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Journal of Clinical Microbiology logoLink to Journal of Clinical Microbiology
. 2011 May;49(5):1799–1809. doi: 10.1128/JCM.02350-10

Automated Identification of Medically Important Bacteria by 16S rRNA Gene Sequencing Using a Novel Comprehensive Database, 16SpathDB▿

Patrick C Y Woo 1,2,3,4,†,*, Jade L L Teng 3,†, Juilian M Y Yeung 3,†, Herman Tse 1,2,3,4, Susanna K P Lau 1,2,3,4,*, Kwok-Yung Yuen 1,2,3,4
PMCID: PMC3122693  PMID: 21389154

Abstract

Despite the increasing use of 16S rRNA gene sequencing, interpretation of 16S rRNA gene sequence results is one of the most difficult problems faced by clinical microbiologists and technicians. To overcome the problems we encountered in the existing databases during 16S rRNA gene sequence interpretation, we built a comprehensive database, 16SpathDB (http://147.8.74.24/16SpathDB) based on the 16S rRNA gene sequences of all medically important bacteria listed in the Manual of Clinical Microbiology and evaluated its use for automated identification of these bacteria. Among 91 nonduplicated bacterial isolates collected in our clinical microbiology laboratory, 71 (78%) were reported by 16SpathDB as a single bacterial species having >98.0% nucleotide identity with the query sequence, 19 (20.9%) were reported as more than one bacterial species having >98.0% nucleotide identity with the query sequence, and 1 (1.1%) was reported as no match. For the 71 bacterial isolates reported as a single bacterial species, all results were identical to their true identities as determined by a polyphasic approach. For the 19 bacterial isolates reported as more than one bacterial species, all results contained their true identities as determined by a polyphasic approach and all of them had their true identities as the “best match in 16SpathDB.” For the isolate (Gordonibacter pamelaeae) reported as no match, the bacterium has never been reported to be associated with human disease and was not included in the Manual of Clinical Microbiology. 16SpathDB is an automated, user-friendly, efficient, accurate, and regularly updated database for 16S rRNA gene sequence interpretation in clinical microbiology laboratories.

INTRODUCTION

In the last decade, as a result of the widespread use of PCR and DNA sequencing, 16S rRNA gene sequencing has played a pivotal role in accurate identification of medically important bacteria in both clinical microbiology and research laboratories (26). For the management of individual patients, accurate and objective identification of clinical isolates has assisted clinicians in the choice and duration of antibiotic therapy, as well as infection control measures. On the population scale, accurate identification has greatly improved our understanding of the epidemiology and, hence, the empirical treatment of infectious disease syndromes. In the past 10 years, our group and others have used this technology for the identification of a large number of medically important bacteria, resulting in major impacts on infectious diseases.

Interpretation of 16S rRNA gene sequence results is one of the most difficult problems faced by inexperienced clinical microbiologists and technical staff, despite the wide range of software and databases available. The best-known software and databases include GenBank (1), the Ribosomal Database Project (RDP-II) (2, 3, 13), MicroSeq (15, 17), Ribosomal Differentiation of Medical Microorganisms (RIDOM) (4–6), and the SmartGene Integrated Database Network System (SmartGene IDNS) (16). The databases of RDP-II and SmartGene IDNS contain sequences downloaded from GenBank, whereas all sequences in the databases of RIDOM and MicroSeq were obtained by sequencing the 16S rRNA genes of bacterial strains from culture collections. Due to the large number of unvalidated 16S rRNA gene sequences in GenBank, it is often not easy for inexperienced users to decide whether the “first hit” or the “closest match” is the real identity of a bacterial isolate. As for the other software and databases, the usefulness is further limited by the choice of bacterial species in the database. If a bacterial species is not included in the database, it would never be the identity of an isolate. If the database includes bacterial species with minimal differences in their 16S rRNA gene sequences, and hence, that cannot be identified with confidence by 16S rRNA gene sequencing, they may also give rise to wrong identification if the software reports only that the “first hit” or “closest match” is the identity of the bacterium.

In view of these problems, in 2005 we started developing our own database, which includes the most representative 16S rRNA gene sequences of all medically important bacteria listed in the most current edition of the Manual of Clinical Microbiology (14), for identification of medically important bacteria using 16S rRNA gene sequencing. In this database, 16SpathDB, we sought to create an automated user-friendly platform that indicated the most likely identity of the 16S rRNA gene sequence of a medically important bacterium, as well as other medically important bacteria with similar 16S rRNA gene sequences that may be alternative identities, which the user should be aware of. In this article, we describe this comprehensive database of 16S rRNA gene sequences of medically important bacteria and its use for automated identification of these bacteria.

MATERIALS AND METHODS

Database design.

