51
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Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time. Genes (Basel) 2022; 13:genes13020170. [PMID: 35205215 PMCID: PMC8872422 DOI: 10.3390/genes13020170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/08/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
Mutation is the driving force of species evolution, which may change the genetic information of organisms and obtain selective competitive advantages to adapt to environmental changes. It may change the structure or function of translated proteins, and cause abnormal cell operation, a variety of diseases and even cancer. Therefore, it is particularly important to identify gene regions with high mutations. Mutations will cause changes in nucleotide distribution, which can be characterized by natural vectors globally. Based on natural vectors, we propose a mathematical formula for measuring the difference in nucleotide distribution over time to investigate the mutations of human immunodeficiency virus. The studied dataset is from public databases and includes gene sequences from twenty HIV-infected patients. The results show that the mutation rate of the nine major genes or gene segment regions in the genome exhibits discrepancy during the infected period, and the Env gene has the fastest mutation rate. We deduce that the peak of virus mutation has a close temporal relationship with viral divergence and diversity. The mutation study of HIV is of great significance to clinical diagnosis and drug design.
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52
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Jamdade R, Al-Shaer K, Al-Sallani M, Al-Harthi E, Mahmoud T, Gairola S, Shabana HA. Multilocus marker-based delimitation of Salicornia persica and its population discrimination assisted by supervised machine learning approach. PLoS One 2022; 17:e0270463. [PMID: 35895732 PMCID: PMC9328517 DOI: 10.1371/journal.pone.0270463] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/10/2022] [Indexed: 11/18/2022] Open
Abstract
The Salicornia L. has been considered one of the most taxonomically challenging genera due to high morphological plasticity, intergradation between related species, and lack of diagnostic features in preserved herbarium specimens. In the United Arab Emirates (UAE), only one species of this genus, Salicornia europaea, has been reported, though investigating its identity at the molecular level has not yet been undertaken. Moreover, based on growth form and morphology variation between the Ras-Al-Khaimah (RAK) population and the Umm-Al-Quwain (UAQ) population, we suspect the presence of different species or morphotypes. The present study aimed to initially perform species identification using multilocus DNA barcode markers from chloroplast DNA (cpDNA) and nuclear ribosomal DNA (nrDNA), followed by the genetic divergence between two populations (RAK and UAQ) belonging to two different coastal localities in the UAE. The analysis resulted in high-quality multilocus barcode sequences subjected to species discrimination through the unsupervised OTU picking and supervised learning methods. The ETS sequence data from our study sites had high identity with the previously reported sequences of Salicornia persica using NCBI blast and was further confirmed using OTU picking methods viz., TaxonDNAs Species identifier and Assemble Species by Automatic Partitioning (ASAP). Moreover, matK sequence data showed a non-monophyletic relationship, and significant discrimination between the two populations through alignment-based unsupervised OTU picking, alignment-free Co-Phylog, and alignment & alignment-free supervised learning approaches. Other markers viz., rbcL, trnH-psbA, ITS2, and ETS could not distinguish the two populations individually, though their combination with matK (cpDNA & cpDNA+nrDNA) showed enough population discrimination. However, the ITS2+ETS (nrDNA) exhibited much higher genetic divergence, further splitting both the populations into four haplotypes. Based on the observed morphology, genetic divergence, and the number of haplotypes predicted using the matK marker, it can be suggested that two distinct populations (RAK and UAQ) do exist. Further extensive morpho-taxonomic studies are required to determine the inter-population variability of Salicornia in the UAE. Altogether, our results suggest that S. persica is the species that grow in the present study area in UAE, and do not support previous treatments as S. europaea.
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Affiliation(s)
- Rahul Jamdade
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
- * E-mail:
| | - Khawla Al-Shaer
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Mariam Al-Sallani
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Eman Al-Harthi
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Tamer Mahmoud
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
- Nature Conservation Sector, Egyptian Environmental Affairs Agency, Cairo, Egypt
| | - Sanjay Gairola
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Hatem A. Shabana
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
- Nature Conservation Sector, Egyptian Environmental Affairs Agency, Cairo, Egypt
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53
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He L, Sun S, Zhang Q, Bao X, Li PK. Alignment-free sequence comparison for virus genomes based on location correlation coefficient. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2021; 96:105106. [PMID: 34626822 PMCID: PMC8493760 DOI: 10.1016/j.meegid.2021.105106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/08/2021] [Accepted: 10/03/2021] [Indexed: 12/18/2022]
Abstract
Coronaviruses (especially SARS-CoV-2) are characterized by rapid mutation and wide spread. As these characteristics easily lead to global pandemics, studying the evolutionary relationship between viruses is essential for clinical diagnosis. DNA sequencing has played an important role in evolutionary analysis. Recent alignment-free methods can overcome the problems of traditional alignment-based methods, which consume both time and space. This paper proposes a novel alignment-free method called the correlation coefficient feature vector (CCFV), which defines a correlation measure of the L-step delay of a nucleotide location from its location in the original DNA sequence. The numerical feature is a 16×L-dimensional numerical vector describing the distribution characteristics of the nucleotide positions in a DNA sequence. The proposed L-step delay correlation measure is interestingly related to some types of L+1 spaced mers. Unlike traditional gene comparison, our method avoids the computational complexity of multiple sequence alignment, and hence improves the speed of sequence comparison. Our method is applied to evolutionary analysis of the common human viruses including SARS-CoV-2, Dengue virus, Hepatitis B virus, and human rhinovirus and achieves the same or even better results than alignment-based methods. Especially for SARS-CoV-2, our method also confirms that bats are potential intermediate hosts of SARS-CoV-2.
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Affiliation(s)
- Lily He
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, PR China.
| | - Siyang Sun
- The High School Affiliated to Renmin University of China, Beijing 100080, PR China
| | - Qianyue Zhang
- The High School Affiliated to Renmin University of China, Beijing 100080, PR China
| | - Xiaona Bao
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, PR China
| | - Peter K Li
- School of Life Sciences, Tsinghua University, Beijing 100084, PR China.
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54
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Bussi Y, Kapon R, Reich Z. Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy. PLoS One 2021; 16:e0258693. [PMID: 34648558 PMCID: PMC8516232 DOI: 10.1371/journal.pone.0258693] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/02/2021] [Indexed: 12/24/2022] Open
Abstract
Information theoretic approaches are ubiquitous and effective in a wide variety of bioinformatics applications. In comparative genomics, alignment-free methods, based on short DNA words, or k-mers, are particularly powerful. We evaluated the utility of varying k-mer lengths for genome comparisons by analyzing their sequence space coverage of 5805 genomes in the KEGG GENOME database. In subsequent analyses on four k-mer lengths spanning the relevant range (11, 21, 31, 41), hierarchical clustering of 1634 genus-level representative genomes using pairwise 21- and 31-mer Jaccard similarities best recapitulated a phylogenetic/taxonomic tree of life with clear boundaries for superkingdom domains and high subtree similarity for named taxons at lower levels (family through phylum). By analyzing ~14.2M prokaryotic genome comparisons by their lowest-common-ancestor taxon levels, we detected many potential misclassification errors in a curated database, further demonstrating the need for wide-scale adoption of quantitative taxonomic classifications based on whole-genome similarity.
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Affiliation(s)
- Yuval Bussi
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ruti Kapon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Ziv Reich
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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55
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Ruohan W, Xianglilan Z, Jianping W, Shuai Cheng LI. DeepHost: phage host prediction with convolutional neural network. Brief Bioinform 2021; 23:6374063. [PMID: 34553750 DOI: 10.1093/bib/bbab385] [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: 06/08/2021] [Revised: 08/10/2021] [Accepted: 08/27/2021] [Indexed: 01/21/2023] Open
Abstract
Next-generation sequencing expands the known phage genomes rapidly. Unlike culture-based methods, the hosts of phages discovered from next-generation sequencing data remain uncharacterized. The high diversity of the phage genomes makes the host assignment task challenging. To solve the issue, we proposed a phage host prediction tool-DeepHost. To encode the phage genomes into matrices, we design a genome encoding method that applied various spaced $k$-mer pairs to tolerate sequence variations, including insertion, deletions, and mutations. DeepHost applies a convolutional neural network to predict host taxonomies. DeepHost achieves the prediction accuracy of 96.05% at the genus level (72 taxonomies) and 90.78% at the species level (118 taxonomies), which outperforms the existing phage host prediction tools by 10.16-30.48% and achieves comparable results to BLAST. For the genomes without hits in BLAST, DeepHost obtains the accuracy of 38.00% at the genus level and 26.47% at the species level, making it suitable for genomes of less homologous sequences with the existing datasets. DeepHost is alignment-free, and it is faster than BLAST, especially for large datasets. DeepHost is available at https://github.com/deepomicslab/DeepHost.
