Bioinformatics 2022; 38:925-932. [PMID: 34718420 DOI: 10.1093/bioinformatics/btab747] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 10/07/2021] [Accepted: 10/26/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e. their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. RESULTS By concentrating on a representative set of word-frequency-based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the 15 functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public. AVAILABILITY AND IMPLEMENTATION The software is available at: https://github.com/pipp8/power_statistics. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Giuseppe Cattaneo
- Dipartimento di Informatica, Università di Salerno, Fisciano, SA 84084, Italy
| | | | - Raffaele Giancarlo
- Dipartimento di Matematica ed Informatica, Università di Palermo, 90133 Palermo, Italy
| | - Francesco Palini
- Dipartimento di Scienze Statistiche, Università di Roma-La Sapienza, 00185 Rome, Italy
| | - Chiara Romualdi
- Dipartimento di Biologia, Università di Padova, 35131 Padova, Italy
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15
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Letiagina AE, Omelina ES, Ivankin AV, Pindyurin AV. MPRAdecoder: Processing of the Raw MPRA Data With a priori Unknown Sequences of the Region of Interest and Associated Barcodes. Front Genet 2021; 12:618189. [PMID: 34046055 PMCID: PMC8148044 DOI: 10.3389/fgene.2021.618189] [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: 03/25/2021] [Indexed: 11/13/2022] Open
Abstract
Massively parallel reporter assays (MPRAs) enable high-throughput functional evaluation of numerous DNA regulatory elements and/or their mutant variants. The assays are based on the construction of reporter plasmid libraries containing two variable parts, a region of interest (ROI) and a barcode (BC), located outside and within the transcription unit, respectively. Importantly, each plasmid molecule in a such a highly diverse library is characterized by a unique BC-ROI association. The reporter constructs are delivered to target cells and expression of BCs at the transcript level is assayed by RT-PCR followed by next-generation sequencing (NGS). The obtained values are normalized to the abundance of BCs in the plasmid DNA sample. Altogether, this allows evaluating the regulatory potential of the associated ROI sequences. However, depending on the MPRA library construction design, the BC and ROI sequences as well as their associations can be a priori unknown. In such a case, the BC and ROI sequences, their possible mutant variants, and unambiguous BC-ROI associations have to be identified, whereas all uncertain cases have to be excluded from the analysis. Besides the preparation of additional "mapping" samples for NGS, this also requires specific bioinformatics tools. Here, we present a pipeline for processing raw MPRA data obtained by NGS for reporter construct libraries with a priori unknown sequences of BCs and ROIs. The pipeline robustly identifies unambiguous (so-called genuine) BCs and ROIs associated with them, calculates the normalized expression level for each BC and the averaged values for each ROI, and provides a graphical visualization of the processed data.
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Affiliation(s)
- Anna E Letiagina
- Institute of Molecular and Cellular Biology of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Faculty of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia
| | - Evgeniya S Omelina
- Institute of Molecular and Cellular Biology of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Anton V Ivankin
- Institute of Molecular and Cellular Biology of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Alexey V Pindyurin
- Institute of Molecular and Cellular Biology of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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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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Sequence Comparison Without Alignment: The SpaM Approaches. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2231:121-134. [PMID: 33289890 DOI: 10.1007/978-1-0716-1036-7_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Sequence alignment is at the heart of DNA and protein sequence analysis. For the data volumes that are nowadays produced by massively parallel sequencing technologies, however, pairwise and multiple alignment methods are often too slow. Therefore, fast alignment-free approaches to sequence comparison have become popular in recent years. Most of these approaches are based on word frequencies, for words of a fixed length, or on word-matching statistics. Other approaches are using the length of maximal word matches. While these methods are very fast, most of them rely on ad hoc measures of sequences similarity or dissimilarity that are hard to interpret. In this chapter, I describe a number of alignment-free methods that we developed in recent years. Our approaches are based on spaced-word matches ("SpaM"), i.e. on inexact word matches, that are allowed to contain mismatches at certain pre-defined positions. Unlike most previous alignment-free approaches, our approaches are able to accurately estimate phylogenetic distances between DNA or protein sequences using a stochastic model of molecular evolution.
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18
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Symbolic Sequence Classification in the Fractal Space. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021. [DOI: 10.1109/tetci.2018.2876528] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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19
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Girgis HZ, James BT, Luczak BB. Identity: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models. NAR Genom Bioinform 2021; 3:lqab001. [PMID: 33554117 PMCID: PMC7850047 DOI: 10.1093/nargab/lqab001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 12/07/2020] [Accepted: 01/08/2021] [Indexed: 11/12/2022] Open
Abstract
Pairwise global alignment is a fundamental step in sequence analysis. Optimal alignment algorithms are quadratic-slow especially on long sequences. In many applications that involve large sequence datasets, all what is needed is calculating the identity scores (percentage of identical nucleotides in an optimal alignment-including gaps-of two sequences); there is no need for visualizing how every two sequences are aligned. For these applications, we propose Identity, which produces global identity scores for a large number of pairs of DNA sequences using alignment-free methods and self-supervised general linear models. For the first time, the new tool can predict pairwise identity scores in linear time and space. On two large-scale sequence databases, Identity provided the best compromise between sensitivity and precision while being faster than BLAST, Mash, MUMmer4 and USEARCH by 2-80 times. Identity was the best performing tool when searching for low-identity matches. While constructing phylogenetic trees from about 6000 transcripts, the tree due to the scores reported by Identity was the closest to the reference tree (in contrast to andi, FSWM and Mash). Identity is capable of producing pairwise identity scores of millions-of-nucleotides-long bacterial genomes; this task cannot be accomplished by any global-alignment-based tool. Availability: https://github.com/BioinformaticsToolsmith/Identity.