16SpathDB is a Web-based 16S rRNA gene sequence database for identification of medically important bacteria. MySQL was employed as the database back end to store the 16S rRNA gene sequence information, and PHP was used to generate HTML web pages for the user interface. The most representative 16S rRNA gene sequence of each medically important bacterial species listed in the most current edition of the Manual of Clinical Microbiology (14) was retrieved from GenBank by manual inspection according to the following criteria. First, strains with good phenotypic and/or genotypic characterization (e.g., type strains and strains with complete genomes sequenced) were preferred. Second, strains isolated from humans were preferred. Third, sequences with fewer undetermined bases were preferred. Fourth, longer sequences, especially those with better coverage of the 5′ end, were preferred. For bacterial species with >2% intragenomic difference in their 16S rRNA gene sequences and those with intervening sequences in their 16S rRNA genes, more than one 16S rRNA gene sequence for each species was included.

Identification of clinical bacterial isolates using 16SpathDB.

To evaluate the usefulness of 16SpathDB, the 16S rRNA gene sequences of 91 nonduplicated medically important bacterial isolates we collected in our clinical microbiology laboratory in the past 10 years were input to the database for analysis (Table 1) (7–12, 18, 20–25, 27–31, 33–35). The exact identities of these isolates were determined by a polyphasic approach using a combination of phenotypic tests and 16S rRNA gene sequencing as described in our publications.

Table 1.