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Affiliation(s)
- Wang Ruohan
- Department of Computer Science at City University of Hong Kong
| | - Zhang Xianglilan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
| | - Wang Jianping
- Department of Computer Science at City University of Hong Kong
| | - L I Shuai Cheng
- Department of Computer Science at City University of Hong Kong
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56
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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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57
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Andreace F, Pizzi C, Comin M. MetaProb 2: Metagenomic Reads Binning Based on Assembly Using Minimizers and K-Mers Statistics. J Comput Biol 2021; 28:1052-1062. [PMID: 34448593 DOI: 10.1089/cmb.2021.0270] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Current technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. The major difficulties of taxonomic analysis are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, and sequencing errors. Microbial communities can be studied with reads clustering, a process referred to as genome binning. In this study, we present MetaProb 2 an unsupervised genome binning method based on reads assembly and probabilistic k-mers statistics. The novelties of MetaProb 2 are the use of minimizers to efficiently assemble reads into unitigs and a community detection algorithm based on graph modularity to cluster unitigs and to detect representative unitigs. The effectiveness of MetaProb 2 is demonstrated in both simulated and real datasets in comparison with state-of-art binning tools such as MetaProb, AbundanceBin, Bimeta, and MetaCluster. On real datasets, it is the only one capable of producing promising results while being parsimonious with computational resources.
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Affiliation(s)
- Francesco Andreace
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Cinzia Pizzi
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Matteo Comin
- Department of Information Engineering, University of Padova, Padova, Italy
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58
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Rachtman E, Bafna V, Mirarab S. CONSULT: accurate contamination removal using locality-sensitive hashing. NAR Genom Bioinform 2021; 3:lqab071. [PMID: 34377979 PMCID: PMC8340999 DOI: 10.1093/nargab/lqab071] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/30/2021] [Accepted: 07/19/2021] [Indexed: 12/27/2022] Open
Abstract
A fundamental question appears in many bioinformatics applications: Does a sequencing read belong to a large dataset of genomes from some broad taxonomic group, even when the closest match in the set is evolutionarily divergent from the query? For example, low-coverage genome sequencing (skimming) projects either assemble the organelle genome or compute genomic distances directly from unassembled reads. Using unassembled reads needs contamination detection because samples often include reads from unintended groups of species. Similarly, assembling the organelle genome needs distinguishing organelle and nuclear reads. While k-mer-based methods have shown promise in read-matching, prior studies have shown that existing methods are insufficiently sensitive for contamination detection. Here, we introduce a new read-matching tool called CONSULT that tests whether k-mers from a query fall within a user-specified distance of the reference dataset using locality-sensitive hashing. Taking advantage of large memory machines available nowadays, CONSULT libraries accommodate tens of thousands of microbial species. Our results show that CONSULT has higher true-positive and lower false-positive rates of contamination detection than leading methods such as Kraken-II and improves distance calculation from genome skims. We also demonstrate that CONSULT can distinguish organelle reads from nuclear reads, leading to dramatic improvements in skim-based mitochondrial assemblies.
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Affiliation(s)
- Eleonora Rachtman
- Bioinformatics and Systems Biology Graduate Program, UC San Diego, CA 92093, USA
| | - Vineet Bafna
- Department of Computer Science and Engineering, UC San Diego, CA 92093, USA
| | - Siavash Mirarab
- Department of Electrical and Computer Engineering, UC San Diego, CA 92093, USA
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59
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Pratas D, Silva JM. Persistent minimal sequences of SARS-CoV-2. Bioinformatics 2021; 36:5129-5132. [PMID: 32730589 PMCID: PMC7559010 DOI: 10.1093/bioinformatics/btaa686] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 07/14/2020] [Accepted: 07/22/2020] [Indexed: 12/22/2022] Open
Abstract
Motivation Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 14
million cases and more than half million deaths. Given the absence of implemented
therapies, new analysis, diagnosis, and therapeutics are of great importance. Results Analysis of SARS-CoV-2 genomes from the current outbreak reveals the presence of short
persistent DNA/RNA sequences that are absent from the human genome and transcriptome
(PmRAWs). For the PmRAWs with length 12, only four exist at the same location in all
SARS-CoV-2. At the gene level, we found one PmRAW of size 13 at the Spike glycoprotein
coding sequence. This protein is fundamental for binding in human ACE2 and further use
as an entry receptor to invade target cells. Applying protein structural prediction, we
localized this PmRAW at the surface of the Spike protein, providing a potential targeted
vector for diagnostics and therapeutics. Additionally, we show a new pattern of relative
absent words (RAWs), characterized by the progressive increase of GC content (Guanine
and Cytosine) according to the decrease of RAWs length, contrarily to the virus and host
genome distributions. New analysis shows the same property during the Ebola virus
outbreak. At a computational level, we improved the alignment-free method to identify
pathogen-specific signatures in balance with GC measures and removed previous size
limitations. Availability and Implementation https://github.com/cobilab/eagle. Supplementary information Supplementary data are
available at Bioinformatics online.
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Affiliation(s)
- Diogo Pratas
- Institute of Electronics and Informatics Engineering of Aveiro, 3810-193 Aveiro, Portugal.,Department of Electronics, Telecommunications and Informatics, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal.,Department of Virology, University of Helsinki, 00014 Helsinki, Finland
| | - Jorge M Silva
- Institute of Electronics and Informatics Engineering of Aveiro, 3810-193 Aveiro, Portugal.,Department of Electronics, Telecommunications and Informatics, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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60
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Sun Q, Peng Y, Liu J. A reference-free approach for cell type classification with scRNA-seq. iScience 2021; 24:102855. [PMID: 34381979 PMCID: PMC8335627 DOI: 10.1016/j.isci.2021.102855] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 05/07/2021] [Accepted: 07/08/2021] [Indexed: 11/29/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to characterize cells under different biological conditions. Unlike bulk RNA-seq, gene expression from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that attribute to gene quantification. To overcome data sparsity and fully utilize original reads, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types with k-mer level features. The compressed k-mer groups (CKGs), identified by the simhash method, contain k-mers with similar abundance profiles and serve as the cells’ features. Our experiments demonstrate that CKG features lend themselves to better performance than gene expression features in scRNA-seq classification accuracy in the majority of experimental cases. Because CKGs are derived from raw reads without alignment to reference genome, scSimClassify offers an effective alternative to existing methods especially when reference genome is incomplete or insufficient to represent subject genomes. Compressed k-mer groups (CKGs) are used to classify cell types without references CKGs are competitive to gene expression features for cell type classification CKGs are associated with genes sharing gene specific k-mers
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Affiliation(s)
- Qi Sun
- Department of Computer Science, University of Kentucky, Lexington, KY, 40508, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jinze Liu
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA
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61
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Akon M, Akon M, Kabir M, Rahman MS, Rahman MS. ADACT: a tool for analysing (dis)similarity among nucleotide and protein sequences using minimal and relative absent words. Bioinformatics 2021; 37:1468-1470. [PMID: 33016997 DOI: 10.1093/bioinformatics/btaa853] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/09/2020] [Accepted: 09/21/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Researchers and practitioners use a number of popular sequence comparison tools that use many alignment-based techniques. Due to high time and space complexity and length-related restrictions, researchers often seek alignment-free tools. Recently, some interesting ideas, namely, Minimal Absent Words (MAW) and Relative Absent Words (RAW), have received much interest among the scientific community as distance measures that can give us alignment-free alternatives. This drives us to structure a framework for analysing biological sequences in an alignment-free manner. RESULTS In this application note, we present Alignment-free Dissimilarity Analysis & Comparison Tool (ADACT), a simple web-based tool that computes the analogy among sequences using a varied number of indexes through the distance matrix, species relation list and phylogenetic tree. This tool basically combines absent word (MAW or RAW) computation, dissimilarity measures, species relationship and thus brings all required software in one platform for the ease of researchers and practitioners alike in the field of bioinformatics. We have also developed a restful API. AVAILABILITY AND IMPLEMENTATION ADACT has been hosted at http://research.buet.ac.bd/ADACT/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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62
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Liang KYH, Orata FD, Boucher YF, Case RJ. Roseobacters in a Sea of Poly- and Paraphyly: Whole Genome-Based Taxonomy of the Family Rhodobacteraceae and the Proposal for the Split of the "Roseobacter Clade" Into a Novel Family, Roseobacteraceae fam. nov. Front Microbiol 2021; 12:683109. [PMID: 34248901 PMCID: PMC8267831 DOI: 10.3389/fmicb.2021.683109] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 05/27/2021] [Indexed: 11/13/2022] Open
Abstract
The family Rhodobacteraceae consists of alphaproteobacteria that are metabolically, phenotypically, and ecologically diverse. It includes the roseobacter clade, an informal designation, representing one of the most abundant groups of marine bacteria. The rapid pace of discovery of novel roseobacters in the last three decades meant that the best practice for taxonomic classification, a polyphasic approach utilizing phenotypic, genotypic, and phylogenetic characteristics, was not always followed. Early efforts for classification relied heavily on 16S rRNA gene sequence similarity and resulted in numerous taxonomic inconsistencies, with several poly- and paraphyletic genera within this family. Next-generation sequencing technologies have allowed whole-genome sequences to be obtained for most type strains, making a revision of their taxonomy possible. In this study, we performed whole-genome phylogenetic and genotypic analyses combined with a meta-analysis of phenotypic data to review taxonomic classifications of 331 type strains (under 119 genera) within the Rhodobacteraceae family. Representatives of the roseobacter clade not only have different environmental adaptions from other Rhodobacteraceae isolates but were also found to be distinct based on genomic, phylogenetic, and in silico-predicted phenotypic data. As such, we propose to move this group of bacteria into a new family, Roseobacteraceae fam. nov. In total, reclassifications resulted to 327 species and 128 genera, suggesting that misidentification is more problematic at the genus than species level. By resolving taxonomic inconsistencies of type strains within this family, we have established a set of coherent criteria based on whole-genome-based analyses that will help guide future taxonomic efforts and prevent the propagation of errors.
