Use of artificial genomes in assessing methods for atypical gene detection

Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods - as well as the evaluation and proper implementation of existing methods - relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, "core" genes - those displaying patterns of mutational biases shared among large numbers of genes - are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple "core" gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes - representing those having experienced lateral gene transfer - were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying "atypical" genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently - i.e., they had different sets of strengths and weaknesses - when identifying atypical genes within chimeric artificial genomes. © 2005 Azad and Lawrence

A Benchmark of Parametric Methods for Horizontal Transfers Detection

Horizontal gene transfer (HGT) has appeared to be of importance for prokaryotic species evolution. As a consequence numerous parametric methods, using only the information embedded in the genomes, have been designed to detect HGTs. Numerous reports of incongruencies in results of the different methods applied to the same genomes were published. The use of artificial genomes in which all HGT parameters are controlled allows testing different methods in the same conditions. The results of this benchmark concerning 16 representative parametric methods showed a great variety of efficiencies. Some methods work very poorly whatever the type of HGTs and some depend on the conditions or on the metrics used. The best methods in terms of total errors were those using tetranucleotides as criterion for the window methods or those using codon usage for gene based methods and the Kullback-Leibler divergence metric. Window methods are very sensitive but less specific and detect badly lone isolated gene. On the other hand gene based methods are often very specific but lack of sensitivity. We propose using two methods in combination to get the best of each category, a gene based one for specificity and a window based one for sensitivity

Detecting laterally transferred genes: use of entropic clustering methods and genome position

Most parametric methods for detecting foreign genes in bacterial genomes use a scoring function that measures the atypicality of a gene with respect to the bulk of the genome. Genes whose features are sufficiently atypical—lying beyond a threshold value—are deemed foreign. Yet these methods fail when the range of features of donor genomes overlaps with that of the recipient genome, leading to misclassification of foreign and native genes; existing parametric methods choose threshold parameters to balance these error rates. To circumvent this problem, we have developed a two-pronged approach to minimize the misclassification of genes. First, beyond classifying genes as merely atypical, a gene clustering method based on Jensen–Shannon entropic divergence identifies classes of foreign genes that are also similar to each other. Second, genome position is used to reassign genes among classes whose composition features overlap. This process minimizes the misclassification of either native or foreign genes that are weakly atypical. The performance of this approach was assessed using artificial chimeric genomes and then applied to the well-characterized Escherichia coli K12 genome. Not only were foreign genes identified with a high degree of accuracy, but genes originating from the same donor organism were effectively grouped

Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

BACKGROUND: Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands. RESULTS: We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU) of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. CONCLUSION: SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired genes

The power of phylogenetic approaches to detect horizontally transferred genes

BACKGROUND: Horizontal gene transfer plays an important role in evolution because it sometimes allows recipient lineages to adapt to new ecological niches. High genes transfer frequencies were inferred for prokaryotic and early eukaryotic evolution. Does horizontal gene transfer also impact phylogenetic reconstruction of the evolutionary history of genomes and organisms? The answer to this question depends at least in part on the actual gene transfer frequencies and on the ability to weed out transferred genes from further analyses. Are the detected transfers mainly false positives, or are they the tip of an iceberg of many transfer events most of which go undetected by current methods? RESULTS: Phylogenetic detection methods appear to be the method of choice to infer gene transfers, especially for ancient transfers and those followed by orthologous replacement. Here we explore how well some of these methods perform using in silico transfers between the terminal branches of a gamma proteobacterial, genome based phylogeny. For the experiments performed here on average the AU test at a 5% significance level detects 90.3% of the transfers and 91% of the exchanges as significant. Using the Robinson-Foulds distance only 57.7% of the exchanges and 60% of the donations were identified as significant. Analyses using bipartition spectra appeared most successful in our test case. The power of detection was on average 97% using a 70% cut-off and 94.2% with 90% cut-off for identifying conflicting bipartitions, while the rate of false positives was below 4.2% and 2.1% for the two cut-offs, respectively. For all methods the detection rates improved when more intervening branches separated donor and recipient. CONCLUSION: Rates of detected transfers should not be mistaken for the actual transfer rates; most analyses of gene transfers remain anecdotal. The method and significance level to identify potential gene transfer events represent a trade-off between the frequency of erroneous identification (false positives) and the power to detect actual transfer events

Whole genome evaluation of horizontal transfers in the pathogenic fungus Aspergillus fumigatus

<p>Abstract</p> <p>Background</p> <p>Numerous cases of horizontal transfers (HTs) have been described for eukaryote genomes, but in contrast to prokaryote genomes, no whole genome evaluation of HTs has been carried out. This is mainly due to a lack of parametric methods specially designed to take the intrinsic heterogeneity of eukaryote genomes into account. We applied a simple and tested method based on local variations of genomic signatures to analyze the genome of the pathogenic fungus <it>Aspergillus fumigatus</it>.</p> <p>Results</p> <p>We detected 189 atypical regions containing 214 genes, accounting for about 1 Mb of DNA sequences. However, the fraction of atypical DNA detected was smaller than the average amount detected in the same conditions in prokaryote genomes (3.1% vs 5.6%). It appeared that about one third of these regions contained no annotated genes, a proportion far greater than in prokaryote genomes. When analyzing the origin of these HTs by comparing their signatures to a home made database of species signatures, 3 groups of donor species emerged: bacteria (40%), fungi (25%), and viruses (22%). It is to be noticed that though inter-domain exchanges are confirmed, we only put in evidence very few exchanges between eukaryotic kingdoms.</p> <p>Conclusions</p> <p>In conclusion, we demonstrated that HTs are not negligible in eukaryote genomes, bearing in mind that in our stringent conditions this amount is a floor value, though of a lesser extent than in prokaryote genomes. The biological mechanisms underlying those transfers remain to be elucidated as well as the biological functions of the transferred genes.</p

HSV-1 genome subnuclear positioning and associations with host-cell PML-NBs and centromeres regulate LAT locus transcription during latency in neurons.

