Search Results for: microbiome

Introduce the QIIME2 microbiome workflow for Biowulf Review key concepts Showcase additional plugins

Try QIIME 2 Galaxy implementation Nephele MicrobiomeAnalyst If you have any questions about your microbiome analysis, do not hesitate to email us at ncibtep@nih.gov.

For general support throughout your microbiome data analysis, see the QIIME 2 documentation and the QIIME 2 forum .

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...

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

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...

10/19/2022 - The QIIME platform, including QIIME 1 and QIIME 2, has been extensively applied in microbiome research, repeatedly making analyses that were challenging or impossible into routine tasks. While QIIME began as a marker gene (e.g., 16 Read More...

08/04/2021 - Registration is required to join this event. If you have not registered, please do so now. The QIIME platform, including QIIME 1 and QIIME 2, has been extensively applied in microbiome research, repeatedly making analyses that Read More...

08/27/2024 - This hands-on workshop will help you advance your microbiome analysis and computing skills, and help you learn new ways to leverage computing resources for your research. What you’ll learn: ● The basics of interacting Read More...

02/21/2024 - https://cap-lab.bio (link is external) https:// (link is external) qiime2.org (link is external) The QIIME platform, including QIIME 1 and QIIME 2 ( https://qiime2.org (link is external) ), has been extensively applied in microbiome Read More...

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...

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 We will look into specific metrics of Read More...

For the help sessions, we will work on processing sequences generated in Zhang Z, Feng Q, Li M, Li Z, Xu Q, Pan X, Chen W. Age-Related Cancer-Associated Microbiota Potentially Promotes Oral Squamous Cell Cancer Read More...

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

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

QIIME2 is a platform for the processing and analysis of microbiome sequencing data. A general amplicon workflow in QIIME2 may look like the following: Image adapted from QIIME2 documentation (Conceptual overview of QIIME2) The first Read More...

learn how to apply different types of filtering to your ASV table and representative sequence data. classify your ASVs. Generate a phylogenetic tree. Now that we have imported and denoised, let's move on to Read More...

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...

BLAST+ - local sequence alignment followed by consensus taxonomy classification VSEARCH - global sequence alignment followed by consensus taxonomy classification These essentially align sequences to references and take the top matches (maxaccepts) above some threshold Read More...

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...

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...

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...

The two methods used for denoising on QIIME 2 include: DADA2 - Uses a run specific error profile - Unclear how an incomplete run profile would impact results - There is a method available for Pacbio Read More...

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...

Practice Lesson 6 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 Read More...

Alpha diversity is within sample diversity. When exploring alpha diversity, we are interested in the distribution of microbes within a sample or metadata category. This distribution not only includes the number of different organisms (richness) Read More...

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...

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 QIIME2 platform.

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...

Practice Lesson 2 For the help sessions, we will work on processing sequences generated in Zhang Z, Feng Q, Li M, Li Z, Xu Q, Pan X, Chen W. Age-Related Cancer-Associated Microbiota Potentially Promotes Oral Squamous Read More...

Now that we know what we mean by denoising, let's apply it to our data. We will use DADA2 , which seems to be the more popular method. To use DADA2, we need to make Read More...

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...

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...

Lesson 5 Practice 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 Read More...

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...

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...

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...

Lesson 4: Feature table filtering, taxonomic classification, and phylogeny Learning objectives learn how to apply different types of filtering to your ASV table and representative sequence data. classify your ASVs. Generate a phylogenetic tree. Now that Read More...

Practice Lesson 4 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 Read More...

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodrí Read More...

Lesson 3: Creating a feature table Lesson Objectives 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 Read More...

Lesson 5: Microbial diversity, alpha rarefaction, alpha diversity Learning Objectives 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 Read More...

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...

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...

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

Lecture slides can be found here .

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...

We will use Qiime2view frequently throughout this course, and you will use it frequently in the future if you plan to use QIIME2 in your research. This is a great tool for exploring QIIME2 Read More...

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...

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...

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...

Image adapted from docs.qiime2.org ( Conceptual Overview of QIIME 2 ) .

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...

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...

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...

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 .

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...

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...

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 .

Let's use our practice data set to run ANCOM. Step 1: Filter out low abundance / low prevalent ASVs. Note: this will shift the composition of the samples, and thus could bias results. mkdir ancom qiime Read More...

q2book Some useful slides from UCSD

The recording can be accessed here .

Obtain sequence data and sample metadata Import data and metadata Discuss other useful QIIME2 features including view QIIME2, provenance tracking, and the QIIME2 forum.

Step 1 : Login to DNAnexus Step 2 : Once you login, you should see the Projects page. If you have used DNAnexus previously, you may see more than one project listed. If this is your first time using Read More...

pwd (print working directory) ls (list) nano (basic editor for creating small text files) rm (remove files) mkdir (make a directory) cd (change directory) mv (rename or move files) less (view files) man (manual) cp ( Read More...

The QIIME2 platform can be used for different types of -omics data. For this course, we will be focusing on targeted amplicon sequencing of the 16S rRNA gene. The 16S rRNA gene (~1500 bp) codes for Read More...

For any next generation sequencing experiment, you will need sample information (sample metadata) to make sense of your data. The key to a good study is to collect good metadata. You should minimally have all Read More...

In our example data, the sequences are paired-end demultiplexed data . Raw fastq files are currently in a directory named /data/data_to_import . QIIME2 has specific functions for importing specific types of raw sequencing data. Read More...

Every qiime2 artifact includes provenance information, which includes things like the unique ID of the artifact(s) used as input, the format, type, method and action, run time, etc. You can check the uuid (universally Read More...

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...

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...

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...

Introduce several beta diversity metrics Discover different ordination methods Learn about statistical methods that are applicable

As mentioned previously, QIIME 2 is installed on Biowulf. To see available versions use module avail qiime Also, check out the QIIME2 Biowulf help page . The default version on Biowulf is qiime2-2021.4, and the latest Read More...

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...

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...

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...

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...

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...

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...