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Affiliation(s)
- Kevin Y H Liang
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Fabini D Orata
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Yann F Boucher
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.,Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University Singapore, Singapore, Singapore
| | - Rebecca J Case
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.,Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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63
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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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64
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Ni H, Mu H, Qi D. Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses. J Mol Graph Model 2021; 107:107942. [PMID: 34058640 DOI: 10.1016/j.jmgm.2021.107942] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/16/2021] [Accepted: 05/10/2021] [Indexed: 11/28/2022]
Abstract
As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis.
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Affiliation(s)
- Haiming Ni
- College of Science, Northeast Forestry University, Hexing Road 26, Harbin, Heilongjiang Province, 150040, PR China.
| | - Hongbo Mu
- College of Science, Northeast Forestry University, Hexing Road 26, Harbin, Heilongjiang Province, 150040, PR China
| | - Dawei Qi
- College of Science, Northeast Forestry University, Hexing Road 26, Harbin, Heilongjiang Province, 150040, PR China.
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Lee B, Smith DK, Guan Y. Alignment free sequence comparison methods and reservoir host prediction. Bioinformatics 2021; 37:3337-3342. [PMID: 33964132 PMCID: PMC8135978 DOI: 10.1093/bioinformatics/btab338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/29/2021] [Accepted: 04/30/2021] [Indexed: 11/19/2022] Open
Abstract
Motivation The emergence and subsequent pandemic of the SARS-CoV-2 virus raised urgent questions about its origin and, particularly, its reservoir host. These types of questions are long-standing problems in the management of emerging infectious diseases and are linked to virus discovery programs and the prediction of viruses that are likely to become zoonotic. Conventional means to identify reservoir hosts have relied on surveillance, experimental studies and phylogenetics. More recently, machine learning approaches have been applied to generate tools to swiftly predict reservoir hosts from sequence data. Results Here, we extend a recent work that combined sequence alignment and a mixture of alignment-free approaches using a gradient boosting machines (GBMs) machine learning model, which integrates genomic traits (GT) and phylogenetic neighbourhood (PN) signatures to predict reservoir hosts. We add a more uniform approach by applying Machine Learning with Digital Signal Processing (MLDSP)-based structural patterns (M-SP). The extended model was applied to an existing virus/reservoir host dataset and to the SARS-CoV-2 and related viruses and generated an improvement in prediction accuracy. Availability and implementation The source code used in this work is freely available at https://github.com/bill1167/hostgbms. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bill Lee
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Rd., Pok Fu Lam, Hong Kong
| | - David K Smith
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Rd., Pok Fu Lam, Hong Kong
| | - Yi Guan
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Rd., Pok Fu Lam, Hong Kong.,Joint Institute of Virology (Shantou University and The University of Hong Kong), Guangdong-Hongkong Joint Laboratory of Emerging Infectious Diseases, Shantou University, Shantou, P. R. China
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66
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Lu YY, Bai J, Wang Y, Wang Y, Sun F. CRAFT: Compact genome Representation toward large-scale Alignment-Free daTabase. Bioinformatics 2021; 37:155-161. [PMID: 32766810 DOI: 10.1093/bioinformatics/btaa699] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 03/11/2020] [Accepted: 07/28/2020] [Indexed: 01/02/2023] Open
Abstract
MOTIVATION Rapid developments in sequencing technologies have boosted generating high volumes of sequence data. To archive and analyze those data, one primary step is sequence comparison. Alignment-free sequence comparison based on k-mer frequencies offers a computationally efficient solution, yet in practice, the k-mer frequency vectors for large k of practical interest lead to excessive memory and storage consumption. RESULTS We report CRAFT, a general genomic/metagenomic search engine to learn compact representations of sequences and perform fast comparison between DNA sequences. Specifically, given genome or high throughput sequencing data as input, CRAFT maps the data into a much smaller embedding space and locates the best matching genome in the archived massive sequence repositories. With 102-104-fold reduction of storage space, CRAFT performs fast query for gigabytes of data within seconds or minutes, achieving comparable performance as six state-of-the-art alignment-free measures. AVAILABILITY AND IMPLEMENTATION CRAFT offers a user-friendly graphical user interface with one-click installation on Windows and Linux operating systems, freely available at https://github.com/jiaxingbai/CRAFT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yang Young Lu
- Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Jiaxing Bai
- Department of Automation, Xiamen University, Xiamen 361000, China
| | - Yiwen Wang
- Department of Automation, Xiamen University, Xiamen 361000, China
| | - Ying Wang
- Department of Automation, Xiamen University, Xiamen 361000, China.,Xiamen Key Lab. of Big Data Intelligent Analysis and Decision, Xiamen 361000, China
| | - Fengzhu Sun
- Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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67
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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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68
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Jacobus AP, Stephens TG, Youssef P, González-Pech R, Ciccotosto-Camp MM, Dougan KE, Chen Y, Basso LC, Frazzon J, Chan CX, Gross J. Comparative Genomics Supports That Brazilian Bioethanol Saccharomyces cerevisiae Comprise a Unified Group of Domesticated Strains Related to Cachaça Spirit Yeasts. Front Microbiol 2021; 12:644089. [PMID: 33936002 PMCID: PMC8082247 DOI: 10.3389/fmicb.2021.644089] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 03/08/2021] [Indexed: 01/05/2023] Open
Abstract
Ethanol production from sugarcane is a key renewable fuel industry in Brazil. Major drivers of this alcoholic fermentation are Saccharomyces cerevisiae strains that originally were contaminants to the system and yet prevail in the industrial process. Here we present newly sequenced genomes (using Illumina short-read and PacBio long-read data) of two monosporic isolates (H3 and H4) of the S. cerevisiae PE-2, a predominant bioethanol strain in Brazil. The assembled genomes of H3 and H4, together with 42 draft genomes of sugarcane-fermenting (fuel ethanol plus cachaça) strains, were compared against those of the reference S288C and diverse S. cerevisiae. All genomes of bioethanol yeasts have amplified SNO2(3)/SNZ2(3) gene clusters for vitamin B1/B6 biosynthesis, and display ubiquitous presence of a particular family of SAM-dependent methyl transferases, rare in S. cerevisiae. Widespread amplifications of quinone oxidoreductases YCR102C/YLR460C/YNL134C, and the structural or punctual variations among aquaporins and components of the iron homeostasis system, likely represent adaptations to industrial fermentation. Interesting is the pervasive presence among the bioethanol/cachaça strains of a five-gene cluster (Region B) that is a known phylogenetic signature of European wine yeasts. Combining genomes of H3, H4, and 195 yeast strains, we comprehensively assessed whole-genome phylogeny of these taxa using an alignment-free approach. The 197-genome phylogeny substantiates that bioethanol yeasts are monophyletic and closely related to the cachaça and wine strains. Our results support the hypothesis that biofuel-producing yeasts in Brazil may have been co-opted from a pool of yeasts that were pre-adapted to alcoholic fermentation of sugarcane for the distillation of cachaça spirit, which historically is a much older industry than the large-scale fuel ethanol production.
