Horizontal gene transfer (HGT) is the transfer of genes between species outside the transmission from parent to offspring. Due to their impact on the genome and biology of various species, HGTs have gained broader attention, but high-throughput methods to robustly identify them are lacking. One rapid method to identify HGT candidates is to calculate the difference in similarity between the most similar gene in closely related species and the most similar gene in distantly related species. Although metrics on similarity associated with taxonomic information can rapidly detect putative HGTs, these methods are hampered by false positives that are difficult to track. Furthermore, they do not inform on the evolutionary trajectory and events such as duplications. Hence, phylogenetic analysis is necessary to confirm HGT candidates and provide a more comprehensive view of their origin and evolutionary history. However, phylogenetic reconstruction requires several time-consuming manual steps to retrieve the homologous sequences, produce a multiple alignment, construct the phylogeny and analyze the topology to assess whether it supports the HGT hypothesis. Here, we present AvP which automatically performs all these steps and detects candidate HGTs within a phylogenetic framework.

MOTIVATION: Identification of conserved motifs in biological sequences is crucial to unveil common shared functions. Many tools exist for motif identification, including some that allow degenerate positions with multiple possible nucleotides or amino acids. Most efficient methods available today search conserved motifs in a set of sequences, but do not check for their specificity regarding to a set of negative sequences. RESULTS: We present a tool to identify degenerate motifs, based on a given classification of amino acids according to their physico-chemical properties. It returns the top K motifs that are most frequent in a positive set of sequences involved in a biological process of interest, and absent from a negative set. Thus, our method discovers discriminative motifs in biological sequences that may be used to identify new sequences involved in the same process. We used this tool to identify candidate effector proteins secreted into plant tissues by the root knot nematode Meloidogyne incognita. Our tool identified a series of motifs specifically present in a positive set of known effectors while totally absent from a negative set of evolutionarily conserved housekeeping proteins. Scanning the proteome of M. incognita, we detected 2579 proteins that contain these specific motifs and can be considered as new putative effectors. AVAILABILITY AND IMPLEMENTATION: The motif discovery tool and the proteins used in the experiments are available at http://dtai.cs.kuleuven.be/ml/systems/merci.

BACKGROUND: Understanding genome evolution provides insight into biological mechanisms. For many years comparative genomics and analysis of conserved chromosomal regions have helped to unravel the mechanisms involved in genome evolution and their implications for the study of biological systems. Detection of conserved regions (descending from a common ancestor) not only helps clarify genome evolution but also makes it possible to identify quantitative trait loci (QTLs) and investigate gene function.The identification and comparison of conserved regions on a genome scale is computationally intensive, making process automation essential. Three key requirements are necessary: consideration of phylogeny to identify orthologs between multiple species, frequent updating of the annotation and panel of compared genomes and computation of statistical tests to assess the significance of identified conserved gene clusters. RESULTS: We developed a modular system superimposed on a multi-agent framework, called CASSIOPE (Clever Agent System for Synteny Inheritance and Other Phenomena in Evolution). CASSIOPE automatically identifies statistically significant conserved regions between multiple genomes based on automated phylogenies and statistical testing. Conserved regions were searched for in 19 species and 1,561 hits were found. To our knowledge, CASSIOPE is the first system to date that integrates evolutionary biology-based concepts and fulfills all three key requirements stated above. All results are available at http://194.57.197.245/cassiopeWeb/displayCluster?clusterId=1 CONCLUSION: CASSIOPE makes it possible to study conserved regions from a chosen query genetic region and to infer conserved gene clusters based on phylogenies and statistical tests assessing the significance of these conserved regions.Source code is freely available, please contact: [email protected].

BACKGROUND: Two of the main objectives of the genomic and post-genomic era are to structurally and functionally annotate genomes which consists of detecting genes' position and structure, and inferring their function (as well as of other features of genomes). Structural and functional annotation both require the complex chaining of numerous different software, algorithms and methods under the supervision of a biologist. The automation of these pipelines is necessary to manage huge amounts of data released by sequencing projects. Several pipelines already automate some of these complex chaining but still necessitate an important contribution of biologists for supervising and controlling the results at various steps. RESULTS: Here we propose an innovative automated platform, FIGENIX, which includes an expert system capable to substitute to human expertise at several key steps. FIGENIX currently automates complex pipelines of structural and functional annotation under the supervision of the expert system (which allows for example to make key decisions, check intermediate results or refine the dataset). The quality of the results produced by FIGENIX is comparable to those obtained by expert biologists with a drastic gain in terms of time costs and avoidance of errors due to the human manipulation of data. CONCLUSION: The core engine and expert system of the FIGENIX platform currently handle complex annotation processes of broad interest for the genomic community. They could be easily adapted to new, or more specialized pipelines, such as for example the annotation of miRNAs, the classification of complex multigenic families, annotation of regulatory elements and other genomic features of interest.