Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

A Semi-Quantitative, Synteny-Based Method to Improve Functional Predictions for Hypothetical and Poorly Annotated Bacterial and Archaeal Genes

Read this article at

Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Abstract

      During microbial evolution, genome rearrangement increases with increasing sequence divergence. If the relationship between synteny and sequence divergence can be modeled, gene clusters in genomes of distantly related organisms exhibiting anomalous synteny can be identified and used to infer functional conservation. We applied the phylogenetic pairwise comparison method to establish and model a strong correlation between synteny and sequence divergence in all 634 available Archaeal and Bacterial genomes from the NCBI database and four newly assembled genomes of uncultivated Archaea from an acid mine drainage (AMD) community. In parallel, we established and modeled the trend between synteny and functional relatedness in the 118 genomes available in the STRING database. By combining these models, we developed a gene functional annotation method that weights evolutionary distance to estimate the probability of functional associations of syntenous proteins between genome pairs. The method was applied to the hypothetical proteins and poorly annotated genes in newly assembled acid mine drainage Archaeal genomes to add or improve gene annotations. This is the first method to assign possible functions to poorly annotated genes through quantification of the probability of gene functional relationships based on synteny at a significant evolutionary distance, and has the potential for broad application.

      Author Summary

      Based on trends between gene sequence divergence and gene order divergence over time, we developed a new synteny-based method to refine functional annotation. This method uses these trends to determine the probability that any two syntenous genes (genes that are sequential in two organisms) are functionally related. Organisms that are distant relatives have few syntenous genes, but these syntenous genes have a very high probability of functional relatedness. We applied this method to newly assembled genomes of co-occurring, uncultivated acid mine drainage Archaea in order to improve their gene annotations. This application revealed important physiological differences between the co-occurring organisms in this clade, including the ability of some but not all of the Archaea to manufacture vitamin B12 and to carry out anaerobic energy metabolism. We also used this method to identify new genes possibly involved in vitamin B12 synthesis, ether lipid synthesis, molybdopterin synthesis and utilization, and microbial immunity through the CRISPR system.

      Related collections

      Most cited references 32

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Prodigal: prokaryotic gene recognition and translation initiation site identification

      Background The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. Results With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. Conclusion We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.
        Bookmark
        • Record: found
        • Abstract: not found
        • Article: not found

        Phylogenies and the Comparative Method

          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Community structure and metabolism through reconstruction of microbial genomes from the environment.

          Microbial communities are vital in the functioning of all ecosystems; however, most microorganisms are uncultivated, and their roles in natural systems are unclear. Here, using random shotgun sequencing of DNA from a natural acidophilic biofilm, we report reconstruction of near-complete genomes of Leptospirillum group II and Ferroplasma type II, and partial recovery of three other genomes. This was possible because the biofilm was dominated by a small number of species populations and the frequency of genomic rearrangements and gene insertions or deletions was relatively low. Because each sequence read came from a different individual, we could determine that single-nucleotide polymorphisms are the predominant form of heterogeneity at the strain level. The Leptospirillum group II genome had remarkably few nucleotide polymorphisms, despite the existence of low-abundance variants. The Ferroplasma type II genome seems to be a composite from three ancestral strains that have undergone homologous recombination to form a large population of mosaic genomes. Analysis of the gene complement for each organism revealed the pathways for carbon and nitrogen fixation and energy generation, and provided insights into survival strategies in an extreme environment.
            Bookmark

            Author and article information

            Affiliations
            [1 ]Department of Environmental Science, Policy, and Management, University of California, Berkeley, California, United States of America
            [2 ]Department of Earth and Planetary Sciences, University of California, Berkeley, California, United States of America
            [3 ]Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, United States of America
            [4 ]Department of Plant and Microbial Biology, University of California, Berkeley, California, United States of America
            University of California Davis, United States of America
            Author notes

            ¤a: Current address: Josephine Bay Paul Center for Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America.

            ¤b: Current address: Department of Environment and Agro-Biotechnologies, Centre de Recherche Public – Gabriel Lippmann, Belvaux, Grand-Duchy of Luxembourg.

            Conceived and designed the experiments: APY JFB. Performed the experiments: APY BCT SLS PW AZ MPT NJ. Analyzed the data: APY. Contributed reagents/materials/analysis tools: BCT. Wrote the paper: APY BCT JFB.

            Contributors
            Role: Editor
            Journal
            PLoS Comput Biol
            plos
            ploscomp
            PLoS Computational Biology
            Public Library of Science (San Francisco, USA )
            1553-734X
            1553-7358
            October 2011
            October 2011
            20 October 2011
            : 7
            : 10
            3197636
            22028637
            PCOMPBIOL-D-11-00418
            10.1371/journal.pcbi.1002230
            (Editor)
            Yelton et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Counts
            Pages: 12
            Categories
            Research Article
            Biology
            Computational Biology
            Genomics
            Microbiology

            Quantitative & Systems biology

            Comments

            Comment on this article