Identification of clinical bacterial isolates using 16SpathDB

Strain no. Identification by polyphasic approach Reference Identification results reported by 16SpathDB
 Best match in 16SpathDB Nucleotide identity (%) Second-best match in 16SpathDB Nucleotide identity (%) Third-best match in 16SpathDB Nucleotide identity (%)
Bacterial species Nucleotide identity (%)
1 Abiotrophia defectiva 21 Abiotrophia defectiva 99.54 Abiotrophia defectiva 99.54 Granulicatella adiacens 92.77 Granulicatella elegans 92.67
2 Actinobaculum schaalii Actinobaculum schaalii 99.35 Actinobaculum schaalii 99.35 Actinobaculum massiliense 95.69 Actinobaculum suis 94.52
3 Actinobaculum urinale Actinobaculum urinale 100.00 Actinobaculum urinale 100.00 Actinobaculum massiliense 91.57 Actinobaculum schaalii 91.01
4 Actinomyces meyeri Actinomyces meyeri 100.00 Actinomyces meyeri 100.00 Actinomyces odontolyticus 97.59 Actinomyces georgiae 97.16
5 Actinomyces odontolyticus 22 Actinomyces odontolyticus 98.92 Actinomyces odontolyticus 98.92 Actinomyces meyeri 97.78 Actinomyces turicensis 96.07
6 Actinomyces urogenitalis Actinomyces urogenitalis 100.00 Actinomyces urogenitalis 100.00 Actinomyces slackii 97.37 Actinomyces radicidentis 97.24
7 Actionmyces turicensis Actinomyces turicensis 99.44 Actinomyces turicensis 99.44 Actinomyces odontolyticus 97.04 Actinomyces meyeri 96.65
8 Aggregatibacter actinomycetemcomitans Aggregatibacter actinomycetemcomitans 98.62 Aggregatibacter actinomycetemcomitans 98.62 Aggregatibacter aphrophilus 94.12 Haemophilus aegyptius 93.73
9 Aggregatibacter aphrophilus 10 Aggregatibacter aphrophilus 99.59 Aggregatibacter aphrophilus 99.59 Aggregatibacter segnis 97.29 Haemophilus parahaemolyticus 95.31
10 Aggregatibacter segnis 10 Aggregatibacter segnis 99.59 Aggregatibacter segnis 99.59 Aggregatibacter aphrophilus 97.54 Haemophilus influenzae 95.73
11 Agrobacterium tumefaciens Agrobacterium tumefaciens 98.86 Agrobacterium tumefaciens 98.86 Ochrobactrum anthropi 94.25 Brucella melitensis 93.95
12 Arcanobacterium haemolyticum Arcanobacterium haemolyticum 99.80 Arcanobacterium haemolyticum 99.80 Arcanobacterium phocae 97.27 Arcanobacterium pluranimalium 94.39
13 Arcobacter butzleri Arcobacter butzleri 99.64 Arcobacter butzleri 99.64 Arcobacter cryaerophilus 96.76 Arcobacter skirrowii 96.61
14 Arcobacter cryaerophilus 7 Arcobacter cryaerophilus 100.00 Arcobacter cryaerophilus 100.00 Arcobacter skirrowii 98.42 Arcobacter butzleri 97.18
15 Averyella dalhousiensis Averyella dalhousiensis 99.18 Averyella dalhousiensis 99.18 Enterobacter cancerogenus 99.02 Enterobacter aerogenes 99.02
Enterobacter cancerogenus 99.02
Enterobacter aerogenes 99.02
16 Bifidobacterium breve Bifidobacterium breve 99.68 Bifidobacterium breve 99.68 Bifidobacterium longum 97.77 Bifidobacterium bifidum 95.96
17 Burkholderia pseudomallei 35 Burkholderia pseudomallei 100.00 Burkholderia pseudomallei 100.00 Burkholderia mallei 99.93 Burkholderia thailandensis 98.99
Burkholderia mallei 99.93
18 Campylobacter coli 7 Campylobacter coli 99.80 Campylobacter coli 99.80 Campylobacter jejuni 99.79 Campylobacter lari 98.33
Campylobacter jejuni 99.79
19 Campylobacter fetus 7 Campylobacter fetus 99.85 Campylobacter fetus 99.85 Campylobacter hyointestinalis 97.98 Campylobacter mucosalis 96.40
20 Campylobacter hyointestinalis Campylobacter hyointestinalis Campylobacter fetus 100.00 Campylobacter hyointestinalis 100.00 Campylobacter fetus 99.01 Campylobacter mucosalis 96.06
99.01
21 Campylobacter rectus Campylobacter rectus 99.34 Campylobacter rectus 99.34 Campylobacter showae 97.94 Campylobacter concisus 96.11
22 Capnocytophaga sputigena Capnocytophaga sputigena 99.24 Capnocytophaga sputigena 99.24 Capnocytophaga ochracea 95.67 Capnocytophaga cynodegmi 92.24
23 Citrobacter koseri Citrobacter koseri 99.92 Citrobacter koseri 99.92 Citrobacter farmeri 98.74 Salmonella enterica 98.74
Citrobacter farmeri 98.74
Salmonella enterica 98.74
24 Clostridium barati 24 Clostridium baratii 99.83 Clostridium baratii 99.83 Eubacterium budayi 99.41 Eubacterium nitritogenes 99.16
Eubacterium budayi 99.41
Eubacterium nitritogenes 99.16
25 Clostridium difficile 24 Clostridium difficile 99.51 Clostridium difficile 99.51 Clostridium sordellii 95.90 Eubacterium tenue 95.80
26 Clostridium disporicum 24 Clostridium disporicum 98.99 Clostridium disporicum 98.99 Clostridium celatum 97.57 Clostridium carnis 96.91
27 Clostridium hathewayi 28 Clostridium hathewayi 98.28 Clostridium hathewayi 98.28 Clostridium indolis 94.17 Clostridium sphenoides 93.86
28 Clostridium Innocuum 24 Clostridium innocuum 99.19 Clostridium innocuum 99.19 Holdemania filiformis 85.33 Erysipelothrix inopinata 84.82
29 Clostridium orbiscindens 24 Clostridium orbiscindens 100.00 Clostridium orbiscindens 100.00 Bacteroides capillosus 97.44 Clostridium sporosphaeroides 86.62
