Open Access

Complete genome sequence of the plant-associated Serratia plymuthica strain AS13

  • Saraswoti Neupane1Email author,
  • Roger D. Finlay1,
  • Nikos C. Kyrpides2,
  • Lynne Goodwin2, 3,
  • Sadhna Alström1,
  • Susan Lucas2,
  • Miriam Land2, 4,
  • James Han2,
  • Alla Lapidus2,
  • Jan-Fang Cheng2,
  • David Bruce2, 3,
  • Sam Pitluck2,
  • Lin Peters2,
  • Galina Ovchinnikova2,
  • Brittany Held2, 3,
  • Cliff Han2, 3,
  • John C. Detter2, 3,
  • Roxanne Tapia2, 3,
  • Loren Hauser2, 4,
  • Natalia Ivanova2,
  • Ioanna Pagani2,
  • Tanja Woyke2,
  • Hans-Peter Klenk5 and
  • Nils Högberg1
Standards in Genomic Sciences20127:7010022

DOI: 10.4056/sigs.2966299

Published: 10 October 2012

Abstract

Serratia plymuthica AS13 is a plant-associated Gammaproteobacteria, isolated from rapeseed roots. It is of special interest because of its ability to inhibit fungal pathogens of rapeseed and to promote plant growth. The complete genome of S. plymuthica AS13 consists of a 5,442,549 bp circular chromosome. The chromosome contains 4,951 protein-coding genes, 87 tRNA genes and 7 rRNA operons. This genome was sequenced as part of the project entitled “Genomics of four rapeseed plant growth promoting bacteria with antagonistic effect on plant pathogens” within the 2010 DOE-JGI Community Sequencing Program (CSP2010).

Keywords

Gram-negative non-sporulating motile plant-associated chemoorganotrophic Enterobacteriaceae

Introduction

The members of the genus Serratia are widely distributed in nature. They are commonly found in soil, water, plants, insects, and other animals including humans [1]. The genus includes biologically and ecologically diverse species – from those beneficial to economically important plants, to pathogenic species that are harmful to humans. The plant-associated species comprise both endophytes and free living taxa, such as S. proteamaculans, S. plymuthica, S. liquefaciens and S. grimesii. Most of them are of interest because of their ability to promote plant growth and inhibit plant pathogenic fungi [26].

There are currently 16 validly named Serratia species. However, there are several unidentified plant-associated Serratia strains that have an impact on agriculture by stimulating plant growth and/or inhibiting soil borne plant pathogens [3]. S. plymuthica AS13 was isolated from rapeseed roots from Uppsala, Sweden. Our interest in S. plymuthica AS13 is due to its ability to stimulate rapeseed plant growth and to inhibit soil borne fungal pathogens such as Verticillium dahlia and Rhizoctonia solani [6]. Here we present a description of the complete genome of S. plymuthica AS13 and its annotation.

Classification and features

A representative sequence of the 16S rRNA gene of S. plymuthica AS13 was compared with the most recently released GenBank databases using NCBI BLAST [7] under default settings. It showed that the strain AS13 shares 99–100% similarity with the genus Serratia. When considering high-scoring segment pairs (HSPs) from the best 250 hits, the most frequent matches were several unspecified Serratia strains (17.2%) with maximum identity of 97–100%, while S. plymuthica (5.2%) had maximum identity of 97–100%, S. proteamaculans (4.8%) maximum identity of 97–99%, S. marcescens (4.8%) maximum identity of 96–97% and also different Rahnella strains (7%) maximum identity of 97–98%.

The phylogenetic relationship of S. plymuthica AS13 is shown in Figure 1 in a 16S rRNA based tree. All Serratia lineages clustered together and were distinct from other enterobacteria (except Obesumbacterium proteus). The tree also shows its very close relation with S. plymuthica strains AS9 and AS12, which was confirmed by digital DNA-DNA hybridization values [12] above 70% when compared with the (unpublished) draft genome sequence of the S. plymuthica type strain Breed K-7T from a culture of DSM 4540, and when compared with the complete genome sequences of S. plymuthica AS9 [13] and S. plymuthica AS12 [14] using the GGDC web server [15].
Figure 1.