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Affiliation(s)
- Hani Z Girgis
- Bioinformatics Toolsmith Laboratory, Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, 700 University Boulevard, Kingsville, TX 78363, USA
| | - Benjamin T James
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA
| | - Brian B Luczak
- Department of Mathematics, Vanderbilt University, 1326 Stevenson Center Lane, Nashville, TN 3721, USA
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20
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Petrillo UF, Palini F, Cattaneo G, Giancarlo R. Alignment-free Genomic Analysis via a Big Data Spark Platform. Bioinformatics 2021; 37:1658-1665. [PMID: 33471066 DOI: 10.1093/bioinformatics/btab014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/28/2020] [Accepted: 01/06/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Alignment-free distance and similarity functions (AF functions, for short) are a well established alternative to pairwise and multiple sequence alignments for many genomic, metagenomic and epigenomic tasks. Due to data-intensive applications, the computation of AF functions is a Big Data problem, with the recent literature indicating that the development of fast and scalable algorithms computing AF functions is a high-priority task. Somewhat surprisingly, despite the increasing popularity of Big Data technologies in computational biology, the development of a Big Data platform for those tasks has not been pursued, possibly due to its complexity. RESULTS We fill this important gap by introducing FADE, the first extensible, efficient and scalable Spark platform for alignment-free genomic analysis. It supports natively eighteen of the best performing AF functions coming out of a recent hallmark benchmarking study. FADE development and potential impact comprises novel aspects of interest. Namely, (a) a considerable effort of distributed algorithms, the most tangible result being a much faster execution time of reference methods like MASH and FSWM; (b) a software design that makes FADE user-friendly and easily extendable by Spark non-specialists; (c) its ability to support data- and compute-intensive tasks. About this, we provide a novel and much needed analysis of how informative and robust AF functions are, in terms of the statistical significance of their output. Our findings naturally extend the ones of the highly regarded benchmarking study, since the functions that can really be used are reduced to a handful of the eighteen included in FADE. AVAILABILITY The software and the datasets are available at https://github.com/fpalini/fade. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Francesco Palini
- Dipartimento di Scienze Statistiche, Università di Roma - La Sapienza, Rome, 00185, Italy
| | - Giuseppe Cattaneo
- Dipartimento di Informatica, Università di Salerno, Fisciano (SA), 84084, Italy
| | - Raffaele Giancarlo
- Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, 90133, Italy
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21
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Abstract
Inferring phylogenetic relationships among hundreds or thousands of microbial genomes is an increasingly common task. The conventional phylogenetic approach adopts multiple sequence alignment to compare gene-by-gene, concatenated multigene or whole-genome sequences, from which a phylogenetic tree would be inferred. These alignments follow the implicit assumption of full-length contiguity among homologous sequences. However, common events in microbial genome evolution (e.g., structural rearrangements and genetic recombination) violate this assumption. Moreover, aligning hundreds or thousands of sequences is computationally intensive and not scalable to the rate at which genome data are generated. Therefore, alignment-free methods present an attractive alternative strategy. Here we describe a scalable alignment-free strategy to infer phylogenetic relationships using complete genome sequences of bacteria and archaea, based on short, subsequences of length k (k-mers). We describe how this strategy can be extended to infer evolutionary relationships beyond a tree-like structure, to better capture both vertical and lateral signals of microbial evolution.
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22
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Song K. Classifying the Lifestyle of Metagenomically-Derived Phages Sequences Using Alignment-Free Methods. Front Microbiol 2020; 11:567769. [PMID: 33304326 PMCID: PMC7693541 DOI: 10.3389/fmicb.2020.567769] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 10/22/2020] [Indexed: 01/20/2023] Open
Abstract
Phages are viruses that infect bacteria. The phages can be classified into two different categories based on their lifestyles: temperate and lytic. Now, the metavirome can generate a large number of fragments from the viral genomic sequences of entire environmental community, which makes it impossible to determine their lifestyles through experiments. Thus, there is a need to development computational methods for annotating phage contigs and making prediction of their lifestyles. Alignment-based methods for classifying phage lifestyle are limited by incomplete assembled genomes and nucleotide databases. Alignment-free methods based on the frequencies of k-mers were widely used for genome and metagenome comparison which did not rely on the completeness of genome or nucleotide databases. To mimic fragmented metagenomic sequences, the temperate and lytic phages genomic sequences were split into non-overlapping fragments with different lengths, then, I comprehensively compared nine alignment-free dissimilarity measures with a wide range of choices of k-mer length and Markov orders for predicting the lifestyles of these phage contigs. The dissimilarity measure, d2S, performed better than other dissimilarity measures for classifying the lifestyles of phages. Thus, I propose that the alignment-free method, d2S, can be used for predicting the lifestyles of phages which derived from the metagenomic data.
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Affiliation(s)
- Kai Song
- School of Mathematics and Statistics, Qingdao University, Qingdao, China
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23
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Huang J, Dai Q, Yao Y, He PA. A Generalized Iterative Map for Analysis of Protein Sequences. Comb Chem High Throughput Screen 2020; 25:381-391. [PMID: 33045963 DOI: 10.2174/1386207323666201012142318] [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: 03/17/2020] [Revised: 07/30/2020] [Accepted: 08/09/2020] [Indexed: 11/22/2022]
Abstract
AIM AND OBJECTIVE The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences. MATERIALS AND METHODS Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species. RESULTS By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach. CONCLUSION Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.