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Affiliation(s)
- Ana Paula Jacobus
- Laboratory for Genomics and Experimental Evolution of Yeasts, Institute for Bioenergy Research, São Paulo State University, Rio Claro, Brazil
| | - Timothy G Stephens
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Pierre Youssef
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Raul González-Pech
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Michael M Ciccotosto-Camp
- Australian Centre for Ecogenomics, The University of Queensland, Brisbane, QLD, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Katherine E Dougan
- Australian Centre for Ecogenomics, The University of Queensland, Brisbane, QLD, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Yibi Chen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.,Australian Centre for Ecogenomics, The University of Queensland, Brisbane, QLD, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Luiz Carlos Basso
- Biological Science Department, Escola Superior de Agricultura Luiz de Queiroz, University of São Paulo (USP), Piracicaba, Brazil
| | - Jeverson Frazzon
- Institute of Food Science and Technology, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Cheong Xin Chan
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.,Australian Centre for Ecogenomics, The University of Queensland, Brisbane, QLD, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Jeferson Gross
- Laboratory for Genomics and Experimental Evolution of Yeasts, Institute for Bioenergy Research, São Paulo State University, Rio Claro, Brazil
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69
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Jonkheer EM, Brankovics B, Houwers IM, van der Wolf JM, Bonants PJM, Vreeburg RAM, Bollema R, de Haan JR, Berke L, Smit S, de Ridder D, van der Lee TAJ. The Pectobacterium pangenome, with a focus on Pectobacterium brasiliense, shows a robust core and extensive exchange of genes from a shared gene pool. BMC Genomics 2021; 22:265. [PMID: 33849459 PMCID: PMC8045196 DOI: 10.1186/s12864-021-07583-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/26/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Bacterial plant pathogens of the Pectobacterium genus are responsible for a wide spectrum of diseases in plants, including important crops such as potato, tomato, lettuce, and banana. Investigation of the genetic diversity underlying virulence and host specificity can be performed at genome level by using a comprehensive comparative approach called pangenomics. A pangenomic approach, using newly developed functionalities in PanTools, was applied to analyze the complex phylogeny of the Pectobacterium genus. We specifically used the pangenome to investigate genetic differences between virulent and avirulent strains of P. brasiliense, a potato blackleg causing species dominantly present in Western Europe. RESULTS Here we generated a multilevel pangenome for Pectobacterium, comprising 197 strains across 19 species, including type strains, with a focus on P. brasiliense. The extensive phylogenetic analysis of the Pectobacterium genus showed robust distinct clades, with most detail provided by 452,388 parsimony-informative single-nucleotide polymorphisms identified in single-copy orthologs. The average Pectobacterium genome consists of 47% core genes, 1% unique genes, and 52% accessory genes. Using the pangenome, we zoomed in on differences between virulent and avirulent P. brasiliense strains and identified 86 genes associated to virulent strains. We found that the organization of genes is highly structured and linked with gene conservation, function, and transcriptional orientation. CONCLUSION The pangenome analysis demonstrates that evolution in Pectobacteria is a highly dynamic process, including gene acquisitions partly in clusters, genome rearrangements, and loss of genes. Pectobacterium species are typically not characterized by a set of species-specific genes, but instead present themselves using new gene combinations from the shared gene pool. A multilevel pangenomic approach, fusing DNA, protein, biological function, taxonomic group, and phenotypes, facilitates studies in a flexible taxonomic context.
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Affiliation(s)
- Eef M Jonkheer
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.
- Biointeractions and Plant Health, Wageningen Plant Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.
| | - Balázs Brankovics
- Biointeractions and Plant Health, Wageningen Plant Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Ilse M Houwers
- Biointeractions and Plant Health, Wageningen Plant Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Jan M van der Wolf
- Biointeractions and Plant Health, Wageningen Plant Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Peter J M Bonants
- Biointeractions and Plant Health, Wageningen Plant Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Robert A M Vreeburg
- Nederlandse Algemene Keuringsdienst voor zaaizaad en pootgoed van landbouwgewassen, Randweg 14, 8304 AS, Emmeloord, The Netherlands
| | - Robert Bollema
- Nederlandse Algemene Keuringsdienst voor zaaizaad en pootgoed van landbouwgewassen, Randweg 14, 8304 AS, Emmeloord, The Netherlands
| | - Jorn R de Haan
- Genetwister Technologies B.V, Nieuwe Kanaal 7b, 6709 PA, Wageningen, The Netherlands
| | - Lidija Berke
- Genetwister Technologies B.V, Nieuwe Kanaal 7b, 6709 PA, Wageningen, The Netherlands
| | - Sandra Smit
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Theo A J van der Lee
- Biointeractions and Plant Health, Wageningen Plant Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
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70
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Sequence Comparison Without Alignment: The SpaM Approaches. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2231:121-134. [PMID: 33289890 DOI: 10.1007/978-1-0716-1036-7_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Sequence alignment is at the heart of DNA and protein sequence analysis. For the data volumes that are nowadays produced by massively parallel sequencing technologies, however, pairwise and multiple alignment methods are often too slow. Therefore, fast alignment-free approaches to sequence comparison have become popular in recent years. Most of these approaches are based on word frequencies, for words of a fixed length, or on word-matching statistics. Other approaches are using the length of maximal word matches. While these methods are very fast, most of them rely on ad hoc measures of sequences similarity or dissimilarity that are hard to interpret. In this chapter, I describe a number of alignment-free methods that we developed in recent years. Our approaches are based on spaced-word matches ("SpaM"), i.e. on inexact word matches, that are allowed to contain mismatches at certain pre-defined positions. Unlike most previous alignment-free approaches, our approaches are able to accurately estimate phylogenetic distances between DNA or protein sequences using a stochastic model of molecular evolution.
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71
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Du N, Shang J, Sun Y. Improving protein domain classification for third-generation sequencing reads using deep learning. BMC Genomics 2021; 22:251. [PMID: 33836667 PMCID: PMC8033682 DOI: 10.1186/s12864-021-07468-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 02/19/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads. RESULTS In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification. CONCLUSIONS In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.
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Affiliation(s)
- Nan Du
- Computer Science and Engineering, Michigan State University, East Lansing, 48824 USA
| | - Jiayu Shang
- Electrical Engineering, City University of Hong Kong, Hong Kong, People’s Republic of China
| | - Yanni Sun
- Electrical Engineering, City University of Hong Kong, Hong Kong, People’s Republic of China
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72
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Břinda K, Baym M, Kucherov G. Simplitigs as an efficient and scalable representation of de Bruijn graphs. Genome Biol 2021; 22:96. [PMID: 33823902 PMCID: PMC8025321 DOI: 10.1186/s13059-021-02297-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 02/10/2021] [Indexed: 12/30/2022] Open
Abstract
de Bruijn graphs play an essential role in bioinformatics, yet they lack a universal scalable representation. Here, we introduce simplitigs as a compact, efficient, and scalable representation, and ProphAsm, a fast algorithm for their computation. For the example of assemblies of model organisms and two bacterial pan-genomes, we compare simplitigs to unitigs, the best existing representation, and demonstrate that simplitigs provide a substantial improvement in the cumulative sequence length and their number. When combined with the commonly used Burrows-Wheeler Transform index, simplitigs reduce memory, and index loading and query times, as demonstrated with large-scale examples of GenBank bacterial pan-genomes.
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Affiliation(s)
- Karel Břinda
- Department of Biomedical Informatics and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA and Broad Institute of MIT and Harvard, Cambridge, USA.
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Michael Baym
- Department of Biomedical Informatics and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA and Broad Institute of MIT and Harvard, Cambridge, USA
| | - Gregory Kucherov
- CNRS/LIGM Univ Gustave Eiffel, Marne-la-Vallée, France
- Skolkovo Institute of Science and Technology, Moscow, Russia
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73
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Rossier V, Warwick Vesztrocy A, Robinson-Rechavi M, Dessimoz C. OMAmer: tree-driven and alignment-free protein assignment to subfamilies outperforms closest sequence approaches. Bioinformatics 2021; 37:2866-2873. [PMID: 33787851 PMCID: PMC8479680 DOI: 10.1093/bioinformatics/btab219] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/18/2021] [Accepted: 03/30/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Assigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive. RESULTS Here, we first show that in multiple animal and plant datasets, 18-62% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using evolutionarily informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND. AVAILABILITYAND IMPLEMENTATION OMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Victor Rossier
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Alex Warwick Vesztrocy
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Marc Robinson-Rechavi
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland,Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland,To whom correspondence should be addressed. or
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland,Department of Genetics, Evolution, and Environment, University College London, London, WC1E 6BT, UK,Department of Computer Science, University College London, London, WC1E 6BT, UK,To whom correspondence should be addressed. or
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74
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Girgis HZ, James BT, Luczak BB. Identity: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models. NAR Genom Bioinform 2021; 3:lqab001. [PMID: 33554117 PMCID: PMC7850047 DOI: 10.1093/nargab/lqab001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 12/07/2020] [Accepted: 01/08/2021] [Indexed: 11/12/2022] Open
Abstract
Pairwise global alignment is a fundamental step in sequence analysis. Optimal alignment algorithms are quadratic-slow especially on long sequences. In many applications that involve large sequence datasets, all what is needed is calculating the identity scores (percentage of identical nucleotides in an optimal alignment-including gaps-of two sequences); there is no need for visualizing how every two sequences are aligned. For these applications, we propose Identity, which produces global identity scores for a large number of pairs of DNA sequences using alignment-free methods and self-supervised general linear models. For the first time, the new tool can predict pairwise identity scores in linear time and space. On two large-scale sequence databases, Identity provided the best compromise between sensitivity and precision while being faster than BLAST, Mash, MUMmer4 and USEARCH by 2-80 times. Identity was the best performing tool when searching for low-identity matches. While constructing phylogenetic trees from about 6000 transcripts, the tree due to the scores reported by Identity was the closest to the reference tree (in contrast to andi, FSWM and Mash). Identity is capable of producing pairwise identity scores of millions-of-nucleotides-long bacterial genomes; this task cannot be accomplished by any global-alignment-based tool. Availability: https://github.com/BioinformaticsToolsmith/Identity.
