Skip to content


  • Open Access

Complete genome sequence of Mahella australiensis type strain (50-1 BONT)

  • Johannes Sikorski1,
  • Hazuki Teshima2, 3,
  • Matt Nolan2,
  • Susan Lucas2,
  • Nancy Hammon2,
  • Shweta Deshpande2,
  • Jan-Fang Cheng2,
  • Sam Pitluck2,
  • Konstantinos Liolios2,
  • Ioanna Pagani2,
  • Natalia Ivanova2,
  • Marcel Huntemann2,
  • Konstantinos Mavromatis2,
  • Galina Ovchinikova2,
  • Amrita Pati2,
  • Roxanne Tapia2, 3,
  • Cliff Han2, 3,
  • Lynne Goodwin2, 3,
  • Amy Chen4,
  • Krishna Palaniappan4,
  • Miriam Land2, 5,
  • Loren Hauser2, 5,
  • Olivier D. Ngatchou-Djao6,
  • Manfred Rohde6,
  • Rüdiger Pukall1,
  • Stefan Spring1,
  • Birte Abt1,
  • Markus Göker1,
  • John C. Detter2, 3,
  • Tanja Woyke2,
  • James Bristow2,
  • Victor Markowitz4,
  • Philip Hugenholtz2, 7,
  • Jonathan A. Eisen2, 8,
  • Nikos C. Kyrpides2,
  • Hans-Peter Klenk1 and
  • Alla Lapidus2
Standards in Genomic Sciences20114:4030331

Published: 1 July 2011


Mahella australiensis Bonilla Salinas et al. 2004 is the type species of the genus Mahella, which belongs to the family Thermoanaerobacteraceae. The species is of interest because it differs from other known anaerobic spore-forming bacteria in its G+C content, and in certain phenotypic traits, such as carbon source utilization and relationship to temperature. Moreover, it has been discussed that this species might be an indigenous member of petroleum and oil reservoirs. This is the first completed genome sequence of a member of the genus Mahella and the ninth completed type strain genome sequence from the family Thermoanaerobacteraceae. The 3,135,972 bp long genome with its 2,974 protein-coding and 59 RNA genes is a part of the Genomic Encyclopedia of Bacteria and Archaea project.


strictly anaerobicmotilespore-formingGram-positivemoderately thermophilicchemoorganotrophic Thermoanaerobacteraceae GEBA


Strain 50-1 BONT (= DSM 15567 = CIP 107919) is the type strain of Mahella australiensis, and the type and only species of the monotypic genus Mahella [1,2]. The genus name is derived from the Neo-Latin word Mahella (named in honor of the American microbiologist R. A. Mah, for his important contribution to the taxonomy of anaerobes) [2]. The species epithet is derived from the Neo-Latin word australiensis (related to Australia) [1]. Strain 50-1 BONT was isolated from the Riverslea Oil Field in the Bowen-Surat basin in Queensland, eastern Australia [1]. No further isolates have been reported for M. australiensis. Here we present a summary classification and a set of features for M. australiensis 50-1 BONT, together with the description of the complete genomic sequencing and annotation.

Classification and features

A representative genomic 16S rRNA sequence of M. australiensis was compared using NCBI BLAST under default settings (e.g., considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database [3] and the relative frequencies, weighted by BLAST scores, of taxa and keywords (reduced to their stem [4] were determined. The three most frequent genera were Clostridium (76.6%), Mahella (18.5%) and Pelotomaculum (4.8%) (36 hits in total). Regarding the two hits to sequences from members of the species, the average identity within HSPs was 99.9%, whereas the average coverage by HSPs was 100.0%. Among all other species, the one yielding the highest score was Pelotomaculum isophthalicicum, which corresponded to an identity of 88.5% and a HSP coverage of 49.0%. (Note that the Greengenes databases uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification.) The highest-scoring environmental sequence was DQ378192 (‘oil-polluted soil clone F28 Pitesti’), which showed an identity of 98.5% and a HSP coverage of 98.0%. The five most frequent keywords within the labels of environmental samples which yielded hits were ‘microbi’ (3.7%), ‘anaerob’ (2.9%), ‘digest’ (2.2%), ‘soil’ (2.0%) and ‘thermophil’ (1.7%) (213 hits in total). The five most frequent keywords within the labels of environmental samples which yielded hits of a higher score than the highest scoring species were ‘microbi’ (4.4%), ‘anaerob’ (3.3%), ‘digest’ (3.2%), ‘soil’ (2.6%) and ‘condit, denitrification-induc, paddi, popul, respons, rice’ (1.9%) (123 hits in total). These keywords reflect some of the ecological and physiological properties reported for strain 50-1 BONT in the original description [1].