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Affiliation(s)
- Jiahe Huang
- School of Science, Zhejiang Sci-Tech University, Hangzhou,China
| | - Qi Dai
- College of Life Science, Zhejiang Sci-Tech University, Hangzhou,China
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou,China
| | - Ping-An He
- School of Science, Zhejiang Sci-Tech University, Hangzhou,China
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24
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Murphy RG, Roddy AC, Srivastava S, Baena E, Waugh D, M. O’Sullivan J, McArt DG, Jain S, LaBonte M. Prostate cancer heterogeneity assessment with multi-regional sampling and alignment-free methods. NAR Genom Bioinform 2020; 2:lqaa062. [PMID: 32856020 PMCID: PMC7440682 DOI: 10.1093/nargab/lqaa062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/16/2020] [Accepted: 08/05/2020] [Indexed: 11/14/2022] Open
Abstract
Combining alignment-free methods for phylogenetic analysis with multi-regional sampling using next-generation sequencing can provide an assessment of intra-patient tumour heterogeneity. From multi-regional sampling divergent branching, we validated two different lesions within a patient's prostate. Where multi-regional sampling has not been used, a single sample from one of these areas could misguide as to which drugs or therapies would best benefit this patient, due to the fact these tumours appear to be genetically different. This application has the power to render, in a fraction of the time used by other approaches, intra-patient heterogeneity and decipher aberrant biomarkers. Another alignment-free method for calling single-nucleotide variants from raw next-generation sequencing samples has determined possible variants and genomic locations that may be able to characterize the differences between the two main branching patterns. Alignment-free approaches have been applied to relevant clinical multi-regional samples and may be considered as a valuable option for comparing and determining heterogeneity to help deliver personalized medicine through more robust efforts in identifying targetable pathways and therapeutic strategies. Our study highlights the application these tools could have on patient-aligned treatment indications.
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Affiliation(s)
- Ross G Murphy
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Aideen C Roddy
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Shambhavi Srivastava
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
- Molecular Oncology, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK
- Belfast–Manchester Movember Centre of Excellence, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK
| | - Esther Baena
- Belfast–Manchester Movember Centre of Excellence, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK
- Prostate Oncobiology, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, UK
| | - David J Waugh
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, QLD 4000, Australia
| | - Joe M. O’Sullivan
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Darragh G McArt
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Suneil Jain
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Melissa J LaBonte
- Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7AE, UK
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25
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Delibaş E, Arslan A, Şeker A, Diri B. A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up. J Mol Graph Model 2020; 100:107693. [PMID: 32805559 DOI: 10.1016/j.jmgm.2020.107693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 06/15/2020] [Accepted: 07/06/2020] [Indexed: 11/17/2022]
Abstract
DNA sequence similarity analysis is an essential task in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods, which result in high computational cost, alignment-free methods have emerged that calculate similarity by digitizing the sequence in a different space. In this paper, we proposed an alignment-free DNA sequence similarity analysis method based on top-k n-gram matches, with the prediction that common repeating DNA subsections indicate high similarity between DNA sequences. In our method, we determined DNA sequence similarities by measuring similarity among feature vectors created according to top-k n-gram match-up scores without the use of similarity functions. We applied the similarity calculation for three different DNA data sets of different lengths. The phylogenetic relationships revealed by our method show that our trees coincide almost completely with the results of the MEGA software, which is based on sequence alignment. Our findings show that a certain number of frequently recurring common sequence patterns have the power to characterize DNA sequences.
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Affiliation(s)
- Emre Delibaş
- Department of Computer Engineering, Faculty of Engineering, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
| | - Ahmet Arslan
- Department of Computer Engineering, Faculty of Engineering, Selçuk University, 42250, Konya, Turkey.
| | - Abdulkadir Şeker
- Department of Computer Engineering, Faculty of Engineering, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
| | - Banu Diri
- Department of Computer Engineering, Faculty of Electrical and Electronics, Yıldız Technical University, 34349, Ístanbul, Turkey.
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26
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Villacrés-Vallejo J, Aranda-Ventura J, Wallis A, Cagle R, Handy SM, Davis J, Reed E, Zhang S, Strain E, Pava-Ripoll M, Erickson D, Ramachandran P, Ottesen A. Using full chloroplast genomes of 'red' and 'yellow' Bixa orellana (achiote) for kmer based identification and phylogenetic inference. BMC Genomics 2020; 21:544. [PMID: 32762642 PMCID: PMC7430826 DOI: 10.1186/s12864-020-06916-0] [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: 03/27/2020] [Accepted: 07/15/2020] [Indexed: 11/18/2022] Open
Abstract
Background Full chloroplast genomes provide high resolution taxonomic discrimination between closely related plant species and are quickly replacing single and multi-locus barcoding regions as reference materials of choice for DNA based taxonomic annotation of plants. Bixa orellana, commonly known as “achiote” and “annatto” is a plant used for both human and animal foods and was thus identified for full chloroplast sequencing for the Center for Veterinary Medicine (CVM) Complete Chloroplast Animal Feed database. This work was conducted in collaboration with the Instituto de Medicina Tradicional (IMET) in Iquitos, Peru. There is a wide range of color variation in pods of Bixa orellana for which genetic loci that distinguish phenotypes have not yet been identified. Here we apply whole chloroplast genome sequencing of “red” and “yellow” individuals of Bixa orellana to provide high quality reference genomes to support kmer database development for use identifying this plant from complex mixtures using shotgun data. Additionally, we describe chloroplast gene content, synteny and phylogeny, and identify an indel and snp that may be associated with seed pod color. Results Fully assembled chloroplast genomes were produced for both red and yellow Bixa orellana accessions (158,918 and 158,823 bp respectively). Synteny and gene content was identical to the only other previously reported full chloroplast genome of Bixa orellana (NC_041550). We observed a 17 base pair deletion at position 58,399–58,415 in both accessions, relative to NC_041550 and a 6 bp deletion at position 75,531–75,526 and a snp at position 86,493 in red Bixa orellana. Conclusions Our data provide high quality reference genomes of individuals of red and yellow Bixa orellana to support kmer based identity markers for use with shotgun sequencing approaches for rapid, precise identification of Bixa orellana from complex mixtures. Kmer based phylogeny of full chloroplast genomes supports monophylly of Bixaceae consistent with alignment based approaches. A potentially discriminatory indel and snp were identified that may be correlated with the red phenotype.