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Affiliation(s)
- Hani Z Girgis
- Bioinformatics Toolsmith Laboratory, Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, 700 University Boulevard, Kingsville, TX 78363, USA
| | - Benjamin T James
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA
| | - Brian B Luczak
- Department of Mathematics, Vanderbilt University, 1326 Stevenson Center Lane, Nashville, TN 3721, USA
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75
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Chakraborty A, Morgenstern B, Bandyopadhyay S. S-conLSH: alignment-free gapped mapping of noisy long reads. BMC Bioinformatics 2021; 22:64. [PMID: 33573603 PMCID: PMC7879691 DOI: 10.1186/s12859-020-03918-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 12/02/2020] [Indexed: 11/16/2022] Open
Abstract
Background The advancement of SMRT technology has unfolded new opportunities of genome analysis with its longer read length and low GC bias. Alignment of the reads to their appropriate positions in the respective reference genome is the first but costliest step of any analysis pipeline based on SMRT sequencing. However, the state-of-the-art aligners often fail to identify distant homologies due to lack of conserved regions, caused by frequent genetic duplication and recombination. Therefore, we developed a novel alignment-free method of sequence mapping that is fast and accurate. Results We present a new mapper called S-conLSH that uses Spaced context based Locality Sensitive Hashing. With multiple spaced patterns, S-conLSH facilitates a gapped mapping of noisy long reads to the corresponding target locations of a reference genome. We have examined the performance of the proposed method on 5 different real and simulated datasets. S-conLSH is at least 2 times faster than the recently developed method lordFAST. It achieves a sensitivity of 99%, without using any traditional base-to-base alignment, on human simulated sequence data. By default, S-conLSH provides an alignment-free mapping in PAF format. However, it has an option of generating aligned output as SAM-file, if it is required for any downstream processing. Conclusions S-conLSH is one of the first alignment-free reference genome mapping tools achieving a high level of sensitivity. The spaced-context is especially suitable for extracting distant similarities. The variable-length spaced-seeds or patterns add flexibility to the proposed algorithm by introducing gapped mapping of the noisy long reads. Therefore, S-conLSH may be considered as a prominent direction towards alignment-free sequence analysis.
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Affiliation(s)
- Angana Chakraborty
- Department of Computer Science, West Bengal Education Service, Kolkata, India
| | - Burkhard Morgenstern
- Department of Bioinformatics (IMG), University of Göttingen, 37077, Göttingen, Germany.
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76
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Petrillo UF, Palini F, Cattaneo G, Giancarlo R. Alignment-free Genomic Analysis via a Big Data Spark Platform. Bioinformatics 2021; 37:1658-1665. [PMID: 33471066 DOI: 10.1093/bioinformatics/btab014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/28/2020] [Accepted: 01/06/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Alignment-free distance and similarity functions (AF functions, for short) are a well established alternative to pairwise and multiple sequence alignments for many genomic, metagenomic and epigenomic tasks. Due to data-intensive applications, the computation of AF functions is a Big Data problem, with the recent literature indicating that the development of fast and scalable algorithms computing AF functions is a high-priority task. Somewhat surprisingly, despite the increasing popularity of Big Data technologies in computational biology, the development of a Big Data platform for those tasks has not been pursued, possibly due to its complexity. RESULTS We fill this important gap by introducing FADE, the first extensible, efficient and scalable Spark platform for alignment-free genomic analysis. It supports natively eighteen of the best performing AF functions coming out of a recent hallmark benchmarking study. FADE development and potential impact comprises novel aspects of interest. Namely, (a) a considerable effort of distributed algorithms, the most tangible result being a much faster execution time of reference methods like MASH and FSWM; (b) a software design that makes FADE user-friendly and easily extendable by Spark non-specialists; (c) its ability to support data- and compute-intensive tasks. About this, we provide a novel and much needed analysis of how informative and robust AF functions are, in terms of the statistical significance of their output. Our findings naturally extend the ones of the highly regarded benchmarking study, since the functions that can really be used are reduced to a handful of the eighteen included in FADE. AVAILABILITY The software and the datasets are available at https://github.com/fpalini/fade. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Francesco Palini
- Dipartimento di Scienze Statistiche, Università di Roma - La Sapienza, Rome, 00185, Italy
| | - Giuseppe Cattaneo
- Dipartimento di Informatica, Università di Salerno, Fisciano (SA), 84084, Italy
| | - Raffaele Giancarlo
- Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, 90133, Italy
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77
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Gemović B, Perović V, Davidović R, Drljača T, Veljkovic N. Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies. PLoS One 2021; 16:e0244948. [PMID: 33395407 PMCID: PMC7781373 DOI: 10.1371/journal.pone.0244948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 12/21/2020] [Indexed: 11/19/2022] Open
Abstract
For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm-Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.
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Affiliation(s)
- Branislava Gemović
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
- * E-mail:
| | - Vladimir Perović
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidović
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Tamara Drljača
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia
- Heliant d.o.o., Belgrade, Serbia
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78
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Abstract
Inferring phylogenetic relationships among hundreds or thousands of microbial genomes is an increasingly common task. The conventional phylogenetic approach adopts multiple sequence alignment to compare gene-by-gene, concatenated multigene or whole-genome sequences, from which a phylogenetic tree would be inferred. These alignments follow the implicit assumption of full-length contiguity among homologous sequences. However, common events in microbial genome evolution (e.g., structural rearrangements and genetic recombination) violate this assumption. Moreover, aligning hundreds or thousands of sequences is computationally intensive and not scalable to the rate at which genome data are generated. Therefore, alignment-free methods present an attractive alternative strategy. Here we describe a scalable alignment-free strategy to infer phylogenetic relationships using complete genome sequences of bacteria and archaea, based on short, subsequences of length k (k-mers). We describe how this strategy can be extended to infer evolutionary relationships beyond a tree-like structure, to better capture both vertical and lateral signals of microbial evolution.
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79
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Almeida JR, Pratas D, Oliveira JL. A semi-automatic methodology for analysing distributed and private biobanks. Comput Biol Med 2020; 130:104180. [PMID: 33360272 DOI: 10.1016/j.compbiomed.2020.104180] [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: 09/24/2020] [Revised: 12/14/2020] [Accepted: 12/14/2020] [Indexed: 10/22/2022]
Abstract
Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent collaboration between entities, constituting a barrier to emergent personalized and public health challenges, namely the discovery of new druggable targets, identification of disease-causing genetic variants, or the study of rare diseases. In this paper, we propose a semi-automatic methodology for the analysis of distributed and private biobanks. The strategies involved in the proposed methodology efficiently enable the creation and execution of unified genomic studies using distributed repositories, without compromising the information present in the datasets. We apply the methodology to a case study in the current Covid-19, ensuring the combination of the diagnostics from multiple entities while maintaining privacy through a completely identical procedure. Moreover, we show that the methodology follows a simple, intuitive, and practical scheme.
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Affiliation(s)
- João Rafael Almeida
- DETI/IEETA, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Diogo Pratas
- DETI/IEETA, University of Aveiro, Aveiro, Portugal; Department of Virology, University of Helsinki, Helsinki, Finland.
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80
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Song K. Classifying the Lifestyle of Metagenomically-Derived Phages Sequences Using Alignment-Free Methods. Front Microbiol 2020; 11:567769. [PMID: 33304326 PMCID: PMC7693541 DOI: 10.3389/fmicb.2020.567769] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 10/22/2020] [Indexed: 01/20/2023] Open
Abstract
Phages are viruses that infect bacteria. The phages can be classified into two different categories based on their lifestyles: temperate and lytic. Now, the metavirome can generate a large number of fragments from the viral genomic sequences of entire environmental community, which makes it impossible to determine their lifestyles through experiments. Thus, there is a need to development computational methods for annotating phage contigs and making prediction of their lifestyles. Alignment-based methods for classifying phage lifestyle are limited by incomplete assembled genomes and nucleotide databases. Alignment-free methods based on the frequencies of k-mers were widely used for genome and metagenome comparison which did not rely on the completeness of genome or nucleotide databases. To mimic fragmented metagenomic sequences, the temperate and lytic phages genomic sequences were split into non-overlapping fragments with different lengths, then, I comprehensively compared nine alignment-free dissimilarity measures with a wide range of choices of k-mer length and Markov orders for predicting the lifestyles of these phage contigs. The dissimilarity measure, d2S, performed better than other dissimilarity measures for classifying the lifestyles of phages. Thus, I propose that the alignment-free method, d2S, can be used for predicting the lifestyles of phages which derived from the metagenomic data.
