1
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Santoni D. An entropy-based study on the mutational landscape of SARS-CoV-2 in USA: Comparing different variants and revealing co-mutational behavior of proteins. Gene 2024; 922:148556. [PMID: 38754568 DOI: 10.1016/j.gene.2024.148556] [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: 02/21/2024] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/18/2024]
Abstract
COVID-19 emergency has pushed the international scientific community to use every resource to combat the spread of the virus, to understand its biology and predict its possible evolution in terms of new variants. Since the first SARS-CoV-2 virus nucleotide and amino acid sequences were made available, information theory was used to study how viral information content was changing over time and then trace the evolution of its mutational landscape. In this work we analyzed SARS-CoV-2 sequences collected mainly in the USA in a period from March 2020 until December 2022 and computed mutation profiles of viral proteins over time through an entropy-based approach using Shannon Entropy and Hellinger distance. This representation allows an at-a-glance view of the mutational landscape of viral proteins over time and can provide new insights on the evolution of the virus from different points of view. Non-structural proteins typically showed flat mutation profiles, characterized by a very low Average mutation Entropy, while accessory and structural proteins showed mostly non uniform and high mutation profiles, often coupled with the predominance of variants. Interestingly NSP2 protein, whose function is currently still debated, falls in the same branch of NSP14 and NSP10 in the phylogenetic tree of mutations constructed through correlations of mutation profiles, suggesting a co-evolution of those proteins and a possible functional link with each other. To the best of our knowledge this is the first study based on a massive amount of data (n = 107,939,973) that analyzes from an entropy point of view the mutational landscape of SARS-CoV-2 over time and depicts a mutational temporal profile of each protein of the virus.
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Affiliation(s)
- Daniele Santoni
- Institute for System Analysis and Computer Science "Antonio Ruberti", National Research Council of Italy, Via dei Taurini 19, Rome 00185, Italy.
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2
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Bajić D. Information Theory, Living Systems, and Communication Engineering. ENTROPY (BASEL, SWITZERLAND) 2024; 26:430. [PMID: 38785679 PMCID: PMC11120474 DOI: 10.3390/e26050430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/08/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
Mainstream research on information theory within the field of living systems involves the application of analytical tools to understand a broad range of life processes. This paper is dedicated to an opposite problem: it explores the information theory and communication engineering methods that have counterparts in the data transmission process by way of DNA structures and neural fibers. Considering the requirements of modern multimedia, transmission methods chosen by nature may be different, suboptimal, or even far from optimal. However, nature is known for rational resource usage, so its methods have a significant advantage: they are proven to be sustainable. Perhaps understanding the engineering aspects of methods of nature can inspire a design of alternative green, stable, and low-cost transmission.
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Affiliation(s)
- Dragana Bajić
- Department of Communications and Signal Processing, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
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3
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Nawaz MS, Fournier-Viger P, Nawaz S, Zhu H, Yun U. SPM4GAC: SPM based approach for genome analysis and classification of macromolecules. Int J Biol Macromol 2024; 266:130984. [PMID: 38513910 DOI: 10.1016/j.ijbiomac.2024.130984] [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: 01/29/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
Genome sequence analysis and classification play critical roles in properly understanding an organism's main characteristics, functionalities, and changing (evolving) nature. However, the rapid expansion of genomic data makes genome sequence analysis and classification a challenging task due to the high computational requirements, proper management, and understanding of genomic data. Recently proposed models yielded promising results for the task of genome sequence classification. Nevertheless, these models often ignore the sequential nature of nucleotides, which is crucial for revealing their underlying structure and function. To address this limitation, we present SPM4GAC, a sequential pattern mining (SPM)-based framework to analyze and classify the macromolecule genome sequences of viruses. First, a large dataset containing the genome sequences of various RNA viruses is developed and transformed into a suitable format. On the transformed dataset, algorithms for SPM are used to identify frequent sequential patterns of nucleotide bases. The obtained frequent sequential patterns of bases are then used as features to classify different viruses. Ten classifiers are employed, and their performance is assessed by using several evaluation measures. Finally, a performance comparison of SPM4GAC with state-of-the-art methods for genome sequence classification/detection reveals that SPM4GAC performs better than those methods.
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Affiliation(s)
- M Saqib Nawaz
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
| | | | - Shoaib Nawaz
- Department of Pharmacy, The University of Lahore, Sargodha Campus, Pakistan.
| | - Haowei Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
| | - Unil Yun
- Sejong University, Seoul, Republic of Korea.
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4
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Wang T, Yu ZG, Li J. CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model. Front Microbiol 2024; 15:1339156. [PMID: 38572227 PMCID: PMC10987876 DOI: 10.3389/fmicb.2024.1339156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/23/2024] [Indexed: 04/05/2024] Open
Abstract
Traditional alignment-based methods meet serious challenges in genome sequence comparison and phylogeny reconstruction due to their high computational complexity. Here, we propose a new alignment-free method to analyze the phylogenetic relationships (classification) among species. In our method, the dynamical language (DL) model and the chaos game representation (CGR) method are used to characterize the frequency information and the context information of k-mers in a sequence, respectively. Then for each DNA sequence or protein sequence in a dataset, our method converts the sequence into a feature vector that represents the sequence information based on CGR weighted by the DL model to infer phylogenetic relationships. We name our method CGRWDL. Its performance was tested on both DNA and protein sequences of 8 datasets of viruses to construct the phylogenetic trees. We compared the Robinson-Foulds (RF) distance between the phylogenetic tree constructed by CGRWDL and the reference tree by other advanced methods for each dataset. The results show that the phylogenetic trees constructed by CGRWDL can accurately classify the viruses, and the RF scores between the trees and the reference trees are smaller than that with other methods.
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Affiliation(s)
- Ting Wang
- National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan, Hunan, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan, China
| | - Zu-Guo Yu
- National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan, Hunan, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan, China
| | - Jinyan Li
- School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
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5
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Formentin M, Chignola R, Favretti M. Optimal entropic properties of SARS-CoV-2 RNA sequences. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231369. [PMID: 38298394 PMCID: PMC10827432 DOI: 10.1098/rsos.231369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/02/2024] [Indexed: 02/02/2024]
Abstract
The reaction of the scientific community against the COVID-19 pandemic has generated a huge (approx. 106 entries) dataset of genome sequences collected worldwide and spanning a relatively short time window. These unprecedented conditions together with the certain identification of the reference viral genome sequence allow for an original statistical study of mutations in the virus genome. In this paper, we compute the Shannon entropy of every sequence in the dataset as well as the relative entropy and the mutual information between the reference sequence and the mutated ones. These functions, originally developed in information theory, measure the information content of a sequence and allows us to study the random character of mutation mechanism in terms of its entropy and information gain or loss. We show that this approach allows us to set in new format known features of the SARS-CoV-2 mutation mechanism like the CT bias, but also to discover new optimal entropic properties of the mutation process in the sense that the virus mutation mechanism track closely theoretically computable lower bounds for the entropy decrease and the information transfer.
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Affiliation(s)
- Marco Formentin
- Department of Mathematics Tullio Levi-Civita, University of Padova, via Trieste 63 35131 Padova, Italy
| | - Roberto Chignola
- Department of Biotechnology, University of Verona, Strada le Grazie 15-CV1, 37134 Verona, Italy
| | - Marco Favretti
- Department of Mathematics Tullio Levi-Civita, University of Padova, via Trieste 63 35131 Padova, Italy
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6
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Chen W, Li W. Application of Feature Definition and Quantification in Biological Sequence Analysis. Curr Genomics 2023; 24:64-65. [PMID: 37994326 PMCID: PMC10662379 DOI: 10.2174/1389202924666230816150732] [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: 04/24/2023] [Revised: 06/01/2023] [Accepted: 07/25/2023] [Indexed: 11/24/2023] Open
Abstract
Biological sequence analysis is the most fundamental work in bioinformatics. Many research methods have been developed in the development of biological sequence analysis. These methods include sequence alignment-based methods and alignment-free methods. In addition, there are also some sequence analysis methods based on the feature definition and quantification of the sequence itself. This editorial introduces the methods of biological sequence analysis and explores the significance of defining features and quantitative research of biological sequences.