Figure 1 shows the phylogenetic neighborhood of M. australiensis 50-1 BONT in a 16S rRNA based tree. The sequences of the three 16S rRNA gene copies in the genome differ from each other by up to two nucleotides, and differ by up to four nucleotides from the previously published 16S rRNA sequence (AY331143).
Figure 1
Figure 1.

Phylogenetic tree highlighting the position of M. australiensis strain 50-1 BONT relative to the other type strains within the order Thermoanaerobacterales. The tree was inferred from 1,275 aligned characters [5,6] of the 16S rRNA gene sequence under the maximum likelihood criterion [7] and rooted in accordance with the current taxonomy. The branches are scaled in terms of the expected number of substitutions per site. Numbers to the right of bifurcations are support values from 950 bootstrap replicates [8] if larger than 60%. Lineages with type strain genome sequencing projects registered in GOLD [9] are labeled with one asterisk, those registered as ‘Complete and Published’ with two asterisks [10,11]. Apparently, even the best BLAST hits show a low degree of similarity to M. australiensis (see above), in agreement with the isolated position of the species in the latest version of the 16S rRNA phylogeny from the All-Species-Living-Tree Project [12]. The species selection for Figure 1 was based on the current taxonomic classification (Table 1).

The cells of strain 50-1 BONT are generally rod-shaped with a size of 3–20 x 0.5 µm (Figure 2). They occur singly or in pairs [1]. Strain 50-1 BONT stains Gram-positive and is spore-forming (Table 1). The organism is described to be motile by peritrichous flagella, with a mean of four flagella per cell [1] (not visible in Figure 2). Strain 50-1 BONT was found to be a strictly anaerobic chemoorganotroph which requires 0.1% NaCl for optimal growth [1], but is also able to grow in the presence of up to 4% NaCl [1]. The organism can use a wide range of carbohydrates as carbon and energy sources, including arabinose, cellobiose, fructose, galactose, glucose, mannose, sucrose, xylose and yeast extract [1]. Lactate, formate, ethanol, acetate, H2, and CO2 are the end products of the glucose metabolism [1]. The temperature range for growth is between 30°C and 60°C, with the optimum at 50°C [1]. Mesothermophilia distinguishes M. australiensis from its closest relatives, such as the members if the genus Thermoanaerobacterium [1]. After seven days of incubation at 50°C, round colonies (1–2 mm diameter) were found in roll tubes [1]. The pH range for growth is between 5.5 and 8.8, with an optimum at pH 7.5 [1]. Strain 50-1 BONT was not able to reduce thiosulfate or to hydrolyze starch [1]. Moreover, it does not use elemental sulfur, sulfate, sulfite, nitrate or nitrite as electron acceptors [1]. The generation time of the strain 50-1 BONT was 11 h [1].
Figure 2
Figure 2.

Scanning electron micrograph of M. australiensis 50-1 BONT

Table 1.

Classification and general features of M. australiensis 50-1 BONT according to the MIGS recommendations [13] and the NamesforLife database [14].