Major human pathologies are caused by nuclear replicative viruses establishing life-long latent infection in their host. During latency the genomes of these viruses are intimately interacting with the cell nucleus environment. A hallmark of herpes simplex virus type 1 (HSV-1) latency establishment is the shutdown of lytic genes expression and the concomitant induction of the latency associated (LAT) transcripts. Although the setting up and the maintenance of the latent genetic program is most likely dependent on a subtle interplay between viral and nuclear factors, this remains uninvestigated. Combining the use of in situ fluorescent-based approaches and high-resolution microscopic analysis, we show that HSV-1 genomes adopt specific nuclear patterns in sensory neurons of latently infected mice (28 days post-inoculation, d.p.i.). Latent HSV-1 genomes display two major patterns, called "Single" and "Multiple", which associate with centromeres, and with promyelocytic leukemia nuclear bodies (PML-NBs) as viral DNA-containing PML-NBs (DCP-NBs). 3D-image reconstruction of DCP-NBs shows that PML forms a shell around viral genomes and associated Daxx and ATRX, two PML partners within PML-NBs. During latency establishment (6 d.p.i.), infected mouse TGs display, at the level of the whole TG and in individual cells, a substantial increase of PML amount consistent with the interferon-mediated antiviral role of PML. "Single" and "Multiple" patterns are reminiscent of low and high-viral genome copy-containing neurons. We show that LAT expression is significantly favored within the "Multiple" pattern, which underlines a heterogeneity of LAT expression dependent on the viral genome copy number, pattern acquisition, and association with nuclear domains. Infection of PML-knockout mice demonstrates that PML/PML-NBs are involved in virus nuclear pattern acquisition, and negatively regulate the expression of the LAT. This study demonstrates that nuclear domains including PML-NBs and centromeres are functionally involved in the control of HSV-1 latency, and represent a key level of host/virus interaction

BMC Genomics

BackgroundFirst pass methods based on BLAST match are commonly used as an initial step to separate the different phylogenetic histories of genes in microbial genomes, and target putative horizontal gene transfer (HGT) events. This will continue to be necessary given the rapid growth of genomic data and the technical difficulties in conducting large-scale explicit phylogenetic analyses. However, these methods often produce misleading results due to their inability to resolve indirect phylogenetic links and their vulnerability to stochastic events.ResultsA new computational method of rapid, exhaustive and genome-wide detection of HGT was developed, featuring the systematic analysis of BLAST hit distribution patterns in the context of a priori defined hierarchical evolutionary categories. Genes that fall beyond a series of statistically determined thresholds are identified as not adhering to the typical vertical history of the organisms in question, but instead having a putative horizontal origin. Tests on simulated genomic data suggest that this approach effectively targets atypically distributed genes that are highly likely to be HGT-derived, and exhibits robust performance compared to conventional BLAST-based approaches. This method was further tested on real genomic datasets, including Rickettsia genomes, and was compared to previous studies. Results show consistency with currently employed categories of HGT prediction methods. In-depth analysis of both simulated and real genomic data suggests that the method is notably insensitive to stochastic events such as gene loss, rate variation and database error, which are common challenges to the current methodology. An automated pipeline was created to implement this approach and was made publicly available at: https://github.com/DittmarLab/HGTector. The program is versatile, easily deployed, has a low requirement for computational resources.ConclusionsHGTector is an effective tool for initial or standalone large-scale discovery of candidate HGT-derived genes.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-717) contains supplementary material, which is available to authorized users

Estimating the extent of horizontal gene transfer in metagenomic sequences

<p>Abstract</p> <p>Background</p> <p>Although the extent of horizontal gene transfer (HGT) in complete genomes has been widely studied, its influence in the evolution of natural communities of prokaryotes remains unknown. The availability of metagenomic sequences allows us to address the study of global patterns of prokaryotic evolution in samples from natural communities. However, the methods that have been commonly used for the study of HGT are not suitable for metagenomic samples. Therefore it is important to develop new methods or to adapt existing ones to be used with metagenomic sequences.</p> <p>Results</p> <p>We have created two different methods that are suitable for the study of HGT in metagenomic samples. The methods are based on phylogenetic and DNA compositional approaches, and have allowed us to assess the extent of possible HGT events in metagenomes for the first time. The methods are shown to be compatible and quite precise, although they probably underestimate the number of possible events. Our results show that the phylogenetic method detects HGT in between 0.8% and 1.5% of the sequences, while DNA compositional methods identify putative HGT in between 2% and 8% of the sequences. These ranges are very similar to these found in complete genomes by related approaches. Both methods act with a different sensitivity since they probably target HGT events of different ages: the compositional method mostly identifies recent transfers, while the phylogenetic is more suitable for the detections of older events. Nevertheless, the study of the number of HGT events in metagenomic sequences from different communities shows a consistent trend for both methods: the lower amount is found for the sequences of the Sargasso Sea metagenome, while the higher quantity is found in the whale fall metagenome from the bottom of the ocean. The significance of these observations is discussed.</p> <p>Conclusion</p> <p>The computational approaches that are used to find possible HGT events in complete genomes can be adapted to work with metagenomic samples, where a level of high performance is shown in different metagenomic samples. The percentage of possible HGT events that were observed is close to that found for complete genomes, and different microbiomes show diverse ratios of putative HGT events. This is probably related with both environmental factors and the composition in the species of each particular community.</p