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Usefulness of rpoB gene sequencing for identification of Afipia and Bosea species, including a strategy for choosing discriminative partial sequences.

ABSTRACT: Bacteria belonging to the genera Afipia and Bosea are amoeba-resisting bacteria that have been recently reported to colonize hospital water supplies and are suspected of being responsible for intensive care unit-acquired pneumonia. Identification of these bacteria is now based on determination of the 16S ribosomal DNA sequence. However, the 16S rRNA gene is not polymorphic enough to ensure discrimination of species defined by DNA-DNA relatedness. The complete rpoB sequences of 20 strains were first determined by both PCR and genome walking methods. The percentage of homology between different species ranged from 83 to 97% and was in all cases lower than that observed with the 16S rRNA gene; this was true even for species that differed in only one position. The taxonomy of Bosea and Afipia is discussed in light of these results. For strain identification that does not require the complete rpoB sequence (4,113 to 4,137 bp), we propose a simple computerized method that allows determination of nucleotide positions of high variability in the sequence that are bordered by conserved sequences and that could be useful for design of universal primers. A fragment of 740 to 752 bp that contained the most highly variable area (positions 408 to 420) was amplified and sequenced with these universal primers for 47 strains. The variability of this sequence allowed identification of all strains and correlated well with results of DNA-DNA relatedness. In the future, this method could be also used for the determination of variability "hot spots" in sets of housekeeping genes, not only for identification purposes but also for increasing the discriminatory power of sequence typing techniques such as multilocus sequence typing.

SUBMITTER: Khamis A

PROVIDER: S-EPMC262318 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOligonucleotide design is known as a time-consuming work in bioinformatics. In order to accelerate and be efficient the oligonucleotide design process, one of widely used approach is the prescreening unreliable regions using a hashing (or seeding) algorithm. Since the seeding algorithm is originally proposed to increase sensitivity for local alignment, the specificity should be considered as well as the sensitivity for the oligonucleotide design problem. However, a measure of evaluating the seeds regarding how adequate and efficient they are in the oligo design is not yet proposed. Here, we propose novel measures of evaluating the seeding algorithms based on the discriminability and the efficiency.ResultsTo evaluate the proposed measures, we examine five seeding algorithms in oligonucleotide design. We carried out a series of experiments to compare the seeding algorithms. As the result, the spaced seed is recorded as the most efficient discriminative seed for oligo design. The performance of transition-constrained seed is slightly lower than the spaced seed. Because BLAT seeding algorithm and Vector seeding algorithm give poor scores in specificity and efficiency, we conclude that these algorithms are not adequate to design oligos. Consequently, we recommend spaced seeds or transition-constrained seeds with 15 approximately 18 weight in order to design oligos with the length of 50 mer. The empirical experiments in real biological data reveal that the recommended seeds show consequently good performance. We also propose a software package which enables the users to get the adequate seeds under their own experimental conditions.ConclusionOur study is valuable to the two points. One is that our study can be applied to the oligo design programs in order to improve the performance by suggesting the experiment-specific seeds. The other is that our study is useful to improve the performance of the mapping assembly in the field of Next-Generation Sequencing. Our proposed measures are originally designed to be used for oligo design but we expect that our study will be helpful to the other genomic tasks.

Project description:BackgroundMotif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.ResultsWe describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.ConclusionUsing artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at http://bioinformatics.org.au/deme/

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Usefulness of rpoB gene sequencing for identification of Afipia and Bosea species, including a strategy for choosing discriminative partial sequences.

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