Evidence code


Domain Bacteria

TAS [15]


Phylum Firmicutes

TAS [16,17]


Class Clostridia

TAS [18,19]


Order Thermoanaerobacterales

TAS [18,20]


Family Thermoanaerobacteraceae

TAS [18,21]


Genus Mahella

TAS [1]


Species Mahella australiensis

TAS [1]


Type strain 50-1 BON

Current classification

TAS [1]


Gram stain


TAS [1]


Cell shape


TAS [1]



motile by peritrichous flagella

TAS [1]



swollen sporangia, terminal spores

TAS [1]


Temperature range


TAS [1]


Optimum temperature


TAS [1]



0.1%–4% NaCl

TAS [1]


Oxygen requirement

strictly anaerobic

TAS [1]


Carbon source

arabinose, cellobiose, fructose, galactose, glucose, mannose, sucrose, xylose and yeast extract

TAS [1]


Energy metabolism


TAS [1]



oil fields

TAS [1]


Biotic relationship





not reported


Biosafety level


TAS [22]



oil well in Queensland

TAS [1]


Geographic location

Riverslea Oil Field in the Bowen-Surat basin, Queensland, Australia

TAS [1]


Sample collection time





roughly −27.32




roughly 148.72




not reported




not reported


Evidence codes - IDA: Inferred from Direct Assay (first time in publication); TAS: Traceable Author Statement (i.e., a direct report exists in the literature); NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from of the Gene Ontology project [23]. If the evidence code is IDA, the property was directly observed by one of the authors or an expert mentioned in the acknowledgements.


No chemotaxonomic information is currently available for the strain 50-1 BONT.

Genome sequencing and annotation

Genome project history

This organism was selected for sequencing on the basis of its phylogenetic position [24], and is part of the Genomic Encyclopedia of Bacteria and Archaea project [25]. The genome project is deposited in the Genome On Line Database [9] and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI). A summary of the project information is shown in Table 2.
Table 2.

Genome sequencing project information





Finishing quality



Libraries used

Three genomic libraries: one 454 pyrosequence standard library, one 454 PE library (10 kb insert size), one Illumina library


Sequencing platforms

Illumina GAii, 454 GS FLX Titanium


Sequencing coverage

52.1 × Illumina; 35.9 × pyrosequence



Newbler version 2.3, Velvet, phrap


Gene calling method

Prodigal 1.4, GenePRIMP





Genbank Date of Release

May 13, 2011





NCBI project ID



Database: IMG-GEBA



Source material identifier

DSM 15567


Project relevance

Tree of Life, GEBA

Growth conditions and DNA isolation

M. australiensis 50-1 BONT, DSM 15567, was grown anaerobically in DSMZ medium 339 (Wilkins-Chalgreen anaerobe broth, Oxoid CM 643) [26] at 50°C. DNA was isolated from 0.5–1 g of cell paste using Jetflex Genomic DNA Purification Kit (GENOMED 600100) following the standard protocol as recommended by the manufacturer. Cell lysis was enhanced by adding 20 µl proteinase K for two hours at 58°C. DNA is available through the DNA Bank Network [27].

Genome sequencing and assembly

The genome was sequenced using a combination of Illumina and 454 sequencing platforms. All general aspects of library construction and sequencing can be found at the JGI website [28]. Pyrosequencing reads were assembled using the Newbler assembler (Roche). The initial Newbler assembly consisting of 40 contigs in one scaffold was converted into a phrap [29] assembly by making fake reads from the consensus, to collect the read pairs in the 454 paired end library.

Illumina GAii sequencing data (444 Mb) was assembled with Velvet [30] and the consensus sequences were shredded into 1.5 kb overlapped fake reads and assembled together with the 454 data. The 454 draft assembly was based on 108.4 Mb 454 draft data and all of the 454 paired end data. Newbler parameters are -consed -a 50 -l 350 -g -m -ml 20. The Phred/Phrap/Consed software package [29] was used for sequence assembly and quality assessment in the subsequent finishing process. After the shotgun stage, reads were assembled with parallel phrap (High Performance Software, LLC). Possible mis-assemblies were corrected with gapResolution [28], Dupfinisher, or sequencing cloned bridging PCR fragments with subcloning [31]. Gaps between contigs were closed by editing in Consed, by PCR and by Bubble PCR primer walks (J.-F. Chang, unpublished). A total of 279 additional reactions were necessary to close gaps and to raise the quality of the finished sequence. Illumina reads were also used to correct potential base errors and increase consensus quality using a software Polisher developed at JGI [32]. The error rate of the completed genome sequence is less than 1 in 100,000. Together, the combination of the Illumina and 454 sequencing platforms provided 88.0 × coverage of the genome. The final assembly contained 364,783 pyrosequence and 4,541,603 Illumina reads.

