Open Access

Complete genome sequence of Arthrobacter sp. strain FB24

  • Cindy H. Nakatsu1Email author,
  • Ravi Barabote2,
  • Sue Thompson2,
  • David Bruce2,
  • Chris Detter2,
  • Thomas Brettin2,
  • Cliff Han2,
  • Federico Beasley1,
  • Weimin Chen1,
  • Allan Konopka3 and
  • Gary Xie2
Standards in Genomic Sciences20139:9010106

DOI: 10.4056/sigs.4438185

Published: 16 October 2013

Abstract

Arthrobacter sp. strain FB24 is a species in the genus Arthrobacter Conn and Dimmick 1947, in the family Micrococcaceae and class Actinobacteria. A number of Arthrobacter genome sequences have been completed because of their important role in soil, especially bioremediation. This isolate is of special interest because it is tolerant to multiple metals and it is extremely resistant to elevated concentrations of chromate. The genome consists of a 4,698,945 bp circular chromosome and three plasmids (96,488, 115,507, and 159,536 bp, a total of 5,070,478 bp), coding 4,536 proteins of which 1,257 are without known function. This genome was sequenced as part of the DOE Joint Genome Institute Program.

Introduction

Arthrobacter sp. strain FB24 was isolated from a microcosm made from soil collected at an Indiana Department of Transport facility in Seymour, Indiana. This site was of particular interest because the soils were contaminated by mixed waste, both petroleum hydrocarbons and extreme metal (chromium and lead) levels [1]. Details of microcosm enrichment and isolation procedures used to obtain the Arthrobacter strain have been described previously [2]. This isolate was of particular interest because of its extreme resistance to chromate [3,4]. This work is a part of a larger study determining the compositional and functional diversity of bacterial communities in soils exposed to long-term contamination with metals [57].

Classification and features

Arthrobacter sp. strain FB24 is a high G+C Gram-positive member of the Micrococcaceae (Figure 1, Table 1). The strain is a facultative, non-motile aerobe with characteristic morphology of rod-shaped cells (Figure 2) that become coccoid in stationary phase. Strain FB24 is able to use a number carbon sources for growth, including glucose, fructose, lactate, succinate, malate, xylose and aromatic hydrocarbons (hydroxybenzoates, phthalate). Additionally, this Arthrobacter sp. strain is resistant to multiple metals: arsenate, arsenite, chromate, cadmium, lead, nickel, and zinc.
Figure 1.

Phylogenetic tree of Arthrobacter strain FB24 relative to nearest neighboring Arthrobacter type strains and Micrococcaceae strains with finished genome sequences: A. arilaitensis re117 (FQ311476) [8], A. aurescens TC1 (NC_008709) [9], A. chlorophenolicus A6 (NC_011886), A. phenanthrenivorans Sphe3 (CP002379 [10], Kocuria rhizophila DC2201, Microccus luteus Fleming NCTC 2665, Renibacterium salmoninarum ATCC 33209, Rothia dentocariosa ATCC 17931, and Rothia mucilaginous DY-18. The sequences were aligned in ClustalX and a consensus tree was generated using a 1,000× repeated bootstrapping process [11,12].

Figure 2.

Transmission electron micrograph of Arthrobacter sp. strain FB24. Cells were grown in nutrient broth for 15 h (early stationary phase), fixed in 3% glutaraldehyde in 0.1 M cacodylate buffer, then fixed in reduced osmium, followed by a series of ethanol dehydration steps. Cells are then embedded in Spurr resin, stained with uranyl acetate and Reynold’s lead citrate. Image was captured on Kodak SO-163 film at 33,000× magnification.

Table 1.

Classification and general features of Arthrobacter strain FB24

MIGS ID

Property

Term

Evidence codea

 

Current classification

Domain Bacteria

TAS [13]

 

Phylum Actinobacteria

TAS [14]

 

Class Actinobacteria

TAS [15]

 

Order Actinomycetales

TAS [1518]

 

Family Micrococcaceae

TAS [1517,19]

 

Genus Arthrobacter

TAS [17,2023]

 

Species Arthrobacter sp.

