Literature DB >> 17050811

Application of SmartGene IDNS software to partial 16S rRNA gene sequences for a diverse group of bacteria in a clinical laboratory.

Keith E Simmon1, Ann C Croft, Cathy A Petti.   

Abstract

Laboratories often receive clinical isolates for bacterial identification that have ambiguous biochemical profiles by conventional testing. With the emergence of 16S rRNA gene sequencing as an identification tool, we evaluated the usefulness of SmartGene IDNS, a 16S rRNA sequence database and software program for microbial identification. Identification by conventional methods of a diverse group of bacterial clinical isolates was compared with gene sequences interrogated by the SmartGene and MicroSeq databases. Of 300 isolates, SmartGene identified 295 (98%) to the genus level and 262 (87%) to the species level, with 5 (2%) being inconclusive. MicroSeq identified 271 (90%) to the genus level and 223 (74%) to the species level, with 29 (10%) being inconclusive. SmartGene and MicroSeq agreed on the genus for 233 (78%) isolates and the species for 212 (71%) isolates. Conventional methods identified 291 (97%) isolates to the genus level and 208 (69%) to the species level, with 9 (3%) being inconclusive. SmartGene, MicroSeq, and conventional identifications agreed for 193 (64%) of the results. Twenty-seven microorganisms were not represented in MicroSeq, compared to only 2 not represented in SmartGene. Overall, SmartGene IDNS provides comprehensive and accurate identification of a diverse group of bacteria and has the added benefit of being a user-friendly program that can be modified to meet the unique needs of clinical laboratories.

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Year:  2006        PMID: 17050811      PMCID: PMC1698390          DOI: 10.1128/JCM.01364-06

Source DB:  PubMed          Journal:  J Clin Microbiol        ISSN: 0095-1137            Impact factor:   5.948


  25 in total

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2.  Usefulness of the MicroSeq 500 16S ribosomal DNA-based bacterial identification system for identification of clinically significant bacterial isolates with ambiguous biochemical profiles.

Authors:  Patrick C Y Woo; Kenneth H L Ng; Susanna K P Lau; Kam-tong Yip; Ami M Y Fung; Kit-wah Leung; Dorothy M W Tam; Tak-lun Que; Kwok-yung Yuen
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3.  Use of 16S rRNA gene sequencing for identification of nonfermenting gram-negative bacilli recovered from patients attending a single cystic fibrosis center.

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4.  Ribosomal DNA sequencing for identification of aerobic gram-positive rods in the clinical laboratory (an 18-month evaluation).

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10.  GenBank.

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  28 in total

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Authors:  Peter M Keller; Silvana K Rampini; Andrea C Büchler; Gerhard Eich; Roger M Wanner; Roberto F Speck; Erik C Böttger; Guido V Bloemberg
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2.  Evaluation of the integrated database network system (IDNS) SmartGene software for analysis of 16S rRNA gene sequences for identification of Nocardia species.

Authors:  Patricia S Conville; Patrick R Murray; Adrian M Zelazny
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3.  Species-Level Identification of Actinomyces Isolates Causing Invasive Infections: Multiyear Comparison of Vitek MS (Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry) to Partial Sequencing of the 16S rRNA Gene.

Authors:  T Lynch; D Gregson; D L Church
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4.  Phylogenetic analysis of viridans group streptococci causing endocarditis.

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5.  Automated identification of medically important bacteria by 16S rRNA gene sequencing using a novel comprehensive database, 16SpathDB.

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6.  GyrB polymorphisms accurately assign invasive viridans group streptococcal species.

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Review 7.  Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory.

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8.  Leptotrichia bacteremia in patients receiving high-dose chemotherapy.

Authors:  Marc Roger Couturier; E Susan Slechta; Claudia Goulston; Mark A Fisher; Kimberly E Hanson
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9.  Carbapenem susceptibility patterns for clinical isolates of Mycobacterium abscessus determined by the Etest method.

Authors:  Shingo Chihara; Geremy Smith; Cathy A Petti
Journal:  J Clin Microbiol       Date:  2009-12-16       Impact factor: 5.948

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Journal:  J Clin Microbiol       Date:  2014-02-26       Impact factor: 5.948

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