Genome annotation

Genes were identified using Prodigal [33] as part of the Oak Ridge National Laboratory genome annotation pipeline, followed by a round of manual curation using the JGI GenePRIMP pipeline [34]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) non-redundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes - Expert Review (IMG-ER) platform [35].

Genome properties

The genome consists of a 3,135,972 bp long chromosome with a G+C content of 43.5% (Table 3 and Figure 3). Of the 3,033 genes predicted, 2,974 were protein-coding genes, and 59 RNAs; 104 pseudogenes were also identified. The majority of the protein-coding genes (70.4%) were assigned with a putative function while the remaining ones were annotated as hypothetical proteins. The distribution of genes into COGs functional categories is presented in Table 4.
Figure 3
Figure 3.

Graphical circular map of the chromosome. From outside to the center: Genes on forward strand (color by COG categories), Genes on reverse strand (color by COG categories), RNA genes (tRNAs green, rRNAs red, other RNAs black), GC content, GC skew.

Table 3.

Genome Statistics



% of Total

Genome size (bp)



DNA coding region (bp)



DNA G+C content (bp)



Number of replicons



Extrachromosomal elements



Total genes



RNA genes



rRNA operons



Protein-coding genes



Pseudo genes



Genes with function prediction



Genes in paralog clusters



Genes assigned to COGs



Genes assigned Pfam domains



Genes with signal peptides



Genes with transmembrane helices



CRISPR repeats


Table 4.

Number of genes associated with the general COG functional categories








Translation, ribosomal structure and biogenesis




RNA processing and modification








Replication, recombination and repair




Chromatin structure and dynamics




Cell cycle control, cell division, chromosome partitioning




Nuclear structure




Defense mechanisms




Signal transduction mechanisms




Cell wall/membrane/envelope biogenesis




Cell motility








Extracellular structures




Intracellular trafficking, secretion, and vesicular transport




Posttranslational modification, protein turnover, chaperones




Energy production and conversion




Carbohydrate transport and metabolism




Amino acid transport and metabolism




Nucleotide transport and metabolism




Coenzyme transport and metabolism




Lipid transport and metabolism




Inorganic ion transport and metabolism




Secondary metabolites biosynthesis, transport and catabolism




General function prediction only




Function unknown




Not in COGs

Insights from the genome sequence

Comparative genomics

Lacking an available genome sequence of the closest relative of M. australiensis, (Thermoanaerobacterium thermosulfurogenes, Figure 1), the following comparative analyses were done with Thermoanaerobacterium thermosaccharolyticum (GenBank CP002171), the closest related organism with a publicly available genome. While the two genomes are similar in size (M. australiensis 3.1 Mb, 2,974 genes; T. thermosaccharolyticum 2.8 Mb, 2,757 genes), they differ significantly in their G+C content (43% vs. 34%). An estimate of the overall similarity between M. australiensis, T. thermosaccharolyticum and Caldicellulosiruptor saccharolyticus [11] (GenBank EKD00000000.1, as an equidistant outgroup, Figure 1), was generated with the GGDC-Genome-to-Genome Distance Calculator [36,37]. This system calculates the distances by comparing the genomes to obtain HSPs (high-scoring segment pairs) and inferring distances from the set of formulae (1, HSP length / total length; 2, identities / HSP length; 3, identities / total length). Table 5 shows the results of the pair wise comparison between the three genomes.
Table 5.

Pairwise comparison of M. australiensis, T. thermosaccharolyticum and C. saccharolyticus using the GGDC-Calculator.


