Lateral Genetic Transfer Research Articles

Genetic recombination-mediated evolutionary interactions between phages of potential industrial importance and prophages of their hosts within or across the domains of Escherichia, Listeria, Salmonella, Campylobacter, and Staphylococcus

BackgroundThe in-depth understanding of the role of lateral genetic transfer (LGT) in phage-prophage interactions is essential to rationalizing phage applications for human and animal therapy, as well as for food and environmental safety. This in silico study aimed to detect LGT between phages of potential industrial importance and their hosts.MethodsA large array of genetic recombination detection algorithms, implemented in SplitsTree and RDP4, was applied to detect LGT between various Escherichia, Listeria, Salmonella, Campylobacter, Staphylococcus, Pseudomonas, and Vibrio phages and their hosts. PHASTER and RAST were employed respectively to identify prophages across the host genome and to annotate LGT-affected genes with unknown functions. PhageAI was used to gain deeper insights into the life cycle history of recombined phages.ResultsThe split decomposition inferences (bootstrap values: 91.3–100; fit: 91.433-100), coupled with the Phi (0.0-2.836E-12) and RDP4 (P being well below 0.05) statistics, provided strong evidence for LGT between certain Escherichia, Listeria, Salmonella, and Campylobacter virulent phages and prophages of their hosts. The LGT events entailed mainly the phage genes encoding for hypothetical proteins, while some of these genetic loci appeared to have been affected even by intergeneric recombination in specific E. coli and S. enterica virulent phages when interacting with their host prophages. Moreover, it is shown that certain L. monocytogenes virulent phages could serve at least as the donors of the gene loci, involved in encoding for the basal promoter specificity factor, for L. monocytogenes. In contrast, the large genetic clusters were determined to have been simultaneously exchanged by many S. aureus prophages and some Staphylococcus temperate phages proposed earlier as potential therapeutic candidates (in their native or modified state). The above genetic clusters were found to encompass multiple genes encoding for various proteins, such as e.g., phage tail proteins, the capsid and scaffold proteins, holins, and transcriptional terminator proteins.ConclusionsIt is suggested that phage-prophage interactions, mediated by LGT (including intergeneric recombination), can have a far-reaching impact on the co-evolutionary trajectories of industrial phages and their hosts especially when excessively present across microbially rich environments.

Identification of a Specific Biomarker of Acinetobacter baumannii Global Clone 1 by Machine Learning and PCR Related to Metabolic Fitness of ESKAPE Pathogens.

Since the emergence of high-risk clones worldwide, constant investigations have been undertaken to comprehend the molecular basis that led to their prevalent dissemination in nosocomial settings over time. So far, the complex and multifactorial genetic traits of this type of epidemic clones have allowed only the identification of biomarkers with low specificity. A machine learning algorithm was able to recognize unequivocally a biomarker for early and accurate detection of Acinetobacter baumannii global clone 1 (GC1), one of the most disseminated high-risk clones. A support vector machine model identified the U1 sequence with a length of 367 nucleotides that matched a fragment of the moaCB gene, which encodes the molybdenum cofactor biosynthesis C and B proteins. U1 differentiates specifically between A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method for early diagnosis based on PCR as shown here. Since the metabolic pathways of Mo enzymes have been recognized as putative therapeutic targets for ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens, our findings highlight that machine learning can also be useful in knowledge gaps of high-risk clones and provides noteworthy support to the literature to identify relevant nosocomial biomarkers for other multidrug-resistant high-risk clones. IMPORTANCE A. baumannii GC1 is an important high-risk clone that rapidly develops extreme drug resistance in the nosocomial niche. Furthermore, several strains have been identified worldwide in environmental samples, exacerbating the risk of human interactions. Early diagnosis is mandatory to limit its dissemination and to outline appropriate antibiotic stewardship schedules. A region with a length of 367 bp (U1) within the moaCB gene that is not subjected to lateral genetic transfer or to antibiotic pressures was successfully found by a support vector machine model that predicts A. baumannii GC1 strains. At the same time, research on the group of Mo enzymes proposed this metabolic pathway related to the superbug's metabolism as a potential future drug target site for ESKAPE pathogens due to its central role in bacterial fitness during infection. These findings confirm that machine learning used for the identification of biomarkers of high-risk lineages can also serve to identify putative novel therapeutic target sites.