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Affiliation(s)
- Jorge Villacrés-Vallejo
- Seguro Social de Salud, Instituto de Medicina Tradicional, Iquitos, Peru.,Universidad Nacional de la Amazonía Peruana, Facultad de Agronomía, Iquitos, Peru
| | | | - Anna Wallis
- Department of Plant Pathology, Cornell University, Ithaca, NY, USA
| | - Robin Cagle
- Department of Plant Pathology, Cornell University, Ithaca, NY, USA
| | - Sara M Handy
- Office of Regulatory Science, Center for Food Safety and Applied Nutrition, FDA, College Park, MD, USA
| | - Jeffery Davis
- Department of Chemistry and Biochemistry, University of Maryland, College Park, USA
| | - Elizabeth Reed
- Office of Regulatory Science, Center for Food Safety and Applied Nutrition, FDA, College Park, MD, USA
| | - Shu Zhang
- DNA4 Technologies LLC, Baltimore, MD, USA
| | - Errol Strain
- Office of Research, Center for Veterinary Medicine, FDA, Laurel, MD, USA
| | - Monica Pava-Ripoll
- Office of Food Safety, Center for Food Safety and Applied Nutrition, FDA, College Park, MD, USA
| | - David Erickson
- Joint Institute for Food Safety and Applied Nutrition, College Park, MD, USA
| | - Padmini Ramachandran
- Office of Regulatory Science, Center for Food Safety and Applied Nutrition, FDA, College Park, MD, USA
| | - Andrea Ottesen
- Office of Research, Center for Veterinary Medicine, FDA, Laurel, MD, USA. .,Department of Plant Sciences and Landscape Architecture, University of Maryland, College Park, MD, USA.
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27
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Wang W, Ren J, Tang K, Dart E, Ignacio-Espinoza JC, Fuhrman JA, Braun J, Sun F, Ahlgren NA. A network-based integrated framework for predicting virus-prokaryote interactions. NAR Genom Bioinform 2020; 2:lqaa044. [PMID: 32626849 PMCID: PMC7324143 DOI: 10.1093/nargab/lqaa044] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 03/12/2020] [Accepted: 06/05/2020] [Indexed: 12/12/2022] Open
Abstract
Metagenomic sequencing has greatly enhanced the discovery of viral genomic sequences; however, it remains challenging to identify the host(s) of these new viruses. We developed VirHostMatcher-Net, a flexible, network-based, Markov random field framework for predicting virus–prokaryote interactions using multiple, integrated features: CRISPR sequences and alignment-free similarity measures (\documentclass[12pt]{minimal}
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}{}$s_2^*$\end{document} and WIsH). Evaluation of this method on a benchmark set of 1462 known virus–prokaryote pairs yielded host prediction accuracy of 59% and 86% at the genus and phylum levels, representing 16–27% and 6–10% improvement, respectively, over previous single-feature prediction approaches. We applied our host prediction tool to crAssphage, a human gut phage, and two metagenomic virus datasets: marine viruses and viral contigs recovered from globally distributed, diverse habitats. Host predictions were frequently consistent with those of previous studies, but more importantly, this new tool made many more confident predictions than previous tools, up to nearly 3-fold more (n > 27 000), greatly expanding the diversity of known virus–host interactions.
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Affiliation(s)
- Weili Wang
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA
| | - Jie Ren
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA
| | - Kujin Tang
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA
| | - Emily Dart
- Biology Department, Clark University, Worcester, MA 01610, USA
| | | | - Jed A Fuhrman
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Jonathan Braun
- Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Fengzhu Sun
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA 90089, USA
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Kaufer A, Stark D, Ellis J. A review of the systematics, species identification and diagnostics of the Trypanosomatidae using the maxicircle kinetoplast DNA: from past to present. Int J Parasitol 2020; 50:449-460. [PMID: 32333942 DOI: 10.1016/j.ijpara.2020.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 02/28/2020] [Accepted: 03/09/2020] [Indexed: 11/25/2022]
Abstract
The Trypanosomatid family are a diverse and widespread group of protozoan parasites that belong to the higher order class Kinetoplastida. Containing predominantly monoxenous species (i.e. those having only a single host) that are confined to invertebrate hosts, this class is primarily known for its pathogenic dixenous species (i.e. those that have two hosts), serving as the aetiological agents of the important neglected tropical diseases including leishmaniasis, American trypanosomiasis (Chagas disease) and human African trypanosomiasis. Over the past few decades, a multitude of studies have investigated the diversity, classification and evolutionary history of the trypanosomatid family using different approaches and molecular targets. The mitochondrial-like DNA of the trypanosomatid parasites, also known as the kinetoplast, has emerged as a unique taxonomic and diagnostic target for exploring the evolution of this diverse group of parasitic eukaryotes. This review discusses recent advancements and important developments that have made a significant impact in the field of trypanosomatid systematics and diagnostics in recent years.
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Affiliation(s)
- Alexa Kaufer
- School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia.
| | - Damien Stark
- Department of Microbiology, St Vincent's Hospital Sydney, Darlinghurst, NSW 2010, Australia
| | - John Ellis
- School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
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Loureiro L, Engstrom M, Lim B. Optimization of Genotype by Sequencing data for phylogenetic purposes. MethodsX 2020; 7:100892. [PMID: 32373482 PMCID: PMC7195544 DOI: 10.1016/j.mex.2020.100892] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 04/03/2020] [Indexed: 12/19/2022] Open
Abstract
• Herein we propose a framework for assembling and analyzing Genotype by Sequencing (GBS) data to better understand evolutionary relationships within a group of closely related species using the mastiff bats (Molossus) as our model system. Many species within this genus have low-levels of genetic variation within and between morphologically distinct species, and the relationships among them remain unresolved using traditional Sanger sequencing methods. Given that both de novo and reference genome pipelines can be used to assemble next generation sequences, and that several tree inference methodologies have been proposed for single nucleotide polymorphism (SNP) data, we test whether different alignments and phylogenetic approaches produce similar results. We also examined how the process of SNP identification and mapping can affect the consistency of the analyses. Different alignments and phylogenetic inferences produced consistent results, supporting the GBS approach for answering evolutionary questions on a macroevolutionary scale when the genetic distance among phenotypically identifiable clades is low. We highlight the importance of exploring the relationships among groups using different assembly assumptions and also distinct phylogenetic inference methods, particularly when addressing phylogenetic questions in genetic and morphologically conservative taxa. • The method uses the comparison of several filter settings, alignments, and tree inference approaches on Genotype by Sequencing data. • Consistent results were found among several approaches. • The methodology successfully recovered well supported species boundaries and phylogenetic relationships among species of mastiff bats not hypothesized by previous methods.