HSP length/total length [%]

identities/HSP length [%]

identities/total length [%]

M. australiensis

T. thermosaccharolyticum




M. australiensis

C. saccharolyticus




C. saccharolyticus

T. thermosaccharolyticum




The fraction of shared genes in the three genomes is shown in a Venn diagram (Figure 4). The numbers of pairwise shared genes were calculated with the phylogenetic profiler function of the IMG ER platform [35]. The homologous genes within the genomes were detected with a maximum E-value of 10-5 and a minimum identity of 30%. About half of all the genes in the genomes (1,313 genes) are shared among the three genomes, with equivalent numbers of genes (265 to 327) shared pairwise to the exclusion of the third genome or occurring in only one genome (866 to 1,069). Within the 1,069 unique genes of M. australiensis that have no detectable homologs in the genomes of T. thermosaccharolyticum and C. saccharolyticus (under the sequence similarity thresholds used for the comparison) the 16 genes encoding xylose isomerases appear to be noteworthy; for seven of these isomerase genes no homologs were detected in the other two genomes; only nine genes were identified in C. saccharolyticus, and five in T. thermosaccharolyticum. The high number of xylose isomerise genes suggests a strong utilization of pentoses by M. australiensis. It is already known that several members of the order Thermoanaerobacterales are capable of xylose metabolism [38]. In addition, a number of extracellular solute-binding proteins were found in the genome of M. australiensis. These proteins belong to a high affinity transport system, which is involved in active transport of solutes across the cytoplasmic membrane. The M. australiensis genome contains 54 genes coding for solute-binding proteins belonging to family 1, whereas in C. saccharolyticus and T. thermosaccharolyticum contain only 16 and 13 solute-binding protein family 1 coding genes, respectively.
Figure 4
Figure 4.

Venn diagram depicting the intersections of protein sets (total number of derived protein sequences in parentheses) of M. australiensis, T. thermosaccharolyticum and C. saccharolyticus.

T. thermosaccharolyticum probably transports sugars via a phosphotransferase system (PTS). A total of 29 genes coding for proteins belonging to the PTS specific for different sugars were found in the genome of T. thermosaccharolyticum. The PTS of Thermoanaerobacter tengcongensis was recently studied in detail [39], with 22 proteins identified as participants in the PTS. In contrast, no genes coding for PTS proteins were identified in the genome of M. australiensis, and only one fructose specific PEP-dependent PTS gene was reported in C. saccharolyticus [11]. In conclusion, the number and distribution of these transport mechanisms seems to be highly variable within the Thermoanaerobacteraceae.



We would like to gratefully acknowledge the help of Katja Steenblock for growing M. australiensis cultures, and Susanne Schneider for DNA extractions and quality control (both at DSMZ). This work was performed under the auspices of the US Department of Energy Office of Science, Biological and Environmental Research Program, and by the University of California, Lawrence Berkeley National Laboratory under contract No. DE-AC02-05CH11231, Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344, and Los Alamos National Laboratory under contract No. DE-AC02-06NA25396, UT-Battelle and Oak Ridge National Laboratory under contract DE-AC05-00OR22725, as well as German Research Foundation (DFG) INST 599/1-2.

Authors’ Affiliations

DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany
DOE Joint Genome Institute, Walnut Creek, USA
Bioscience Division, Los Alamos National Laboratory, Los Alamos, USA
Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, USA
Oak Ridge National Laboratory, Oak Ridge, USA
HZI - Helmholtz Centre for Infection Research, Braunschweig, Germany
Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
University of California Davis Genome Center, Davis, USA