TAS [14]

 

Type strain

TAS [15]

 

Gram stain

Positive

IDA

 

Cell shape

Polymorphic: Coccus to rod shape

IDA

 

Motility

Non-motile

IDA

 

Sporulation

Non-sporulating

IDA

 

Temperature range

4–37°C

IDA

 

Optimum temperature

30°C

IDA

 

Carbon source

Yeast extract, glucose, fructose, lactate, succinate, malate, xylose, hydroxybenzoates, phthalate

 
 

Energy source

Yeast extract, glucose, fructose, lactate, succinate, malate, xylose, hydroxybenzoates, phthalate

 
 

Terminal electron receptor

Oxygen or nitrate

IDA

MIGS-6

Habitat

Soil

TAS [1]

 

Isolation

Chromate and xylene enriched microcosm composed of anthropogenically disturbed soils

TAS [2]

MIGS-6.3

Salinity

  

MIGS-22

Oxygen

Facultative aerobe

IDA

MIGS-15

Biotic relationship

Free-living

IDA

MIGS-14

Pathogenicity

Non-pathogenic

NAS

MIGS-4

Geographic location

Seymour, Indiana, USA

TAS [1,2]

MIGS-5

Sample collection time

June 27, 2001

IDA

MIGS-4.1

Latitude

38.9591667

NAS

MIGS-4.2

Longitude

-85.8902778

NAS

MIGS-4.3

Depth

40–90 cm

NAS

MIGS-4.4

Altitude

583 feet

NAS

aEvidence 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 [24].

Genome sequencing information

Genome project history

Arthrobacter sp. strain FB24 was chosen for sequencing by DOE-JGI because of its extreme resistance to chromate. Table 2 presents the project information and its association with MIGS version 2.0 compliance [25].
Table 2.

Project information

MIGS ID

Property

Term

MIGS-31

Finishing quality

Finished

MIGS-28

Libraries used

Small and medium random shotgun clones

MIGS-29

Sequencing platforms

Sanger

MIGS-31.2

Fold coverage

~15-fold

MIGS-30

Assemblers

Parallel PHRAP

MIGS-32

Gene calling method

Critica, Generation, Glimmer

 

Genome Database release

March 1, 2007

 

Genbank ID

12640

 

Genbank Date of Release

October 24, 2006

 

GOLD ID

Gc00445

 

Project relevance

Bioremediation, biotechnological, environmental

Growth conditions and DNA isolation

The FB24 culture used for DNA extraction was started from the glycerol stock (stored at −80 ºC) that was made from the original isolate. Cells were streaked onto a 0.1× nutrient agar plate, incubated at 30̱C, then a single colony was used to grow a culture in 0.25× nutrient broth (NB) (Difco, USA). Total genomic DNA was extracted from cells grown in liquid culture using the standard CTAB procedure [26].

Genome properties

The 5,070,478-base pair genome of Arthrobacter FB24 is composed of a single 4,698,945-base pair circular chromosome and three large circular plasmids (96,488, 115,507, and 159,536 bp) (Table 3) with GC content of 65.5, 64.7, 63.3 and 65.0%, respectively. Based on a summary of genomic features listed on the Integrated Microbial Genomes (IMG) [34] there are 4,536 protein coding sequences identified, of which 3,279 (70.94%, Table 4) have been assigned to a COG functional category (Table 5, Figure 3 and Figure 4). There are 1,257 (27.19%) predicted genes without an associated function.
Figure 3.

Circular map of FB24 chromosome, graphical depiction from outside to the center: genes on forward strand, genes on reverse strand (colored by COG categories), RNA genes (tRNAs green, rRNAs red, other RNAs black), GC content, GC skew. Chromosome is not to scale with plasmid maps.

Figure 4.

Circular map of three plasmids in FB24, graphical depiction from outside to the center: genes on forward strand, genes on reverse strand (colored by COG categories), RNA genes (tRNAs green, rRNAs red, other RNAs black), GC content, GC skew. Plasmid maps not to scale with each other or with chromosome map.

Table 3.

Summary of genome

Label

Size (bp)

Topology

INSDC identifier

RefSeq ID

Chromosome 1

4,698,945

Circular

CP000454.1

NC_008541.1

Plasmid pFB104

96,488

Circular

CP000457.1

NC_008539.1

Plasmid pFB105

115,507

Circular

CP000456.1

NC_008538.1

Plasmid pFB136

159,536

Circular

CP000455.1

NC_008537.1

Table 4.

