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Items: 1 to 20 of 129

1.

STITCH: algorithm to splice, trim, identify, track, and capture the uniqueness of 16S rRNAs sequence pairs using public or in-house database.

Zhu D, Vaishampayan PA, Venkateswaran K, Fox GE.

Microb Ecol. 2011 Apr;61(3):669-75. doi: 10.1007/s00248-010-9779-2. Epub 2010 Nov 27.

PMID:
21113709
2.

EzTaxon: a web-based tool for the identification of prokaryotes based on 16S ribosomal RNA gene sequences.

Chun J, Lee JH, Jung Y, Kim M, Kim S, Kim BK, Lim YW.

Int J Syst Evol Microbiol. 2007 Oct;57(Pt 10):2259-61.

PMID:
17911292
3.
4.

V-REVCOMP: automated high-throughput detection of reverse complementary 16S rRNA gene sequences in large environmental and taxonomic datasets.

Hartmann M, Howes CG, Veldre V, Schneider S, Vaishampayan PA, Yannarell AC, Quince C, Johansson P, Björkroth KJ, Abarenkov K, Hallam SJ, Mohn WW, Nilsson RH.

FEMS Microbiol Lett. 2011 Jun;319(2):140-5. doi: 10.1111/j.1574-6968.2011.02274.x. Epub 2011 Apr 27.

5.

The All-Species Living Tree project: a 16S rRNA-based phylogenetic tree of all sequenced type strains.

Yarza P, Richter M, Peplies J, Euzeby J, Amann R, Schleifer KH, Ludwig W, Glöckner FO, Rosselló-Móra R.

Syst Appl Microbiol. 2008 Sep;31(4):241-50. doi: 10.1016/j.syapm.2008.07.001. Epub 2008 Aug 9.

PMID:
18692976
6.

MiCA: a web-based tool for the analysis of microbial communities based on terminal-restriction fragment length polymorphisms of 16S and 18S rRNA genes.

Shyu C, Soule T, Bent SJ, Foster JA, Forney LJ.

Microb Ecol. 2007 May;53(4):562-70. Epub 2007 Apr 4.

PMID:
17406775
7.

Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data.

Aßhauer KP, Wemheuer B, Daniel R, Meinicke P.

Bioinformatics. 2015 Sep 1;31(17):2882-4. doi: 10.1093/bioinformatics/btv287. Epub 2015 May 7.

8.

Automatic identification of large collections of protein-coding or rRNA sequences.

Arigon AM, Perrière G, Gouy M.

Biochimie. 2008 Apr;90(4):609-14. Epub 2007 Sep 2.

PMID:
17920750
9.

spyder, a new method for in silico design and assessment of 16S rRNA gene primers for molecular microbial ecology.

Thomas MC, Thomas DK, Selinger LB, Inglis GD.

FEMS Microbiol Lett. 2011 Jul;320(2):152-9. doi: 10.1111/j.1574-6968.2011.02302.x. Epub 2011 May 25.

10.

Identification of characteristic oligonucleotides in the bacterial 16S ribosomal RNA sequence dataset.

Zhang Z, Willson RC, Fox GE.

Bioinformatics. 2002 Feb;18(2):244-50.

PMID:
11847072
11.

FastGroupII: a web-based bioinformatics platform for analyses of large 16S rDNA libraries.

Yu Y, Breitbart M, McNairnie P, Rohwer F.

BMC Bioinformatics. 2006 Feb 7;7:57.

13.

Analysis of mixed sequencing chromatograms and its application in direct 16S rRNA gene sequencing of polymicrobial samples.

Kommedal O, Karlsen B, Saebø O.

J Clin Microbiol. 2008 Nov;46(11):3766-71. doi: 10.1128/JCM.00213-08. Epub 2008 Sep 3.

14.

CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization.

Hwang K, Oh J, Kim TK, Kim BK, Yu DS, Hou BK, Caetano-Anollés G, Hong SG, Kim KM.

PLoS One. 2013 May 1;8(5):e62623. doi: 10.1371/journal.pone.0062623. Print 2013. Erratum in: PLoS One. 2013;8(5). doi:10.1371/annotation/bb3baaad-6b58-41a0-96cc-7bdc819de411.

15.

Necessity of quality-controlled 16S rRNA gene sequence databases: identifying nontuberculous Mycobacterium species.

Turenne CY, Tschetter L, Wolfe J, Kabani A.

J Clin Microbiol. 2001 Oct;39(10):3637-48. Erratum in: J Clin Microbiol 2002 Jun;40(6):2316.

16.
17.

On the suitability of short reads of 16S rRNA for phylogeny-based analyses in environmental surveys.

Jeraldo P, Chia N, Goldenfeld N.

Environ Microbiol. 2011 Nov;13(11):3000-9. doi: 10.1111/j.1462-2920.2011.02577.x. Epub 2011 Sep 13.

PMID:
21910812
18.

High-density universal 16S rRNA microarray analysis reveals broader diversity than typical clone library when sampling the environment.

DeSantis TZ, Brodie EL, Moberg JP, Zubieta IX, Piceno YM, Andersen GL.

Microb Ecol. 2007 Apr;53(3):371-83. Epub 2007 Mar 2.

PMID:
17334858
19.

In silico analysis of 16S ribosomal RNA gene sequencing-based methods for identification of medically important anaerobic bacteria.

Woo PC, Chung LM, Teng JL, Tse H, Pang SS, Lau VY, Wong VW, Kam KL, Lau SK, Yuen KY.

J Clin Pathol. 2007 May;60(5):576-9. Epub 2006 Oct 17.

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