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Affiliation(s)
- Kai Song
- School of Mathematics and Statistics, Qingdao University, Qingdao, China
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81
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Horizontal Gene Transfer in Eukaryotes: Not if, but How Much? Trends Genet 2020; 36:915-925. [DOI: 10.1016/j.tig.2020.08.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 07/31/2020] [Accepted: 08/10/2020] [Indexed: 12/17/2022]
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82
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Criscuolo A. On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference. F1000Res 2020; 9:1309. [PMID: 33335719 PMCID: PMC7713896 DOI: 10.12688/f1000research.26930.1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/12/2020] [Indexed: 12/29/2022] Open
Abstract
Recently developed MinHash-based techniques were proven successful in quickly estimating the level of similarity between large nucleotide sequences. This article discusses their usage and limitations in practice to approximating uncorrected distances between genomes, and transforming these pairwise dissimilarities into proper evolutionary distances. It is notably shown that complex distance measures can be easily approximated using simple transformation formulae based on few parameters. MinHash-based techniques can therefore be very useful for implementing fast yet accurate alignment-free phylogenetic reconstruction procedures from large sets of genomes. This last point of view is assessed with a simulation study using a dedicated bioinformatics tool.
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Affiliation(s)
- Alexis Criscuolo
- Hub de Bioinformatique et Biostatistique - Département Biologie Computationnelle, Institut Pasteur, USR 3756, CNRS, 75015 Paris, France
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83
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Pornputtapong N, Acheampong DA, Patumcharoenpol P, Jenjaroenpun P, Wongsurawat T, Jun SR, Yongkiettrakul S, Chokesajjawatee N, Nookaew I. KITSUNE: A Tool for Identifying Empirically Optimal K-mer Length for Alignment-Free Phylogenomic Analysis. Front Bioeng Biotechnol 2020; 8:556413. [PMID: 33072720 PMCID: PMC7538862 DOI: 10.3389/fbioe.2020.556413] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/24/2020] [Indexed: 12/22/2022] Open
Abstract
Genomic DNA is the best “unique identifier” for organisms. Alignment-free phylogenomic analysis, simple, fast, and efficient method to compare genome sequences, relies on looking at the distribution of small DNA sequence of a particular length, referred to as k-mer. The k-mer approach has been explored as a basis for sequence analysis applications, including assembly, phylogenetic tree inference, and classification. Although this approach is not novel, selecting the appropriate k-mer length to obtain the optimal resolution is rather arbitrary. However, it is a very important parameter for achieving the appropriate resolution for genome/sequence distances to infer biologically meaningful phylogenetic relationships. Thus, there is a need for a systematic approach to identify the appropriate k-mer from whole-genome sequences. We present K-mer–length Iterative Selection for UNbiased Ecophylogenomics (KITSUNE), a tool for assessing the empirically optimal k-mer length of any given set of genomes of interest for phylogenomic analysis via a three-step approach based on (1) cumulative relative entropy (CRE), (2) average number of common features (ACF), and (3) observed common features (OCF). Using KITSUNE, we demonstrated the feasibility and reliability of these measurements to obtain empirically optimal k-mer lengths of 11, 17, and ∼34 from large genome datasets of viruses, bacteria, and fungi, respectively. Moreover, we demonstrated a feature of KITSUNE for accurate species identification for the two de novo assembled bacterial genomes derived from error-prone long-reads sequences, and for a published yeast genome. In addition, KITSUNE was used to identify the shortest species-specific k-mer accurately identifying viruses. KITSUNE is freely available at https://github.com/natapol/kitsune.
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Affiliation(s)
- Natapol Pornputtapong
- Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, and Research Unit of DNA Barcoding of Thai Medicinal Plants, Chulalongkorn University, Bangkok, Thailand
| | - Daniel A Acheampong
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States.,Joint Graduate Program in Bioinformatics, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Preecha Patumcharoenpol
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Piroon Jenjaroenpun
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Thidathip Wongsurawat
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Se-Ran Jun
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Suganya Yongkiettrakul
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Nipa Chokesajjawatee
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Intawat Nookaew
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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84
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Huang J, Dai Q, Yao Y, He PA. A Generalized Iterative Map for Analysis of Protein Sequences. Comb Chem High Throughput Screen 2020; 25:381-391. [PMID: 33045963 DOI: 10.2174/1386207323666201012142318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/30/2020] [Accepted: 08/09/2020] [Indexed: 11/22/2022]
Abstract
AIM AND OBJECTIVE The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences. MATERIALS AND METHODS Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species. RESULTS By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach. CONCLUSION Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.
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Affiliation(s)
- Jiahe Huang
- School of Science, Zhejiang Sci-Tech University, Hangzhou,China
| | - Qi Dai
- College of Life Science, Zhejiang Sci-Tech University, Hangzhou,China
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou,China
| | - Ping-An He
- School of Science, Zhejiang Sci-Tech University, Hangzhou,China
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85
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Klötzl F, Haubold B. Phylonium: fast estimation of evolutionary distances from large samples of similar genomes. Bioinformatics 2020; 36:2040-2046. [PMID: 31790149 PMCID: PMC7141870 DOI: 10.1093/bioinformatics/btz903] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/01/2019] [Accepted: 11/28/2019] [Indexed: 11/13/2022] Open
Abstract
Motivation Tracking disease outbreaks by whole-genome sequencing leads to the collection of large samples of closely related sequences. Five years ago, we published a method to accurately compute all pairwise distances for such samples by indexing each sequence. Since indexing is slow, we now ask whether it is possible to achieve similar accuracy when indexing only a single sequence. Results We have implemented this idea in the program phylonium and show that it is as accurate as its predecessor and roughly 100 times faster when applied to all 2678 Escherichia coli genomes contained in ENSEMBL. One of the best published programs for rapidly computing pairwise distances, mash, analyzes the same dataset four times faster but, with default settings, it is less accurate than phylonium. Availability and implementation Phylonium runs under the UNIX command line; its C++ sources and documentation are available from github.com/evolbioinf/phylonium. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fabian Klötzl
- Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany
| | - Bernhard Haubold
- Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany
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86
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Nugent CM, Adamowicz SJ. Alignment-free classification of COI DNA barcode data with the Python package Alfie. METABARCODING AND METAGENOMICS 2020. [DOI: 10.3897/mbmg.4.55815] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Characterization of biodiversity from environmental DNA samples and bulk metabarcoding data is hampered by off-target sequences that can confound conclusions about a taxonomic group of interest. Existing methods for isolation of target sequences rely on alignment to existing reference barcodes, but this can bias results against novel genetic variants. Effectively parsing targeted DNA barcode data from off-target noise improves the quality of biodiversity estimates and biological conclusions by limiting subsequent analyses to a relevant subset of available data. Here, we present Alfie, a Python package for the alignment-free classification of cytochrome c oxidase subunit I (COI) DNA barcode sequences to taxonomic kingdoms. The package determines k-mer frequencies of DNA sequences, and the frequencies serve as input for a neural network classifier that was trained and tested using ~58,000 publicly available COI sequences. The classifier was designed and optimized through a series of tests that allowed for the optimal set of DNA k-mer features and optimal machine learning algorithm to be selected. The neural network classifier rapidly assigns COI sequences of varying lengths to kingdoms with greater than 99% accuracy and is shown to generalize effectively and make accurate predictions about data from previously unseen taxonomic classes. The package contains an application programming interface that allows the Alfie package’s functionality to be extended to different DNA sequence classification tasks to suit a user’s need, including classification of different genes and barcodes, and classification to different taxonomic levels. Alfie is free and publicly available through GitHub (https://github.com/CNuge/alfie) and the Python package index (https://pypi.org/project/alfie/).
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87
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Delibaş E, Arslan A, Şeker A, Diri B. A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up. J Mol Graph Model 2020; 100:107693. [PMID: 32805559 DOI: 10.1016/j.jmgm.2020.107693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 06/15/2020] [Accepted: 07/06/2020] [Indexed: 11/17/2022]
Abstract
DNA sequence similarity analysis is an essential task in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods, which result in high computational cost, alignment-free methods have emerged that calculate similarity by digitizing the sequence in a different space. In this paper, we proposed an alignment-free DNA sequence similarity analysis method based on top-k n-gram matches, with the prediction that common repeating DNA subsections indicate high similarity between DNA sequences. In our method, we determined DNA sequence similarities by measuring similarity among feature vectors created according to top-k n-gram match-up scores without the use of similarity functions. We applied the similarity calculation for three different DNA data sets of different lengths. The phylogenetic relationships revealed by our method show that our trees coincide almost completely with the results of the MEGA software, which is based on sequence alignment. Our findings show that a certain number of frequently recurring common sequence patterns have the power to characterize DNA sequences.