A Global Survey of Hypervirulent Aeromonas hydrophila (vAh) Identified vAh Strains in the Lower Mekong River Basin and Diverse Opportunistic Pathogens from Farmed Fish and Other Environmental Sources.

Hypervirulent Aeromonas hydrophila (vAh) has emerged as the etiologic agent of epidemic outbreaks of motile Aeromonas septicemia (MAS) in high-density aquaculture of farmed carp in China and catfish in the United States, which has caused millions of tons of lost fish. We conducted a global survey to better understand the evolution, geographical distribution, and phylogeny of vAh. Aeromonas isolates were isolated from fish that showed clinical symptoms of MAS, and pure cultures were screened for the ability to utilize myo-inositol as the sole carbon source. A total of 113 myo-inositol-utilizing bacterial strains were included in this study, including additional strains obtained from previously published culture collections. Based on a gyrB phylogeny, this collection included 66 A. hydrophila isolates, 48 of which were vAh. This collection also included five new vAh isolates from diseased Pangas catfish (Pangasius pangasius) and striped catfish (Pangasianodon hypophthalmus) obtained in Cambodia and Vietnam, respectively. Genome sequences were generated from representative vAh and non-vAh isolates to evaluate the potential for lateral genetic transfer of the myo-inositol catabolism pathway. Phylogenetic analyses of each of the nine genes required for myo-inositol utilization revealed the close affiliation of vAh strains regardless of geographic origin and suggested lateral genetic transfer of this catabolic pathway from an Enterobacter species. Prediction of virulence factors was conducted to determine differences between vAh and non-vAh strains in terms of virulence and secretion systems. Core genome phylogenetic analyses on vAh isolates and Aeromonas spp. disease isolates (55 in total) were conducted to evaluate the evolutionary relationships among vAh and other Aeromonas sp. isolates, which supported the clonal nature of vAh isolates. IMPORTANCE This global survey of vAh brought together scientists that study fish disease to evaluate the evolution, geographical distribution, phylogeny, and hosts of vAh and other Aeromonas sp. isolates. In addition to vAh isolates from China and the United States, four new vAh isolates were isolated from the lower Mekong River basin in Cambodia and Vietnam, indicating the significant threat of vAh to modern aquaculture and the need for improved biosecurity to prevent vAh spread.

Novel Mobile Integrons and Strain-Specific Integrase Genes within Shewanella spp. Unveil Multiple Lateral Genetic Transfer Events within The Genus.

Shewanella spp. are Gram-negative bacteria that thrive in aquatic niches and also can cause infectious diseases as opportunistic pathogens. Chromosomal (CI) and mobile integrons (MI) were previously described in some Shewanella isolates. Here, we evaluated the occurrence of integrase genes, the integron systems and their genetic surroundings in the genus. We identified 22 integrase gene types, 17 of which were newly described, showing traits of multiple events of lateral genetic transfer (LGT). Phylogenetic analysis showed that most of them were strain-specific, except for Shewanella algae, where SonIntIA-like may have co-evolved within the host as typical CIs. It is noteworthy that co-existence of up to five different integrase genes within a strain, as well as their wide dissemination to Alteromonadales, Vibrionales, Chromatiales, Oceanospirillales and Enterobacterales was observed. In addition, identification of two novel MIs suggests that continuous LGT events may have occurred resembling the behavior of class 1 integrons. The constant emergence of determinants associated to antimicrobial resistance worldwide, concomitantly with novel MIs in strains capable to harbor several types of integrons, may be an alarming threat for the recruitment of novel antimicrobial resistance gene cassettes in the genus Shewanella, with its consequent contribution towards multidrug resistance in clinical isolates.

