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Search Results for: microbiome

Total Results Found: 106

Total Results Found: 106

Statistical Analysis of Microbiome Data

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Bioinformatics

11/08/2024 - Learn microbiome analysis basics in R with phyloseq. This workshop will cover different types of analysis frequently used in microbiome studies, including sample diversity, community composition, and differential taxa. The techniques we learn will Read More...

Microbiome Analysis with QIIME2: Microbiome Course

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Bioinformatics

Microbiome Course Course Outline Work through DNAnexus? Find a nice cancer data set for 16S and shotgun Divide into two workshops or courses? Course structure or workshop structure? Amplicon processing and analysis (5 week course) (4 weeks) 1. Read More...

Microbiome Analysis with QIIME2: QIIME2 Introduction

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Bioinformatics

Lesson 1: Toward fully reproducible microbiome multi-omics bioinformatics with QIIME 2 Lesson 1 does not include a hands on component, but rather includes an introduction to QIIME2 by guest speaker, Dr. Greg Caporaso, a leading developer of the Read More...

Microbiome Analysis with QIIME2: Meet our presenter

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Bioinformatics

Greg Caporaso, PhD Professor at Northern Arizona University A microbiome expert with 100+ related publications Lead developer of the QIIME 2 Platform Visit his lab website at https://caporasolab.us

Poisson Hurdle Model-based Clustering for Microbiome Data

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Bioinformatics

02/08/2023 - Join Dr. Peng Liu of Iowa State University as she proposes a model-based  algorithm  based on Poisson hurdle models for sparse microbiome count data. Simulation results demonstrate that the proposed methods provide better clustering Read More...

Microbiome Analysis with QIIME2: References

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Bioinformatics

This course series primarily used information from QIIME2.org and the QIIME2 forum . Specifically, this course series focused on data and code from the QIIME2 Cancer Microbiome Intervention Tutorial . Special thanks goes to the QIIME 2 Read More...

Microbiome Analysis with QIIME2: References

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Bioinformatics

https://www.zymoresearch.com/blogs/blog/microbiome-informatics-otu-vs-asv www.drive5.com Elbrecht V, Vamos EE, Steinke D, Leese F. 2018. Estimating intraspecific genetic diversity from community DNA metabarcoding data. PeerJ 6:e4644 https://doi.org/10.7717/peerj.4644

Microbiome Analysis with QIIME2: Beta diversity

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Bioinformatics

Beta diversity is between sample diversity. This is useful for answering the question, how different are these microbial communities? Image modified from https://www.genome.gov/genetics-glossary/Microbiome Beta diversity is measured using distance and Read More...

Microbiome Analysis with QIIME2: Examining the metadata

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Bioinformatics

Let's take a look at the metadata associated with QIIME 2 Cancer Microbiome Intervention tutorial. qiime metadata tabulate \ --m-input-file /data/sample-metadata.tsv \ --o-visualization metadata-summary.qzv This command allows us to interactively explore the metadata. If Read More...

Microbiome Analysis with QIIME2: Course Outline

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Bioinformatics

Work through DNAnexus? Find a nice cancer data set for 16S and shotgun Divide into two workshops or courses? Course structure or workshop structure? Amplicon processing and analysis (5 week course) (4 weeks) 1. Talk by Greg Caparaso? 1. Read More...

Microbiome Analysis with QIIME2: What is QIIME 2?

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Bioinformatics

A powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical Read More...

Microbiome Analysis with QIIME2: Data import

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Bioinformatics

As mentioned previously, the first step of any QIIME 2 analysis will be to import the data. Each type of data will be stored in its own QIIME2 artifact. For example, sample metadata, ASV / OTU tables, Read More...

Microbiome Analysis with QIIME2: Learning Objectives

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Bioinformatics

Understand the difference between alpha and beta diversity Introduce several alpha diversity metrics Understand what rarefaction is and why it is important Introduce the debate regarding rarefaction and other methods of normalization Often many questions Read More...