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Affiliation(s)
- Emre Delibaş
- Department of Computer Engineering, Faculty of Engineering, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
| | - Ahmet Arslan
- Department of Computer Engineering, Faculty of Engineering, Selçuk University, 42250, Konya, Turkey.
| | - Abdulkadir Şeker
- Department of Computer Engineering, Faculty of Engineering, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
| | - Banu Diri
- Department of Computer Engineering, Faculty of Electrical and Electronics, Yıldız Technical University, 34349, Ístanbul, Turkey.
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88
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Pratas D, Toppinen M, Pyöriä L, Hedman K, Sajantila A, Perdomo MF. A hybrid pipeline for reconstruction and analysis of viral genomes at multi-organ level. Gigascience 2020; 9:giaa086. [PMID: 32815536 PMCID: PMC7439602 DOI: 10.1093/gigascience/giaa086] [Citation(s) in RCA: 12] [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] [Received: 01/17/2020] [Revised: 05/25/2020] [Accepted: 07/23/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Advances in sequencing technologies have enabled the characterization of multiple microbial and host genomes, opening new frontiers of knowledge while kindling novel applications and research perspectives. Among these is the investigation of the viral communities residing in the human body and their impact on health and disease. To this end, the study of samples from multiple tissues is critical, yet, the complexity of such analysis calls for a dedicated pipeline. We provide an automatic and efficient pipeline for identification, assembly, and analysis of viral genomes that combines the DNA sequence data from multiple organs. TRACESPipe relies on cooperation among 3 modalities: compression-based prediction, sequence alignment, and de novo assembly. The pipeline is ultra-fast and provides, additionally, secure transmission and storage of sensitive data. FINDINGS TRACESPipe performed outstandingly when tested on synthetic and ex vivo datasets, identifying and reconstructing all the viral genomes, including those with high levels of single-nucleotide polymorphisms. It also detected minimal levels of genomic variation between different organs. CONCLUSIONS TRACESPipe's unique ability to simultaneously process and analyze samples from different sources enables the evaluation of within-host variability. This opens up the possibility to investigate viral tissue tropism, evolution, fitness, and disease associations. Moreover, additional features such as DNA damage estimation and mitochondrial DNA reconstruction and analysis, as well as exogenous-source controls, expand the utility of this pipeline to other fields such as forensics and ancient DNA studies. TRACESPipe is released under GPLv3 and is available for free download at https://github.com/viromelab/tracespipe.
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Affiliation(s)
- Diogo Pratas
- Department of Virology, University of Helsinki, Haartmaninkatu 3, Helsinki, 00290, Finland
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro, Portugal
| | - Mari Toppinen
- Department of Virology, University of Helsinki, Haartmaninkatu 3, Helsinki, 00290, Finland
| | - Lari Pyöriä
- Department of Virology, University of Helsinki, Haartmaninkatu 3, Helsinki, 00290, Finland
| | - Klaus Hedman
- Department of Virology, University of Helsinki, Haartmaninkatu 3, Helsinki, 00290, Finland
- HUSLAB, Helsinki University Hospital, Topeliuksenkatu 32, 00290 Helsinki, Finland
| | - Antti Sajantila
- Department of Forensic Medicine, University of Helsinki, Kytösuontie 11, 00300, Helsinki, Finland
- Forensic Medicine Unit, Finnish Institute of Health and Welfare, PO Box 30 FI-00271 Helsinki, Finland
| | - Maria F Perdomo
- Department of Virology, University of Helsinki, Haartmaninkatu 3, Helsinki, 00290, Finland
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89
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Libkind D, Čadež N, Opulente DA, Langdon QK, Rosa CA, Sampaio JP, Gonçalves P, Hittinger CT, Lachance MA. Towards yeast taxogenomics: lessons from novel species descriptions based on complete genome sequences. FEMS Yeast Res 2020; 20:5876348. [DOI: 10.1093/femsyr/foaa042] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 07/23/2020] [Indexed: 01/23/2023] Open
Abstract
ABSTRACT
In recent years, ‘multi-omic’ sciences have affected all aspects of fundamental and applied biological research. Yeast taxonomists, though somewhat timidly, have begun to incorporate complete genomic sequences into the description of novel taxa, taking advantage of these powerful data to calculate more reliable genetic distances, construct more robust phylogenies, correlate genotype with phenotype and even reveal cryptic sexual behaviors. However, the use of genomic data in formal yeast species descriptions is far from widespread. The present review examines published examples of genome-based species descriptions of yeasts, highlights relevant bioinformatic approaches, provides recommendations for new users and discusses some of the challenges facing the genome-based systematics of yeasts.
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Affiliation(s)
- D Libkind
- Centro de Referencia en Levaduras y Tecnología Cervecera (CRELTEC), Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales (IPATEC) – CONICET / Universidad Nacional del Comahue, Bariloche, Argentina
| | - N Čadež
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia
| | - D A Opulente
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Q K Langdon
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI, USA
| | - C A Rosa
- Departamento de Microbiologia, ICB, C.P. 486, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270–901, Brazil
| | - J P Sampaio
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
| | - P Gonçalves
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
| | - C T Hittinger
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - M A Lachance
- Department of Biology, University of Western Ontario, London N6A 5B7, Ontario, Canada
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90
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Bohmann K, Mirarab S, Bafna V, Gilbert MTP. Beyond DNA barcoding: The unrealized potential of genome skim data in sample identification. Mol Ecol 2020; 29:2521-2534. [PMID: 32542933 PMCID: PMC7496323 DOI: 10.1111/mec.15507] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 02/06/2023]
Abstract
Genetic tools are increasingly used to identify and discriminate between species. One key transition in this process was the recognition of the potential of the ca 658bp fragment of the organelle cytochrome c oxidase I (COI) as a barcode region, which revolutionized animal bioidentification and lead, among others, to the instigation of the Barcode of Life Database (BOLD), containing currently barcodes from >7.9 million specimens. Following this discovery, suggestions for other organellar regions and markers, and the primers with which to amplify them, have been continuously proposed. Most recently, the field has taken the leap from PCR-based generation of DNA references into shotgun sequencing-based "genome skimming" alternatives, with the ultimate goal of assembling organellar reference genomes. Unfortunately, in genome skimming approaches, much of the nuclear genome (as much as 99% of the sequence data) is discarded, which is not only wasteful, but can also limit the power of discrimination at, or below, the species level. Here, we advocate that the full shotgun sequence data can be used to assign an identity (that we term for convenience its "DNA-mark") for both voucher and query samples, without requiring any computationally intensive pretreatment (e.g. assembly) of reads. We argue that if reference databases are populated with such "DNA-marks," it will enable future DNA-based taxonomic identification to complement, or even replace PCR of barcodes with genome skimming, and we discuss how such methodology ultimately could enable identification to population, or even individual, level.
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Affiliation(s)
- Kristine Bohmann
- Section for Evolutionary GenomicsThe GLOBE InstituteUniversity of CopenhagenCopenhagenDenmark
| | - Siavash Mirarab
- Department of Electrical and Computer EngineeringUniversity of CaliforniaSan DiegoCAUSA
| | - Vineet Bafna
- Department of Computer Science and EngineeringUniversity of CaliforniaSan DiegoCAUSA
| | - M. Thomas P. Gilbert
- Section for Evolutionary GenomicsThe GLOBE InstituteUniversity of CopenhagenCopenhagenDenmark
- Center for Evolutionary HologenomicsThe GLOBE InstituteUniversity of CopenhagenCopenhagenDenmark
- NTNU University MuseumTrondheimNorway
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91
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ProtPCV: A Fixed Dimensional Numerical Representation of Protein Sequence to Significantly Reduce Sequence Search Time. Interdiscip Sci 2020; 12:276-287. [PMID: 32524529 DOI: 10.1007/s12539-020-00380-w] [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: 11/18/2019] [Revised: 05/19/2020] [Accepted: 06/02/2020] [Indexed: 10/24/2022]
Abstract
Protein sequence is a wealth of experimental information which is yet to be exploited to extract information on protein homologues. Consequently, it is observed from publications that dynamic programming, heuristics and HMM profile-based alignment techniques along with the alignment free techniques do not directly utilize ordered profile of physicochemical properties of a protein to identify its homologue. Also, it is found that these works lack crucial bench-marking or validation in absence of which their incorporation in search engines may appears to be questionable. In this direction this research approach offers fixed dimensional numerical representation of protein sequences extending the concept of periodicity count value of nucleotide types (2017) to accommodate Euclidean distance as direct similarity measure between two proteins. Instead of bench-marking with BLAST and PSI-BLAST only, this new similarity measure was also compared with Needleman-Wunsch and Smith-Waterman. For enhancing the strength of comparison, this work for the first time introduces two novel benchmarking methods based on correlation of "similarity scores" and "proximity of ranked outputs from a standard sequence alignment method" between all possible pairs of search techniques including the new one presented in this paper. It is found that the novel and unique numerical representation of a protein can reduce computational complexity of protein sequence search to the tune of O(log(n)). It may also help implementation of various other similarity-based operation possible, such as clustering, phylogenetic analysis and classification of proteins on the basis of the properties used to build this numerical representation of protein.