Nucleotide content and gene count levels of the genome

Attribute

Value

% of totala

Genome size (bp)

5,070,478

100.0

DNA coding region (bp)

4,552,065

89.78

DNA G+C content (bp)

3,315,507

65.39

Total genesb

4,622

100.00

RNA genes

86

1.86

Protein-coding genes with function prediction

3,279

70.94

Protein coding genes without function prediction

1,257

27.19

Genes in paralog clusters

965

20.88

Genes assigned to COGs

3,361

72.72

Genes with signal peptides

1,098

23.76

Genes with transmembrane helices

1,168

25.27

Paralogous groups

373

100.00

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

b) Also includes 54 pseudogenes and 5 other genes

Table 5.

Number of genes associated with general COG functional categories

Code

Value

%agea

Description

J

162

4.27

Translation, ribosomal structure and biogenesis

A

1

0.03

RNA processing and modification

K

363

9.57

Transcription

L

164

4.32

Replication, recombination and repair

B

1

0.03

Chromatin structure and dynamics

D

32

0.84

Cell cycle control, cell division, chromosome partitioning

Y

-

-

Nuclear structure

V

49

1.29

Defense mechanisms

T

162

4.27

Signal transduction mechanisms

M

171

4.51

Cell wall/membrane/envelope biogenesis

N

3

0.08

Cell motility

Z

1

0.03

Cytoskeleton

W

0

0.0

Extracellular structures

U

48

1.27

Intracellular trafficking, secretion, and vesicular transport

O

124

3.27

Posttranslational modification, protein turnover, chaperones

C

239

6.3

Energy production and conversion

G

436

11.49

Carbohydrate transport and metabolism

E

364

9.6

Amino acid transport and metabolism

H

98

2.58

Nucleotide transport and metabolism

I

155

4.09

Lipid transport and metabolism

P

207

5.46

Inorganic ion transport and metabolism

Q

112

2.95

Secondary metabolites biosynthesis, transport and catabolism

R

458

12.07

General function prediction only

S

286

7.54

Function unknown

-

1,261

27.28

Not in COG

a) The total is based on the total number of protein coding genes in the annotated genome.

Genome sequencing and assembly

The random shotgun method was used in Sanger sequencing the genome of Arthrobacter sp. strain FB24 at the DOE-Joint Genome Institution (DOE-JGI). Medium (8 kb) and small (3 kb) insert random libraries were partially sequenced with average success rate of 88% and average high-quality read lengths of 614 nucleotides. Sequences were assembled with parallel phrap (High Performance Software, LLC). Possible mis-assemblies were corrected with Dupfinisher [27] or by analysis of transposon insertions in bridge clones. Gaps between contigs were closed by editing, custom primer walk or PCR amplification. The completed genome sequence of Arthrobacter sp. FB24 contains 89530 reads, achieving an average of 15-fold sequence coverage per base with an error rate less than 1 in 100,000. The sequences of Arthrobacter sp. FB24 can be accessed using the GenBank accession number NC_008541 for the chromosome and NC_008537, NC_008538, NC_008539 for three plasmids.

Genome annotation

Automated gene prediction was performed by using the output of Critica [28], combined with the output of Generation and Glimmer [29]. The assignment of product descriptions was made by using search results of the following curated databases in this order: TIGRFam; PRIAM (e–30 cutoff); Pfam; Smart; COGs; Swissprot/TrEMBL (SPTR); and KEGG. If there was no significant similarity to any protein in another organism, it was described as “hypothetical protein.” “Conserved hypothetical protein” was used if at least one match was found to a hypothetical protein in another organism. EC numbering was based on searches in PRIAM at an e–10 cutoff; COG and KEGG functional classifications were based on homology searches in the respective databases. Additionally, the tRNAScanSE tool [30] was used to find tRNA genes, whereas ribosomal RNAs were found by using BLASTn vs. the 16S and 23S ribosomal RNA databases. Other “standard” structural RNAs (e.g., 5S rRNA, rnpB, tmRNA, SRP RNA) were found by using covariance models with the Infernal search tool [31]. The HMMTOP program was used to predict the number of transmembrane segments (TMSs) in each protein. Those predicted to have two or more TMSs (about 918 proteins) were used to interrogate the transporter database (TCDB). Peter Karp’s pathologic tool was used for pathway prediction [32]. This method largely relies on the keyword matching and other automatic methods to manually curate some of the pathways, such as aromatic compound degradation. Metabolic pathways were constructed using MetaCyc as a reference data set [33].