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Affiliation(s)
- Weiyang Chen
- School of Cyber Science and Engineering, Qufu Normal University, Qufu, China
| | - Weiwei Li
- Qilu Institute of Technology, Shandong, China
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7
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Boughter CT, Meier-Schellersheim M. Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions. eLife 2023; 12:e90681. [PMID: 37861280 PMCID: PMC10631762 DOI: 10.7554/elife.90681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/19/2023] [Indexed: 10/21/2023] Open
Abstract
T cells are critically important components of the adaptive immune system primarily responsible for identifying and responding to pathogenic challenges. This recognition of pathogens is driven by the interaction between membrane-bound T cell receptors (TCRs) and antigenic peptides presented on major histocompatibility complex (MHC) molecules. The formation of the TCR-peptide-MHC complex (TCR-pMHC) involves interactions among germline-encoded and hypervariable amino acids. Germline-encoded and hypervariable regions can form contacts critical for complex formation, but only interactions between germline-encoded contacts are likely to be shared across many of all the possible productive TCR-pMHC complexes. Despite this, experimental investigation of these interactions have focused on only a small fraction of the possible interaction space. To address this, we analyzed every possible germline-encoded TCR-MHC contact in humans, thereby generating the first comprehensive characterization of these largely antigen-independent interactions. Our computational analysis suggests that germline-encoded TCR-MHC interactions that are conserved at the sequence level are rare due to the high amino acid diversity of the TCR CDR1 and CDR2 loops, and that such conservation is unlikely to dominate the dynamic protein-protein binding interface. Instead, we propose that binding properties such as the docking orientation are defined by regions of biophysical compatibility between these loops and the MHC surface.
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Affiliation(s)
- Christopher T Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of HealthBethesdaUnited States
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of HealthBethesdaUnited States
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8
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Orlov YL, Orlova NG. Bioinformatics tools for the sequence complexity estimates. Biophys Rev 2023; 15:1367-1378. [PMID: 37974990 PMCID: PMC10643780 DOI: 10.1007/s12551-023-01140-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 09/01/2023] [Indexed: 11/19/2023] Open
Abstract
We review current methods and bioinformatics tools for the text complexity estimates (information and entropy measures). The search DNA regions with extreme statistical characteristics such as low complexity regions are important for biophysical models of chromosome function and gene transcription regulation in genome scale. We discuss the complexity profiling for segmentation and delineation of genome sequences, search for genome repeats and transposable elements, and applications to next-generation sequencing reads. We review the complexity methods and new applications fields: analysis of mutation hotspots loci, analysis of short sequencing reads with quality control, and alignment-free genome comparisons. The algorithms implementing various numerical measures of text complexity estimates including combinatorial and linguistic measures have been developed before genome sequencing era. The series of tools to estimate sequence complexity use compression approaches, mainly by modification of Lempel-Ziv compression. Most of the tools are available online providing large-scale service for whole genome analysis. Novel machine learning applications for classification of complete genome sequences also include sequence compression and complexity algorithms. We present comparison of the complexity methods on the different sequence sets, the applications for gene transcription regulatory regions analysis. Furthermore, we discuss approaches and application of sequence complexity for proteins. The complexity measures for amino acid sequences could be calculated by the same entropy and compression-based algorithms. But the functional and evolutionary roles of low complexity regions in protein have specific features differing from DNA. The tools for protein sequence complexity aimed for protein structural constraints. It was shown that low complexity regions in protein sequences are conservative in evolution and have important biological and structural functions. Finally, we summarize recent findings in large scale genome complexity comparison and applications for coronavirus genome analysis.
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Affiliation(s)
- Yuriy L. Orlov
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow, 119991 Russia
- Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, 117198 Moscow, Russia
| | - Nina G. Orlova
- Department of Mathematics, Financial University under the Government of the Russian Federation, Moscow, 125167 Russia
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9
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Lainscsek X, Taher L. Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA. Brief Bioinform 2023; 24:bbad198. [PMID: 37264486 PMCID: PMC10359093 DOI: 10.1093/bib/bbad198] [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: 11/16/2022] [Revised: 04/18/2023] [Accepted: 05/08/2023] [Indexed: 06/03/2023] Open
Abstract
Three-dimensional (3D) genome architecture is characterized by multi-scale patterns and plays an essential role in gene regulation. Chromatin conformation capturing experiments have revealed many properties underlying 3D genome architecture, such as the compartmentalization of chromatin based on transcriptional states. However, they are complex, costly and time consuming, and therefore only a limited number of cell types have been examined using these techniques. Increasing effort is being directed towards deriving computational methods that can predict chromatin conformation and associated structures. Here we present DNA-delay differential analysis (DDA), a purely sequence-based method based on chaos theory to predict genome-wide A and B compartments. We show that DNA-DDA models derived from a 20 Mb sequence are sufficient to predict genome wide compartmentalization at the scale of 100 kb in four different cell types. Although this is a proof-of-concept study, our method shows promise in elucidating the mechanisms responsible for genome folding as well as modeling the impact of genetic variation on 3D genome architecture and the processes regulated thereby.
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Affiliation(s)
- Xenia Lainscsek
- Institute of Biomedical Informatics, Graz University of Technology, Austria
| | - Leila Taher
- Institute of Biomedical Informatics, Graz University of Technology, Austria
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10
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de la Fuente R, Díaz-Villanueva W, Arnau V, Moya A. Genomic Signature in Evolutionary Biology: A Review. BIOLOGY 2023; 12:biology12020322. [PMID: 36829597 PMCID: PMC9953303 DOI: 10.3390/biology12020322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/19/2023]
Abstract
Organisms are unique physical entities in which information is stored and continuously processed. The digital nature of DNA sequences enables the construction of a dynamic information reservoir. However, the distinction between the hardware and software components in the information flow is crucial to identify the mechanisms generating specific genomic signatures. In this work, we perform a bibliometric analysis to identify the different purposes of looking for particular patterns in DNA sequences associated with a given phenotype. This study has enabled us to make a conceptual breakdown of the genomic signature and differentiate the leading applications. On the one hand, it refers to gene expression profiling associated with a biological function, which may be shared across taxa. This signature is the focus of study in precision medicine. On the other hand, it also refers to characteristic patterns in species-specific DNA sequences. This interpretation plays a key role in comparative genomics, identifying evolutionary relationships. Looking at the relevant studies in our bibliographic database, we highlight the main factors causing heterogeneities in genome composition and how they can be quantified. All these findings lead us to reformulate some questions relevant to evolutionary biology.
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Affiliation(s)
- Rebeca de la Fuente
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
- Correspondence:
| | - Wladimiro Díaz-Villanueva
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
| | - Vicente Arnau
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
| | - Andrés Moya
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
- Foundation for the Promotion of Sanitary and Biomedical Research of the Valencian Community (FISABIO), 46020 Valencia, Spain
- CIBER in Epidemiology and Public Health (CIBEResp), 28029 Madrid, Spain
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11
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Dey S, Das S, Bhattacharya DK. Biochemical Property Based Positional Matrix: A New Approach Towards Genome Sequence Comparison. J Mol Evol 2023; 91:93-131. [PMID: 36587178 PMCID: PMC9805373 DOI: 10.1007/s00239-022-10082-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 12/01/2022] [Indexed: 01/01/2023]
Abstract
The growth of the genome sequence has become one of the emerging areas in the study of bioinformatics. It has led to an excessive demand for researchers to develop advanced methodologies for evolutionary relationships among species. The alignment-free methods have been proved to be more efficient and appropriate related to time and space than existing alignment-based methods for sequence analysis. In this study, a new alignment-free genome sequence comparison technique is proposed based on the biochemical properties of nucleotides. Each genome sequence can be distributed in four parameters to represent a 21-dimensional numerical descriptor using the Positional Matrix. To substantiate the proposed method, phylogenetic trees are constructed on the viral and mammalian datasets by applying the UPGMA/NJ clustering method. Further, the results of this method are compared with the results of the Feature Frequency Profiles method, the Positional Correlation Natural Vector method, the Graph-theoretic method, the Multiple Encoding Vector method, and the Fuzzy Integral Similarity method. In most cases, it is found that the present method produces more accurate results than the prior methods. Also, in the present method, the execution time for computation is comparatively small.
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Affiliation(s)
- Sudeshna Dey
- grid.440742.10000 0004 1799 6713Computer Science and Engineering, Narula Institute of Technology, Kolkata, 700109 India
| | - Subhram Das
- grid.440742.10000 0004 1799 6713Computer Science and Engineering, Narula Institute of Technology, Kolkata, 700109 India
| | - D. K. Bhattacharya
- grid.59056.3f0000 0001 0664 9773Pure Mathematics, Calcutta University, Kolkata, 700019 India
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12
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Bonidia RP, Avila Santos AP, de Almeida BLS, Stadler PF, Nunes da Rocha U, Sanches DS, de Carvalho ACPLF. Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1398. [PMID: 37420418 DOI: 10.3390/e24101398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/16/2022] [Accepted: 09/24/2022] [Indexed: 07/09/2023]
Abstract
In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.
