P235 - GCB 2014 - German Conference on Bioinformatics 2014

https://dl.gi.de/handle/20.500.12116/21227

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Konferenzbeitrag
On the limits of computational functional genomics for bacterial lifestyle prediction
(German conference on bioinformatics 2014, 2014) Barbosa, Eudes; Röttger, Richard; Hauschild, Anne-Christin; Azevedo, Vasco; Baumbach, Jan
We review the level of genomic specificity regarding actinobacterial pathogenicity. As they occupy various niches in diverse habitats, one may assume the existence of lifestyle-specific genomic features. We include 240 actinobacteria classified into four pathogenicity classes: human pathogens (HP), broad-spectrum pathogens (BP), opportunistic pathogens (OP), and non-pathogenic (NP). We hypothesize: (H1) Pathogens (HPs and BPs) possess specific pathogenicity signature genes. (H2) The same holds for opportunistic pathogens. (H3) Broadspectrum and exclusively human pathogens cannot be distinguished from each other due to an observation bias, i.e. many HPs might be yet unclassified BPs. (H4) There is no intrinsic genomic characteristic of opportunistic pathogens compared to pathogens, as small mutations are likely to play a more dominant role in order to survive the immune system. To study these hypotheses, we implemented a bioinformatics pipeline that combines evolutionary sequence analysis with statistical learning methods (Random Forest with feature selection, model tuning and robustness analysis). Essentially, we present orthologous gene sets that computationally distinguish pathogens from non-pathogens (H1). We further show a clear limit in differentiating opportunistic pathogens from both, non-pathogens (H2) and pathogens (H4). Human pathogens may also not be distinguished from bacteria annotated as broad-spectrum pathogens based on a small set of orthologous genes only (H3), as many human pathogens might as well target a broad range of mammals but have not been annotated accordingly. In conclusion, we illustrate that even in the post-genome era and despite next-generation sequencing technology our ability to efficiError: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in bfrange block in ToUnicode CMap Error: Illegal entry in 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We now wonder to what extend we can deduce real-world qualitative information from this treasure of data. The aim of this paper was to investigate the power of computational functional genomics to predict bacterial lifestyles utilizing DNA sequence information only. Specifically, we asked the question if we may utilize sequence similarity to identify dedicated lifestyle-specific protein-coding genes. We restrict our report, first, to a set of 240 well-studied actinobacterial genomes and, second, to four pathogenicity lifestyles, namely: human pathogens (HP), broad-spectrum pathogens (BP), opportunistic pathogens (OP), and non-pathogenic (NP). In [EB14], we illustrate and quantify the boundaries we face when trying to deduce a specific microbial pathogenicity class if given the genomic repertoire only, at least in the case of actinobacteria (see figure 1 below). In summary, we show that we indeed find signature genes that differentiate pathogens from non-pathogens. Only a small set of three genes for each bias, i.e. classification direction, is sufficient to reach an approximately 90\% accuracy (figures 2-4). When trying to classify the different pathogenicity lifestyles though, it appears that too many confounding factors unbalance our data sets such that we cannot differentiate, for instance, a strain-specific from a lifestyle-specific gene. We conclude that even in the post-genome era, and even for supposedly simple questions, our ability to efficiently deduce real-world implications from large-scale de novo next-generation sequencing data remains quite limited. 80 Figure 1: Illustration of our bias introduction strategy. Distribution of homologous gene clusters over two lifestyles (pathogens vs. non-pathogens) and illustration of our strategy to

Auflistung P235 - GCB 2014 - German Conference on Bioinformatics 2014 nach Autor:in "Barbosa, Eudes"

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