Genome comparisons

A comparative analysis of genome sizes and protein coding genes in Arthrobacter sp. FB24 and other Arthrobacter species with finished sequences (Table 6) was made from data listed on the IMG website [34]. Included in the comparison is A. arilaitensis re117 (Gc01419, FQ311476) [8], A. aurescens TC1 (Gc00480, NC_008709) [9], A. chlorophenolicus A6 (Gc00930, NC_011886), A. nitroguajacolicus Rue61a (Gc0006272, CP003203), and A. phenanthrenivorans Sphe3 (Gc01621, CP002379) [10]. In addition, the draft genome of A. globiformis NBRC 12137 was included because its phylogenetic relatedness to FB24 based on the 16S rRNA gene sequence. Similarity between functional protein groups (based on COG, clusters of orthologous groups) in the genomes of these strains were made and visualized using hierarchical clustering (Figure 5) with tools available on the Joint Genome Institute (JGI) Integrated Microbial Genomes (IMG) site. Also included in the tree were closely related species in the family Micrococcaceae with finished genomes Kocuria rhizophila DC2201 (Gc00769), Microccus luteus Fleming NCTC 2665 (Gc01033), Renibacterium salmoninarum ATCC 33209 (Gc00698), Rothia dentocariosa ATCC 17931 (Gc01662), and Rothia mucilaginosa DY-18 (Gc01162). Detailed information about the genome properties and genome annotation of these strains can be obtained from the JGI-IMG website at the JGI website [35].
Figure 5.

Hierarchical tree based on similarity of COG groups between genomes. Included are genomes of bacteria in the family Micrococcaceae with finished genome sequences.

Table 6.

Comparison of genomes of the genus Arthrobacter with finished genome sequences

Genome Name

Genome size (bp)

Gene count

Protein coding

Protein with function

Without function

Plasmid number

rRNA operons

Arthrobacter arilaitensis re117, CIP108037

3,918,192

3,518

3,436

2,390

1,046

2

6

Arthrobacter aurescens TC1

5,226,648

4,793

4,699

3,419

1,280

2

6

Arthrobacter chlorophenolicus A6

4,980,870

4,744

4,641

3,125

1,516

2

5

Arthrobacter nitroguajacolicus Rue61a

5,081,038

4,655

4,584

3,800

784

2

6

Arthrobacter phenanthrenivorans Sphe3

4,535,320

4,273

4,209

3,101

1,108

2

4

Arthrobacter sp. FB24

5,070,478

4,622

4,536

3,279

1,257

3

5

Arthrobacter globiformis NBRC 12137*

4,954,410

4,582

4,529

2,784

1,745

?

1

*Sequence not fully assembled

Abbreviations

IMG: 

Integrated microbial genomes

DOE-JGI: 

Department of Energy Joint Genome Institution

NCBI: 

National Center for Biotechnology Information (Bethesda, MD, USA)

RDP: 

Ribosomal Database Project (East Lansing, MI, USA)

Authors’ Affiliations

(1)
Department of Agronomy, Purdue University
(2)
Los Alamos National Laboratories
(3)
Pacific Northwest National Laboratory