Phylogenetic tree highlighting the position of S. plymuthica AS13 in relation to other genera within the family Enterobacteriaceae, based on 1,472 characters of the 16S rRNA gene sequence aligned in ClustalW2 [8]. The tree was constructed under the maximum likelihood criterion using MEGA5 software [9] and rooted with Xanthomonas cucurbitae (a member of the Xanthomonadaceae family). The branches are scaled based on the expected number of substitutions per site. The numbers above branches are support values from 1,000 bootstrap replicates if larger than 60% [10]. The lineages shown in blue color are the genome sequences of bacterial strains that are registered in GOLD [11].

Strain AS13 is a rod shaped bacterium, 1–2 µm long, 0.5–0.7 µm wide (Figure 2 and Table 1), is Gram-negative, motile, and a member of the family Enterobacteriaceae. The bacterium is a facultative anaerobe and grows within the temperature range 4 °C–40 °C and within a pH range of 4–10. It has chitinolytic, cellulolytic, proteolytic, and phospholytic activity [6] and can easily grow on different carbon sources such as glucose, cellobiose, succinate, mannitol, arabinose and inositol. It forms red to pink colored colonies that are 1–2 mm in diameter on potato dextrose agar at low temperature. The color of the bacterium depends on the growth substrate, temperature and pH of the culture medium [30]. The bacterium is deposited in the Culture Collection, University of Göteborg, Sweden (CCUG) as S. plymuthica AS13 (= CCUG 61398).
Figure 2.

Scanning electron micrograph of S. plymuthica AS13

Table 1.

Classification and general features of S. plymuthica AS13 according to the MIGS recommendations [16]

MIGS ID

Property

Term

Evidence codea

 

Current classification

Domain Bacteria

TAS [17]

 

Phylum Proteobacteria

TAS [18]

 

Class Gammaproteobacteria

TAS [19,20]

 

Order “Enterobacteriales

TAS [21]

 

Family Enterobacteriaceae

TAS [2224]

 

Genus Serratia

TAS [22,25,26]

 

Species Serratia plymuthica

TAS [22,27]

 

Strain AS13

IDA

 

Gram stain

Negative

IDA

 

Cell shape

Rod-shaped

IDA

 

Motility

Motile

IDA

 

Sporulation

Non-sporulating

IDA

 

Temperature range

Mesophilic

IDA

 

Optimum temperature

28°C

IDA

 

Carbon source

Glucose, inositol, arabinose, succinate, sucrose, fructose

IDA

 

Energy metabolism

Chemoorganotrophic

IDA

MIGS-6

Habitat

Rapeseed roots

IDA

MIGS-6.3

Salinity

Medium

IDA

MIGS-22

Oxygen

Facultative

IDA

MIGS-15

Biotic relationship

Plant associated

TAS [6]

MIGS-14

Pathogenicity

None

IDA

 

Biosafety level

1

TAS [28]

MIGS-4

Geographic location

Uppsala, Sweden

NAS

MIGS-5

Sample collection time

Summer 1998

IDA

MIGS-4.1

Latitude

59.8

NAS

MIGS-4.2

Longitude

17.65

NAS

MIGS-4.3

Depth

0.1 m

NAS

MIGS-4.4

Altitude

24–25 m

NAS

a) Evidence codes - IDA: Inferred from Direct Assay; 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 the Gene Ontology project [29]. If the evidence code is IDA, then the property should have been directly observed, for the purpose of this specific publication, for a live isolate by one of the authors, or an expert or reputable institution mentioned in the acknowledgements.

Chemotaxonomy

Little is known about the chemotaxonomy of S. plymuthica AS13. Fatty acid methyl ester (FAME) analysis showed the main fatty acid in strain AS13 comprises C16:0 (25.27%), C16:1ω7c (15.41%), C18:1ω7c (18.17%), C14:0 (5.21%), C17:0 cyclo (18.53%), along with other minor fatty acid components. Previously it has been shown that Serratia spp. contain a mixture of C14:0, C16:0, C16:1 and C18:1+2 fatty acids in which 50–80% of the total fatty acid in the cell is C14:0 and other fatty acids are less than 3% each [31]. This is consistent with the fact that C14:0 fatty acid is characteristic of the family Enterobacteriaceae.

Genome sequencing information

S. plymuthica AS13, a bacterial strain isolated from rapeseed roots was selected for sequencing on the basis of its biocontrol activity against fungal pathogens of rapeseed and its plant growth promoting ability. The genome project is deposited in the Genomes On Line Database [11] (GOLD ID = Gc01776) and the complete genome sequence is deposited in GenBank (INSDC ID = CP002775). Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI). A summary of the project information is shown in Table 2 and its association with MIGS identifiers.
Table 2.

