scholarly journals Exploring lateral genetic transfer among microbial genomes using TF-IDF

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yingnan Cong ◽  
Yao-ban Chan ◽  
Mark A. Ragan
Keyword(s):  
Genetic Transfer ◽  
Microbial Genomes ◽  
Lateral Genetic Transfer

scholarly journals k-mer Similarity, Networks of Microbial Genomes, and Taxonomic Rank

mSystems ◽  
2018 ◽  
Vol 3 (6) ◽  
Author(s):  
Guillaume Bernard ◽  
Paul Greenfield ◽  
Mark A. Ragan ◽  
Cheong Xin Chan
Keyword(s):  
Genome Evolution ◽  
Energy Production ◽  
Ribosomal Rnas ◽  
Taxonomic Rank ◽  
Genetic Transfer ◽  
Microbial Genomes ◽  
Evolutionary Diversification ◽  
Niche Specialization ◽  
Alignment Free ◽  
Lateral Genetic Transfer

ABSTRACTMicrobial genomes have been shaped by parent-to-offspring (vertical) descent and lateral genetic transfer. These processes can be distinguished by alignment-based inference and comparison of phylogenetic trees for individual gene families, but this approach is not scalable to whole-genome sequences, and a tree-like structure does not adequately capture how these processes impact microbial physiology. Here we adopted alignment-free approaches based onk-mer statistics to infer phylogenomic networks involving 2,783 completely sequenced bacterial and archaeal genomes and compared the contributions of rRNA, protein-coding, and plasmid sequences to these networks. Our results show that the phylogenomic signal arising from ribosomal RNAs is strong and extends broadly across all taxa, whereas that from plasmids is strong but restricted to closely related groups, particularlyProteobacteria. However, the signal from the other chromosomal regions is restricted in breadth. We show that meank-mer similarity can correlate with taxonomic rank. We also link the implicatedk-mers to genome annotation (thus, functions) and define corek-mers (thus, core functions) in specific phyletic groups. Highly conserved functions in most phyla include amino acid metabolism and transport as well as energy production and conversion. Intracellular trafficking and secretion are the most prominent core functions amongSpirochaetes, whereas energy production and conversion are not highly conserved among the largely parasitic or commensalTenericutes. These observations suggest that differential conservation of functions relates to niche specialization and evolutionary diversification of microbes. Our results demonstrate thatk-mer approaches can be used to efficiently identify phylogenomic signals and conserved core functions at the multigenome scale.IMPORTANCEGenome evolution of microbes involves parent-to-offspring descent, and lateral genetic transfer that convolutes the phylogenomic signal. This study investigated phylogenomic signals among thousands of microbial genomes based on short subsequences without using multiple-sequence alignment. The signal from ribosomal RNAs is strong across all taxa, and the signal of plasmids is strong only in closely related groups, particularlyProteobacteria. However, the signal from other chromosomal regions (∼99% of the genomes) is remarkably restricted in breadth. The similarity of subsequences is found to correlate with taxonomic rank and informs on conserved and differential core functions relative to niche specialization and evolutionary diversification of microbes. These results provide a comprehensive, alignment-free view of microbial genome evolution as a network, beyond a tree-like structure.

scholarly journals Are Protein Domains Modules of Lateral Genetic Transfer?

PLoS ONE ◽  
2009 ◽  
Vol 4 (2) ◽  
pp. e4524 ◽  
Author(s):  
Cheong Xin Chan ◽  
Aaron E. Darling ◽  
Robert G. Beiko ◽  
Mark A. Ragan
Keyword(s):  
Protein Domains ◽  
Genetic Transfer ◽  
Lateral Genetic Transfer

Lateral Genetic Transfer and Cellular Networks

2013 ◽  
pp. 123-135 ◽  
Author(s):  
Elizabeth Skippington ◽  
Mark A. Ragan
Keyword(s):  
Cellular Networks ◽  
Genetic Transfer ◽  
Lateral Genetic Transfer

scholarly journals A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yingnan Cong ◽  
Yao-ban Chan ◽  
Mark A. Ragan
Keyword(s):  
Genetic Transfer ◽  
Alignment Free ◽  
Lateral Genetic Transfer

GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome

2016 ◽  
Vol 14 (01) ◽  
pp. 1640003 ◽  
Author(s):  
Bingxin Lu ◽  
Hon Wai Leong
Keyword(s):  
Parameter Tuning ◽  
Genomic Islands ◽  
Support Vector ◽  
Bacterial Genomes ◽  
Genetic Transfer ◽  
Single Genome ◽  
First Pass ◽  
Lateral Genetic Transfer ◽  
Novel Method ◽  
Sensitive Method

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.

Do different surrogate methods detect lateral genetic transfer events of different relative ages?

2006 ◽  
Vol 14 (1) ◽  
pp. 4-8 ◽  
Author(s):  
Mark A. Ragan ◽  
Timothy J. Harlow ◽  
Robert G. Beiko
Keyword(s):  
Genetic Transfer ◽  
Lateral Genetic Transfer
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