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Affiliation(s)
- Robson P Bonidia
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil
| | - Anderson P Avila Santos
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ GmbH, 04318 Leipzig, Germany
| | - Breno L S de Almeida
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil
| | - Peter F Stadler
- Department of Computer Science and Interdisciplinary Center of Bioinformatics, University of Leipzig, 04107 Leipzig, Germany
| | - Ulisses Nunes da Rocha
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ GmbH, 04318 Leipzig, Germany
| | - Danilo S Sanches
- Department of Computer Science, Federal University of Technology-Paraná-UTFPR, Cornélio Procópio 86300-000, Brazil
| | - André C P L F de Carvalho
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil
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13
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Abstract
The human genome carries a vast amount of information within its DNA sequences. The chemical bases A, T, C, and G are the basic units of information content, that are arranged into patterns and codes. Expansive areas of the genome contain codes that are not yet well understood. To decipher these, mathematical and computational tools are applied here to study genomic signatures or general designs of sequences. A novel binary components analysis is devised and utilized. This seeks to isolate the physical and chemical properties of DNA bases, which reveals sequence design and function. Here, information theory tools break down the information content within DNA bases, in order to study them in isolation for their genomic signatures and non-random properties. In this way, the RY (purine/pyrimidine), WS (weak/strong), and KM (keto/amino) general designs are observed in the sequences. The results show that RY, KM, and WS components have a similar and stable overall profile across all human chromosomes. It reveals that the RY property of a sequence is most distant from randomness in the human genome with respect to the genomic signatures. This is true across all human chromosomes. It is concluded that there exists a widespread potential RY code, and furthermore, that this is likely a structural code. Ascertaining this feature of general design, and potential RY structural code has far-reaching implications. This is because it aids in the understanding of cell biology, growth, and development, as well as downstream in the study of human disease and potential drug design.
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14
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Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics. ENTROPY 2022; 24:e24070925. [PMID: 35885148 PMCID: PMC9323281 DOI: 10.3390/e24070925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022]
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15
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Sun N, Zhao X, Yau SST. An efficient numerical representation of genome sequence: natural vector with covariance component. PeerJ 2022; 10:e13544. [PMID: 35729905 PMCID: PMC9206847 DOI: 10.7717/peerj.13544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/16/2022] [Indexed: 01/17/2023] Open
Abstract
Background The characterization and comparison of microbial sequences, including archaea, bacteria, viruses and fungi, are very important to understand their evolutionary origin and the population relationship. Most methods are limited by the sequence length and lack of generality. The purpose of this study is to propose a general characterization method, and to study the classification and phylogeny of the existing datasets. Methods We present a new alignment-free method to represent and compare biological sequences. By adding the covariance between each two nucleotides, the new 18-dimensional natural vector successfully describes 24,250 genomic sequences and 95,542 DNA barcode sequences. The new numerical representation is used to study the classification and phylogenetic relationship of microbial sequences. Results First, the classification results validate that the six-dimensional covariance vector is necessary to characterize sequences. Then, the 18-dimensional natural vector is further used to conduct the similarity relationship between giant virus and archaea, bacteria, other viruses. The nearest distance calculation results reflect that the giant viruses are closer to bacteria in distribution of four nucleotides. The phylogenetic relationships of the three representative families, Mimiviridae, Pandoraviridae and Marsellieviridae from giant viruses are analyzed. The trees show that ten sequences of Mimiviridae are clustered with Pandoraviridae, and Mimiviridae is closer to the root of the tree than Marsellieviridae. The new developed alignment-free method can be computed very fast, which provides an effective numerical representation for the sequence of microorganisms.
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Affiliation(s)
- Nan Sun
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Xin Zhao
- Beijing Electronic Science and Technology Institute, Beijing, China
| | - Stephen S.-T. Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing, China,Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China
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16
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Zandavi SM, Koch FC, Vijayan A, Zanini F, Mora F, Ortega D, Vafaee F. Disentangling single-cell omics representation with a power spectral density-based feature extraction. Nucleic Acids Res 2022; 50:5482-5492. [PMID: 35639509 PMCID: PMC9178020 DOI: 10.1093/nar/gkac436] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 04/26/2022] [Accepted: 05/10/2022] [Indexed: 12/13/2022] Open
Abstract
Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obscure discriminatory information, mask cellular subtype variations and complicate downstream analyses which can limit our understanding of cell function and tissue heterogeneity. Here, we present a novel pre-processing method (scPSD) inspired by power spectral density analysis that enhances the accuracy for cell subtype separation from large-scale single-cell omics data. We comprehensively benchmarked our method on a wide range of single-cell RNA-sequencing datasets and showed that scPSD pre-processing, while being fast and scalable, significantly reduces data complexity, enhances cell-type separation, and enables rare cell identification. Additionally, we applied scPSD to transcriptomics and chromatin accessibility cell atlases and demonstrated its capacity to discriminate over 100 cell types across the whole organism and across different modalities of single-cell omics data.
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Affiliation(s)
- Seid Miad Zandavi
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Forrest C Koch
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia
| | - Abhishek Vijayan
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia
| | - Fabio Zanini
- Prince of Wales Clinical School, UNSW Sydney, Australia
- Cellular Genomics Future Institute, UNSW Sydney, Australia
| | - Fatima Valdes Mora
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Australia
- School of Women's and Children's Health, Faculty of Medicine, UNSW, Sydney, Australia
| | - David Gallego Ortega
- School of Biomedical Engineering, University of Technology Sydney (UTS), Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia
- Cellular Genomics Future Institute, UNSW Sydney, Australia
- UNSW Data Science Hub (uDASH), UNSW Sydney, Australia
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17
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Barker TS, Pierobon M, Thomas PJ. Subjective Information and Survival in a Simulated Biological System. ENTROPY 2022; 24:e24050639. [PMID: 35626524 PMCID: PMC9142001 DOI: 10.3390/e24050639] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 03/25/2022] [Accepted: 04/25/2022] [Indexed: 02/01/2023]
Abstract
Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a “subjective information”, i.e., trade-off between a living system’s capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival.
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Affiliation(s)
- Tyler S. Barker
- School of Computing, College of Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
| | - Massimiliano Pierobon
- School of Computing, College of Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
- Correspondence:
| | - Peter J. Thomas
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH 44106, USA;
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18
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Bohnsack KS, Kaden M, Abel J, Saralajew S, Villmann T. The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1357. [PMID: 34682081 PMCID: PMC8534762 DOI: 10.3390/e23101357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/11/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022]
Abstract
In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.
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Affiliation(s)
- Katrin Sophie Bohnsack
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
| | - Marika Kaden
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
| | - Julia Abel
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
| | - Sascha Saralajew
- Bosch Center for Artificial Intelligence, 71272 Renningen, Germany;
| | - Thomas Villmann
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
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19
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Jin Y, Jiang J, Wang R, Qin ZS. Systematic Evaluation of DNA Sequence Variations on in vivo Transcription Factor Binding Affinity. Front Genet 2021; 12:667866. [PMID: 34567058 PMCID: PMC8458901 DOI: 10.3389/fgene.2021.667866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/02/2021] [Indexed: 02/01/2023] Open
Abstract
The majority of the single nucleotide variants (SNVs) identified by genome-wide association studies (GWAS) fall outside of the protein-coding regions. Elucidating the functional implications of these variants has been a major challenge. A possible mechanism for functional non-coding variants is that they disrupted the canonical transcription factor (TF) binding sites that affect the in vivo binding of the TF. However, their impact varies since many positions within a TF binding motif are not well conserved. Therefore, simply annotating all variants located in putative TF binding sites may overestimate the functional impact of these SNVs. We conducted a comprehensive survey to study the effect of SNVs on the TF binding affinity. A sequence-based machine learning method was used to estimate the change in binding affinity for each SNV located inside a putative motif site. From the results obtained on 18 TF binding motifs, we found that there is a substantial variation in terms of a SNV’s impact on TF binding affinity. We found that only about 20% of SNVs located inside putative TF binding sites would likely to have significant impact on the TF-DNA binding.
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Affiliation(s)
- Yutong Jin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Jiahui Jiang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Ruixuan Wang
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
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20
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Ricci L, Perinelli A, Castelluzzo M. Estimating the variance of Shannon entropy. Phys Rev E 2021; 104:024220. [PMID: 34525589 DOI: 10.1103/physreve.104.024220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 08/10/2021] [Indexed: 11/07/2022]
Abstract
The statistical analysis of data stemming from dynamical systems, including, but not limited to, time series, routinely relies on the estimation of information theoretical quantities, most notably Shannon entropy. To this purpose, possibly the most widespread tool is provided by the so-called plug-in estimator, whose statistical properties in terms of bias and variance were investigated since the first decade after the publication of Shannon's seminal works. In the case of an underlying multinomial distribution, while the bias can be evaluated by knowing support and data set size, variance is far more elusive. The aim of the present work is to investigate, in the multinomial case, the statistical properties of an estimator of a parameter that describes the variance of the plug-in estimator of Shannon entropy. We then exactly determine the probability distributions that maximize that parameter. The results presented here allow one to set upper limits to the uncertainty of entropy assessments under the hypothesis of memoryless underlying stochastic processes.