Genome sequencing project information

MIGS ID

Property

Term

MIGS-31

Finishing quality

Finished

MIGS-28

Libraries used

Three libraries: one 454 standard library, one paired end 454 library (9.0 kb insert size) and one Illumina library)

MIGS-29

Sequencing platforms

Illumina GAii, 454 GS FLX Titanium

MIGS-31.2

Fold coverage

262.2 × Illumina, 8.7 × pyrosequencing

MIGS-30

Assemblers

Newbler version 2.3, Velvet 1.0.13, phrap version SPS - 4.24

MIGS-32

Gene calling method

Prodigal 1.4, GenePRIMP

 

NCBI project ID

60455

 

INSDC ID

CP002775

 

Genbank Date of Release

October 12, 2011

 

GOLD ID

Gc01776

 

Project relevance

Biocontrol, Agriculture

Growth conditions and DNA isolation

S. plymuthica AS13 was grown in Luria Broth (LB) medium at 28 °C until early stationary phase. The DNA was extracted from the cells by using a standard CTAB protocol for bacterial genomic DNA isolation that is available at JGI [32].

Genome sequencing and assembly

The genome of S. plymuthica AS13 was sequenced using a combination of Illumina and 454 sequencing platforms. The details of library construction and sequencing can be found at the JGI [32]. The sequence data from Illumina GAii (1,457.3 Mb) were assembled with Velvet [33] and the consensus sequence was computationally shredded into 1.5 kb overlapping fake reads. The sequencing data from 454 pyrosequencing (79.5 Mb) were assembled with Newbler and consensus sequences were computationally shredded into 2 kb overlapping fake reads. The initial draft assembly contained 86 contigs in 1 scaffold. The 454 Newbler consensus reads, the Illumina Velvet consensus reads and the read pairs in the 454 paired end library were assembled and quality assessment performed in the subsequent finishing process by using software phrap package [3437]. Possible mis-assemblies were corrected with gapResolution [32], Dupfinisher [38], or by sequencing cloned bridging PCR fragments with subcloning. The gaps between contigs were closed by editing in the software Consed [37], by PCR and by Bubble PCR primer walks (J.-F. Chang, unpublished). Fifty one additional reactions were necessary to close gaps and to raise the quality of the finished sequence. The sequence reads from Illumina were used to correct potential base errors and increase consensus quality using the software Polisher developed at JGI [39]. The final assembly is based on 46.8 Mb of 454 draft data which provides an average 8.7 × coverage of the genome and 1,415.6 Mb of Illumina draft data which provides an average 262.2 × coverage of the genome.

Genome annotation

The S. plymuthica AS13 genes were identified using Prodigal [40] as part of the genome annotation pipeline at Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA, followed by a round of manual curation using the JGI GenePRIMP pipeline [41]. The predicted CDS were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, Uniport, TIGR-Fam, Pfam, PRIAM, KEGG, COG and InterPro databases. Non-coding genes and miscellaneous features were predicted using tRNAscan-SE [42], RNAmmer [43], Rfam [44], TMHMM [45], and signalP [46]. Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes – Expert Review (IMG-ER) platform developed by the Joint Genome Institute, Walnut Creek, CA, USA [47].

Genome properties

The genome of S. plymuthica AS13 has a single circular chromosome of 5,442,549 bp with 55.96% GC content (Table 3 and Figure 3). It has 5,139 predicted genes, of which 4,951 were assigned as protein-coding genes. Among them, most of the protein coding genes (84.41%) were functionally assigned while the remaining ones were annotated as hypothetical proteins. 112 genes were assigned as RNA genes and 76 as pseudogenes. The distribution of genes into COG functional categories is presented in Table 4.
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 blue, rRNAs red, other RNAs black), GC content, GC skew.

Table 3.