30 Clostridium paraputrificum 24 Clostridium paraputrificum 99.42 Clostridium paraputrificum 99.42 Clostridium carnis 96.81 Clostridium disporicum 96.53
31 Clostridium perfringens Clostridium perfringens 99.92 Clostridium perfringens 99.92 Clostridium baratii 94.60 Eubacterium budayi 94.19
32 Clostridium ramosum 24 Clostridium ramosum 99.92 Clostridium ramosum 99.92 Clostridium spiroforme 95.25 Lactobacillus vitulinus 86.78
33 Clostridium sordellii Clostridium sordellii 99.33 Clostridium sordellii 99.33 Eubacterium tenue 98.44 Clostridium ghonii 98.15
Eubacterium tenue 98.44
34 Clostridium symbiosum Clostridium symbiosum 99.49 Clostridium symbiosum 99.49 Clostridium bolteae 94.05 Clostridium clostridioforme 93.37
35 Clostridium tertium 24 Clostridium tertium 100.00 Clostridium tertium 100.00 Clostridium carnis 98.89 Clostridium chauvoei 97.90
36 Clostridium sporosphaeroides 24 Clostridium sporosphaeroides 98.80 Clostridium sporosphaeroides 98.80 Clostridium orbiscindens 87.17 Bacteroides capillosus 85.97
37 Desulfovibrio desulfuricans Desulfovibrio desulfuricans 99.07 Desulfovibrio desulfuricans 99.07 Desulfovibrio fairfieldensis 95.58 Desulfovibrio piger 95.07
38 Desulfovibrio fairfieldensis Desulfovibrio fairfieldensis 99.92 Desulfovibrio fairfieldensis 99.92 Desulfovibrio desulfuricans 96.75 Desulfovibrio piger 95.83
39 Dolosigranulum pigrum Dolosigranulum pigrum 98.97 Dolosigranulum pigrum 98.97 Alloiococcus otitis 91.40 Aerococcus sanguinicola 88.79
40 Eggerthella hongkongensis 11 Eggerthella hongkongensis 100.00 Eggerthella hongkongensis 100.00 Eggerthella lenta 93.08 Slackia exigua 90.09
41 Eggerthella lenta 9 Eggerthella lenta 99.74 Eggerthella lenta 99.74 Slackia exigua 89.48 Slackia heliotrinireducens 89.10
42 Enterobacter hormaechei Enterobacter hormaechei 98.60 Enterobacter hormaechei 98.60 Enterobacter asburiae 98.45 Enterobacter ludwigii 98.22
Enterobacter asburiae 98.45
Enterobacter ludwigii 98.22
43 Enterococcus cecorum 29 Enterococcus cecorum 98.72 Enterococcus cecorum 98.72 Enterococcus columbae 96.50 Enterococcus casseliflavus 96.06
44 Enterococcus raffinosus Enterococcus raffinosus 99.93 Enterococcus raffinosus 99.93 Enterococcus gilvus 99.39 Enterococcus malodoratus 99.25
Enterococcus gilvus 99.39
Enterococcus malodoratus 99.25
45 Facklamia hominis Facklamia hominis 99.38 Facklamia hominis 99.38 Facklamia languida 96.82 Facklamia ignava 93.16
46 Fusobacterium necrophorum Fusobacterium necrophorum 99.78 Fusobacterium necrophorum 99.78 Fusobacterium gonidiaformans 97.65 Fusobacterium russii 93.95
47 Gemella haemolysans 7 Gemella haemolysans 99.62 Gemella haemolysans 99.62 Gemella sanguinis 97.67 Gemella morbillorum 97.15
48 Gemella morbillorum Gemella morbillorum 99.68 Gemella morbillorum 99.68 Gemella haemolysans 98.30 Gemella sanguinis 97.50
49 Gordonia terrae Gordonia terrae 100.00 Gordonia terrae 100.00 Gordonia polyisoprenivorans 98.31 Gordonia bronchialis 98.30
50 Gordonibacter pamelaeae 30 No match - Eggerthella sinensis 94.00 Eggerthella hongkongensis 93.53 Eggerthella lenta 91.74
51 Granulicatella adiacens 21 Granulicatella adiacens 99.77 Granulicatella adiacens 99.77 Granulicatella elegans 97.40 Enterococcus villorum 94.58
52 Haemophilus parainfluenzae 10 Haemophilus parainfluenzae 99.53 Haemophilus parainfluenzae 99.53 Haemophilus pittmaniae 96.51 Haemophilus haemolyticus 95.33
53 Helcococcus kunzii 34 Helcococcus kunzii 99.59 Helcococcus kunzii 99.59 Parvimonas micra 84.57 Anaerococcus hydrogenalis 83.62
54 Kytococcus sedentarius Kytococcus sedentarius 98.12 Kytococcus sedentarius 98.12 Dermacoccus nishinomiyaensis 94.48 Janibacter sanguinis 93.24
55 Lactobacillus casei 7 Lactobacillus casei 100.00 Lactobacillus casei 100.00 Lactobacillus rhamnosus 99.51 Pediococcus pentosaceus 92.94
Lactobacillus rhamnosus 99.51
56 Lactobacillus fermentum 25 Lactobacillus fermentum 99.77 Lactobacillus fermentum 99.77 Pediococcus acidilactici 93.6 Pediococcus pentosaceus 93.5
57 Lactobacillus gasseri Lactobacillus gasseri 99.92 Lactobacillus gasseri 99.92 Lactobacillus acidophilus 93.94 Lactobacillus plantarum 90.32
58 Lactobacillus rhamnosus 7 Lactobacillus rhamnosus 99.83 Lactobacillus rhamnosus 99.83 Lactobacillus casei 99.66 Pediococcus pentosaceus 93.31
Lactobacillus casei 99.66
59 Lactococcus garvieae Lactococcus garvieae 99.92 Lactococcus garvieae 99.92 Lactococcus lactis 92.07 Streptococcus iniae 89.32
60 Lactococcus lactis Lactococcus lactis 99.52 Lactococcus lactis 99.52 Lactococcus garvieae 92.75 Streptococcus agalactiae 89.34
61 Laribacter hongkongensis 27 Laribacter hongkongensis 99.93 Laribacter hongkongensis 99.93 Chromobacterium violaceum 92.39 Eikenella corrodens 89.82
62 Leptotrichia buccalis Leptotrichia buccalis 99.56 Leptotrichia buccalis 99.56 Streptobacillus moniliformis 86.68 Sneathia sanguinegens 83.96