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Affiliation(s)
- L.O. Loureiro
- University of Toronto, Canada
- Royal Ontario Museum, Canada
| | - M.D. Engstrom
- University of Toronto, Canada
- Royal Ontario Museum, Canada
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30
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Luczak BB, James BT, Girgis HZ. A survey and evaluations of histogram-based statistics in alignment-free sequence comparison. Brief Bioinform 2020; 20:1222-1237. [PMID: 29220512 PMCID: PMC6781583 DOI: 10.1093/bib/bbx161] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 10/13/2017] [Indexed: 11/29/2022] Open
Abstract
Motivation Since the dawn of the bioinformatics field, sequence alignment scores have been the main method for comparing sequences. However, alignment algorithms are quadratic, requiring long execution time. As alternatives, scientists have developed tens of alignment-free statistics for measuring the similarity between two sequences. Results We surveyed tens of alignment-free k-mer statistics. Additionally, we evaluated 33 statistics and multiplicative combinations between the statistics and/or their squares. These statistics are calculated on two k-mer histograms representing two sequences. Our evaluations using global alignment scores revealed that the majority of the statistics are sensitive and capable of finding similar sequences to a query sequence. Therefore, any of these statistics can filter out dissimilar sequences quickly. Further, we observed that multiplicative combinations of the statistics are highly correlated with the identity score. Furthermore, combinations involving sequence length difference or Earth Mover’s distance, which takes the length difference into account, are always among the highest correlated paired statistics with identity scores. Similarly, paired statistics including length difference or Earth Mover’s distance are among the best performers in finding the K-closest sequences. Interestingly, similar performance can be obtained using histograms of shorter words, resulting in reducing the memory requirement and increasing the speed remarkably. Moreover, we found that simple single statistics are sufficient for processing next-generation sequencing reads and for applications relying on local alignment. Finally, we measured the time requirement of each statistic. The survey and the evaluations will help scientists with identifying efficient alternatives to the costly alignment algorithm, saving thousands of computational hours. Availability The source code of the benchmarking tool is available as Supplementary Materials.
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Affiliation(s)
| | | | - Hani Z Girgis
- Corresponding author. Hani Z. Girgis, Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA. E-mail:
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31
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Daoud M. Detecting outliers in segmented genomes of flu virus using an alignment-free approach. Genomics Inform 2020; 18:e2. [PMID: 32224835 PMCID: PMC7120353 DOI: 10.5808/gi.2020.18.1.e2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 11/21/2019] [Indexed: 11/29/2022] Open
Abstract
In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.
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Affiliation(s)
- Mosaab Daoud
- Independent Research Scientist, Toronto, ON M1S1G2, Canada
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32
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Dencker T, Leimeister CA, Gerth M, Bleidorn C, Snir S, Morgenstern B. 'Multi-SpaM': a maximum-likelihood approach to phylogeny reconstruction using multiple spaced-word matches and quartet trees. NAR Genom Bioinform 2020; 2:lqz013. [PMID: 33575565 PMCID: PMC7671388 DOI: 10.1093/nargab/lqz013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/31/2019] [Accepted: 10/13/2019] [Indexed: 02/03/2023] Open
Abstract
Word-based or 'alignment-free' methods for phylogeny inference have become popular in recent years. These methods are much faster than traditional, alignment-based approaches, but they are generally less accurate. Most alignment-free methods calculate 'pairwise' distances between nucleic-acid or protein sequences; these distance values can then be used as input for tree-reconstruction programs such as neighbor-joining. In this paper, we propose the first word-based phylogeny approach that is based on 'multiple' sequence comparison and 'maximum likelihood'. Our algorithm first samples small, gap-free alignments involving four taxa each. For each of these alignments, it then calculates a quartet tree and, finally, the program 'Quartet MaxCut' is used to infer a super tree for the full set of input taxa from the calculated quartet trees. Experimental results show that trees produced with our approach are of high quality.
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Affiliation(s)
- Thomas Dencker
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Chris-André Leimeister
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Michael Gerth
- Institute for Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, L69 7ZB Liverpool, UK
| | - Christoph Bleidorn
- Department of Animal Evolution and Biodiversity, Universität Göttingen, Untere Karspüle 2, 37073 Göttingen, Germany
- Museo Nacional de Ciencias Naturales, Spanish National Research Council (CSIC), 28006 Madrid, Spain
| | - Sagi Snir
- Institute of Evolution, Department of Evolutionary and Environmental Biology, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, Israel
| | - Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
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33
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Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method. ENTROPY 2020; 22:e22020255. [PMID: 33286029 PMCID: PMC7516702 DOI: 10.3390/e22020255] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/07/2020] [Accepted: 02/20/2020] [Indexed: 12/31/2022]
Abstract
HIV-1 viruses, which are predominant in the family of HIV viruses, have strong pathogenicity and infectivity. They can evolve into many different variants in a very short time. In this study, we propose a new and effective alignment-free method for the phylogenetic analysis of HIV-1 viruses using complete genome sequences. Our method combines the position distribution information and the counts of the k-mers together. We also propose a metric to determine the optimal k value. We name our method the Position-Weighted k-mers (PWkmer) method. Validation and comparison with the Robinson-Foulds distance method and the modified bootstrap method on a benchmark dataset show that our method is reliable for the phylogenetic analysis of HIV-1 viruses. PWkmer can resolve within-group variations for different known subtypes of Group M of HIV-1 viruses. This method is simple and computationally fast for whole genome phylogenetic analysis.