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Affiliation(s)
- Leonardo Ricci
- Department of Physics, University of Trento, 38123 Trento, Italy.,CIMeC, Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
| | - Alessio Perinelli
- CIMeC, Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
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21
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VanWallendael A, Alvarez M. Alignment-free methods for polyploid genomes: Quick and reliable genetic distance estimation. Mol Ecol Resour 2021; 22:612-622. [PMID: 34478242 DOI: 10.1111/1755-0998.13499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 08/20/2021] [Indexed: 01/10/2023]
Abstract
Polyploid genomes pose several inherent challenges to population genetic analyses. While alignment-based methods are fundamentally limited in their applicability to polyploids, alignment-free methods bypass most of these limits. We investigated the use of Mash, a k-mer analysis tool that uses the MinHash method to reduce complexity in large genomic data sets, for basic population genetic analyses of polyploid sequences. We measured the degree to which Mash correctly estimated pairwise genetic distance in simulated haploid and polyploid short-read sequences with various levels of missing data. Mash-based estimates of genetic distance were comparable to alignment-based estimates, and were less impacted by missing data. We also used Mash to analyse publicly available short-read data for three polyploid and one diploid species, then compared Mash results to published results. For both simulated and real data, Mash accurately estimated pairwise genetic differences for polyploids as well as diploids as much as 476 times faster than alignment-based methods, though we found that Mash genetic distance estimates could be biased by per-sample read depth. Mash may be a particularly useful addition to the toolkit of polyploid geneticists for rapid confirmation of alignment-based results and for basic population genetics in reference-free systems or those with only poor-quality sequence data available.
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Affiliation(s)
- Acer VanWallendael
- Department of Plant Biology, Michigan State University, East Lansing, MI, USA
| | - Mariano Alvarez
- Biology Department, Wesleyan University, Middletown, CT, USA
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22
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López-Cortegano E, Craig RJ, Chebib J, Samuels T, Morgan AD, Kraemer SA, Böndel KB, Ness RW, Colegrave N, Keightley PD. De Novo Mutation Rate Variation and Its Determinants in Chlamydomonas. Mol Biol Evol 2021; 38:3709-3723. [PMID: 33950243 PMCID: PMC8383909 DOI: 10.1093/molbev/msab140] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
De novo mutations are central for evolution, since they provide the raw material for natural selection by regenerating genetic variation. However, studying de novo mutations is challenging and is generally restricted to model species, so we have a limited understanding of the evolution of the mutation rate and spectrum between closely related species. Here, we present a mutation accumulation (MA) experiment to study de novo mutation in the unicellular green alga Chlamydomonas incerta and perform comparative analyses with its closest known relative, Chlamydomonas reinhardtii. Using whole-genome sequencing data, we estimate that the median single nucleotide mutation (SNM) rate in C. incerta is μ = 7.6 × 10-10, and is highly variable between MA lines, ranging from μ = 0.35 × 10-10 to μ = 131.7 × 10-10. The SNM rate is strongly positively correlated with the mutation rate for insertions and deletions between lines (r > 0.97). We infer that the genomic factors associated with variation in the mutation rate are similar to those in C. reinhardtii, allowing for cross-prediction between species. Among these genomic factors, sequence context and complexity are more important than GC content. With the exception of a remarkably high C→T bias, the SNM spectrum differs markedly between the two Chlamydomonas species. Our results suggest that similar genomic and biological characteristics may result in a similar mutation rate in the two species, whereas the SNM spectrum has more freedom to diverge.
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Affiliation(s)
- Eugenio López-Cortegano
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Rory J Craig
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Jobran Chebib
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Toby Samuels
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew D Morgan
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Katharina B Böndel
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Rob W Ness
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Nick Colegrave
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Peter D Keightley
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
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23
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An Information-theoretic approach to dimensionality reduction in data science. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2021. [DOI: 10.1007/s41060-021-00272-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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24
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CVTree: A Parallel Alignment-free Phylogeny and Taxonomy Tool based on Composition Vectors of Genomes. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:662-667. [PMID: 34119695 PMCID: PMC9040009 DOI: 10.1016/j.gpb.2021.03.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 02/23/2021] [Accepted: 03/06/2021] [Indexed: 11/21/2022]
Abstract
CVTree is an alignment-free algorithm to infer phylogenetic relationships from genome sequences. It had been successfully applied to study phylogeny and taxonomy of viruses, prokaryotes, and fungi based on the whole genomes, as well as chloroplasts, mitochondria, and metagenomes. Here we presented the standalone software for the CVTree algorithm. In the software, an extensible parallel workflow for the CVTree algorithm was designed. Based on the workflow, new alignment-free methods were also implemented. And by examining the phylogeny and taxonomy of 13,903 prokaryotes based on 16S rRNA sequences, we showed that CVTree software is an efficient and effective tool for the studying of phylogeny and taxonomy based on genome sequences. Code availability: https://github.com/ghzuo/cvtree.
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25
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Kaden M, Bohnsack KS, Weber M, Kudła M, Gutowska K, Blazewicz J, Villmann T. Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences. Neural Comput Appl 2021; 34:67-78. [PMID: 33935376 PMCID: PMC8076884 DOI: 10.1007/s00521-021-06018-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 04/07/2021] [Indexed: 02/06/2023]
Abstract
We present an approach to discriminate SARS-CoV-2 virus types based on their RNA sequence descriptions avoiding a sequence alignment. For that purpose, sequences are preprocessed by feature extraction and the resulting feature vectors are analyzed by prototype-based classification to remain interpretable. In particular, we propose to use variants of learning vector quantization (LVQ) based on dissimilarity measures for RNA sequence data. The respective matrix LVQ provides additional knowledge about the classification decisions like discriminant feature correlations and, additionally, can be equipped with easy to realize reject options for uncertain data. Those options provide self-controlled evidence, i.e., the model refuses to make a classification decision if the model evidence for the presented data is not sufficient. This model is first trained using a GISAID dataset with given virus types detected according to the molecular differences in coronavirus populations by phylogenetic tree clustering. In a second step, we apply the trained model to another but unlabeled SARS-CoV-2 virus dataset. For these data, we can either assign a virus type to the sequences or reject atypical samples. Those rejected sequences allow to speculate about new virus types with respect to nucleotide base mutations in the viral sequences. Moreover, this rejection analysis improves model robustness. Last but not least, the presented approach has lower computational complexity compared to methods based on (multiple) sequence alignment. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00521-021-06018-2.
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Affiliation(s)
- Marika Kaden
- University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany
- Saxon Institute for Computational Intelligence and Machine Learning, Technikumplatz 17, 09648 Mittweida, Germany
| | - Katrin Sophie Bohnsack
- University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany
- Saxon Institute for Computational Intelligence and Machine Learning, Technikumplatz 17, 09648 Mittweida, Germany
| | - Mirko Weber
- University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany
- Saxon Institute for Computational Intelligence and Machine Learning, Technikumplatz 17, 09648 Mittweida, Germany
| | - Mateusz Kudła
- University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Kaja Gutowska
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- European Centre for Bioinformatics and Genomics, Piotrowo 2, 60-965 Poznan, Poland
| | - Jacek Blazewicz
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
- European Centre for Bioinformatics and Genomics, Piotrowo 2, 60-965 Poznan, Poland
| | - Thomas Villmann
- University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany
- Saxon Institute for Computational Intelligence and Machine Learning, Technikumplatz 17, 09648 Mittweida, Germany
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26
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Sy P, Nagaraj N. Causal discovery using compression-complexity measures. J Biomed Inform 2021; 117:103724. [PMID: 33722730 DOI: 10.1016/j.jbi.2021.103724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 12/23/2020] [Accepted: 02/22/2021] [Indexed: 12/30/2022]
Abstract
Causal inference is one of the most fundamental problems across all domains of science. We address the problem of inferring a causal direction from two observed discrete symbolic sequences X and Y. We present a framework which relies on lossless compressors for inferring context-free grammars (CFGs) from sequence pairs and quantifies the extent to which the grammar inferred from one sequence compresses the other sequence. We infer X causes Y if the grammar inferred from X better compresses Y than in the other direction. To put this notion to practice, we propose three models that use the Compression-Complexity Measures (CCMs) - Lempel-Ziv (LZ) complexity and Effort-To-Compress (ETC) to infer CFGs and discover causal directions without demanding temporal structures. We evaluate these models on synthetic and real-world benchmarks and empirically observe performances competitive with current state-of-the-art methods. Lastly, we present two unique applications of the proposed models for causal inference directly from pairs of genome sequences belonging to the SARS-CoV-2 virus. Using numerous sequences, we show that our models capture causal information exchanged between genome sequence pairs, presenting novel opportunities for addressing key issues in sequence analysis to investigate the evolution of virulence and pathogenicity in future applications.