Genome statistics

Attribute

Value

% of totala

Genome size (bp)

5,442,549

100.00%

DNA Coding region (bp)

4,770,475

87.65%

DNA G+C content (bp)

3,045,680

55.96%

Total genes

5,139

100.00%

RNA genes

112

2.18%

rRNA operons

7

0.14%

Protein-coding genes

4,951

96.34%

Pseudogenes

76

1.48%

Genes in paralog clusters

112

2.18%

Genes assigned to COGs

3,805

74.04%

Genes assigned in Pfam domains

4,183

81.39%

Genes with signal peptides

676

13.15%

Genes with transmembrane helices

1,228

23.89%

CRISPR repeats

1

% of totala

a) The total is based on either the size of the genome in base pairs or the total number of protein coding genes in the annotated genome.

Table 4.

Number of genes associated with the 25 general COG functional categories

Code

Value

% age

Description

J

201

4.27

Translation, ribosomal structure and biogenesis

A

1

0.02

RNA processing and modification

K

480

10.20

Transcription

L

161

3.42

Replication, recombination and repair

B

1

0.02

Chromatin structure and dynamics

D

37

0.79

Cell division and chromosome partitioning

Y

0

0.00

Nuclear structure

V

64

1.36

Defense mechanisms

T

187

3.97

Signal transduction mechanisms

M

265

5.63

Cell envelope biogenesis, outer membrane

N

94

2.00

Cell motility and secretion

Z

0

0.00

Cytoskeleton

W

0

0.00

Extracellular structure

U

116

2.47

Intracellular trafficking and secretion

O

153

3.25

Posttranslational modification, protein turnover, chaperones

C

272

5.78

Energy production and conversion

G

424

9.01

Carbohydrate transport and metabolism

E

470

9.99

Amino acid transport and metabolism

F

106

2.25

Nucleotide transport and metabolism

H

185

3.93

Coenzyme metabolism

I

135

2.87

Lipid metabolism

P

285

6.06

Inorganic ion transport and metabolism

Q

133

2.83

Secondary metabolite biosynthesis, transport and catabolism

R

537

11.41

General function prediction only

S

398

8.46

Function unknown

-

918

17.86

Not in COG

Declarations

Acknowledgements

We gratefully acknowledge the help of Elke Lang for providing cell cultures of the reference bacterial strain, Evelyne-Marie Brambilla for extraction of DNA and Anne Fiebig for assembly of the reference genome required for digital DNA-DNA hybridizations (all at DSMZ). The work conducted by the US Department of Energy Joint Genome Institute is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231.

Authors’ Affiliations

(1)
Department of Forest Mycology and Pathology, Swedish University of Agricultural Sciences
(2)
DOE Joint Genome Institute
(3)
Bioscience Division, Los Alamos National Laboratory
(4)
Oak Ridge National Laboratory
(5)
Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures

