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      Genomic and metagenomic surveys of hydrogenase distribution indicate H 2 is a widely utilised energy source for microbial growth and survival

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          Abstract

          Recent physiological and ecological studies have challenged the long-held belief that microbial metabolism of molecular hydrogen (H 2) is a niche process. To gain a broader insight into the importance of microbial H 2 metabolism, we comprehensively surveyed the genomic and metagenomic distribution of hydrogenases, the reversible enzymes that catalyse the oxidation and evolution of H 2. The protein sequences of 3286 non-redundant putative hydrogenases were curated from publicly available databases. These metalloenzymes were classified into multiple groups based on (1) amino acid sequence phylogeny, (2) metal-binding motifs, (3) predicted genetic organisation and (4) reported biochemical characteristics. Four groups (22 subgroups) of [NiFe]-hydrogenase, three groups (6 subtypes) of [FeFe]-hydrogenases and a small group of [Fe]-hydrogenases were identified. We predict that this hydrogenase diversity supports H 2-based respiration, fermentation and carbon fixation processes in both oxic and anoxic environments, in addition to various H 2-sensing, electron-bifurcation and energy-conversion mechanisms. Hydrogenase-encoding genes were identified in 51 bacterial and archaeal phyla, suggesting strong pressure for both vertical and lateral acquisition. Furthermore, hydrogenase genes could be recovered from diverse terrestrial, aquatic and host-associated metagenomes in varying proportions, indicating a broad ecological distribution and utilisation. Oxygen content ( pO 2) appears to be a central factor driving the phylum- and ecosystem-level distribution of these genes. In addition to compounding evidence that H 2 was the first electron donor for life, our analysis suggests that the great diversification of hydrogenases has enabled H 2 metabolism to sustain the growth or survival of microorganisms in a wide range of ecosystems to the present day. This work also provides a comprehensive expanded system for classifying hydrogenases and identifies new prospects for investigating H 2 metabolism.

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              Is Open Access

              BLAST+: architecture and applications

              Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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                Author and article information

                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group
                1751-7362
                1751-7370
                March 2016
                25 September 2015
                : 10
                : 3
                : 761-777
                Affiliations
                [1 ] Department of Microbiology and Immunology, University of Otago , Dunedin, New Zealand
                [2 ] The Commonwealth Scientific and Industrial Research Organisation, Land and Water Flagship , Acton, Australian Capital Territory, Australia
                [3 ] GNS Science, Wairakei Research Centre , Taupō, New Zealand
                [4 ] Scion, Te Papa Tipu Innovation Park , Rotorua, New Zealand
                [5 ] Australian National University, Research School of Chemistry , Acton, Australian Capital Territory, Australia
                [6 ] University of Auckland, Maurice Wilkins Centre for Molecular Biodiscovery , Auckland, New Zealand
                Author notes
                [* ] The Commonwealth Scientific and Industrial Organisation, Land and Water Flagship , Clunies Ross Street, Acton, Australian Capital Territory 2060, Australia. E-mail: chris.greening@ 123456csiro.au
                [* ] Department of Microbiology and Immunology, University of Otago , 720 Cumberland Street, North Dunedin, Dunedin 9054, New Zealand. E-mail: sergio.morales@ 123456otago.ac.nz
                Article
                PMC4817680 PMC4817680 4817680 ismej2015153
                10.1038/ismej.2015.153
                4817680
                26405831
                979fa58e-04ad-45b2-bcab-e826e4cec02e
                Copyright © 2016 International Society for Microbial Ecology
                History
                : 03 May 2015
                : 20 June 2015
                : 20 July 2015
                Categories
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