Crop type exerts greater influence upon rhizosphere phosphohydrolase gene abundance and phylogenetic diversity than phosphorus fertilization.

Rock phosphate is an alternative form of phosphorus (P) fertilizer; however, there is no information regarding the influence of P fertilizer sources in Brazilian Cerrado soils upon microbial genes coding for phosphohydrolase enzymes in crop rhizospheres. Here, we analyze a field experiment comparing maize and sorghum grown under different P fertilization (rock phosphate and triple superphosphate) upon crop performance, phosphatase activity and rhizosphere microbiomes at three levels of diversity: small subunit rRNA marker genes of bacteria, archaea and fungi; a suite of alkaline and acid phosphatase and phytase genes; and ecotypes of individual genes. We found no significant difference in crop performance between the fertilizer sources, but the accumulation of fertilizer P into pools of organic soil P differed. Phosphatase activity was the only biological parameter influenced by P fertilization. Differences in rhizosphere microbiomes were observed at all levels of biodiversity due to crop type, but not fertilization. Inspection of phosphohydrolase gene ecotypes responsible for differences between the crops suggests a role for lateral genetic transfer in establishing ecotype distributions. Moreover, they were not reflected in microbial community composition, suggesting that they confer competitive advantage to individual cells rather than species in the sorghum rhizosphere.

Autoimmunity in celiac disease: Extra-intestinal manifestations.

Celiac disease is an autoimmune condition of the small intestine caused by prolamins in genetically susceptible individuals evoked by multiple environmental factors. The pathological luminal intricate eco-events produce multiple signals that irradiate the entire body, resulting in a plethora of extra-intestinal manifestations. Nutrients, dysbiosis, dysbiotic components and their mobilome, post-translational modification of naive proteins, inter-enterocyte's tight junction dysfunction resulting in a leaky gut, microbial lateral genetic transfer of virulent genes, the sensing network of the enteric nervous systems and the ensuing pro-inflammatory messengers are mutually orchestrating the autoimmune interplay. Genetic-environmental-luminal events-mucosal changes are driving centrifugally the remote organs autoimmunity, establishing extra-intestinal multi organ injury. Exploring the underlying intestinal eco-events, the sensing and the delivery pathways and mechanisms that induce the peripheral tissues' damages might unravel new therapeutical strategies to prevent and help the gluten affected patients.

K-mer Similarity, Networks of Microbial Genomes, and Taxonomic Rank.

Microbial 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 on k-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, particularly Proteobacteria. However, the signal from the other chromosomal regions is restricted in breadth. We show that mean k-mer similarity can correlate with taxonomic rank. We also link the implicated k-mers to genome annotation (thus, functions) and define core k-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 among Spirochaetes, whereas energy production and conversion are not highly conserved among the largely parasitic or commensal Tenericutes. These observations suggest that differential conservation of functions relates to niche specialization and evolutionary diversification of microbes. Our results demonstrate that k-mer approaches can be used to efficiently identify phylogenomic signals and conserved core functions at the multigenome scale. IMPORTANCE Genome 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, particularly Proteobacteria. 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.

Calculating the Unrooted Subtree Prune-and-Regraft Distance.

The subtree prune-and-regraft (SPR) distance metric is a fundamental way of comparing evolutionary trees. It has wide-ranging applications, such as to study lateral genetic transfer, viral recombination, and Markov chain Monte Carlo phylogenetic inference. Although the rooted version of SPR distance can be computed relatively efficiently between rooted trees using fixed-parameter-tractable maximum agreement forest (MAF) algorithms, no MAF formulation is known for the unrooted case. Correspondingly, previous algorithms are unable to compute unrooted SPR distances larger than 7.