References

  1. Grimont F, Grimont PAD. (1992). The genus Serratia. In: Balows A, Trüper HG, Dworkin M, Harder W, Schleifer KH (eds) The Prokaryotes, pp 2822–2848. Springer, New York.Google Scholar
  2. Kalbe C, Marten P, Berg G. Strains of genus Serratia as beneficial rhizobacteria of oilseed rape with antifungal properties. Microbiol Res 1996; 151:433–439. PubMed http://dx.doi.org/10.1016/S0944-5013(96)80014-0View ArticlePubMedGoogle Scholar
  3. Müller H, Berg G. Impact of formulation procedures on the effect of the biocontrol agent Serratia plymuthica HRO-C48 on Verticillium wilt in oilseed rape. BioControl 2008; 53:905–916. http://dx.doi.org/10.1007/s10526-007-9111-3View ArticleGoogle Scholar
  4. Kurze S, Bahl H, Dahl R, Berg G. Biological control of fungal strawberry diseases by Serratia plymuthica HRO-C48. Plant Dis 2001; 85:529–534. http://dx.doi.org/10.1094/PDIS.2001.85.5.529View ArticleGoogle Scholar
  5. Taghavi S, Garafola C, Monchy S, Newman L, Hoffman A, Weyens N, Barac T, Vangronsveld J, van der Lelie D. Genome survey and characterization of endophytic bacteria exhibiting a beneficial effect on growth and development of poplar trees. Appl Environ Microbiol 2009; 75:748–757. PubMed http://dx.doi.org/10.1128/AEM.02239-08PubMed CentralView ArticlePubMedGoogle Scholar
  6. Alström S. Characteristics of bacteria from oilseed rape in relation to their biocontrol activity against Verticillium dahliae. J Phytopathol 2001; 149:57–64. http://dx.doi.org/10.1046/j.1439-0434.2001.00585.xView ArticleGoogle Scholar
  7. Altschul SF, Thomas LS, Alejandro AS, Jingui Z, Webb M, David JL. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res 1997; 25:3389–3402. PubMed http://dx.doi.org/10.1093/nar/25.17.3389PubMed CentralView ArticlePubMedGoogle Scholar
  8. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, et al. Clustal W and Clustal X version 2.0. Bioinformatics 2007; 23:2947–2948. PubMed http://dx.doi.org/10.1093/bioinformatics/btm404View ArticlePubMedGoogle Scholar
  9. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Mol Biol Evol 2011; 28:2731–2739. PubMed http://dx.doi.org/10.1093/molbev/msr121PubMed CentralView ArticlePubMedGoogle Scholar
  10. 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. http://dx.doi.org/10.1007/978-3-642-02008-7_13View ArticleGoogle Scholar
  11. 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 http://dx.doi.org/10.1093/nar/gkp848PubMed CentralView ArticlePubMedGoogle Scholar
  12. 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 http://dx.doi.org/10.4056/sigs.531120PubMed CentralView ArticlePubMedGoogle Scholar
  13. Neupane S, Högberg N, Alström S, Lucas S, Han J, Lapidus A, Cheng JF, Bruce D, Goodwin L, Pitluck S, et al. Complete genome sequence of the rapeseed plant-growth promoting Serratia plymuthica strain AS9. Stand Genomic Sci 2012; 6:54–62. PubMed http://dx.doi.org/10.4056/sigs.2595762PubMed CentralView ArticlePubMedGoogle Scholar
  14. Neupane S, Finlay RD, Alström S, Goodwin L, Kyrpides NC, Lucas S, Lapidus A, Bruce D, Pitluck S, Peters L, et al. Complete genome sequence of Serratia plymuthica strain AS12. Stand Genomic Sci 2012; 6:165–173. PubMed http://dx.doi.org/10.4056/sigs.2705996PubMed CentralView ArticlePubMedGoogle Scholar
  15. 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 http://dx.doi.org/10.4056/sigs.541628PubMed CentralView ArticlePubMedGoogle Scholar
  16. 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 http://dx.doi.org/10.1038/nbt1360PubMed CentralView ArticlePubMedGoogle Scholar
  17. Woese CR, Kandler O, Wheelis ML. Towards a natural system of organisms: proposal for the domainsArchaea, Bacteria and Eucarya. Proc Natl Acad Sci USA 1990; 87:4576–4579. PubMed http://dx.doi.org/10.1073/pnas.87.12.4576PubMed CentralView ArticlePubMedGoogle Scholar
  18. Garrity GM, Bell JA, Lilburn T. Phylum XIV. Proteobacteria phyl. nov. In: Garrity GM, Brenner DJ, Krieg NR, Staley JT (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 2, Part B, Springer, New York, 2005, p. 1.View ArticleGoogle Scholar
  19. List Editor. Validation of publication of new names and new combinations previously effectively published outside the IJSEM. List no. 106. Int J Syst Evol Microbiol 2005; 55:2235–2238. http://dx.doi.org/10.1099/ijs.0.64108-0
  20. Garrity GM, Bell JA, Lilburn T. Class III. Gammaproteobacteria class. nov. In: Garrity GM, Brenner DJ, Krieg NR, Staley JT (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 2, Part B, Springer, New York, 2005, p. 1.View ArticleGoogle Scholar