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Affiliation(s)
- Pranay Sy
- Consciousness Studies Programme, National Institute of Advanced Studies, Bengaluru, India.
| | - Nithin Nagaraj
- Consciousness Studies Programme, National Institute of Advanced Studies, Bengaluru, India.
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27
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Tan Y, Schneider T, Shukla PK, Chandrasekharan MB, Aravind L, Zhang D. Unification and extensive diversification of M/Orf3-related ion channel proteins in coronaviruses and other nidoviruses. Virus Evol 2021; 7:veab014. [PMID: 33692906 PMCID: PMC7928690 DOI: 10.1093/ve/veab014] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The coronavirus, Severe Acute Respiratory Syndrome (SARS)-CoV-2, responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic, has emphasized the need for a better understanding of the evolution of virus-host interactions. ORF3a in both SARS-CoV-1 and SARS-CoV-2 are ion channels (viroporins) implicated in virion assembly and membrane budding. Using sensitive profile-based homology detection methods, we unify the SARS-CoV ORF3a family with several families of viral proteins, including ORF5 from MERS-CoVs, proteins from beta-CoVs (ORF3c), alpha-CoVs (ORF3b), most importantly, the Matrix (M) proteins from CoVs, and more distant homologs from other nidoviruses. We present computational evidence that these viral families might utilize specific conserved polar residues to constitute an aqueous pore within the membrane-spanning region. We reconstruct an evolutionary history of these families and objectively establish the common origin of the M proteins of CoVs and Toroviruses. We also show that the divergent ORF3 clade (ORF3a/ORF3b/ORF3c/ORF5 families) represents a duplication stemming from the M protein in alpha- and beta-CoVs. By phyletic profiling of major structural components of primary nidoviruses, we present a hypothesis for their role in virion assembly of CoVs, ToroVs, and Arteriviruses. The unification of diverse M/ORF3 ion channel families in a wide range of nidoviruses, especially the typical M protein in CoVs, reveal a conserved, previously under-appreciated role of ion channels in virion assembly and membrane budding. We show that M and ORF3 are under different evolutionary pressures; in contrast to the slow evolution of M as core structural component, the ORF3 clade is under selection for diversification, which suggests it might act at the interface with host molecules and/or immune attack.
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Affiliation(s)
- Yongjun Tan
- Department of Biology, College of Arts and Sciences, Saint Louis University, St. Louis, MO 63103, USA
| | - Theresa Schneider
- Department of Biology, College of Arts and Sciences, Saint Louis University, St. Louis, MO 63103, USA
| | - Prakash K Shukla
- Department of Radiation Oncology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Mahesh B Chandrasekharan
- Department of Radiation Oncology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - L Aravind
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Dapeng Zhang
- Department of Biology, College of Arts and Sciences, Saint Louis University, St. Louis, MO 63103, USA
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28
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Bonidia RP, Sampaio LDH, Domingues DS, Paschoal AR, Lopes FM, de Carvalho ACPLF, Sanches DS. Feature extraction approaches for biological sequences: a comparative study of mathematical features. Brief Bioinform 2021; 22:6135010. [PMID: 33585910 DOI: 10.1093/bib/bbab011] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 12/13/2020] [Accepted: 01/07/2021] [Indexed: 11/14/2022] Open
Abstract
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification. Availability: https://github.com/Bonidia/FeatureExtraction_BiologicalSequences.
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Affiliation(s)
- Robson P Bonidia
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil.,Institute of Mathematics and Computer Sciences, University of São Paulo - USP, São Carlos, 13566-590, Brazil
| | - Lucas D H Sampaio
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
| | - Douglas S Domingues
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil.,Department of Botany, Institute of Biosciences, São Paulo State University (UNESP), Rio Claro 13506-900, Brazil
| | - Alexandre R Paschoal
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
| | - Fabrício M Lopes
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
| | - André C P L F de Carvalho
- Institute of Mathematics and Computer Sciences, University of São Paulo - USP, São Carlos, 13566-590, Brazil
| | - Danilo S Sanches
- Department of Computer Science, Bioinformatics Graduate Program (PPGBIOINFO), Federal University of Technology - Paraná, UTFPR, Campus Cornélio Procópio, 86300-000, Brazil
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29
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Blokh D, Gitarts J, Stambler I. An information-theoretical analysis of gene nucleotide sequence structuredness for a selection of aging and cancer-related genes. Genomics Inform 2020; 18:e41. [PMID: 33412757 PMCID: PMC7808870 DOI: 10.5808/gi.2020.18.4.e41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 11/27/2020] [Indexed: 12/02/2022] Open
Abstract
We provide an algorithm for the construction and analysis of autocorrelation (information) functions of gene nucleotide sequences. As a measure of correlation between discrete random variables, we use normalized mutual information. The information functions are indicative of the degree of structuredness of gene sequences. We construct the information functions for selected gene sequences. We find a significant difference between information functions of genes of different types. We hypothesize that the features of information functions of gene nucleotide sequences are related to phenotypes of these genes.
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Affiliation(s)
- David Blokh
- C.D. Technologies Ltd., Beer Sheba 8445914, Israel
| | - Joseph Gitarts
- Efi Arazi School of Computer Science, Interdisciplinary Center, Herzliya 4673304, Israel
| | - Ilia Stambler
- Department of Science, Technology and Society, Bar Ilan University, Ramat Gan 5290002, Israel
- Corresponding author: E-mail:
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Boughter CT, Borowska MT, Guthmiller JJ, Bendelac A, Wilson PC, Roux B, Adams EJ. Biochemical patterns of antibody polyreactivity revealed through a bioinformatics-based analysis of CDR loops. eLife 2020; 9:61393. [PMID: 33169668 PMCID: PMC7755423 DOI: 10.7554/elife.61393] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 11/09/2020] [Indexed: 12/20/2022] Open
Abstract
Antibodies are critical components of adaptive immunity, binding with high affinity to pathogenic epitopes. Antibodies undergo rigorous selection to achieve this high affinity, yet some maintain an additional basal level of low affinity, broad reactivity to diverse epitopes, a phenomenon termed ‘polyreactivity’. While polyreactivity has been observed in antibodies isolated from various immunological niches, the biophysical properties that allow for promiscuity in a protein selected for high-affinity binding to a single target remain unclear. Using a database of over 1000 polyreactive and non-polyreactive antibody sequences, we created a bioinformatic pipeline to isolate key determinants of polyreactivity. These determinants, which include an increase in inter-loop crosstalk and a propensity for a neutral binding surface, are sufficient to generate a classifier able to identify polyreactive antibodies with over 75% accuracy. The framework from which this classifier was built is generalizable, and represents a powerful, automated pipeline for future immune repertoire analysis. To defend itself against bacteria and viruses, the body depends on a group of proteins known as antibodies. Each subset of antibodies undergoes a rigorous training regimen to ensure it recognizes a single epitope well – that is, one specific region on the surface of foreign, harmful organisms. Most antibodies stick extremely tightly to their one unique epitope, but some can also weakly bind to molecules that are vastly different from their main trained targets. This feature – known as polyreactivity – can in some cases help the immune system fight against multiple strains of viruses. On the other hand, when antibodies are designed in the laboratory to treat diseases, this characteristic can sometimes lead to the failure of pre-clinical trials. Yet it is currently unclear why some antibodies are polyreactive when others are not. To investigate this question, Boughter et al. compared over 1,000 polyreactive and non-polyreactive antibody sequences from a large database, revealing differences in the physical properties of the region of the antibodies that attaches to epitopes. Using these defining features, Boughter et al. went on to design a new piece of freely available, automated software that could predict which antibodies would be polyreactive more than 75% of the time. Such software could ultimately help to guide the design of antibody-based treatments, while bypassing the need for costly laboratory tests.
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Affiliation(s)
| | - Marta T Borowska
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States
| | - Jenna J Guthmiller
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, United States
| | - Albert Bendelac
- Committee on Immunology, University of Chicago, Chicago, United States.,Department of Pathology, University of Chicago, Chicago, United States
| | - Patrick C Wilson
- Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, United States.,Committee on Immunology, University of Chicago, Chicago, United States
| | - Benoit Roux
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States
| | - Erin J Adams
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States.,Committee on Immunology, University of Chicago, Chicago, United States
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31
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Humphrey S, Kerr A, Rattray M, Dive C, Miller CJ. A model of k-mer surprisal to quantify local sequence information content surrounding splice regions. PeerJ 2020; 8:e10063. [PMID: 33194378 PMCID: PMC7648452 DOI: 10.7717/peerj.10063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 09/08/2020] [Indexed: 12/22/2022] Open
Abstract
Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment.
