The identification of replication origin in bacterial genomes by cumulated phase signal

Maderankova, Denisa; Sedlář, Karel; Vítek, Martin; Škutková, Helena

doi:10.1109/cibcb.2017.8058561

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…For example, the majority of bacterial genomes have a characteristic arrow shape when the cumulated phase signal begins in the region of replication origin (oriC). This helps to predict the position of the oriC region in newly assembled genomes [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

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Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations

Maderankova

Jugas

Sedlář

et al. 2019

Computational and Structural Biotechnology Journal

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Species delineation based on bacterial genomes is an essential part of the research of prokaryotes. In silico genome-to-genome comparison methods are computationally demanding, but much less tedious and error prone than the wet-lab methods. In this paper, we present a novel method for the delineation of bacterial genomes based on genomic signal processing. The proposed method uses numerical representations of whole bacterial genomes, phase signal and cumulated phase signal, from which four parameters are derived for each genome. The parameters characterize a genome and their calculation is independent of the other genomes comprising a delineation dataset. The delineation itself is processed as a calculation of the parameters' average similarity. The method was statistically verified on 1826 bacterial genomes. A similarity threshold of 96% was set based on the receiver operating characteristic curve that featured sensitivity of 99.78% and specificity of 97.25%. Additionally, comparative analysis on another 33 bacterial genomes was conducted using standard delineation tools as these tools were not able to process the dataset of 1826 genomes using desktop computer. The proposed method achieved comparable or better delineation results in comparison with the standard tools. Besides the excellent delineation results, another great advantage of the method is its small computational demands, which enables the delineation of thousands of genomes on a desktop computer. The calculation of the parameters takes tens of minutes for thousands of genomes. Moreover, they can be calculated in advance by creating a database, meaning the delineation itself is then completed in a matter of seconds.

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Section: Methodsmentioning

confidence: 99%

“…Unfortunately, genomes starting in another position may still occur in the GenBank database. To eliminate the possible influence of different starts, a simple signal-based rearrangement of sequences was made in a similar manner as was used for the purpose of oriC localization [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations

Maderankova

Jugas

Sedlář

et al. 2019

Computational and Structural Biotechnology Journal

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“…All currently available computational methods for the identification of oriC sequences in bacterial chromosomes rely on nucleotide disparities on the leading and lagging strand of the DNA double helix (13)(14)(15)(16)(17). As replication usually extends from oriC bidirectionally, it is one of two chromosomal sites where the leading and lagging strand switch places.…”

Section: Introductionmentioning

confidence: 99%

γBOriS: Identification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning

Sperlea

Muth

Martin

et al. 2019

Preprint

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The biology of bacterial cells is, in general, based on the information encoded on circular chromosomes. Regulation of chromosome replication is an essential process which mostly takes place at the origin of replication (oriC). Identification of high numbers of oriC is a prerequisite to enable systematic studies that could lead to insights of oriC functioning as well as novel drug targets for antibiotic development. Current methods for identyfing oriC sequences rely on chromosome-wide nucleotide disparities and are therefore limited to fully sequenced genomes, leaving a superabundance of genomic fragments unstudied. Here, we present γBOriS (Gammaproteobacterial oriCSearcher), which accurately identifies oriC sequences on gammaproteobacterial chromosomal fragments by employing motif-based DNA classification. Using γBOriS, we created BOriS DB, which currently contains 25,827 oriC sequences from 1,217 species, thus making it the largest available database for oriC sequences to date.

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“…All currently available computational methods for the identification of oriC sequences in bacterial chromosomes rely on nucleotide disparities on the leading and lagging strand of the DNA double helix [16][17][18][19][20] . As replication usually extends from oriC bidirectionally, it is one of two chromosomal sites where the leading and lagging strand switch places.…”

mentioning

confidence: 99%

gammaBOriS: Identification and Taxonomic Classification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning

Sperlea

Muth

Martin

et al. 2020

Sci Rep

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The biology of bacterial cells is, in general, based on information encoded on circular chromosomes. Regulation of chromosome replication is an essential process that mostly takes place at the origin of replication (oriC), a locus unique per chromosome. Identification of high numbers of oriC is a prerequisite for systematic studies that could lead to insights into oriC functioning as well as the identification of novel drug targets for antibiotic development. Current methods for identifying oriC sequences rely on chromosome-wide nucleotide disparities and are therefore limited to fully sequenced genomes, leaving a large number of genomic fragments unstudied. Here, we present gammaBOriS (Gammaproteobacterial oriC Searcher), which identifies oriC sequences on gammaproteobacterial chromosomal fragments. It does so by employing motif-based machine learning methods. Using gammaBOriS, we created BOriS DB, which currently contains 25,827 gammaproteobacterial oriC sequences from 1,217 species, thus making it the largest available database for oriC sequences to date. Furthermore, we present gammaBOriTax, a machine-learning based approach for taxonomic classification of oriC sequences, which was trained on the sequences in BOriS DB. Finally, we extracted the motifs relevant for identification and classification decisions of the models. Our results suggest that machine learning sequence classification approaches can offer great support in functional motif identification. Before every cell division, bacteria need to duplicate their genetic material to ensure that this information can faithfully be passed on to both daughter cells. This essential process, called DNA replication, initiates in a highly regulated manner at chromosomal sites called oriC and is coordinated with many other cellular processes 1,2. With notable exceptions as e.G. Vibrionales, usually, bacteria contain one or multiple copies of a single chromosome, which carries a single oriC sequence 3,4. Since many different proteins need to bind to and act upon oriC for initiation to occur, oriC contains many protein binding sites and DNA motifs 5,6. While there is a high level of variation between oriC sequences of different organisms, there are also nearly universally occurring DNA motifs in oriC sequences 7-9. Central among these are 9 bp short DNA motifs called DnaA boxes, which act as binding sites for the initiator protein DnaA, and exhibit differing protein binding characteristics depending on the exact sequence. Starting from these motifs, DnaA polymerizes and spreads across multiple DnaA boxes and DnaA trio motifs 10 , which then, in interplay with the protein IHF 11 , leads to double helix unwinding at a closely positioned AT-rich region called DNA unwinding element (DUE) so that the replication machinery can be loaded onto the DNA 12,13. As oriC contains binding sites for proteins that relay information on the status of the cell, it can be considered as a biological information compiler and processor 14,15. Taken together, these properties make oriC sequ...

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The identification of replication origin in bacterial genomes by cumulated phase signal

Cited by 3 publications

References 14 publications

Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations

Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations

γBOriS: Identification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning

gammaBOriS: Identification and Taxonomic Classification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning

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