Microbiome Analysis with QIIME2: Course Overview

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Bioinformatics

This course was designed to teach the basics of targeted amplicon data processing and analysis using the QIIME2 platform. Attendees will learn how to format data and metadata, import data, demultiplex sequences, trim sequences, denoise Read More...

Microbiome Analysis with QIIME2: Home

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Bioinformatics

Course Overview This course was designed to teach the basics of targeted amplicon data processing and analysis using the QIIME2 platform. Attendees will learn how to format data and metadata, import data, demultiplex sequences, trim Read More...

Microbiome Analysis with QIIME2: Optional filtering of samples

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Bioinformatics

If we want to retain only the samples included in our diversity analyses, we can use qiime feature-table filter-samples to drop samples with read depths less than 10,000. qiime feature-table filter-samples \ --i-table filtered-table-3.qza \ --p-min-frequency 10000 \ --o-filtered-table Read More...

Microbiome Analysis with QIIME 2

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Bioinformatics

10/19/2022 - Welcome to the Microbiome Analysis with QIIME 2 course series!                   This course series was designed to teach the basics of targeted amplicon data processing and analysis using the QIIME 2 platform. Attendees will learn how to Read More...

Microbiome Analysis with QIIME2: Q2-longitudinal

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Bioinformatics

Luckily, there is a q2-longitudinal plugin to handle dependent longitudinal data. The q2-longitudinal plugin includes: interactive plotting (e.g., volatility plots) linear mixed effects models paired differences and distances non-metric microbial interdependence testing ( Read More...

Microbiome Analysis with QIIME2: Lesson Objectives

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Bioinformatics

Check for primers Generate an ASV count table and representative sequence file Understand the difference between OTU picking and denoising The two primary files that will be used throughout any microbiome analysis are the feature Read More...

Microbiome Analysis with QIIME2: Feature filtering

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Bioinformatics

We can filter features observed in only a single sample. qiime feature-table filter-features \ --i-table filtered-table-1.qza \ --p-min-samples 2 \ --o-filtered-table filtered-table-2.qza Unless we explicitly remove them, removed features will remain in our FeatureData (Representative Sequences). This Read More...

Microbiome Analysis with QIIME2: Course Wrap-Up

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Bioinformatics

Lesson 7: Course Wrap-Up Learning Objectives Introduce the QIIME2 microbiome workflow for Biowulf Review key concepts Showcase additional plugins QIIME 2 on Biowulf As mentioned previously, QIIME 2 is installed on Biowulf. To see available versions use module Read More...

Microbiome Analysis with QIIME2: Practice Lesson 6

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Bioinformatics

This practice lesson is associated with Lesson 6 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will view beta diversity results and determine whether our two conditions (old vs young) demonstrate significant differences Read More...

Microbiome Analysis with QIIME2: References

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Bioinformatics

References This course series primarily used information from QIIME2.org and the QIIME2 forum . Specifically, this course series focused on data and code from the QIIME2 Cancer Microbiome Intervention Tutorial . Special thanks goes to the Read More...

Microbiome Analysis with QIIME2: Core microbiome

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Bioinformatics

If interested in highly prevalent taxa, you could use qiime feature-table core-features , which identifies "features observed in a user-defined fraction of the samples." By default, this will return features observed in at least 50% Read More...

Microbiome Analysis with QIIME2: Lesson 5 Practice

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Bioinformatics

This practice lesson is associated with Lesson 5 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on choosing a sampling depth to rarefy, running core-metrics, and comparing alpha diveristy between our Read More...

Microbiome Analysis with QIIME2: Metadata and importing

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Bioinformatics

Lesson 2: Getting Started with QIIME2 Lesson Objectives Obtain sequence data and sample metadata Import data and metadata Discuss other useful QIIME2 features including view QIIME2, provenance tracking, and the QIIME2 forum. DNAnexus DNAnexus provides a Read More...