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Affiliation(s)
- Sam Humphrey
- CRUK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, United Kingdom
- CRUK Manchester Institute, CRUK Lung Cancer Centre of Excellence, Manchester, United Kingdom
| | - Alastair Kerr
- CRUK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, United Kingdom
- CRUK Manchester Institute, CRUK Lung Cancer Centre of Excellence, Manchester, United Kingdom
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Caroline Dive
- CRUK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, United Kingdom
- CRUK Manchester Institute, CRUK Lung Cancer Centre of Excellence, Manchester, United Kingdom
| | - Crispin J. Miller
- Computational Biology Group, CRUK Beatson Institute, Glasgow, United Kingdom
- Institute of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom
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Mapping sequence to feature vector using numerical representation of codons targeted to amino acids for alignment-free sequence analysis. Gene 2020; 766:145096. [PMID: 32919006 DOI: 10.1016/j.gene.2020.145096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 08/16/2020] [Accepted: 08/24/2020] [Indexed: 12/17/2022]
Abstract
The phylogenetic analysis based on sequence similarity targeted to real biological taxa is one of the major challenging tasks. In this paper, we propose a novel alignment-free method, CoFASA (Codon Feature based Amino acid Sequence Analyser), for similarity analysis of nucleotide sequences. At first, we assign numerical weights to the four nucleotides. We then calculate a score of each codon based on the numerical value of the constituent nucleotides, termed as degree of codons. Accordingly, we obtain the degree of each amino acid based on the degree of codons targeted towards a specific amino acid. Utilizing the degree of twenty amino acids and their relative abundance within a given sequence, we generate 20-dimensional features for every coding DNA sequence or protein sequence. We use the features for performing phylogenetic analysis of the set of candidate sequences. We use multiple protein sequences derived from Beta-globin (BG), NADH dehydrogenase subunit 5 (ND5), Transferrins (TFs), Xylanases, low identity (<40%) and high identity (⩾40%) protein sequences (encompassing 533 and 1064 protein families) for experimental assessments. We compare our results with sixteen (16) well-known methods, including both alignment-based and alignment-free methods. Various assessment indices are used, such as the Pearson correlation coefficient, RF (Robinson-Foulds) distance and ROC score for performance analysis. While comparing the performance of CoFASA with alignment-based methods (ClustalW, ClustalΩ, MAFFT, and MUSCLE), it shows very similar results. Further, CoFASA shows better performance in comparison to well-known alignment-free methods, including LZW-Kernal, jD2Stat, FFP, spaced, and AFKS-D2s in predicting taxonomic relationship among candidate taxa. Overall, we observe that the features derived by CoFASA are very much useful in isolating the sequences according to their taxonomic labels. While our method is cost-effective, at the same time, produces consistent and satisfactory outcomes.
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Ding Y, Xue H, Ding X, Zhao Y, Zhao Z, Wang D, Wu J. On the complexity measures of mutation hotspots in human TP53 protein. CHAOS (WOODBURY, N.Y.) 2020; 30:073118. [PMID: 32752620 DOI: 10.1063/1.5143584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 06/15/2020] [Indexed: 06/11/2023]
Abstract
The role of sequence complexity in 23 051 somatic missense mutations including 73 well-known mutation hotspots across 22 major cancers was studied in human TP53 proteins. A role for sequence complexity in TP53 protein mutations is suggested since (i) the mutation rate significantly increases in low amino acid pair bias complexity; (ii) probability distribution complexity increases following single point substitution mutations and strikingly increases after mutation at the mutation hotspots including six detectable hotspot mutations (R175, G245, R248, R249, R273, and R282); and (iii) the degree of increase in distribution complexity is significantly correlated with the frequency of missense mutations (r = -0.5758, P < 0.0001) across 20 major types of solid tumors. These results are consistent with the hypothesis that amino acid pair bias and distribution probability may be used as novel measures for protein sequence complexity, and the degree of complexity is related to its susceptibility to mutation, as such, it may be used as a predictor for modeling protein mutations in human cancers.
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Affiliation(s)
- Yan Ding
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Hongsheng Xue
- Institute for Translational Medicine, The Affiliated Zhongshan Hospital of Dalian University, Dalian 116001, China
| | - Xinjia Ding
- Department of Urology, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China
| | - Yuqing Zhao
- Department of Urology, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China
| | - Zhilong Zhao
- Institute for Translational Medicine, The Affiliated Zhongshan Hospital of Dalian University, Dalian 116001, China
| | - Dazhi Wang
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Jianlin Wu
- Institute for Translational Medicine, The Affiliated Zhongshan Hospital of Dalian University, Dalian 116001, China
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34
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Chanda P, Costa E, Hu J, Sukumar S, Van Hemert J, Walia R. Information Theory in Computational Biology: Where We Stand Today. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E627. [PMID: 33286399 PMCID: PMC7517167 DOI: 10.3390/e22060627] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/31/2020] [Accepted: 06/03/2020] [Indexed: 12/30/2022]
Abstract
"A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.
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Affiliation(s)
- Pritam Chanda
- Corteva Agriscience™, Indianapolis, IN 46268, USA
- Computer and Information Science, Indiana University-Purdue University, Indianapolis, IN 46202, USA
| | - Eduardo Costa
- Corteva Agriscience™, Mogi Mirim, Sao Paulo 13801-540, Brazil
| | - Jie Hu
- Corteva Agriscience™, Indianapolis, IN 46268, USA
| | | | | | - Rasna Walia
- Corteva Agriscience™, Johnston, IA 50131, USA
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Positional Correlation Natural Vector: A Novel Method for Genome Comparison. Int J Mol Sci 2020; 21:ijms21113859. [PMID: 32485813 PMCID: PMC7312176 DOI: 10.3390/ijms21113859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/17/2020] [Accepted: 05/26/2020] [Indexed: 12/17/2022] Open
Abstract
Advances in sequencing technology have made large amounts of biological data available. Evolutionary analysis of data such as DNA sequences is highly important in biological studies. As alignment methods are ineffective for analyzing large-scale data due to their inherently high costs, alignment-free methods have recently attracted attention in the field of bioinformatics. In this paper, we introduce a new positional correlation natural vector (PCNV) method that involves converting a DNA sequence into an 18-dimensional numerical feature vector. Using frequency and position correlation to represent the nucleotide distribution, it is possible to obtain a PCNV for a DNA sequence. This new numerical vector design uses six suitable features to characterize the correlation among nucleotide positions in sequences. PCNV is also very easy to compute and can be used for rapid genome comparison. To test our novel method, we performed phylogenetic analysis with several viral and bacterial genome datasets with PCNV. For comparison, an alignment-based method, Bayesian inference, and two alignment-free methods, feature frequency profile and natural vector, were performed using the same datasets. We found that the PCNV technique is fast and accurate when used for phylogenetic analysis and classification of viruses and bacteria.
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36
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Dehghanzadeh H, Ghaderi-Zefrehei M, Mirhoseini SZ, Esmaeilkhaniyan S, Haruna IL, Amirpour Najafabadi H. A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering. J Appl Genet 2020; 61:231-238. [PMID: 31981184 DOI: 10.1007/s13353-020-00543-x] [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: 05/20/2019] [Revised: 09/07/2019] [Accepted: 01/08/2020] [Indexed: 11/29/2022]
Abstract
Information theory is a branch of mathematics that overlaps with communications, biology, and medical engineering. Entropy is a measure of uncertainty in the set of information. In this study, for each gene and its exons sets, the entropy was calculated in orders one to four. Based on the relative entropy of genes and exons, Kullback-Leibler divergence was calculated. After obtaining the Kullback-Leibler distance for genes and exons sets, the results were entered as input into 7 clustering algorithms: single, complete, average, weighted, centroid, median, and K-means. To aggregate the results of clustering, the AdaBoost algorithm was used. Finally, the results of the AdaBoost algorithm were investigated by GeneMANIA prediction server to explore the results from gene annotation point of view. All calculations were performed using the MATLAB Engineering Software (2015). Following our findings on investigating the results of genes metabolic pathways based on the gene annotations, it was revealed that our proposed clustering method yielded correct, logical, and fast results. This method at the same that had not had the disadvantages of aligning allowed the genes with actual length and content to be considered and also did not require high memory for large-length sequences. We believe that the performance of the proposed method could be used with other competitive gene clustering methods to group biologically relevant set of genes. Also, the proposed method can be seen as a predictive method for those genes bearing up weak genomic annotations.
