BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

170 related articles for article (PubMed ID: 34074026)

  • 1. General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways.
    Murovec B; Deutsch L; Stres B
    Metabolites; 2021 May; 11(6):. PubMed ID: 34074026
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer.
    Armour CR; Topçuoğlu BD; Garretto A; Schloss PD
    mBio; 2022 Feb; 13(1):e0316121. PubMed ID: 35012354
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 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. 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]  

  • 8.
    Abdala Asbun A; Besseling MA; Balzano S; van Bleijswijk JDL; Witte HJ; Villanueva L; Engelmann JC
    Front Genet; 2020; 11():489357. PubMed ID: 33329686
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Decontamination of 16S rRNA gene amplicon sequence datasets based on bacterial load assessment by qPCR.
    Lazarevic V; Gaïa N; Girard M; Schrenzel J
    BMC Microbiol; 2016 Apr; 16():73. PubMed ID: 27107811
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing.
    Shahi SK; Zarei K; Guseva NV; Mangalam AK
    J Vis Exp; 2019 Oct; (152):. PubMed ID: 31680682
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Binary Metabolic Phenotypes and Phenotype Diversity Metrics for the Functional Characterization of Microbial Communities.
    Iablokov SN; Novichkov PS; Osterman AL; Rodionov DA
    Front Microbiol; 2021; 12():653314. PubMed ID: 34113324
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution.
    Rohwer RR; Hamilton JJ; Newton RJ; McMahon KD
    mSphere; 2018 Sep; 3(5):. PubMed ID: 30185512
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering.
    Franzén O; Hu J; Bao X; Itzkowitz SH; Peter I; Bashir A
    Microbiome; 2015 Oct; 3():43. PubMed ID: 26434730
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome.
    Liu Y; Li W; Yang H; Zhang X; Wang W; Jia S; Xiang B; Wang Y; Miao L; Zhang H; Wang L; Wang Y; Song J; Sun Y; Chai L; Tian X
    Front Cell Infect Microbiol; 2021; 11():645951. PubMed ID: 34178718
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling.
    Reitmeier S; Hitch TCA; Treichel N; Fikas N; Hausmann B; Ramer-Tait AE; Neuhaus K; Berry D; Haller D; Lagkouvardos I; Clavel T
    ISME Commun; 2021 Jun; 1(1):31. PubMed ID: 37938227
    [TBL] [Abstract][Full Text] [Related]  

  • 17. MetAmp: combining amplicon data from multiple markers for OTU analysis.
    Zhbannikov IY; Foster JA
    Bioinformatics; 2015 Jun; 31(11):1830-2. PubMed ID: 25630378
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deciphering chicken gut microbial dynamics based on high-throughput 16S rRNA metagenomics analyses.
    Mohd Shaufi MA; Sieo CC; Chong CW; Gan HM; Ho YW
    Gut Pathog; 2015; 7():4. PubMed ID: 25806087
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health.
    Vernocchi P; Del Chierico F; Putignani L
    Front Microbiol; 2016; 7():1144. PubMed ID: 27507964
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies.
    Thorsen J; Brejnrod A; Mortensen M; Rasmussen MA; Stokholm J; Al-Soud WA; Sørensen S; Bisgaard H; Waage J
    Microbiome; 2016 Nov; 4(1):62. PubMed ID: 27884206
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.