  21. Garrity GM, Holt JG. Taxonomic Outline of the Archaea andBacteria. In: Garrity GM, Boone DR, Castenholz RW (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 1, Springer, New York, 2001, p. 155–166.Google Scholar
  22. Skerman VBD, McGowan V, Sneath PHA. Approved Lists of Bacterial Names. Int J Syst Bacteriol 1980; 30:225–420. http://dx.doi.org/10.1099/00207713-30-1-225View ArticleGoogle Scholar
  23. Rahn O. New principles for the classification of bacteria. Zentralbl Bakteriol Parasitenkd Infektionskr Hyg 1937; 96:273–286.Google Scholar
  24. Judicial Commission. Conservation of the family nameEnterobacteriaceae, of the name of the type genus, and designation of the type species OPINION no. 15. Int Bull Bacteriol Nomencl Taxon 1958; 8:73–74.Google Scholar
  25. Sakazaki R. Genus IX. Serratia Bizio 1823, 288. In: Buchanan RE, Gibbons NE (eds), Bergey’s Manual of Determinative Bacteriology, Eighth Edition, The Williams and Wilkins Co., Baltimore, 1974, p. 326.Google Scholar
  26. Bizio B. Lettera di Bartolomeo Bizio al chiarissimo canonico Angelo Bellani sopra il fenomeno della polenta porporina. Biblioteca Italiana o sia Giornale di Letteratura. [Anno VIII]. Scienze e Arti 1823; 30:275–295Google Scholar
  27. Breed RS, Murray EGD, Hitchens AP. In: Breed RS, Murray EGD, Hitchens AP (eds), Bergey’s Manual of Determinative Bacteriology, Sixth Edition, The Williams and Wilkins Co., Baltimore, 1948, p. 481–482.Google Scholar
  28. BAuA. 2010, Classification of bacteria and archaea in risk groups. http://www.baua.de TRBA 466, p. 200.
  29. 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 http://dx.doi.org/10.1038/75556PubMed CentralView ArticlePubMedGoogle Scholar
  30. Alström S, Gerhardson B. Characteirtics of a Serratia plymuthica isolate from plant rhizospheres. Plant Soil 1987; 103:185–189. http://dx.doi.org/10.1007/BF02370387View ArticleGoogle Scholar
  31. Bergan T, Grimont AD, Grimont F. Fatty acids of Serratia determined by gas chromatography. Curr Microbiol 1983; 8:7–11. http://dx.doi.org/10.1007/BF01567306View ArticleGoogle Scholar
  32. DOE Joint Genome Institute. http://www.jgi.doe.gov
  33. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008; 18:821–829. PubMed http://dx.doi.org/10.1101/gr.074492.107PubMed CentralView ArticlePubMedGoogle Scholar
  34. Phrap and Phred for Windows. MacOS, Linux, and Unix. http://www.phrap.com
  35. Ewing B, Green P. Base-Calling of automated sequencer traces using Phred. II. error probabilities. Genome Res 1998; 8:186–194. PubMedView ArticlePubMedGoogle Scholar
  36. Ewing B, Hillier L, Wendl MC, Green P. Base-Calling of automated sequencer traces using Phred. I. accuracy assessment. Genome Res 1998; 8:175–185. PubMedView ArticlePubMedGoogle Scholar
  37. Gordon D, Abajian C, Green P. Consed: a graphical tool for sequence finishing. Genome Res 1998; 8:195–202. PubMedView ArticlePubMedGoogle Scholar
  38. Han C, Chain P. Finishing repeat regions automatically with Dupfinisher. In: Proceeding of the 2006 international conference on bioinformatics & computational biology. Arabina HR, Valafar H (eds), CSREA Press. June 26–29, 2006: 141–146.
  39. 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, 2008.Google Scholar
  40. 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 http://dx.doi.org/10.1186/1471-2105-11-119PubMed CentralView ArticlePubMedGoogle Scholar
  41. 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 http://dx.doi.org/10.1038/nmeth.1457View ArticlePubMedGoogle Scholar
  42. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 1997; 25:955–964. PubMedPubMed CentralView ArticlePubMedGoogle Scholar
  43. Lagesen K, Hallin P, Rødland EA, Stærfeldt HH, Rognes T, Ussery DW. RNAmmer: consistent annotation of rRNA genes in genomic sequences. Nucleic Acids Res 2007; 35:3100–3108. PubMed http://dx.doi.org/10.1093/nar/gkm160PubMed CentralView ArticlePubMedGoogle Scholar
  44. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. Rfam: an RNA family database. Nucleic Acids Res 2003; 31:439–441. PubMed http://dx.doi.org/10.1093/nar/gkg006PubMed CentralView ArticlePubMedGoogle Scholar
  45. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J Mol Biol 2001; 305:567–580. PubMed http://dx.doi.org/10.1006/jmbi.2000.4315View ArticlePubMedGoogle Scholar
  46. Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 2004; 340:783–795. PubMed http://dx.doi.org/10.1016/j.jmb.2004.05.028View ArticlePubMedGoogle Scholar
  47. Markowitz VM, Mavromatis K, 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 http://dx.doi.org/10.1093/bioinformatics/btp393View ArticlePubMedGoogle Scholar

Copyright

© The Author(s) 2012