  1. Bonilla Salinas MB, Fardeau ML, Thomas P, Cayol JL, Patel BKC, Ollivier B. Mahella australiensis gen. nov., sp. nov., a moderately thermophilic anaerobic bacterium isolated from an Australian oil well. Int J Syst Evol Microbiol 2004; 54:2169–2173. PubMed doi:10.1099/ijs.0.02926-0View ArticleGoogle Scholar
  2. Euzéby JP. List of bacterial names with standing in nomenclature: A folder available on the Internet. Int J Syst Bacteriol 1997; 47:590–592. PubMed doi:10.1099/00207713-47-2-590View ArticlePubMedGoogle Scholar
  3. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie E, Keller K, Huber T, Dalevi D, Hu P, Andersen G. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006; 72:5069–5072. PubMed doi:10.1128/AEM.03006-05PubMed CentralView ArticlePubMedGoogle Scholar
  4. Porter MF. An algorithm for suffix stripping. Program: electronic library and information systems. 1980; 14:130–137.View ArticleGoogle Scholar
  5. Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol 2000; 17:540–552. PubMedView ArticlePubMedGoogle Scholar
  6. Lee C, Grasso C, Sharlow MF. Multiple sequence alignment using partial order graphs. Bioinformatics 2002; 18:452–464. PubMed doi:10.1093/bioinformatics/18.3.452View ArticlePubMedGoogle Scholar
  7. Stamatakis A, Hoover P, Rougemont J. A rapid bootstrap algorithm for the RAxML web servers. Syst Biol 2008; 57:758–771. PubMed doi:10.1080/10635150802429642View ArticlePubMedGoogle Scholar
  8. Pattengale ND, Alipour M, Bininda-Emonds ORP, Moret BME, Stamatakis A. How many bootstrap replicates are necessary? Lect Notes Comput Sci 2009; 5541:184–200. doi:10.1007/978-3-642-02008-7_13View ArticleGoogle Scholar
  9. Liolios K, Chen IM, Mavromatis K, Tavernarakis N, Hugenholtz P, Markowitz VM, Kyrpides NC. The Genomes On Line Database (GOLD) in 2009: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res 2009; 38:D346–D354. PubMed doi:10.1093/nar/gkp848PubMed CentralView ArticlePubMedGoogle Scholar
  10. Pitluck S, Yasawong M, Munk C, Nolan M, Lapidus A, Lucas S, Glavina Del Rio T, Tice H, Cheng JF, Bruce D, et al. Complete genome sequence of Thermosediminibacter oceani type strain (JW/IW-1228PT). Stand Genomic Sci 2010; 3:108–116. PubMed doi:10.4056/sigs.1133078PubMed CentralView ArticlePubMedGoogle Scholar
  11. van de Werken HJ, Verhaart MR, VanFossen AL, Willquist K, Lewis DL, Nichols JD, Goorissen HP, Mongodin EF, Nelson KE, van Niel EW, et al. Hydrogenomics of the extremely thermophilic bacterium Caldicellulosiruptor saccharolyticus. Appl Environ Microbiol 2008; 74:6720–6729. PubMed doi:10.1128/AEM.00968-08PubMed CentralView ArticlePubMedGoogle Scholar
  12. Yarza P, Ludwig W, Euzéby J, Amman R, Schleifer KH, Glöckner FO, Rosselló-Mora R. Updates of the All-Species Living Tree Project based on 16S and 23S rRNA sequence analyses. Syst Appl Microbiol 2010; 33:291–299. PubMed doi:10.1016/j.syapm.2010.08.001View ArticlePubMedGoogle Scholar
  13. Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, Tatusova T, Thomson N, Allen MJ, Angiuoli SV, et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol 2008; 26:541–547. PubMed doi:10.1038/nbt1360PubMed CentralView ArticlePubMedGoogle Scholar
  14. Garrity G. NamesforLife. BrowserTool takes expertise out of the database and puts it right in the browser. Microbiol Today 2010; 37:9.Google Scholar
  15. Woese CR, Kandler O, Wheelis ML. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proc Natl Acad Sci USA 1990; 87:4576–4579. PubMed doi:10.1073/pnas.87.12.4576PubMed CentralView ArticlePubMedGoogle Scholar
  16. Garrity GM, Holt JG. The Road Map to the Manual. In: Garrity GM, Boone DR, Castenholz RW (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 1, Springer, New York, 2001, p. 119–169.View ArticleGoogle Scholar
  17. Gibbons NE, Murray RGE. Proposals concerning the higher taxa of Bacteria. Int J Syst Bacteriol 1978; 28:1–6. doi:10.1099/00207713-28-1-1View ArticleGoogle Scholar
  18. Validation list 132. List of new names and new combinations previously effectively, but not validly, published. Int J Syst Evol Microbiol 2010; 60:469–472. doi:10.1099/ijs.0.022855-0Google Scholar