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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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35
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Agüero-Chapin G, Galpert D, Molina-Ruiz R, Ancede-Gallardo E, Pérez-Machado G, De la Riva GA, Antunes A. Graph Theory-Based Sequence Descriptors as Remote Homology Predictors. Biomolecules 2019; 10:E26. [PMID: 31878100 PMCID: PMC7022958 DOI: 10.3390/biom10010026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/16/2019] [Accepted: 12/18/2019] [Indexed: 12/23/2022] Open
Abstract
Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical-numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Deborah Galpert
- Departamento de Ciencia de la Computación. Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Reinaldo Molina-Ruiz
- Centro de Bioactivos Químicos (CBQ), Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Evys Ancede-Gallardo
- Programa de Doctorado en Fisicoquímica Molecular, Facultad de Ciencias Exactas, Universidad Andrés Bello, Av. República 239, Santiago 8370146, Chile;
| | - Gisselle Pérez-Machado
- EpiDisease S.L. Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Gustavo A. De la Riva
- Laboratorio de Biotecnología Aplicada S. de R.L. de C.V., GRECA Inc., Carretera La Piedad-Carapán, km 3.5, La Piedad, Michoacán 59300, Mexico;
- Tecnológico Nacional de México, Instituto Tecnológico de la Piedad, Av. Ricardo Guzmán Romero, Santa Fe, La Piedad de Cavadas, Michoacán 59370, Mexico
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
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Qian J, Comin M. MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage. BMC Bioinformatics 2019; 20:367. [PMID: 31757198 PMCID: PMC6873667 DOI: 10.1186/s12859-019-2904-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 05/15/2019] [Indexed: 11/30/2022] Open
Abstract
Motivation Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths. Results In this context we present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases. The effectiveness of MetaCon is demonstrated in both simulated and real datasets in comparison with state-of-art binning approaches such as CONCOCT, MaxBin and MetaBAT. Electronic supplementary material The online version of this article (10.1186/s12859-019-2904-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jia Qian
- Department of Information Engineering, University of Padova, Via Giovanni Gradenigo 6, Padova, Italy
| | - Matteo Comin
- Department of Information Engineering, University of Padova, Via Giovanni Gradenigo 6, Padova, Italy.
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37
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Song K, Ren J, Sun F. Reads Binning Improves Alignment-Free Metagenome Comparison. Front Genet 2019; 10:1156. [PMID: 31824565 PMCID: PMC6881972 DOI: 10.3389/fgene.2019.01156] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 10/22/2019] [Indexed: 12/26/2022] Open
Abstract
Comparing metagenomic samples is a critical step in understanding the relationships among microbial communities. Recently, next-generation sequencing (NGS) technologies have produced a massive amount of short reads data for microbial communities from different environments. The assembly of these short reads can, however, be time-consuming and challenging. In addition, alignment-based methods for metagenome comparison are limited by incomplete genome and/or pathway databases. In contrast, alignment-free methods for metagenome comparison do not depend on the completeness of genome or pathway databases. Still, the existing alignment-free methods,d 2 S andd 2 * , which model k-tuple patterns using only one Markov chain for each sample, neglect the heterogeneity within metagenomic data wherein potentially thousands of types of microorganisms are sequenced. To address this imperfection ind 2 S andd 2 * , we organized NGS sequences into different reads bins and constructed several corresponding Markov models. Next, we modified the definition of our previous alignment-free methods,d 2 S andd 2 * , to make them more compatible with a scheme of analysis which uses the proposed reads bins. We then used two simulated and three real metagenomic datasets to test the effect of the k-tuple size and Markov orders of background sequences on the performance of these de novo alignment-free methods. For dependable comparison of metagenomic samples, our newly developed alignment-free methods with reads binning outperformed alignment-free methods without reads binning in detecting the relationship among microbial communities, including whether they form groups or change according to some environmental gradients.
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Affiliation(s)
- Kai Song
- School of Mathematics and Statistics, Qingdao University, Qingdao, China
| | - Jie Ren
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, United States
| | - Fengzhu Sun
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, United States
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38
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Ma Y, He T, Jiang X. Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction. Front Genet 2019; 10:1148. [PMID: 31824563 PMCID: PMC6880730 DOI: 10.3389/fgene.2019.01148] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 10/21/2019] [Indexed: 12/25/2022] Open
Abstract
Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.
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Affiliation(s)
- Yingjun Ma
- School of Mathematics & Statistics, Central China Normal University, Wuhan, China.,Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China
| | - Tingting He
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China.,School of Computer, Central China Normal University, Wuhan, China
| | - Xingpeng Jiang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China.,School of Computer, Central China Normal University, Wuhan, China
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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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40
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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: 53] [Impact Index Per Article: 10.6] [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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41
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Huang GD, Liu XM, Huang TL, Xia LC. The statistical power of k-mer based aggregative statistics for alignment-free detection of horizontal gene transfer. Synth Syst Biotechnol 2019; 4:150-156. [PMID: 31508512 PMCID: PMC6723412 DOI: 10.1016/j.synbio.2019.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/14/2019] [Accepted: 08/05/2019] [Indexed: 12/21/2022] Open
Abstract
Alignment-based database search and sequence comparison are commonly used to detect horizontal gene transfer (HGT). However, with the rapid increase of sequencing depth, hundreds of thousands of contigs are routinely assembled from metagenomics studies, which challenges alignment-based HGT analysis by overwhelming the known reference sequences. Detecting HGT by k-mer statistics thus becomes an attractive alternative. These alignment-free statistics have been demonstrated in high performance and efficiency in whole-genome and transcriptome comparisons. To adapt k-mer statistics for HGT detection, we developed two aggregative statistics TsumS and Tsum*, which subsample metagenome contigs by their representative regions, and summarize the regional D2S and D2* metrics by their upper bounds. We systematically studied the aggregative statistics’ power at different k-mer size using simulations. Our analysis showed that, in general, the power of TsumS and Tsum* increases with sequencing coverage, and reaches a maximum power >80% at k = 6, with 5% Type-I error and the coverage ratio >0.2x. The statistical power of TsumS and Tsum* was evaluated with realistic simulations of HGT mechanism, sequencing depth, read length, and base error. We expect these statistics to be useful distance metrics for identifying HGT in metagenomic studies.