Lateral Antimicrobial Resistance Genetic Transfer is active in the open environment

Historically, the environment has been viewed as a passive deposit of antimicrobial resistance mechanisms, where bacteria show biological cost for maintenance of these genes. Thus, in the absence of antimicrobial pressure, it is expected that they disappear from environmental bacterial communities. To test this scenario, we studied native IntI1 functionality of 11 class 1 integron-positive environmental strains of distant genera collected in cold and subtropical forests of Argentina. We found natural competence and successful site-specific insertion with no significant fitness cost of both aadB and blaVIM-2 antimicrobial resistance gene cassettes, in a model system without antibiotic pressure. A bidirectional flow of antimicrobial resistance gene cassettes between natural and nosocomial habitats is proposed, which implies an active role of the open environment as a reservoir, recipient and source of antimicrobial resistance mechanisms, outlining an environmental threat where novel concepts of rational use of antibiotics are extremely urgent and mandatory.

Robust Inference of Genetic Exchange Communities from Microbial Genomes Using TF-IDF

Bacteria and archaea can exchange genetic material across lineages through processes of lateral genetic transfer (LGT). Collectively, these exchange relationships can be modeled as a network and analyzed using concepts from graph theory. In particular, densely connected regions within an LGT network have been defined as genetic exchange communities (GECs). However, it has been problematic to construct networks in which edges solely represent LGT. Here we apply term frequency-inverse document frequency (TF-IDF), an alignment-free method originating from document analysis, to infer regions of lateral origin in bacterial genomes. We examine four empirical datasets of different size (number of genomes) and phyletic breadth, varying a key parameter (word length k) within bounds established in previous work. We map the inferred lateral regions to genes in recipient genomes, and construct networks in which the nodes are groups of genomes, and the edges natively represent LGT. We then extract maximum and maximal cliques (i.e., GECs) from these graphs, and identify nodes that belong to GECs across a wide range of k. Most surviving lateral transfer has happened within these GECs. Using Gene Ontology enrichment tests we demonstrate that biological processes associated with metabolism, regulation and transport are often over-represented among the genes affected by LGT within these communities. These enrichments are largely robust to change of k.

Recapitulating phylogenies using k-mers: from trees to networks

Ernst Haeckel based his landmark Tree of Life on the supposed ontogenic recapitulation of phylogeny, i.e. that successive embryonic stages during the development of an organism re-trace the morphological forms of its ancestors over the course of evolution. Much of this idea has since been discredited. Today, phylogenies are often based on families of molecular sequences. The standard approach starts with a multiple sequence alignment, in which the sequences are arranged relative to each other in a way that maximises a measure of similarity position-by-position along their entire length. A tree (or sometimes a network) is then inferred. Rigorous multiple sequence alignment is computationally demanding, and evolutionary processes that shape the genomes of many microbes (bacteria, archaea and some morphologically simple eukaryotes) can add further complications. In particular, recombination, genome rearrangement and lateral genetic transfer undermine the assumptions that underlie multiple sequence alignment, and imply that a tree-like structure may be too simplistic. Here, using genome sequences of 143 bacterial and archaeal genomes, we construct a network of phylogenetic relatedness based on the number of shared k-mers (subsequences at fixed length k). Our findings suggest that the network captures not only key aspects of microbial genome evolution as inferred from a tree, but also features that are not treelike. The method is highly scalable, allowing for investigation of genome evolution across a large number of genomes. Instead of using specific regions or sequences from genome sequences, or indeed Haeckel’s idea of ontogeny, we argue that genome phylogenies can be inferred using k-mers from whole-genome sequences. Representing these networks dynamically allows biological questions of interest to be formulated and addressed quickly and in a visually intuitive manner.