  19. Rainey FA. Class II. Clostridia class nov. In:De Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer KH, Whitman WB (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 3, Springer-Verlag, New York, 2009, p. 736.Google Scholar
  20. Wiegel J. 2009. Order III. Thermoanaerobacterales ord. nov. In:De Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer KH, Whitman WB (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 3, Springer-Verlag, New York, p. 1224.Google Scholar
  21. Wiegel J. 2009. Family I. Thermoanaerobacteraceae fam. nov. In:De Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer KH, Whitman WB (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 3, Springer-Verlag, New York, p. 1225.Google Scholar
  22. BAuA. Classification of Bacteria and Archaea in risk groups. TRBA 2010; 466:123.Google Scholar
  23. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene Ontology: tool for the unification of biology. Nat Genet 2000; 25:25–29. PubMed doi:10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
  24. Klenk HP, Göker M. En route to a genome-based classification of Archaea and Bacteria? Syst Appl Microbiol 2010; 33:175–182. PubMed doi:10.1016/j.syapm.2010.03.003View ArticlePubMedGoogle Scholar
  25. Wu D, Hugenholtz P, Mavromatis K, Pukall R, Dalin E, Ivanova NN, Kunin V, Goodwin L, Wu M, Tindall BJ, et al. A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea. Nature 2009; 462:1056–1060. PubMed doi:10.1038/nature08656PubMed CentralView ArticlePubMedGoogle Scholar
  26. List of growth media used at DSMZ:
  27. Gemeinholzer B, Dröge G, Zetzsche H, Haszprunar G, Klenk HP, Güntsch A, Berendsohn WG, Wägele JW. The DNA Bank Network: the start from a German initiative. Biopreservation and Biobanking 2011; 9:51–55. doi:10.1089/bio.2010.0029View ArticlePubMedGoogle Scholar
  28. JGI website.
  29. The Phred/Phrap/Consed software package.
  30. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008; 18:821–829. PubMed doi:10.1101/gr.074492.107PubMed CentralView ArticlePubMedGoogle Scholar
  31. Han C, Chain P. Finishing repeat regions automatically with Dupfinisher. In: Proceeding of the 2006 international conference on bioinformatics & computational biology. Arabnia HR, Valafar H (eds), CSREA Press. June 26–29, 2006: 141–146.Google Scholar
  32. Lapidus A, LaButti K, Foster B, Lowry S, Trong S, Goltsman E. POLISHER: An effective tool for using ultra short reads in microbial genome assembly and finishing. AGBT, Marco Island, FL, 2008Google Scholar
  33. Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119. PubMed doi:10.1186/1471-2105-11-119PubMed CentralView ArticlePubMedGoogle Scholar
  34. Pati A, Ivanova NN, Mikhailova N, Ovchinnikova G, Hooper SD, Lykidis A, Kyrpides NC. GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes. Nat Methods 2010; 7:455–457. PubMed doi:10.1038/nmeth.1457View ArticlePubMedGoogle Scholar
  35. Markowitz VM, Ivanova NN, Chen IMA, Chu K, Kyrpides NC. IMG ER: a system for microbial genome annotation expert review and curation. Bioinformatics 2009; 25:2271–2278. PubMed doi:10.1093/bioinformatics/btp393View ArticlePubMedGoogle Scholar
  36. Auch AF, von Jan M, Klenk HP, Göker M. Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison. Stand Genomic Sci 2010; 2:117–134. PubMed doi:10.4056/sigs.531120PubMed CentralView ArticlePubMedGoogle Scholar
  37. Auch AF, Klenk HP, Göker M. Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs. Stand Genomic Sci 2010; 2:142–148. PubMed doi:10.4056/sigs.541628PubMed CentralView ArticlePubMedGoogle Scholar
  38. Uffen RL. Xylan degradation: a glimpse at microbial diversity. J Ind Microbiol Biotechnol 1997; 19:1–6. doi:10.1038/sj.jim.2900417View ArticleGoogle Scholar
  39. Navdaeva V, Zurbriggen A, Waltersperger S, Schneider P, Oberholzer AE, Bähler P, Bächler C, Grieder A, Baumann U, Erni B. Phosphoenolpyruvate: Sugar phosphotransferase system from the hyperthermophilic Thermoanaerobacter tengcongensis. Biochemistry 2011; 50:1184–1193. PubMed doi:10.1021/bi101721fView ArticlePubMedGoogle Scholar


© The Author(s) 2011