63 Microbacterium paraoxydans Microbacterium paraoxydans 99.49 Microbacterium paraoxydans 99.49 Microbacterium oxydans 98.87 Microbacterium resistens 98.25
Microbacterium oxydans 98.87
64 Micrococcus luteus 7 Micrococcus luteus 98.11 Micrococcus luteus 98.11 Micrococcus lylae 97.78 Micrococcus antarcticus 97.32
Micrococcus lylae 97.78
Micrococcus antarcticus 97.32
65 Moraxella osloensis Moraxella osloensis 99.53 Moraxella osloensis 99.53 Moraxella lincolnii 92.49 Moraxella lacunata 92.41
66 Mycobacterium chelonae 7 Mycobacterium chelonae 99.79 Mycobacterium chelonae 99.79 Mycobacterium abscessus 99.79 Mycobacterium immunogenum 98.97
Mycobacterium abscessus 99.79
Mycobacterium immunogenum 98.97
67 Mycobacterium marinum Mycobacterium marinum 100.00 Mycobacterium marinum 100.00 Mycobacterium ulcerans 100.00 Mycobacterium asiaticum 98.71
Mycobacterium ulcerans 100.00
68 Mycobacterium nonchromogenicum 7 Mycobacterium nonchromogenicum 98.13 Mycobacterium nonchromogenicum 98.13 Mycobacterium terrae 96.69 Mycobacterium cookii 95.56
69 Mycoplasma hominis 7 Mycoplasma hominis 100.00 Mycoplasma hominis 100.00 Brevibacillus parabrevis 76.75 Clostridium ramosum 76.07
70 Nocardia cyriacigeorgica Nocardia cyriacigeorgica 100.00 Nocardia cyriacigeorgica 100.00 Nocardia abscessus 98.73 Nocardia paucivorans 98.33
71 Olsenella uli 7 Olsenella uli 100.00 Olsenella uli 100.00 Atopobium vaginae 92.15 Atopobium parvulum 91.97
72 Pantoea dispersa Pantoea dispersa 99.92 Pantoea dispersa 99.92 Enterobacter cancerogenus 97.49 Kluyvera cryocrescens 97.49
73 Propionibacterium acnes 7 Propionibacterium acnes 99.80 Propionibacterium acnes 99.80 Propionibacterium propionicum 93.46 Propionibacterium avidum 93.27
74 Propionibacterium avidum Propionibacterium avidum 99.45 Propionibacterium avidum 99.45 Propionibacterium propionicum 99.30 Propionibacterium acnes 96.10
Propionibacterium propionicum 99.30
75 Providencia stuartii Providencia stuartii 99.77 Providencia stuartii 99.77 Providencia rettgeri 98.75 Providencia rustigianii 98.13
76 Pseudomonas oryzihabitans Pseudomonas oryzihabitans 99.79 Pseudomonas oryzihabitans 99.79 Pseudomonas pseudoalcaligenes 96.84 Pseudomonas aeruginosa 96.21
77 Salmonella enterica 23 Salmonella enterica 99.93 Salmonella enterica 99.93 Citrobacter farmeri 98.47 Enterobacter cloacae 98.46
78 Shewanella algae Shewanella algae 99.92 Shewanella algae 99.92 Shewanella putrefaciens 95.02 Aeromonas hydrophila 90.90
79 Solobacterium moorei 8 Solobacterium moorei 98.33 Solobacterium moorei 98.33 Bulleidia extructa 93.20 Holdemania filiformis 88.86
80 Staphylococcus aureus 7 Staphylococcus aureus 99.63 Staphylococcus aureus 99.63 Staphylococcus epidermidis 97.66 Staphylococcus caprae 97.64
81 Staphylococcus epidermidis 7 Staphylococcus epidermidis 100.00 Staphylococcus epidermidis 100.00 Staphylococcus caprae 99.21 Staphylococcus capitis 99.02
Staphylococcus caprae 99.21
Staphylococcus capitis 99.02
82 Staphylococcus lugdunensis 18 Staphylococcus lugdunensis 99.93 Staphylococcus lugdunensis 99.93 Staphylococcus haemolyticus 99.05 Staphylococcus hominis 98.84
Staphylococcus haemolyticus 99.05
83 Streptococcus anginosus 33 Streptococcus anginosus 99.77 Streptococcus anginosus 99.77 Streptococcus intermedius 97.37 Streptococcus constellatus 96.62
84 Streptococcus dysgalactiae 31 Streptococcus dysgalactiae 99.62 Streptococcus dysgalactiae 99.62 Streptococcus iniae 97.47 Streptococcus agalactiae 97.33
85 Streptococcus iniae 7 Streptococcus iniae 99.62 Streptococcus iniae 99.62 Streptococcus porcinus 95.21 Streptococcus dysgalactiae 95.11
86 Streptococcus porcinus Streptococcus porcinus 99.76 Streptococcus porcinus 99.76 Streptococcus agalactiae 96.79 Streptococcus iniae 96.79
87 Streptococcus pyogenes 12 Streptococcus pyogenes 100.00 Streptococcus pyogenes 100.00 Streptococcus canis 98.24 Streptococcus agalactiae 96.93
88 Tsukamurella pulmonis 20 Tsukamurella pulmonis 99.13 Tsukamurella pulmonis 99.13 Tsukamurella tyrosinosolvens 99.05 Tsukamurella strandjordii 98.90
Tsukamurella tyrosinosolvens 99.05
Tsukamurella strandjordii 98.90
Tsukamurella inchonensis 98.74
Tsukamurella paurometabola 98.66
89 Tsukamurella tyrosinosolvens 20 Tsukamurella tyrosinosolvens 100.00 Tsukamurella tyrosinosolvens 100.00 Tsukamurella pulmonis 99.69 Tsukamurella strandjordii 99.69
Tsukamurella pulmonis 99.69
Tsukamurella strandjordii 99.69
Tsukamurella inchonensis 99.39
Tsukamurella paurometabola 99.24
90 Veillonella atypica Veillonella atypica 98.95 Veillonella atypica 98.95 Veillonella dispar 98.42 Veillonella parvula 97.71
Veillonella dispar 98.42
91 Vibrio furnissii Vibrio furnissii 99.62 Vibrio furnissii 99.62 Vibrio fluvialis 98.85 Vibrio vulnificus 97.02
Vibrio fluvialis 98.85
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Availability and update of 16SpathDB.