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Affiliation(s)
- Houshang Dehghanzadeh
- Department of Animal Science Research, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, Iran
| | - Mostafa Ghaderi-Zefrehei
- Department of Animal Science, Faculty of Agriculture, University of Yasouj, P. O. Box: 75914, Yasouj, Iran.
| | | | - Saeid Esmaeilkhaniyan
- Animal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Ishaku Lemu Haruna
- Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln, New Zealand
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37
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Hosseini M, Pratas D, Morgenstern B, Pinho AJ. Smash++: an alignment-free and memory-efficient tool to find genomic rearrangements. Gigascience 2020; 9:giaa048. [PMID: 32432328 PMCID: PMC7238676 DOI: 10.1093/gigascience/giaa048] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 04/06/2020] [Accepted: 04/20/2020] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The development of high-throughput sequencing technologies and, as its result, the production of huge volumes of genomic data, has accelerated biological and medical research and discovery. Study on genomic rearrangements is crucial owing to their role in chromosomal evolution, genetic disorders, and cancer. RESULTS We present Smash++, an alignment-free and memory-efficient tool to find and visualize small- and large-scale genomic rearrangements between 2 DNA sequences. This computational solution extracts information contents of the 2 sequences, exploiting a data compression technique to find rearrangements. We also present Smash++ visualizer, a tool that allows the visualization of the detected rearrangements along with their self- and relative complexity, by generating an SVG (Scalable Vector Graphics) image. CONCLUSIONS Tested on several synthetic and real DNA sequences from bacteria, fungi, Aves, and Mammalia, the proposed tool was able to accurately find genomic rearrangements. The detected regions were in accordance with previous studies, which took alignment-based approaches or performed FISH (fluorescence in situ hybridization) analysis. The maximum peak memory usage among all experiments was ∼1 GB, which makes Smash++ feasible to run on present-day standard computers.
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Affiliation(s)
- Morteza Hosseini
- IEETA/DETI, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - Diogo Pratas
- IEETA/DETI, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
- Department of Virology, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
| | - Burkhard Morgenstern
- Department of Bioinformatics, University of Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
| | - Armando J Pinho
- IEETA/DETI, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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38
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Reyes-Valdés MH, Kantartzi SK. An information theory approach to biocultural complexity. Sci Rep 2020; 10:7203. [PMID: 32350371 PMCID: PMC7190823 DOI: 10.1038/s41598-020-64260-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 04/13/2020] [Indexed: 11/29/2022] Open
Abstract
The study of biocultural diversity requires the use of appropriate concepts and analytical tools. Particularly, there is a need of indices capable to show the degree of stratification in the set of interactions among cultures and groups of plants and animals in a given region. Here, we present a mathematical approach based on the mutual Shannon information theory to study the relationships among cultural and biological groups. Biocultural complexity was described in terms of effective biocultural units, a new concept defined in this work. From the mathematical formulation of biocultural complexity, formulas were derived to measure the specificity of biological groups and the specialization of cultures, based on the association of human societies with plant or animal groups. To exemplify the concepts and tools, two data sets were analyzed; 1) a set that included artificial data in order to demonstrate the use of the formulas and calculate the indices, and 2) a set that included published data on the use of 18 mushroom species by people in five villages of eastern India. Analysis of the first data set revealed a clear case of biocultural complexity, whereas that of the second set showed that the villages and the use of biological resources composed a single biocultural unit. Overall, hypothesis testing of the association among cultures and biological species was consistent with the information that was provided by the new indices.
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Affiliation(s)
- M Humberto Reyes-Valdés
- Universidad Autónoma Agraria Antonio Narro, Graduate Program on Plant Genetic Resources for Arid Lands, Saltillo, Coahuila, 25315, Mexico.
| | - Stella K Kantartzi
- Southern Illinois University, Department of Plant, Soil and Agricultural Systems, Carbondale, IL, 62901, USA
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Czech L, Barbera P, Stamatakis A. Methods for automatic reference trees and multilevel phylogenetic placement. Bioinformatics 2020; 35:1151-1158. [PMID: 30169747 PMCID: PMC6449752 DOI: 10.1093/bioinformatics/bty767] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 07/24/2018] [Accepted: 08/30/2018] [Indexed: 12/28/2022] Open
Abstract
MOTIVATION In most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phylogeny. These placement methods do however face certain limitations: The manual selection of reference sequences is labor-intensive; the computational effort to infer reference phylogenies is substantially larger than for methods that rely on sequence similarity; the number of taxa in the reference phylogeny should be small enough to allow for visually inspecting the results. RESULTS We present algorithms to overcome the above limitations. First, we introduce a method to automatically construct representative sequences from databases to infer reference phylogenies. Second, we present an approach for conducting large-scale phylogenetic placements on nested phylogenies. Third, we describe a preprocessing pipeline that allows for handling huge sequence datasets. Our experiments on empirical data show that our methods substantially accelerate the workflow and yield highly accurate placement results. AVAILABILITY AND IMPLEMENTATION Freely available under GPLv3 at http://github.com/lczech/gappa. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lucas Czech
- Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Pierre Barbera
- Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Alexandros Stamatakis
- Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.,Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
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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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Evidence of genomic information and structural restrictions of HIV-1 PR and RT gene regions from individuals experiencing antiretroviral virologic failure. INFECTION GENETICS AND EVOLUTION 2019; 78:104134. [PMID: 31837484 DOI: 10.1016/j.meegid.2019.104134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 10/25/2022]
Abstract
OBJECTIVES This study analyzed Protease-PR and Reverse Transcriptase-RT HIV-1 genomic information entropy metrics among patients under antiretroviral virologic failure, according to the numbers of virologic failures or resistance mutations. METHODS For this purpose, we used genomic sequences from PR and RT of HIV-1 from a cohort of chronic patients followed up at São Paulo Hospital. RESULTS Informational entropy proportionally increases with the number of antiretroviral virologic failures in PR and RT (p < .001). Affected regions of PR were related to catalytic and structural functions, such as Fulcrum (K20) Flap (M46) and Cantilever (A71). In RT, this occurred at Fingers (E44) and Palm (K219). Informational entropy increases according to the number of resistance mutations in PR and RT (p < .001). Higher PR entropy was proportional to the resistance mutation numbers in Fulcrum (L10), Active site (L24) Flap (M46), Cantilever (L63) and near Interface (L90). In RT, they related to regions responsible for protein stability such as Fingers (T39) and Palm (L100). CONCLUSIONS The antiretroviral selective pressure affects HIV genomic informational entropy at the PR and RT regions, leading to the emergence of more unstable virions. Mapping the three-dimensional structure in these HIV-1 proteins is relevant to designing new antiretroviral targeting resistant strains.
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Roddy AC, Jurek-Loughrey A, Souza J, Gilmore A, O’Reilly PG, Stupnikov A, Gonzalez de Castro D, Prise KM, Salto-Tellez M, McArt DG. NUQA: Estimating Cancer Spatial and Temporal Heterogeneity and Evolution through Alignment-Free Methods. Mol Biol Evol 2019; 36:2883-2889. [PMID: 31424551 PMCID: PMC6878956 DOI: 10.1093/molbev/msz182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Longitudinal next-generation sequencing of cancer patient samples has enhanced our understanding of the evolution and progression of various cancers. As a result, and due to our increasing knowledge of heterogeneity, such sampling is becoming increasingly common in research and clinical trial sample collections. Traditionally, the evolutionary analysis of these cohorts involves the use of an aligner followed by subsequent stringent downstream analyses. However, this can lead to large levels of information loss due to the vast mutational landscape that characterizes tumor samples. Here, we propose an alignment-free approach for sequence comparison-a well-established approach in a range of biological applications including typical phylogenetic classification. Such methods could be used to compare information collated in raw sequence files to allow an unsupervised assessment of the evolutionary trajectory of patient genomic profiles. In order to highlight this utility in cancer research we have applied our alignment-free approach using a previously established metric, Jensen-Shannon divergence, and a metric novel to this area, Hellinger distance, to two longitudinal cancer patient cohorts in glioma and clear cell renal cell carcinoma using our software, NUQA. We hypothesize that this approach has the potential to reveal novel information about the heterogeneity and evolutionary trajectory of spatiotemporal tumor samples, potentially revealing early events in tumorigenesis and the origins of metastases and recurrences. Key words: alignment-free, Hellinger distance, exome-seq, evolution, phylogenetics, longitudinal.
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Affiliation(s)
- Aideen C Roddy
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Anna Jurek-Loughrey
- School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, United Kingdom
| | - Jose Souza
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Alan Gilmore
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Paul G O’Reilly
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Alexey Stupnikov
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD
| | - David Gonzalez de Castro
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Kevin M Prise
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Manuel Salto-Tellez
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
| | - Darragh G McArt
- Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
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43
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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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Entropy and Information within Intrinsically Disordered Protein Regions. ENTROPY 2019; 21:e21070662. [PMID: 33267376 PMCID: PMC7515160 DOI: 10.3390/e21070662] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 06/27/2019] [Accepted: 07/01/2019] [Indexed: 02/06/2023]
Abstract
Bioinformatics and biophysical studies of intrinsically disordered proteins and regions (IDRs) note the high entropy at individual sequence positions and in conformations sampled in solution. This prevents application of the canonical sequence-structure-function paradigm to IDRs and motivates the development of new methods to extract information from IDR sequences. We argue that the information in IDR sequences cannot be fully revealed through positional conservation, which largely measures stable structural contacts and interaction motifs. Instead, considerations of evolutionary conservation of molecular features can reveal the full extent of information in IDRs. Experimental quantification of the large conformational entropy of IDRs is challenging but can be approximated through the extent of conformational sampling measured by a combination of NMR spectroscopy and lower-resolution structural biology techniques, which can be further interpreted with simulations. Conformational entropy and other biophysical features can be modulated by post-translational modifications that provide functional advantages to IDRs by tuning their energy landscapes and enabling a variety of functional interactions and modes of regulation. The diverse mosaic of functional states of IDRs and their conformational features within complexes demands novel metrics of information, which will reflect the complicated sequence-conformational ensemble-function relationship of IDRs.
