| scholarly article | Q13442814 |
| P356 | DOI | 10.1002/CPBI.100 |
| P698 | PubMed publication ID | 32343490 |
| P50 | author | Amanda Birmingham | Q92172536 |
| Lingjing Jiang | Q99616540 | ||
| Yoshiki Vázquez-Baeza | Q110853916 | ||
| Rob Knight | Q20657126 | ||
| Qiyun Zhu | Q57274084 | ||
| J Gregory Caporaso | Q88261455 | ||
| Daniel McDonald | Q88798031 | ||
| Evan Bolyen | Q92154202 | ||
| P2093 | author name string | Nicholas A Bokulich | |
| Antonio González | |||
| Tomasz Kosciolek | |||
| Cameron Martino | |||
| Mehrbod Estaki | |||
| Matthew R Dillon | |||
| P2860 | cites work | FEAST: fast expectation-maximization for microbial source tracking | Q92643433 |
| Interactive Tree Of Life (iTOL) v4: recent updates and new developments | Q92735580 | ||
| redbiom: a Rapid Sample Discovery and Feature Characterization System | Q93024078 | ||
| Modeling sample variables with an Experimental Factor Ontology | Q24596875 | ||
| Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics | Q24803868 | ||
| Fast gapped-read alignment with Bowtie 2 | Q27860699 | ||
| IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies | Q28250973 | ||
| The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation | Q28598380 | ||
| RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies | Q28658397 | ||
| FastTree 2--approximately maximum-likelihood trees for large alignments | Q28748616 | ||
| ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level | Q28749402 | ||
| Associations among Wine Grape Microbiome, Metabolome, and Fermentation Behavior Suggest Microbial Contribution to Regional Wine Characteristics | Q28829007 | ||
| Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 | Q29547403 | ||
| UniFrac: a new phylogenetic method for comparing microbial communities | Q29547435 | ||
| Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms | Q29614454 | ||
| An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea | Q29617497 | ||
| EMPeror: a tool for visualizing high-throughput microbial community data | Q30702273 | ||
| Waste not, want not: why rarefying microbiome data is inadmissible | Q30793257 | ||
| Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis | Q30829889 | ||
| It's all relative: analyzing microbiome data as compositions | Q31090161 | ||
| Normalization and microbial differential abundance strategies depend upon data characteristics | Q31169829 | ||
| Differential abundance analysis for microbial marker-gene surveys | Q33566352 | ||
| Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences | Q34090756 | ||
| Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing | Q34497700 | ||
| Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities | Q34601325 | ||
| Supervised classification of microbiota mitigates mislabeling errors | Q35018800 | ||
| Analysis of composition of microbiomes: a novel method for studying microbial composition | Q35668886 | ||
| Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications | Q36008546 | ||
| Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights | Q36072739 | ||
| Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets | Q37000950 | ||
| Bayesian community-wide culture-independent microbial source tracking | Q37217955 | ||
| Balance Trees Reveal Microbial Niche Differentiation. | Q37604562 | ||
| Antibiotics, birth mode, and diet shape microbiome maturation during early life. | Q37641944 | ||
| Exact sequence variants should replace operational taxonomic units in marker-gene data analysis | Q38668202 | ||
| VSEARCH: a versatile open source tool for metagenomics | Q39249686 | ||
| A broken promise: microbiome differential abundance methods do not control the false discovery rate | Q46297098 | ||
| A communal catalogue reveals Earth's multiscale microbial diversity | Q46349678 | ||
| Microbiome Datasets Are Compositional: And This Is Not Optional. | Q47149781 | ||
| Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information. | Q55224528 | ||
| Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. | Q55339477 | ||
| Cutadapt removes adapter sequences from high-throughput sequencing reads | Q55953584 | ||
| Data Organization in Spreadsheets | Q56200060 | ||
| Qiita: rapid, web-enabled microbiome meta-analysis | Q57060051 | ||
| Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians | Q57155462 | ||
| MetaPhlAn2 for enhanced metagenomic taxonomic profiling | Q57255766 | ||
| Striped UniFrac: enabling microbiome analysis at unprecedented scale | Q58083882 | ||
| Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches | Q58795625 | ||
| Evaluating the Information Content of Shallow Shotgun Metagenomics | Q59137646 | ||
| Modelling and Analysis of Compositional Data | Q59410954 | ||
| q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data | Q59793297 | ||
| Isometric Logratio Transformations for Compositional Data Analysis | Q60182806 | ||
| Best practices for analysing microbiomes | Q60547176 | ||
| A Novel Sparse Compositional Technique Reveals Microbial Perturbations | Q64238781 | ||
| Performance of Microbiome Sequence Inference Methods in Environments with Varying Biomass | Q64258708 | ||
| Establishing microbial composition measurement standards with reference frames. | Q64890630 | ||
| A field guide for the compositional analysis of any-omics data | Q90207879 | ||
| q2-sample-classifier: machine-learning tools for microbiome classification and regression | Q90266622 | ||
| The Statistical Analysis of Compositional Data | Q90314312 | ||
| Species abundance information improves sequence taxonomy classification accuracy | Q90663038 | ||
| Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 | Q92154246 | ||
| P433 | issue | 1 | |
| P304 | page(s) | e100 | |
| P577 | publication date | 2020-06-01 | |
| P1433 | published in | Current Protocols in Bioinformatics | Q26842707 |
| P1476 | title | QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data | |
| P478 | volume | 70 |
| Q108844055 | Advancements in capturing and mining mass spectrometry data are transforming natural products research |
| Q96473059 | COVID-19 pandemic reveals the peril of ignoring metadata standards |
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