16SpathDB is available at no charge at http://147.8.74.24/16SpathDB and will be updated periodically for every new edition of the Manual of Clinical Microbiology.

RESULTS

16SpathDB and functionality of the database.

As of November 2010, 16SpathDB contained 1,014 16S rRNA gene sequences from 1,010 unique bacterial species.

Identification of medically important bacteria.

The main goal for setting up 16SpathDB was to provide a convenient and efficient platform for identification of medically important bacterial isolates using their 16S rRNA gene sequences. The interfaces of the database are simple and user friendly. From the home page, one can enter the “query page” by clicking the “identify bacteria by 16S rRNA gene sequence” hyperlink (Fig. 1a). From this page, users can input one or more query 16S rRNA gene sequences of 50 bp to 1,800 bp by pasting the sequences in FASTA format in the textbox or uploading a file that contains the sequences in FASTA format by clicking the “browse” button. By clicking the “begin identification” button, the query sequence(s) will be aligned with each of the 16S rRNA gene sequences in 16SpathDB using the pairwise global alignment algorithm with free end gap penalty. The percent nucleotide identity calculated from the alignment between the query sequence and each of the sequences in 16SpathDB is then used for the evaluation of the identity of the query sequence, using the algorithm depicted in Fig. 2.

Fig. 1.

Fig. 1.

Fig. 1.

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(a) “Query page” of 16SpathDB. (b) Example of query results showing one species (Gemella morbillorum) in 16SpathDB with >98.0% nucleotide identity to the query sequence. (c) Example of query results showing more than one species (Mycobacterium chelonae, Mycobacterium abscessus, and Mycobacterium immunogenum) in 16SpathDB with >98.0% nucleotide identity to the query sequence. (d) Example of query results showing no species in 16SpathDB with >98.0% nucleotide identity to the query sequence but one or more species in 16SpathDB with >96.0% nucleotide identity to the query sequence. (e) Example of query results showing no species in 16SpathDB with >96.0% nucleotide identity to the query sequence. (f) Example of query results showing the identity of the query sequence (Clostridium paraputrificum) and the 10 most closely matched sequences from 16SpathDB compared to the query sequence.

Fig. 2.

Fig. 2.

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Algorithm for reporting results in 16SpathDB.

The results of the comparison will be shown on the results page. If there is one species in 16SpathDB with >98.0% nucleotide identity to the query sequence, this bacterial species, as well as the percent nucleotide identity between the query sequence and the sequence of the most likely bacterial species, will be reported (category 1) (Fig. 1b and 2). If there is more than one species in 16SpathDB with >98.0% nucleotide identity to the query sequence, the species that showed the highest nucleotide identity to the query sequence (“best match in 16SpathDB”), as well as those with 16S rRNA gene sequences having <1% difference from the “best match in 16SpathDB,” will be reported to alert the user that further tests, such as biochemical tests or sequencing additional genes, may be necessary to distinguish between the most probable identities (category 2) (Fig. 1c and 2). If there are no species in 16SpathDB with >98.0% nucleotide identity to the query sequence but there are one or more species in 16SpathDB with >96.0% nucleotide identity to the query sequence, only the genus will be reported (category 3) (Fig. 1d and 2). The user will also be reminded that further tests are necessary for definite species identification. If there is no species in 16SpathDB with >96.0% nucleotide identity to the query sequence, the results page will show “no species in 16SpathDB was found to be sharing high nucleotide identity to your query sequence” (category 4) (Fig. 1e and 2). This indicates that the query sequence may represent a bacterial species not included in the Manual of Clinical Microbiology or a novel bacterial species. Users are advised to perform a BLAST search against the GenBank nr database to differentiate between the two possibilities. By clicking the “run BLAST” hyperlink, users can enter the “query page” of the NCBI BLAST website. The cutoffs of 98.0% for reporting the identity at the species level and 96.0% for reporting the identity at the genus level were found to be the optimal cutoffs by using 400 16S rRNA gene sequences of bacteria isolated from patients found in the GenBank database as the query sequences (data not shown).