Microbiome Analysis with QIIME2: Practice Lesson 4

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Bioinformatics

This practice lesson is associated with Lesson 4 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on filtering our feature table and representative sequences, classify our features, and generate a phylogenetic Read More...

Microbiome Analysis with QIIME2: Practice Lesson 3

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Bioinformatics

This practice lesson is associated with Lesson 3 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on generating a feature table and representative sequences. We will continue working with the data Read More...

Microbiome Analysis with QIIME2: Lesson 3: Denoising

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Bioinformatics

Practice Lesson 3 This practice lesson is associated with Lesson 3 of the Microbiome Analysis with QIIME 2. In this practice lesson, we will work on generating a feature table and representative sequences. We will continue working with Read More...

Microbiome Analysis with QIIME2: Beta Diversity

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Bioinformatics

Lesson 6 . Learning Objectives Introduce several beta diversity metrics Discover different ordination methods Learn about statistical methods that are applicable Beta diversity Beta diversity is between sample diversity. This is useful for answering the question, how Read More...

Microbiome Analysis with QIIME2: What have we done?

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Bioinformatics

Using a small subset of data: Imported raw fastq files using qiime tools import . Data was paired-end CASAVA format. Checked for primers using qiime cutadapt trim-paired . Denoised with qiime dada2 denoise-paired and generated summaries of Read More...

Microbiome Analysis with QIIME2: Linux help

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Bioinformatics

Introduction to Unix from the Bioinformatics Workbook Unix from Happy Belly Bioinformatics Linux Command List (from fosswire.com) Bioinformatics for Beginners: Module 1 - Linux and Biowulf

Microbiome Analysis with QIIME2: DNAnexus

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Bioinformatics

DNAnexus provides a secure cloud based platform for the analysis and sharing of next generation sequencing data. This class will use a pre-built teaching environment, the GOLD platform, which includes all of the software needed Read More...

Microbiome Analysis with QIIME2: Denoising

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Bioinformatics

The field has moved to denoising sequences rather than OTU clustering. In a denoising approach, the exact biological sequence is inferred and noise is removed from the dataset via error correction. This is generally done Read More...

Microbiome Analysis with QIIME2: Taxonomy

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Bioinformatics

After denoising, we have unique ASVs or sequences, which in itself can be quite revealing. However, often we want to classify our sequences to know more about the organisms represented by our ASVs, particularly regarding Read More...

Microbiome Analysis with QIIME2: What is rarefaction?

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Bioinformatics

Rarefaction is the process of subsampling reads without replacement to a defined sequencing depth, thereby creating a standardized library size across samples. Any sample with a total read count less than the defined sequencing depth Read More...

Microbiome Analysis with QIIME2: Phylogeny

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Bioinformatics

In addition to clasifying our organisms, we also want to reconstruct their phylogenetic relationships by generating a phylogenetic tree. We often assume that phylogenetic closeness can elucidate commonalities in phenotypic properties / functions, so it is Read More...

Microbiome Analysis with QIIME2: Rarefaction

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Bioinformatics

To rarefy or not to rarefy? Feature tables are composed of sparse and compositional data. Measuring microbial diversity using 16S rRNA sequencing is dependent on sequencing depth. By chance, a sample that is more deeply Read More...

Microbiome Analysis with QIIME2: PCoA

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Bioinformatics

PCoA was included by default in our core-metrics-phylogenetic pipeline. Because these are longitudinal data, we will customize the axis to include the varaible, week-relative-to-hct . qiime emperor plot \ --i-pcoa diversity-core-metrics-phylogenetic/unweighted_unifrac_pcoa_results.qza \ --m-metadata-file / Read More...

Microbiome Analysis with QIIME2: Getting the Data

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Bioinformatics

Getting the Data The data used in this course are freely available from the Sequence Read Archive (SRA-NCBI) and qiime2.org . For your convenience, we are also including a compressed archive containing the data here .