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Affiliation(s)
- Guan-Da Huang
- School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China
| | - Xue-Mei Liu
- School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China
| | - Tian-Lai Huang
- School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China
| | - Li-C Xia
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
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42
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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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43
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Lu YY, Tang K, Ren J, Fuhrman JA, Waterman MS, Sun F. CAFE: aCcelerated Alignment-FrEe sequence analysis. Nucleic Acids Res 2019; 45:W554-W559. [PMID: 28472388 PMCID: PMC5793812 DOI: 10.1093/nar/gkx351] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 04/20/2017] [Indexed: 12/13/2022] Open
Abstract
Alignment-free genome and metagenome comparisons are increasingly important with the development of next generation sequencing (NGS) technologies. Recently developed state-of-the-art k-mer based alignment-free dissimilarity measures including CVTree, \documentclass[12pt]{minimal}
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}{}$d_2^S$\end{document} are more computationally expensive than measures based solely on the k-mer frequencies. Here, we report a standalone software, aCcelerated Alignment-FrEe sequence analysis (CAFE), for efficient calculation of 28 alignment-free dissimilarity measures. CAFE allows for both assembled genome sequences and unassembled NGS shotgun reads as input, and wraps the output in a standard PHYLIP format. In downstream analyses, CAFE can also be used to visualize the pairwise dissimilarity measures, including dendrograms, heatmap, principal coordinate analysis and network display. CAFE serves as a general k-mer based alignment-free analysis platform for studying the relationships among genomes and metagenomes, and is freely available at https://github.com/younglululu/CAFE.
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Affiliation(s)
- Yang Young Lu
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089, USA
| | - Kujin Tang
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089, USA
| | - Jie Ren
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089, USA
| | - Jed A Fuhrman
- Department of Biological Sciences and Wrigley Institute for Environmental Studies, University of Southern California, Los Angeles, CA 90089, USA
| | - Michael S Waterman
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089, USA.,Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, 200433 Shanghai, China
| | - Fengzhu Sun
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089, USA.,Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, 200433 Shanghai, China
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Farkaš T, Sitarčík J, Brejová B, Lucká M. SWSPM: A Novel Alignment-Free DNA Comparison Method Based on Signal Processing Approaches. Evol Bioinform Online 2019; 15:1176934319849071. [PMID: 31210725 PMCID: PMC6545658 DOI: 10.1177/1176934319849071] [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: 04/12/2019] [Accepted: 04/12/2019] [Indexed: 11/16/2022] Open
Abstract
Computing similarity between 2 nucleotide sequences is one of the fundamental problems in bioinformatics. Current methods are based mainly on 2 major approaches: (1) sequence alignment, which is computationally expensive, and (2) faster, but less accurate, alignment-free methods based on various statistical summaries, for example, short word counts. We propose a new distance measure based on mathematical transforms from the domain of signal processing. To tolerate large-scale rearrangements in the sequences, the transform is computed across sliding windows. We compare our method on several data sets with current state-of-art alignment-free methods. Our method compares favorably in terms of accuracy and outperforms other methods in running time and memory requirements. In addition, it is massively scalable up to dozens of processing units without the loss of performance due to communication overhead. Source files and sample data are available at https://bitbucket.org/fiitstubioinfo/swspm/src.
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Affiliation(s)
- Tomáš Farkaš
- Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Jozef Sitarčík
- Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Broňa Brejová
- Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
| | - Mária Lucká
- Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Bratislava, Slovakia
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45
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Randhawa GS, Hill KA, Kari L. ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels. BMC Genomics 2019; 20:267. [PMID: 30943897 PMCID: PMC6448311 DOI: 10.1186/s12864-019-5571-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 02/27/2019] [Indexed: 11/11/2022] Open
Abstract
Background Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results We propose a novel combination of supervised Machine Learning with Digital Signal Processing, resulting in ML-DSP: an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97%. A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy. Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes. Conclusions Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.
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Affiliation(s)
- Gurjit S Randhawa
- Department of Computer Science, University of Western Ontario, London, ON, Canada.
| | - Kathleen A Hill
- Department of Biology, University of Western Ontario, London, ON, Canada
| | - Lila Kari
- School of Computer Science, University of Waterloo, Waterloo, ON, Canada
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46
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Interpretable genotype-to-phenotype classifiers with performance guarantees. Sci Rep 2019; 9:4071. [PMID: 30858411 PMCID: PMC6411721 DOI: 10.1038/s41598-019-40561-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 02/19/2019] [Indexed: 01/15/2023] Open
Abstract
Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use in this setting. The high dimensionality of the data tends to hinder generalization and challenges the scalability of most learning algorithms. Additionally, most algorithms produce models that are complex and difficult to interpret. We alleviate these limitations by proposing strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models. We show that these guarantees can be leveraged to accelerate learning and improve model interpretability. Our approach is validated through an application to the genomic prediction of antimicrobial resistance, an important public health concern. Highly accurate models were obtained for 12 species and 56 antibiotics, and their interpretation revealed known resistance mechanisms, as well as some potentially new ones. An open-source disk-based implementation that is both memory and computationally efficient is provided with this work. The implementation is turnkey, requires no prior knowledge of machine learning, and is complemented by comprehensive tutorials.
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47
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Saw AK, Tripathy BC, Nandi S. Alignment-free similarity analysis for protein sequences based on fuzzy integral. Sci Rep 2019; 9:2775. [PMID: 30808983 PMCID: PMC6391537 DOI: 10.1038/s41598-019-39477-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 01/15/2019] [Indexed: 12/12/2022] Open
Abstract
Sequence comparison is an essential part of modern molecular biology research. In this study, we estimated the parameters of Markov chain by considering the frequencies of occurrence of the all possible amino acid pairs from each alignment-free protein sequence. These estimated Markov chain parameters were used to calculate similarity between two protein sequences based on a fuzzy integral algorithm. For validation, our result was compared with both alignment-based (ClustalW) and alignment-free methods on six benchmark datasets. The results indicate that our developed algorithm has a better clustering performance for protein sequence comparison.