Recapitulating phylogenies using k-mers: from trees to networks

Ernst Haeckel based his landmark Tree of Life on the supposed ontogenic recapitulation of phylogeny, i.e. that successive embryonic stages during the development of an organism re-trace the morphological forms of its ancestors over the course of evolution. Much of this idea has since been discredited. Today, phylogenies are often based on families of molecular sequences. The standard approach starts with a multiple sequence alignment, in which the sequences are arranged relative to each other in a way that maximises a measure of similarity position-by-position along their entire length. A tree (or sometimes a network) is then inferred. Rigorous multiple sequence alignment is computationally demanding, and evolutionary processes that shape the genomes of many microbes (bacteria, archaea and some morphologically simple eukaryotes) can add further complications. In particular, recombination, genome rearrangement and lateral genetic transfer undermine the assumptions that underlie multiple sequence alignment, and imply that a tree-like structure may be too simplistic. Here, using genome sequences of 143 bacterial and archaeal genomes, we construct a network of phylogenetic relatedness based on the number of shared k-mers (subsequences at fixed length k). Our findings suggest that the network captures not only key aspects of microbial genome evolution as inferred from a tree, but also features that are not treelike. The method is highly scalable, allowing for investigation of genome evolution across a large number of genomes. Instead of using specific regions or sequences from genome sequences, or indeed Haeckel’s idea of ontogeny, we argue that genome phylogenies can be inferred using k-mers from whole-genome sequences. Representing these networks dynamically allows biological questions of interest to be formulated and addressed quickly and in a visually intuitive manner.

Scaling Up the Phylogenetic Detection of Lateral Gene Transfer Events.

Lateral genetic transfer (LGT) is the process by which genetic material moves between organisms (and viruses) in the biosphere. Among the many approaches developed for the inference of LGT events from DNA sequence data, methods based on the comparison of phylogenetic trees remain the gold standard for many types of problem. Identifying LGT events from sequenced genomes typically involves a series of steps in which homologous sequences are identified and aligned, phylogenetic trees are inferred, and their topologies are compared to identify unexpected or conflicting relationships. These types of approach have been used to elucidate the nature and extent of LGT and its physiological and ecological consequences throughout the Tree of Life. Advances in DNA sequencing technology have led to enormous increases in the number of sequenced genomes, including ultra-deep sampling of specific taxonomic groups and single cell-based sequencing of unculturable "microbial dark matter." Environmental shotgun sequencing enables the study of LGT among organisms that share the same habitat.This abundance of genomic data offers new opportunities for scientific discovery, but poses two key problems. As ever more genomes are generated, the assembly and annotation of each individual genome receives less scrutiny; and with so many genomes available it is tempting to include them all in a single analysis, but thousands of genomes and millions of genes can overwhelm key algorithms in the analysis pipeline. Identifying LGT events of interest therefore depends on choosing the right dataset, and on algorithms that appropriately balance speed and accuracy given the size and composition of the chosen set of genomes.

Recapitulating phylogenies using k-mers: from trees to networks

Ernst Haeckel based his landmark Tree of Life on the supposed ontogenic recapitulation of phylogeny, i.e. that successive embryonic stages during the development of an organism re-trace the morphological forms of its ancestors over the course of evolution. Much of this idea has since been discredited. Today, phylogenies are often based on molecular sequences. A typical phylogenetic inference aims to capture and represent, in the form of a tree, the evolutionary history of a family of molecular sequences. The standard approach starts with a multiple sequence alignment, in which the sequences are arranged relative to each other in a way that maximises a measure of similarity position-by-position along their entire length. However, this approach ignores important evolutionary processes that are known to shape the genomes of microbes (bacteria, archaea and some morphologically simple eukaryotes). Recombination, genome rearrangement and lateral genetic transfer undermine the assumptions that underlie multiple sequence alignment, and imply that a tree-like structure may be too simplistic. Here, using genome sequences of 143 bacterial and archaeal genomes, we construct a network of phylogenetic relatedness based on the number of shared k-mers (subsequences at fixed length k). Our findings suggest that the network captures not only key aspects of microbial genome evolution as inferred from a tree, but also features that are not treelike. The method is highly scalable, allowing for investigation of genome evolution across a large number of genomes. Instead of using specific regions or sequences from genome sequences, or indeed Haeckel's idea of ontogeny, we argue that genome phylogenies can be inferred using k-mers from whole-genome sequences. Representing these networks dynamically allows biological questions of interest to be formulated and addressed quickly and in a visually intuitive manner.

Exploring lateral genetic transfer among microbial genomes using TF-IDF.

