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A high-resolution genomic composition-based method with the ability to distinguish similar bacterial organisms.

ABSTRACT: BACKGROUND:Genomic composition has been found to be species specific and is used to differentiate bacterial species. To date, almost no published composition-based approaches are able to distinguish between most closely related organisms, including intra-genus species and intra-species strains. Thus, it is necessary to develop a novel approach to address this problem. RESULTS:Here, we initially determine that the "tetranucleotide-derived z-value Pearson correlation coefficient" (TETRA) approach is representative of other published statistical methods. Then, we devise a novel method called "Tetranucleotide-derived Z-value Manhattan Distance" (TZMD) and compare it with the TETRA approach. Our results show that TZMD reflects the maximal genome difference, while TETRA does not in most conditions, demonstrating in theory that TZMD provides improved resolution. Additionally, our analysis of real data shows that TZMD improves species differentiation and clearly differentiates similar organisms, including similar species belonging to the same genospecies, subspecies and intraspecific strains, most of which cannot be distinguished by TETRA. Furthermore, TZMD is able to determine clonal strains with the TZMD?=?0 criterion, which intrinsically encompasses identical composition, high average nucleotide identity and high percentage of shared genomes. CONCLUSIONS:Our extensive assessment demonstrates that TZMD has high resolution. This study is the first to propose a composition-based method for differentiating bacteria at the strain level and to demonstrate that composition is also strain specific. TZMD is a powerful tool and the first easy-to-use approach for differentiating clonal and non-clonal strains. Therefore, as the first composition-based algorithm for strain typing, TZMD will facilitate bacterial studies in the future.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC6805505 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A high-resolution genomic composition-based method with the ability to distinguish similar bacterial organisms.

Zhou Yizhuang Y Zhang Wenting W Wu Huixian H Huang Kai K Jin Junfei J

BMC genomics 20191021 1

<h4>Background</h4>Genomic composition has been found to be species specific and is used to differentiate bacterial species. To date, almost no published composition-based approaches are able to distinguish between most closely related organisms, including intra-genus species and intra-species strains. Thus, it is necessary to develop a novel approach to address this problem.<h4>Results</h4>Here, we initially determine that the "tetranucleotide-derived z-value Pearson correlation coefficient" (T ...[more]

PMID: 31638897

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Project description:BackgroundSignificant efforts have recently been put into the investigation of the spatial organization and the chromatin-interaction networks of genomes. Chromosome conformation capture (3C) technology and its derivatives are important tools used in this effort. However, many of these have limitations, such as being limited to one viewpoint, expensive with moderate to low resolution, and/or requiring a large sequencing effort. Techniques like Hi-C provide a genome-wide analysis. However, it requires massive sequencing effort with considerable costs. Here we describe a new technique termed Targeted Chromatin Capture (T2C), to interrogate large selected regions of the genome. T2C provides an unbiased view of the spatial organization of selected loci at superior resolution (single restriction fragment resolution, from 2 to 6 kbp) at much lower costs than Hi-C due to the lower sequencing effort.ResultsWe applied T2C on well-known model regions, the mouse β-globin locus and the human H19/IGF2 locus. In both cases we identified all known chromatin interactions. Furthermore, we compared the human H19/IGF2 locus data obtained from different chromatin conformation capturing methods with T2C data. We observed the same compartmentalization of the locus, but at a much higher resolution (single restriction fragments vs. the common 40 kbp bins) and higher coverage. Moreover, we compared the β-globin locus in two different biological samples (mouse primary erythroid cells and mouse fetal brain), where it is either actively transcribed or not, to identify possible transcriptional dependent interactions. We identified the known interactions in the β-globin locus and the same topological domains in both mouse primary erythroid cells and in mouse fetal brain with the latter having fewer interactions probably due to the inactivity of the locus. Furthermore, we show that interactions due to the important chromatin proteins, Ldb1 and Ctcf, in both tissues can be analyzed easily to reveal their role on transcriptional interactions and genome folding.ConclusionsT2C is an efficient, easy, and affordable with high (restriction fragment) resolution tool to address both genome compartmentalization and chromatin-interaction networks for specific genomic regions at high resolution for both clinical and non-clinical research.

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A high-resolution genomic composition-based method with the ability to distinguish similar bacterial organisms.

Publications

A high-resolution genomic composition-based method with the ability to distinguish similar bacterial organisms.

Similar Datasets

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