References

  1. Joynt J, Bischoff M, Turco RF, Konopka A, Nakatsu CH. Microbial community analysis of soils contaminated with lead, chromium and organic solvents. Microb Ecol 2006; 51:209–219. PubMed http://dx.doi.org/10.1007/s00248-005-0205-0View ArticlePubMedGoogle Scholar
  2. Nakatsu CH, Carmosini N, Baldwin B, Beasley F, Kourtev P, Konopka A. Soil microbial community responses to additions of organic carbon substrates and heavy metals (Pb and Cr). Appl Environ Microbiol 2005; 71:7679–7689. PubMed http://dx.doi.org/10.1128/AEM.71.12.7679-7689.2005PubMed CentralView ArticlePubMedGoogle Scholar
  3. Henne KL, Nakatsu CH, Thompson DK, Konopka AE. High-level chromate resistance in Arthrobactersp. strain FB24 requires previously uncharacterized accessory genes. BMC Microbiol 2009; 9:199. PubMed http://dx.doi.org/10.1186/1471-2180-9-199PubMed CentralView ArticlePubMedGoogle Scholar
  4. Henne KL, Turse JE, Nicora CD, Lipton MS, Tollaksen SL, Lindberg C, Babnigg G, Giometti CS, Nakatsu CH, Thompson DK, et al. Global proteomic analysis of the chromate response in Arthrobacter sp strain FB24. J Proteome Res 2009; 8:1704–1716. PubMed http://dx.doi.org/10.1021/pr800705fView ArticlePubMedGoogle Scholar
  5. Becker JM, Parkin T, Nakatsu CH, Wilbur JD, Konopka A. Bacterial activity, community structure, and centimeter-scale spatial heterogeneity in contaminated soil. Microb Ecol 2006; 51:220–231. PubMed http://dx.doi.org/10.1007/s00248-005-0002-9View ArticlePubMedGoogle Scholar
  6. Kourtev PS, Nakatsu CH, Konopka A. Responses of the anaerobic bacterial community to addition of organic C in chromium(VI)- and iron(III)-amended microcosms. Appl Environ Microbiol 2006; 72:628–637. PubMed http://dx.doi.org/10.1128/AEM.72.1.628-637.2006PubMed CentralView ArticlePubMedGoogle Scholar
  7. Kourtev PS, Nakatsu CH, Konopka A. Inhibition of nitrate reduction by chromium(VI) in anaerobic soil microcosms. Appl Environ Microbiol 2009; 75:6249–6257. PubMed http://dx.doi.org/10.1128/AEM.00347-09PubMed CentralView ArticlePubMedGoogle Scholar
  8. Monnet C, Loux V. Gibrat J-Fo, Spinnler E, Barbe Vr, Vacherie B, Gavory F, Gourbeyre E, Siguier P, Chandler MI and others. The Arthrobacter arilaitensis Re117 Genome Sequence Reveals Its Genetic Adaptation to the Surface of Cheese. PLoS ONE 2010; 5:e15489. PubMed http://dx.doi.org/10.1371/journal.pone.0015489PubMed CentralView ArticlePubMedGoogle Scholar
  9. Mongodin EF, Shapir N, Daugherty SC, Deboy RT, Emerson JB, Shvartzbeyn A, Radune D, Vamathevan J, Riggs F, Grinberg V, et al. Secrets of soil survival revealed by the genome sequence of Arthrobacter aurescens TC1. PLoS Genet 2006; 2:e214. PubMed http://dx.doi.org/10.1371/journal.pgen.0020214PubMed CentralView ArticlePubMedGoogle Scholar
  10. Kallimanis A, LaButti K, Lapidus A, Clum A, Lykidis A, Mavromatis K, Pagani I, Liolios K, Ivanova N, Goodwin L, et al. Complete genome sequence of Arthrobacter phenanthrenivorans type strain (Sphe3T). Stand Genomic Sci 2011; 4:123–130. PubMed http://dx.doi.org/10.4056/sigs.1393494PubMed CentralView ArticlePubMedGoogle Scholar
  11. Cole JR, Chai B, Farris RJ, Wang Q, Kulam-Syed-Mohideen AS, McGarrell DM, Bandela AM, Cardenas E, Garrity GM, Tiedje JM. The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucleic Acids Res 2007; 35:D169–D172. PubMed http://dx.doi.org/10.1093/nar/gkl889PubMed CentralView ArticlePubMedGoogle Scholar
  12. 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
  13. 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 http://dx.doi.org/10.1073/pnas.87.12.4576PubMed CentralView ArticlePubMedGoogle Scholar
  14. Garrity GM, Holt JG. The Road Map to the Manual. In: Garrity GM, Boone DR, Castenholz RW, editors. Bergey’s Manual of Systematic Bacteriology. Second Edition ed. Volume 1. New York: Springer; 2001. p 119–169.View ArticleGoogle Scholar