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Ghosh B, Sarma U, Sourjik V, Legewie S. Sharing of Phosphatases Promotes Response Plasticity in Phosphorylation Cascades. Biophys J 2019; 114:223-236. [PMID: 29320690 DOI: 10.1016/j.bpj.2017.10.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 10/06/2017] [Accepted: 10/17/2017] [Indexed: 01/06/2023] Open
Abstract
Sharing of positive or negative regulators between multiple targets is frequently observed in cellular signaling cascades. For instance, phosphatase sharing between multiple kinases is ubiquitous within the MAPK pathway. Here we investigate how such phosphatase sharing could shape robustness and evolvability of the phosphorylation cascade. Through modeling and evolutionary simulations, we demonstrate that 1) phosphatase sharing dramatically increases robustness of a bistable MAPK response, and 2) phosphatase-sharing cascades evolve faster than nonsharing cascades. This faster evolution is particularly pronounced when evolving from a monostable toward a bistable phenotype, whereas the transition speed of a population from a bistable to monostable response is not affected by phosphatase sharing. This property may enable the phosphatase-sharing design to adapt better in a changing environment. Analysis of the respective mutational landscapes reveal that phosphatase sharing reduces the number of limiting mutations required for transition from monostable to bistable responses, hence facilitating a faster transition to such response types. Taken together, using MAPK cascade as an example, our study offers a general theoretical framework to explore robustness and evolutionary plasticity of signal transduction cascades.
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Affiliation(s)
- Bhaswar Ghosh
- Department of Systems and Synthetic Microbiology, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany; LOEWE Research Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany.
| | - Uddipan Sarma
- Modelling of Biological Networks Group, Institute of Molecular Biology (IMB), Mainz, Germany.
| | - Victor Sourjik
- Department of Systems and Synthetic Microbiology, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany; LOEWE Research Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany.
| | - Stefan Legewie
- Modelling of Biological Networks Group, Institute of Molecular Biology (IMB), Mainz, Germany.
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46
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Xi B, Tao J, Liu X, Xu X, He P, Dai Q. RaaMLab: A MATLAB toolbox that generates amino acid groups and reduced amino acid modes. Biosystems 2019; 180:38-45. [PMID: 30904554 DOI: 10.1016/j.biosystems.2019.03.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 12/25/2018] [Accepted: 03/06/2019] [Indexed: 01/31/2023]
Abstract
Amino acid (AA) classification and its different biophysical and chemical characteristics have been widely applied to analyze and predict the structural, functional, expression and interaction profiles of proteins and peptides. We present RaaMLab, a free and open-source MATLAB toolbox, to facilitate studies on proteins and peptides, to generate AA groups and to extract the structural and physicochemical features of reduced AAs (RedAA). This toolbox offers 4 kinds of databases, including the physicochemical properties of AAs and their groupings, 49 AA classification methods and 5 types of biophysicochemical features of RedAAs. These factors can be easily computed based on user-defined alphabet size and AA properties of AA groupings. RaaMLab is an open source freely available at https://github.com/bioinfo0706/RaaMLab. This website also contains a tutorial, extensive documentation and examples.
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Affiliation(s)
- Baohang Xi
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Jin Tao
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Xiaoqing Liu
- College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
| | - Xinnan Xu
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Pingan He
- College of Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Qi Dai
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
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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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48
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Zhao Y, Xue X, Xie X. An alignment-free measure based on physicochemical properties of amino acids for protein sequence comparison. Comput Biol Chem 2019; 80:10-15. [PMID: 30851619 DOI: 10.1016/j.compbiolchem.2019.01.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Revised: 12/30/2018] [Accepted: 01/17/2019] [Indexed: 01/21/2023]
Abstract
Sequence comparison is an important topic in bioinformatics. With the exponential increase of biological sequences, the traditional protein sequence comparison methods - the alignment methods become limited, so the alignment-free methods are widely proposed in the past two decades. In this paper, we considered not only the six typical physicochemical properties of amino acids, but also their frequency and positional distribution. A 51-dimensional vector was obtained to describe the protein sequence. We got a pairwise distance matrix by computing the standardized Euclidean distance, and discriminant analysis and phylogenetic analysis can be made. The results on the Influenza A virus and ND5 datasets indicate that our method is accurate and efficient for classifying proteins and inferring the phylogeny of species.
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Affiliation(s)
- Yunxiu Zhao
- College of Science, Northwest A&F University, Yangling, Shaanxi 712100, PR China
| | - Xiaolong Xue
- College of Science, Northwest A&F University, Yangling, Shaanxi 712100, PR China
| | - Xiaoli Xie
- College of Science, Northwest A&F University, Yangling, Shaanxi 712100, PR China.
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49
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Xu W, Zhu L, Huang DS. DCDE: An Efficient Deep Convolutional Divergence Encoding Method for Human Promoter Recognition. IEEE Trans Nanobioscience 2019; 18:136-145. [PMID: 30624223 DOI: 10.1109/tnb.2019.2891239] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Efficient human promoter feature extraction is still a major challenge in genome analysis as it can better understand human gene regulation and will be useful for experimental guidance. Although many machine learning algorithms have been developed for eukaryotic gene recognition, performance on promoters is unsatisfactory due to the diverse nature. To extract discriminative features from human promoters, an efficient deep convolutional divergence encoding method (DCDE) is proposed based on statistical divergence (SD) and convolutional neural network (CNN). SD can help optimize kmer feature extraction for human promoters. CNN can also be used to automatically extract features in gene analysis. In DCDE, we first perform informative kmers settlement to encode original gene sequences. A series of SD methods can optimize the most discriminative kmers distributions while maintaining important positional information. Then, CNN is utilized to extract lower dimensional deep features by secondary encoding. Finally, we construct a hybrid recognition architecture with multiple support vector machines and a bilayer decision method. It is flexible to add new features or new models and can be extended to identify other genomic functional elements. The extensive experiments demonstrate that DCDE is effective in promoter encoding and can significantly improve the performance of promoter recognition.
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50
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Wasik S, Szostak N, Kudla M, Wachowiak M, Krawiec K, Blazewicz J. Detecting life signatures with RNA sequence similarity measures. J Theor Biol 2018; 463:110-120. [PMID: 30562502 DOI: 10.1016/j.jtbi.2018.12.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 10/25/2018] [Accepted: 12/14/2018] [Indexed: 12/20/2022]
Abstract
The RNA World is currently the most plausible hypothesis for explaining the origins of life on Earth. The supporting body of evidence is growing and it comes from multiple areas, including astrobiology, chemistry, biology, mathematics, and, in particular, from computer simulations. Such methods frequently assume the existence of a hypothetical species on Earth, around three billion years ago, with a base sequence probably dissimilar from any in known genomes. However, it is often hard to verify whether or not a hypothetical sequence has the characteristics of biological sequences, and is thus likely to be functional. The primary objective of the presented research was to verify the possibility of building a computational 'life probe' for determining whether a given genetic sequence is biological, and assessing the sensitivity of such probes to the signatures of life present in known biological sequences. We have proposed decision algorithms based on the normalized compression distance (NCD) and Levenshtein distance (LD). We have validated the proposed method in the context of the RNA World hypothesis using short genetic sequences shorter than the error threshold value (i.e., 100 nucleotides). We have demonstrated that both measures can be successfully used to construct life probes that are significantly better than a random decision procedure, while varying from each other when it comes to detailed characteristics. We also observed that fragments of sequences related to replication have better discriminatory power than sequences having other molecular functions. In a broader context, this shows that the signatures of life in short RNA samples can be effectively detected using relatively simple means.
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Affiliation(s)
- Szymon Wasik
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland; Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland; European Centre for Bioinformatics and Genomics, Poznan, Poland.
| | - Natalia Szostak
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland; Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland; European Centre for Bioinformatics and Genomics, Poznan, Poland
| | - Mateusz Kudla
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Michal Wachowiak
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Krzysztof Krawiec
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Jacek Blazewicz
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland; Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland; European Centre for Bioinformatics and Genomics, Poznan, Poland
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