A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria.

By S. Chakravorty, Danica A Helb, Michele S. Burday, Nancy D. Connell, D. Alland. Journal of Microbiological Methods, 2007

1,136 citations recorded by the source corpus.

Subfields: Clinical Biochemistry, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e78b94a0b5d7a9bed16cf4d1b603770eb5307d3f

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Citations in the corpus

Comparative Analysis of Pyrosequencing and a Phylogenetic Microarray for Exploring Microbial Community Structures in the Human Distal Intestine (2009, positive): “When investigated in another study at a much smaller scale [39], combining the three regions V2, V3 and V6 allowed assignment of all tested 110 bacterial species down to genus-level, but only a subset of these to specieslevel.”
Efficient phenol degradation by a newly characterized Pseudomonas sp. SA01 isolated from pharmaceutical wastewaters (2009, methodology): “The DNA extracts from bacterial isolate were amplified using primers V6F (961-78) 5NTCG AtGCAACGCGAAGAA3N and V6R (1062-85) 5NACATtTCACaACACGAGCTGACGA3N for partial bacterial 16SrRNA sequence analysis [30].”
Deciphering Diversity Indices for a Better Understanding of Microbial Communities. (2017, background): “However, the recent application of targeted DNA sequence analysis of the 16S rRNA gene coupled with next-generation sequencing technologies now enables us to intensively explore microbial communities of the GI tract and describe overall diversity [7, 8].”
Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions (2010, neutral): “A few comparative studies have focused on assessing region suitability: after using different methodological approaches Sundquist et (11).”
A deep learning approach to pattern recognition for short DNA sequences (2018, background): “As these hypervariable regions are well-known and exploited features of 16S sequences, 44,45 the ability to learn the importance of these regions from training data is an important demonstration that 1) the DNN learned important biological structure of 16S, and 2) we might be able to discover…”
Microbiologically induced concrete corrosion: A case study from a combined sewer network (2015, methodology): “The released DNA was used for amplification of a 298 base pair 16S rDNA fragment using primers in conserved regions flanking two highly variable regions V5 and V6 [32] in standard PCR reactions using Phusion DNA polymerase (New England Biolabs).”
Performance, Accuracy, and Web Server for Evolutionary Placement of Short Sequence Reads under Maximum Likelihood (2011, positive): “Position-specific effects on placement accuracy have previously been identified as a problem by Chakravorty et al. (2007).”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, methodology): “Sequences that surpass a dissimilarity threshold (bit-score) are progressively placed on higher taxonomy levels.”
Microbial Co-occurrence Relationships in the Human Microbiome (2012, background): “The network discussed here, for example, leverages two specific hypervariable regions for taxonomic classification, each with strengths and weaknesses, and neither individually adequate for sequence classification at the species level [69,70].”
Bacteria in Weathered Basaltic Glass, Iceland (2009, methodology): “For phylogenetic tree construction we used the hypervariable regions V5 and V6 of the 16S rDNA gene (Chakravorty et al. 2007) corresponding to E.”