Many microbes can acquire genetic material from their environment and incorporate it into their genome, a process known as lateral genetic transfer (LGT). Computational approaches have been developed to detect genomic regions of lateral origin, but typically lack sensitivity, ability to distinguish donor from recipient, and scalability to very large datasets. To address these issues we have introduced an alignment-free method based on ideas from document analysis, term frequency-inverse document frequency (TF-IDF). Here we examine the performance of TF-IDF on three empirical datasets: 27 genomes of Escherichia coli and Shigella, 110 genomes of enteric bacteria, and 143 genomes across 12 bacterial and three archaeal phyla. We investigate the effect of k-mer size, gap size and delineation of groups on the inference of genomic regions of lateral origin, finding an interplay among these parameters and sequence divergence. Because TF-IDF identifies donor groups and delineates regions of lateral origin within recipient genomes, aggregating these regions by gene enables us to explore, for the first time, the mosaic nature of lateral genes including the multiplicity of biological sources, ancestry of transfer and over-writing by subsequent transfers. We carry out Gene Ontology enrichment tests to investigate which biological processes are potentially affected by LGT.

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

Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of k-mers in the sequences. If a set of contiguous k-mers appears sufficiently more frequently in another phyletic group than in its own, we infer that they have been transferred from the first group to the second. We performed rigorous tests of TF-IDF using simulated and empirical datasets. With the simulated data, we tested our method under different parameter settings for sequence length, substitution rate between and within groups and post-LGT, deletion rate, length of transferred region and k size, and found that we can detect LGT events with high precision and recall. Our method performs better than an established method, ALFY, which has high recall but low precision. Our method is efficient, with runtime increasing approximately linearly with sequence length.

Genetic relationship between the Echinococcus granulosus sensu stricto cysts located in lung and liver of hosts

G1 genotype of Echinococcus granulosus sensu stricto is the major cause of hydatidosis in Northern Africa, Tunisia included. The genetic relationship between lung and liver localization were studied in ovine, bovine and human hydatid cysts in Tunisia. Allozyme variation and single strand conformation polymorphism were used for genetic differentiation. The first cause of genetic differentiation was the host species and the second was the localization (lung or liver). The reticulated genetic relationship between the liver or the lung human isolates and isolates from bovine lung, is indicative of recombination (sexual reproduction) or lateral genetic transfer. The idea of two specialized populations (one for the lung one for the liver) that are more or less successful according to host susceptibility is thus proposed.

Alignment-free microbial phylogenomics under scenarios of sequence divergence, genome rearrangement and lateral genetic transfer.

Alignment-free (AF) approaches have recently been highlighted as alternatives to methods based on multiple sequence alignment in phylogenetic inference. However, the sensitivity of AF methods to genome-scale evolutionary scenarios is little known. Here, using simulated microbial genome data we systematically assess the sensitivity of nine AF methods to three important evolutionary scenarios: sequence divergence, lateral genetic transfer (LGT) and genome rearrangement. Among these, AF methods are most sensitive to the extent of sequence divergence, less sensitive to low and moderate frequencies of LGT, and most robust against genome rearrangement. We describe the application of AF methods to three well-studied empirical genome datasets, and introduce a new application of the jackknife to assess node support. Our results demonstrate that AF phylogenomics is computationally scalable to multi-genome data and can generate biologically meaningful phylogenies and insights into microbial evolution.

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

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.

Phylogenomics of Marine Algae

De novo genome and transcriptome data from a number of marine algal species have recently become available, ranging from red, green and brown algae, as well as other photosynthetic eukaryotes, e.g. diatoms and dinofl agellates. Phylogenomic approaches are widely adopted to decipher the evolutionary relationships among diverse lineages. Novel algal genomes therefore provide an exciting analysis platform for understanding algal biology, ecophysiology and diversity, and at a broader scale, eukaryote evolution. In this brief communication, I highlight major fi ndings from recent phylogenomic studies of marine algae and their impact to the research fi eld. I then discuss the current trends and future directions of phylogenomics, and how we can apply this approach in studying biodiversity in the South China Sea. (Keywords: algal genomics, biodiversity, endosymbiosis, lateral genetic transfer, marine algae, phylogenomics)