From the results page, users can click the bacterial species name and go to the page in GenBank that contains detailed information on the representative 16S rRNA gene sequence selected for that bacterial species in 16SpathDB. From the results page, users can also click “show top 10 matches” to show the 10 most closely matched sequences from 16SpathDB compared to the query sequence (Fig. 1f).

Browsing 16S rRNA gene sequences of medically important bacteria.

In addition to identification of 16S rRNA gene sequences, users can also inspect the detailed contents of the database, as well as the information on the individual sequences. From the home page, one can enter the “sequence information” page by clicking the “browse bacteria 16S rRNA gene information” hyperlink (Fig. 3). From this page, the user can go to the page in GenBank that contains the detailed information on the 16S rRNA gene sequence selected for the corresponding bacterial species by entering the name of the bacterial species in the text box and click “view sequence information” and then the GenBank accession number. In addition, users can visualize the 10 bacterial species with 16S rRNA gene sequences having the highest nucleotide identities with the input bacterial species and inspect the detailed information on their 16S rRNA gene sequences in GenBank accordingly (Fig. 3).

Fig. 3.

Fig. 3.

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Example showing the 10 bacterial species with 16S rRNA gene sequences having the highest nucleotide identities to the input bacterial species (Streptococcus acidominimus).

Identification of clinical bacterial isolates using 16SpathDB.

Among the 91 nonduplicated medically important bacterial isolates we collected in our clinical microbiology laboratory in the past 10 years (Table 1), 71 (78%) were reported by 16SpathDB as a single bacterial species having >98.0% nucleotide identity with the query sequence (category 1), 19 (20.9%) were reported by 16SpathDB as more than one bacterial species having >98.0% nucleotide identity with the query sequence (category 2), none was reported by 16SpathDB to the genus level (category 3), and 1 (1.1%) was reported by 16SpathDB as “no species in 16SpathDB was found to be sharing high nucleotide identity to your query sequence” (category 4). For the 71 bacterial isolates reported by 16SpathDB as a single bacterial species, all results were identical to the true identities of the isolates as determined by the polyphasic approach. For the 19 bacterial isolates reported by 16SpathDB as more than one bacterial species, all results contained the true identities of the isolates as determined by the polyphasic approach. In fact, all 19 of these isolates had their true identities as the “best match in 16SpathDB.”

DISCUSSION

Rapid and accurate interpretation of 16S rRNA gene sequence results is the cornerstone for identification of medically important bacteria by 16S rRNA gene sequencing. During the process of using 16S rRNA gene sequencing for identification of a large number of medically important bacteria in the past 5 years, we have developed an automated, user-friendly, and comprehensive database, 16SpathDB, of the most representative 16S rRNA gene sequences of all medically important bacteria listed in the most current edition of the Manual of Clinical Microbiology (14). In contrast to RDP-II and SmartGene IDNS software (Table 2), 16SpathDB includes only 16S rRNA gene sequences of medically important bacteria. Since more than 99.9% of the bacterial strains recovered from patients were included in the Manual of Clinical Microbiology (unpublished data), the inclusion of other bacterial species that have never been isolated from patients would create ambiguity during data interpretation, as the target users of 16SpathDB are technicians and clinical microbiologists who work on 16S rRNA gene sequencing for clinical isolates. As for the MicroSeq databases (Table 2), one of their main drawbacks for identification of medically important bacteria is that the databases do not include a significant number of medically important bacteria that 16S rRNA gene sequencing is able to identify. For example, 98 to 108 (53.3 to 67.1%), 38 to 39 (22.7 to 37.3%), and 23 to 39 (19.8 to 41.9%) medically important anaerobic, aerobic Gram-positive, and aerobic Gram-negative bacteria that should be confidently identified by 16S rRNA gene sequencing are not included in the full- and 500-MicroSeq databases, respectively (19, 32). If the query sequence is from one of these bacteria, the MicroSeq databases would automatically give wrong results. Furthermore, the results from the MicroSeq databases show only a single “identity” of the query sequence. Other bacterial species with similar 16S rRNA gene sequences, which may also be the identity of the isolate, are ignored. For example, it is well known that 16S rRNA gene sequencing is not useful for distinguishing some bacterial species, such as Streptococcus pneumoniae, Streptococcus pseudopneumoniae, Streptococcus mitis, and Streptococcus oralis, as their 16S rRNA gene sequences show more than 99% identity. In 16SpathDB, in addition to the species that shows the highest nucleotide identity to the query sequence, those species with 16S rRNA gene sequences having less than 1% difference from the species that showed the highest nucleotide identity to the query sequence will also be reported (Fig. 1c). This helps to alert the user that further tests have to be carried out in order to distinguish between these probable identities.

