247 related articles for article (PubMed ID: 35202411)
1. Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold.
Chiarello M; McCauley M; Villéger S; Jackson CR
PLoS One; 2022; 17(2):e0264443. PubMed ID: 35202411
[TBL] [Abstract][Full Text] [Related]
2. Impact of DNA Sequencing and Analysis Methods on 16S rRNA Gene Bacterial Community Analysis of Dairy Products.
Xue Z; Kable ME; Marco ML
mSphere; 2018 Oct; 3(5):. PubMed ID: 30333179
[TBL] [Abstract][Full Text] [Related]
3. Amplicon Sequence Variants Artificially Split Bacterial Genomes into Separate Clusters.
Schloss PD
mSphere; 2021 Aug; 6(4):e0019121. PubMed ID: 34287003
[TBL] [Abstract][Full Text] [Related]
4. Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches.
Nearing JT; Douglas GM; Comeau AM; Langille MGI
PeerJ; 2018; 6():e5364. PubMed ID: 30123705
[TBL] [Abstract][Full Text] [Related]
5. OTUs and ASVs Produce Comparable Taxonomic and Diversity from Shrimp Microbiota 16S Profiles Using Tailored Abundance Filters.
García-López R; Cornejo-Granados F; Lopez-Zavala AA; Cota-Huízar A; Sotelo-Mundo RR; Gómez-Gil B; Ochoa-Leyva A
Genes (Basel); 2021 Apr; 12(4):. PubMed ID: 33924545
[TBL] [Abstract][Full Text] [Related]
6. An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur.
Liu G; Li T; Zhu X; Zhang X; Wang J
Front Microbiol; 2023; 14():1178744. PubMed ID: 37560524
[TBL] [Abstract][Full Text] [Related]
7. Ecological Observations Based on Functional Gene Sequencing Are Sensitive to the Amplicon Processing Method.
Cholet F; Lisik A; Agogué H; Ijaz UZ; Pineau P; Lachaussée N; Smith CJ
mSphere; 2022 Aug; 7(4):e0032422. PubMed ID: 35938727
[TBL] [Abstract][Full Text] [Related]
8. Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing.
Abellan-Schneyder I; Matchado MS; Reitmeier S; Sommer A; Sewald Z; Baumbach J; List M; Neuhaus K
mSphere; 2021 Feb; 6(1):. PubMed ID: 33627512
[TBL] [Abstract][Full Text] [Related]
9. Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences.
Narayan NR; Weinmaier T; Laserna-Mendieta EJ; Claesson MJ; Shanahan F; Dabbagh K; Iwai S; DeSantis TZ
BMC Genomics; 2020 Jan; 21(1):56. PubMed ID: 31952477
[TBL] [Abstract][Full Text] [Related]
10. Patterns of Relative Bacterial Richness and Community Composition in Seawater and Marine Sediment Are Robust for Both Operational Taxonomic Units and Amplicon Sequence Variants.
Kerrigan Z; D'Hondt S
Front Microbiol; 2022; 13():796758. PubMed ID: 35197949
[TBL] [Abstract][Full Text] [Related]
11. Taxonomic annotation of 16S rRNA sequences of pig intestinal samples using MG-RAST and QIIME2 generated different microbiota compositions.
Lima J; Manning T; Rutherford KM; Baima ET; Dewhurst RJ; Walsh P; Roehe R
J Microbiol Methods; 2021 Jul; 186():106235. PubMed ID: 33974954
[TBL] [Abstract][Full Text] [Related]
12. Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing.
Prodan A; Tremaroli V; Brolin H; Zwinderman AH; Nieuwdorp M; Levin E
PLoS One; 2020; 15(1):e0227434. PubMed ID: 31945086
[TBL] [Abstract][Full Text] [Related]
13. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding.
Brandt MI; Trouche B; Quintric L; Günther B; Wincker P; Poulain J; Arnaud-Haond S
Mol Ecol Resour; 2021 Aug; 21(6):1904-1921. PubMed ID: 33835712
[TBL] [Abstract][Full Text] [Related]
14. A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome.
Allali I; Arnold JW; Roach J; Cadenas MB; Butz N; Hassan HM; Koci M; Ballou A; Mendoza M; Ali R; Azcarate-Peril MA
BMC Microbiol; 2017 Sep; 17(1):194. PubMed ID: 28903732
[TBL] [Abstract][Full Text] [Related]
15. hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units Based on Homopolymer Compaction.
Park S; Choi HS; Lee B; Chun J; Won JH; Yoon S
IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(2):441-451. PubMed ID: 26930691
[TBL] [Abstract][Full Text] [Related]
16. Primary progressive multiple sclerosis in a Russian cohort: relationship with gut bacterial diversity.
Kozhieva M; Naumova N; Alikina T; Boyko A; Vlassov V; Kabilov MR
BMC Microbiol; 2019 Dec; 19(1):309. PubMed ID: 31888483
[TBL] [Abstract][Full Text] [Related]
17. Evaluating the accuracy of amplicon-based microbiome computational pipelines on simulated human gut microbial communities.
Golob JL; Margolis E; Hoffman NG; Fredricks DN
BMC Bioinformatics; 2017 May; 18(1):283. PubMed ID: 28558684
[TBL] [Abstract][Full Text] [Related]
18. A streamlined pipeline based on HmmUFOtu for microbial community profiling using 16S rRNA amplicon sequencing.
Kim H; Kim J; Choi JW; Ahn KS; Park DI; Kim S
Genomics Inform; 2023 Sep; 21(3):e40. PubMed ID: 37813636
[TBL] [Abstract][Full Text] [Related]
19. Evaluation of the reproducibility of amplicon sequencing with Illumina MiSeq platform.
Wen C; Wu L; Qin Y; Van Nostrand JD; Ning D; Sun B; Xue K; Liu F; Deng Y; Liang Y; Zhou J
PLoS One; 2017; 12(4):e0176716. PubMed ID: 28453559
[TBL] [Abstract][Full Text] [Related]
20. The effect of low-abundance OTU filtering methods on the reliability and variability of microbial composition assessed by 16S rRNA amplicon sequencing.
Nikodemova M; Holzhausen EA; Deblois CL; Barnet JH; Peppard PE; Suen G; Malecki KM
Front Cell Infect Microbiol; 2023; 13():1165295. PubMed ID: 37377642
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]