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Affiliation(s)
- Ajay Kumar Saw
- Institute of Advanced Study in Science and Technology, Mathematical Sciences Division, Guwahati, 781035, India
| | | | - Soumyadeep Nandi
- Institute of Advanced Study in Science and Technology, Life Science Division, Guwahati, 781035, India.
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48
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Li W, Freudenberg J, Freudenberg J. Alignment-free approaches for predicting novel Nuclear Mitochondrial Segments (NUMTs) in the human genome. Gene 2019; 691:141-152. [PMID: 30630097 DOI: 10.1016/j.gene.2018.12.040] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/07/2018] [Accepted: 12/14/2018] [Indexed: 10/27/2022]
Abstract
The nuclear human genome harbors sequences of mitochondrial origin, indicating an ancestral transfer of DNA from the mitogenome. Several Nuclear Mitochondrial Segments (NUMTs) have been detected by alignment-based sequence similarity search, as implemented in the Basic Local Alignment Search Tool (BLAST). Identifying NUMTs is important for the comprehensive annotation and understanding of the human genome. Here we explore the possibility of detecting NUMTs in the human genome by alignment-free sequence similarity search, such as k-mers (k-tuples, k-grams, oligos of length k) distributions. We find that when k=6 or larger, the k-mer approach and BLAST search produce almost identical results, e.g., detect the same set of NUMTs longer than 3 kb. However, when k=5 or k=4, certain signals are only detected by the alignment-free approach, and these may indicate yet unrecognized, and potentially more ancestral NUMTs. We introduce a "Manhattan plot" style representation of NUMT predictions across the genome, which are calculated based on the reciprocal of the Jensen-Shannon divergence between the nuclear and mitochondrial k-mer frequencies. The further inspection of the k-mer-based NUMT predictions however shows that most of them contain long-terminal-repeat (LTR) annotations, whereas BLAST-based NUMT predictions do not. Thus, similarity of the mitogenome to LTR sequences is recognized, which we validate by finding the mitochondrial k-mer distribution closer to those for transposable sequences and specifically, close to some types of LTR.
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Affiliation(s)
- Wentian Li
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, USA.
| | - Jerome Freudenberg
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Jan Freudenberg
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
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49
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Tang K, Ren J, Cronn R, Erickson DL, Milligan BG, Parker-Forney M, Spouge JL, Sun F. Alignment-free genome comparison enables accurate geographic sourcing of white oak DNA. BMC Genomics 2018; 19:896. [PMID: 30526482 PMCID: PMC6288960 DOI: 10.1186/s12864-018-5253-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 11/15/2018] [Indexed: 01/14/2023] Open
Abstract
Background The application of genomic data and bioinformatics for the identification of restricted or illegally-sourced natural products is urgently needed. The taxonomic identity and geographic provenance of raw and processed materials have implications in sustainable-use commercial practices, and relevance to the enforcement of laws that regulate or restrict illegally harvested materials, such as timber. Improvements in genomics make it possible to capture and sequence partial-to-complete genomes from challenging tissues, such as wood and wood products. Results In this paper, we report the success of an alignment-free genome comparison method, \documentclass[12pt]{minimal}
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\begin{document}$$ {d}_2^{\ast }, $$\end{document}d2∗, that differentiates different geographic sources of white oak (Quercus) species with a high level of accuracy with very small amount of genomic data. The method is robust to sequencing errors, different sequencing laboratories and sequencing platforms. Conclusions This method offers an approach based on genome-scale data, rather than panels of pre-selected markers for specific taxa. The method provides a generalizable platform for the identification and sourcing of materials using a unified next generation sequencing and analysis framework. Electronic supplementary material The online version of this article (10.1186/s12864-018-5253-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kujin Tang
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jie Ren
- Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
| | - Richard Cronn
- Pacific Northwest Research Station, USDA Forest Service, Corvallis, OR, 97331, USA.
| | - David L Erickson
- DNA4 Technologies LLC, bwtech@UMBC Research & Technology Park, Baltimore, MD, 21227, USA
| | - Brook G Milligan
- Conservation Genomics Laboratory, Department of Biology, New Mexico State University, Las Cruces, NM, 88003, USA
| | | | - John L Spouge
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Fengzhu Sun
- 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.
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50
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Abstract
Background Spaced-seeds, i.e. patterns in which some fixed positions are allowed to be wild-cards, play a crucial role in several bioinformatics applications involving substrings counting and indexing, by often providing better sensitivity with respect to k-mers based approaches. K-mers based approaches are usually fast, being based on efficient hashing and indexing that exploits the large overlap between consecutive k-mers. Spaced-seeds hashing is not as straightforward, and it is usually computed from scratch for each position in the input sequence. Recently, the FSH (Fast Spaced seed Hashing) approach was proposed to improve the time required for computation of the spaced seed hashing of DNA sequences with a speed-up of about 1.5 with respect to standard hashing computation. Results In this work we propose a novel algorithm, Fast Indexing for Spaced seed Hashing (FISH), based on the indexing of small blocks that can be combined to obtain the hashing of spaced-seeds of any length. The method exploits the fast computation of the hashing of runs of consecutive 1 in the spaced seeds, that basically correspond to k-mer of the length of the run. Conclusions We run several experiments, on NGS data from simulated and synthetic metagenomic experiments, to assess the time required for the computation of the hashing for each position in each read with respect to several spaced seeds. In our experiments, FISH can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.9x to 6.03x, depending on the structure of the spaced seeds. Electronic supplementary material The online version of this article (10.1186/s12859-018-2415-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Samuele Girotto
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy
| | - Matteo Comin
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy.
| | - Cinzia Pizzi
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy.
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