Table 2.

Comparison of 16SpathDB and other commonly used software for bacterial identification using 16S rRNA gene sequencing

Software Yr of first description Company/organization Website Partial/full 16S rRNA gene sequence included Database size Source of sequences Cost Quality control Updates
Ribosomal Database Project (RDP) 1992 Michigan State University, East Lansing, MI http://rdp.cme.msu.edu/ Partial and full 1,418,497 (release 10.22) GenBank No Partial Periodically
MicroSeq Microbial Identification Systema 1998 Applied Biosystems, Foster City, California http://www.microseq.com Partial and fulla 1,834 and 1,261 in the two databases, respectivelya Sequence of 16S rRNA gene of one strain from each species Yes All type strains from culture collections Periodically
Ribosomal Differentiation of Medical Microorganisms (RIDOM) 1999 Ridom GmbH, Würzburg, Germany http://rdna2.ridom.de/ Partial 236b Sequences of 16S rRNA genes of medically relevant bacteria, mainly belonging to the Neisseriaceae, Moraxellaceae, and genus Mycobacterium No All strains from culture collections Periodically
SmartGene IDNS software 2006 SmartGene GmbH, Switzerland http://www.smartgene.com/mod_bacteria.html Partial and full 243,000 GenBank Yes Partial Daily
16SpathDB 2010 Department of Microbiology, University of Hong Kong, Hong Kong http://147.8.74.24/16SpathDB Partial and full 1,010 GenBank No All sequences manually selected from GenBank Periodically
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a

Contains two databases, MicroSeq ID 16S rDNA 500 Library v2.2, which contains sequences from the 5′-end 527 bp of 16S rRNA genes, and MicroSeq ID 16S rDNA Full Gene Library v2.0, which contains full 16S rRNA gene sequences.

b

Number from from http://rdna2.ridom.de/ridom2/servlet/link?page = list (8 Nov 2010).

16SpathDB offers efficient and accurate analysis of 16S rRNA gene sequences from medically important bacteria. In the present study, all 91 bacteria recovered in our clinical microbiology laboratory in the past 10 years were successfully identified using 16SpathDB (Table 1). These 91 isolates included 55 aerobic and 36 anaerobic bacteria, and 62 Gram-positive bacteria, 28 Gram-negative bacteria, and one Mycoplasma hominis isolate. Among these 91 bacteria, 20.9% showed multiple possible identities, which reflected an inherent limitation of using 16S rRNA gene sequencing for bacterial identification. For this 20.9% of the strains, phenotypic tests or sequencing of additional gene loci should be performed to differentiate among the reported bacterial species. For example, when Tsukamurella pulmonis or Tsukamurella tyrosinosolvens (strain no. 88 and 89) (Table 1) were the exact identities of the query isolates, 16S rRNA gene sequencing was not able to distinguish them from the other medically important Tsukamurella spp. The API 20C AUX, and API 50 CH systems (bioMérieux, Lyon, France) have to be used to distinguish among these Tsukamurella spp. (20).

One limitation of 16SpathDB is that our database includes only the bacterial species that are known to be associated with infections, as described in the Manual of Clinical Microbiology. This was deliberately done because if those bacterial species that have never been reported to cause infections were also included, as in the RDP-II and SmartGene IDNS software, the proportion of results that showed multiple possible identities would be markedly increased. This would defeat the original purpose of designing the database, which is used for identification of medically important bacteria in clinical microbiology laboratories. For example, the 16S rRNA gene sequence of strain no. 50 (Table 1) was reported as “no species in 16SpathDB was found to be sharing high nucleotide identity to your query sequence.” This is because Gordonibacter pamelaeae has never been reported to be associated with human disease and was not included in the Manual of Clinical Microbiology. In such a situation, users should perform a BLAST search against the GenBank nr database for the identification of the 16S rRNA gene sequence (30). Although the database will be updated regularly whenever there is a new edition of the Manual of Clinical Microbiology, users should bear in mind that those bacterial species that have never been reported to be associated with infections may still have the potential to be so associated.

ACKNOWLEDGMENTS

This work is partly supported by the HKSAR Research Fund for the Control of Infectious Diseases of the Health, Welfare and Food Bureau, Research Grants Council Grant, and University Development Fund, The University of Hong Kong.

We thank Kit-Wah Leung and Ami M. Y. Fung for their critical comments on the database.

Footnotes

▿

Published ahead of print on 9 March 2011.

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