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92
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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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93
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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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94
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Acman M, van Dorp L, Santini JM, Balloux F. Large-scale network analysis captures biological features of bacterial plasmids. Nat Commun 2020; 11:2452. [PMID: 32415210 PMCID: PMC7229196 DOI: 10.1038/s41467-020-16282-w] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 04/23/2020] [Indexed: 11/30/2022] Open
Abstract
Many bacteria can exchange genetic material through horizontal gene transfer (HGT) mediated by plasmids and plasmid-borne transposable elements. Here, we study the population structure and dynamics of over 10,000 bacterial plasmids, by quantifying their genetic similarities and reconstructing a network based on their shared k-mer content. We use a community detection algorithm to assign plasmids into cliques, which correlate with plasmid gene content, bacterial host range, GC content, and existing classifications based on replicon and mobility (MOB) types. Further analysis of plasmid population structure allows us to uncover candidates for yet undescribed replicon genes, and to identify transposable elements as the main drivers of HGT at broad phylogenetic scales. Our work illustrates the potential of network-based analyses of the bacterial 'mobilome' and opens up the prospect of a natural, exhaustive classification framework for bacterial plasmids.
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Affiliation(s)
- Mislav Acman
- UCL Genetics Institute, University College London, Gower Street, London, WC1E 6BT, UK.
| | - Lucy van Dorp
- UCL Genetics Institute, University College London, Gower Street, London, WC1E 6BT, UK
| | - Joanne M Santini
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Francois Balloux
- UCL Genetics Institute, University College London, Gower Street, London, WC1E 6BT, UK.
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95
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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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96
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Abstract
Background Phylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues. Recently, an approach to large-scale phylogeny has been proposed that divides a set of species into disjoint subsets, computes trees on the subsets, and then merges the trees together using a computed matrix of pairwise distances between the species. The novel component of these approaches is the last step: Disjoint Tree Merger (DTM) methods. Results We present GTM (Guide Tree Merger), a polynomial time DTM method that adds edges to connect the subset trees, so as to provably minimize the topological distance to a computed guide tree. Thus, GTM performs unblended mergers, unlike the previous DTM methods. Yet, despite the potential limitation, our study shows that GTM has excellent accuracy, generally matching or improving on two previous DTMs, and is much faster than both. Conclusions The proposed GTM approach to the DTM problem is a useful new tool for large-scale phylogenomic analysis, and shows the surprising potential for unblended DTM methods.
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Affiliation(s)
- Vladimir Smirnov
- Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N Goodwin Ave, Urbana, 61801, IL, US
| | - Tandy Warnow
- Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N Goodwin Ave, Urbana, 61801, IL, US.
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97
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Identifying genetic variants underlying phenotypic variation in plants without complete genomes. Nat Genet 2020; 52:534-540. [PMID: 32284578 PMCID: PMC7610390 DOI: 10.1038/s41588-020-0612-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 03/10/2020] [Indexed: 12/11/2022]
Abstract
Structural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies (GWAS). Here, we expand the type of genetic variants detected in GWAS to include major deletions, insertions and rearrangements. We first use raw sequencing data directly to derive short sequences, k-mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k-mers associated with phenotypes to specific genomic regions. Using this approach, we reanalyzed 2,000 traits in Arabidopsis thaliana, tomato and maize populations. Associations identified with k-mers recapitulate those found with SNPs, but with stronger statistical support. Importantly, we discovered new associations with structural variants and with regions missing from reference genomes. Our results demonstrate the power of performing GWAS before linking sequence reads to specific genomic regions, which allows the detection of a wider range of genetic variants responsible for phenotypic variation.
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98
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Dencker T, Leimeister CA, Gerth M, Bleidorn C, Snir S, Morgenstern B. 'Multi-SpaM': a maximum-likelihood approach to phylogeny reconstruction using multiple spaced-word matches and quartet trees. NAR Genom Bioinform 2020; 2:lqz013. [PMID: 33575565 PMCID: PMC7671388 DOI: 10.1093/nargab/lqz013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/31/2019] [Accepted: 10/13/2019] [Indexed: 02/03/2023] Open
Abstract
Word-based or 'alignment-free' methods for phylogeny inference have become popular in recent years. These methods are much faster than traditional, alignment-based approaches, but they are generally less accurate. Most alignment-free methods calculate 'pairwise' distances between nucleic-acid or protein sequences; these distance values can then be used as input for tree-reconstruction programs such as neighbor-joining. In this paper, we propose the first word-based phylogeny approach that is based on 'multiple' sequence comparison and 'maximum likelihood'. Our algorithm first samples small, gap-free alignments involving four taxa each. For each of these alignments, it then calculates a quartet tree and, finally, the program 'Quartet MaxCut' is used to infer a super tree for the full set of input taxa from the calculated quartet trees. Experimental results show that trees produced with our approach are of high quality.
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Affiliation(s)
- Thomas Dencker
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Chris-André Leimeister
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Michael Gerth
- Institute for Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, L69 7ZB Liverpool, UK
| | - Christoph Bleidorn
- Department of Animal Evolution and Biodiversity, Universität Göttingen, Untere Karspüle 2, 37073 Göttingen, Germany
- Museo Nacional de Ciencias Naturales, Spanish National Research Council (CSIC), 28006 Madrid, Spain
| | - Sagi Snir
- Institute of Evolution, Department of Evolutionary and Environmental Biology, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, Israel
| | - Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
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99
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Röhling S, Linne A, Schellhorn J, Hosseini M, Dencker T, Morgenstern B. The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances. PLoS One 2020; 15:e0228070. [PMID: 32040534 PMCID: PMC7010260 DOI: 10.1371/journal.pone.0228070] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 01/08/2020] [Indexed: 12/14/2022] Open
Abstract
We study the number Nk of length-k word matches between pairs of evolutionarily related DNA sequences, as a function of k. We show that the Jukes-Cantor distance between two genome sequences-i.e. the number of substitutions per site that occurred since they evolved from their last common ancestor-can be estimated from the slope of a function F that depends on Nk and that is affine-linear within a certain range of k. Integers kmin and kmax can be calculated depending on the length of the input sequences, such that the slope of F in the relevant range can be estimated from the values F(kmin) and F(kmax). This approach can be generalized to so-called Spaced-word Matches (SpaM), where mismatches are allowed at positions specified by a user-defined binary pattern. Based on these theoretical results, we implemented a prototype software program for alignment-free sequence comparison called Slope-SpaM. Test runs on real and simulated sequence data show that Slope-SpaM can accurately estimate phylogenetic distances for distances up to around 0.5 substitutions per position. The statistical stability of our results is improved if spaced words are used instead of contiguous words. Unlike previous alignment-free methods that are based on the number of (spaced) word matches, Slope-SpaM produces accurate results, even if sequences share only local homologies.
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Affiliation(s)
- Sophie Röhling
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Alexander Linne
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Jendrik Schellhorn
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | | | - Thomas Dencker
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Burkhard Morgenstern
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Göttingen, Germany
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100
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Abstract
Tree of life (ToL) is a metaphorical tree that captures a simplified narrative of the evolutionary course and kinship among all living organisms of today. We have reconstructed a whole-proteome ToL for over 4,000 different extant species for which complete or near-complete genome sequences are available in public databases. The ToL suggests that 1) all extant organisms of this study can be grouped into 2 “Supergroups,” 6 “Major Groups,” or 35+ “Groups”; 2) the order of emergence of the “founders” of all the groups may be assigned on an evolutionary progression scale; and 3) all of the founders of the groups have emerged in a “deep burst” near the root of the ToL—an explosive birth of life’s diversity. An organism tree of life (organism ToL) is a conceptual and metaphorical tree to capture a simplified narrative of the evolutionary course and kinship among the extant organisms. Such a tree cannot be experimentally validated but may be reconstructed based on characteristics associated with the organisms. Since the whole-genome sequence of an organism is, at present, the most comprehensive descriptor of the organism, a whole-genome sequence-based ToL can be an empirically derivable surrogate for the organism ToL. However, experimentally determining the whole-genome sequences of many diverse organisms was practically impossible until recently. We have constructed three types of ToLs for diversely sampled organisms using the sequences of whole genome, of whole transcriptome, and of whole proteome. Of the three, whole-proteome sequence-based ToL (whole-proteome ToL), constructed by applying information theory-based feature frequency profile method, an “alignment-free” method, gave the most topologically stable ToL. Here, we describe the main features of a whole-proteome ToL for 4,023 species with known complete or almost complete genome sequences on grouping and kinship among the groups at deep evolutionary levels. The ToL reveals 1) all extant organisms of this study can be grouped into 2 “Supergroups,” 6 “Major Groups,” or 35+ “Groups”; 2) the order of emergence of the “founders” of all of the groups may be assigned on an evolutionary progression scale; 3) all of the founders of the groups have emerged in a “deep burst” at the very beginning period near the root of the ToL—an explosive birth of life’s diversity.
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