  15. Stackebrandt E, Rainey F, Ward-Rainey N. Proposal for a new hierarchic classification system, Actinobacteria classis nov. Int J Syst Bacteriol 1997; 47:479–491. http://dx.doi.org/10.1099/00207713-47-2-479View ArticleGoogle Scholar
  16. Zhi XY, Li WJ, Stackebrandt E. An update of the structure and 16S rRNA gene sequence-based definition of higher ranks of the class Actinobacteria, with the proposal of two new suborders and four new families and emended descriptions of the existing higher taxa. Int J Syst Bacteriol 2009; 59:589–608. PubMedView ArticleGoogle Scholar
  17. 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
  18. Buchanan RE. Studies in the nomenclature and classification of bacteria. II. The primary subdivisions of the Schizomycetes. J Bacteriol 1917; 2:155–164. PubMedPubMed CentralPubMedGoogle Scholar
  19. Pribram E. A contribution to the classification of microorganisms. J Bacteriol 1929; 18:361–394. PubMedPubMed CentralPubMedGoogle Scholar
  20. Conn HJ, Dimmick I. Soil bacteria similar In morphology to Mycobacterium and Corynebacterium. J Bacteriol 1947; 54:291–303. PubMedPubMed CentralPubMedGoogle Scholar
  21. Koch C, Schumann P, Stackebrandt E. Reclassification of Micrococcus agilis (Ali-Cohen 1889) to the genus Arthrobacter as Arthrobacter agilis comb. nov. and emendation of the genus Arthrobacter. Int J Syst Bacteriol 1995; 45:837–839. PubMed http://dx.doi.org/10.1099/00207713-45-4-837View ArticlePubMedGoogle Scholar
  22. Keddie RM. Genus II. Arthrobacter Conn and Dimmick 1947, 300. In: Buchanan RE, Gibbons NE (eds), Bergey’s Manual of Determinative Bacteriology, Eighth Edition, The Williams and Wilkins Co., Baltimore, 1974, p. 618–625.Google Scholar
  23. Judicial Commission. Opinion 24. Rejection of the Generic Name Arthrobacter Fischer 1895 and Conservation of the Generic Name Arthrobacter Conn and Dimmick 1947. Int Bull Bacteriol Nomencl Taxon 1958; 8:171–172.Google Scholar
  24. 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
  25. 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
  26. Ausubel FM, Brent R, Kingston RE, Moore DD, Seidman JG, Smith JA, Struhl K, eds. Current Protocols in Molecular Biology. Hoboken NJ: John Wiley & Sons; 2003.Google Scholar
  27. Han C, Chain P. Finishing repetitive regions automatically with Dupfinisher. In: Arabnia HR, Valafar H, editors 2006; Las Vegas, Nevada, USA. CSREA Press. p 142–147.Google Scholar
  28. Badger JH, Olsen GJ. CRITICA: Coding region identification tool invoking comparative analysis. Mol Biol Evol 1999; 16:512–524. PubMed http://dx.doi.org/10.1093/oxfordjournals.molbev.a026133View ArticlePubMedGoogle Scholar
  29. Delcher AL, Harmon D, Kasif S, White O, Salzberg SL. Improved microbial gene identification with GLIMMER. Nucleic Acids Res 1999; 27:4636–4641. PubMed http://dx.doi.org/10.1093/nar/27.23.4636PubMed CentralView ArticlePubMedGoogle Scholar
  30. Lowe TM, Eddy SR. tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence. Nucleic Acids Res 1997;25:0955–964.View ArticleGoogle Scholar
  31. Eddy SR. A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinform 2002;3.
  32. Karp PD, Paley S, Romero P. The Pathway Tools software. Bioinformatics 2002; 18:S225–S232. PubMed http://dx.doi.org/10.1093/bioinformatics/18.suppl1.S225View ArticlePubMedGoogle Scholar
  33. Caspi R, Foerster H, Fulcher CA, Hopkinson R, Ingraham J, Kaipa P, Krummenacker M, Paley S, Pick J, Rhee SY, et al. MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 2006; 34:D511–D516. PubMed http://dx.doi.org/10.1093/nar/gkj128PubMed CentralView ArticlePubMedGoogle Scholar
  34. Markowitz VM, Chen IMA, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Anderson I, Lykidis A, Mavromatis K, et al. The integrated microbial genomes system: an expanding comparative analysis resource. Nucleic Acids Res 2010; 38:D382–D390. PubMed http://dx.doi.org/10.1093/nar/gkp887PubMed CentralView ArticlePubMedGoogle Scholar
  35. DOE Joint Genome Institute. http://img.jgi.doe.gov/cgi-binAv/main.cgi

Copyright

© The Author(s) 2013