Microbiome Analysis with QIIME2: UMAP

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Bioinformatics

First, we perform the ordination. qiime diversity umap \ --i-distance-matrix diversity-core-metrics-phylogenetic/unweighted_unifrac_distance_matrix.qza \ --o-umap uu-umap.qza qiime diversity umap \ --i-distance-matrix diversity-core-metrics-phylogenetic/weighted_unifrac_distance_matrix.qza \ --o-umap wu-umap.qza Then we use emperor Read More...

Microbiome Analysis with QIIME2: Statistics

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Bioinformatics

Some typical statitstical tests applied to beta diversity metrics include the following: Adonis (PERMANOVA) Similar to a MANOVA, but is permutational and non-parametric. Sensitive to group dispersion, so it is worth running alongside a beta-dispersion Read More...

Microbiome Analysis with QIIME2: Getting the Data

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Bioinformatics

The data used in this course are freely available from the Sequence Read Archive (SRA-NCBI) and qiime2.org . For your convenience, we are also including a compressed archive containing the data here .

Microbiome Analysis with QIIME2: Denoising stats

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Bioinformatics

Let's take a look at our DADA2 stats. This will give us an idea about the number of reads filtered at various steps. qiime metadata tabulate \ --m-input-file dada2-stats.qza \ --o-visualization dada2-stats-summ.qzv Read More...

Microbiome Analysis with QIIME2: Visualizing our taxonomy

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Bioinformatics

We can visualize sample by sample taxonomic composition using a stacked bar plot generated with qiime taxa barplot . Let's take a look. qiime taxa barplot \ --i-table filtered-table-3.qza \ --i-taxonomy taxonomy.qza \ --m-metadata-file /data/sample-metadata. Read More...

Microbiome Analysis with QIIME2: Methods of filtering

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Bioinformatics

Frequency based filtering, contingency based filtering, metadata based filtering qiime feature-table filter-samples Filter samples based on total number of sequences (e.g., --p-min-frequency 1000 ) Filter samples with a minimal number of features (e.g., --p-min-features 10 ) Metadata Read More...

Microbiome Analysis with QIIME2: Other notable plugins

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Bioinformatics

DEICODE compositional beta diversity with biplots performs a Robust Aitchison PCA q2-clawback can improve taxonomic classifications uses taxonomic weights based on environment q2-picrust2 functional prediction from 16S rRNA data q2-sidle a new Read More...

CCR Genomics Technology Laboratory (GTL)
Frederick, MD

Core Facility

The Genomics Technology Laboratory is an integrated, high-throughput molecular biology laboratory focusing on the development of genetics and genomics technologies, data analysis, and information management tools, in support of CCR Investigators. The laboratory develops integrated Read More...

November 2021

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CREx News & Updates November 2021 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Site Spotlight FACILITY HIGLIGHTS Learn more about services from the NINDS Quantitative Magnetic Resonance Imaging Core. NINDS Read More...

NCI LASP Gnotobiotics Facility (GF)
Frederick, MD

Core Facility

The Laboratory Animal Sciences Program (LASP) of the Frederick National Laboratory operates a Gnotobiotics Facility (GF) to support research focused on the role of microbiota in cancer inflammation, pathogenesis, and treatment response. The GF can Read More...

Genomics Technology Laboratory

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The Genomics Laboratory (formerly Laboratory of Molecular Technology) is an integrated, high-throughput molecular biology laboratory focusing on the development of genetics and genomics technologies, together with associated laboratory automation systems, data analysis, and information management Read More...

CCR Genomics Core
Bethesda, MD

Core Facility

The CCR Genomics Core is located in Building 41 on the NIH Bethesda campus. The primary goal of the Core is to provide investigators from CCR/NCI and other NIH Institutes access to genomic technologies and Read More...