"
to the "Exception Site List" in the Java Security tab.
**Download Data - Abundance**
Click the folder icon to be redirected to the CyVerse Discovery
Environment (you may be required to log in). You will be directed
to all outputs from you Kallisto analysis. You may preview them in
the Discovery Environment or use the path listed to download the
files using Cyberduck (see [Data Store Guide](https://unm-carc.github.io/cyverse/data-store/sftp/cyberduck/)). A tab-separated file of abundances
for each sequence pair is available at the download link.
------------------------------------------------------------------------
### DNA Subway Green Line: Kallisto/Sleuth- Visualize data using Sleuth
**Differential analysis - Shiny App**
Click the "Sleuth R Shiny" link to launch an interactive window which contains data and graphics from your analysis.
**R Shiny App Walkthrough**
The R Shiny App allows you to explore your differential expression
results as generated by the . We will cover highlights to for each
menu in the app.
??? tip "Data Transfer Timings"
It can take a few minutes for data to be transferred to the R
Shiny server after the quantification step completes. If R Shiny
does not load, try again in a few minutes. If you still have an
issue, use the link and include your project number in the
feedback form.
**Results Menu**
{width="800px" height="400px"}
This menu is an interactive table of your results. You can choose
which columns to display in the table using the checkboxes on the
left of the screen. Several important values selected by default
include:
- **Target_id**: This is the name of the transcript (gene) from the selected reference transcriptome.
- **qval**: This is a corrected (for multiple testing) p-value indicating the significance test of differential abundance. Lower numbers indicate greater significance.
- **b**: This is an estimate of the fold change between the conditions
- **ext_gene**: If available, these are gene names pulled from Ensemble
!!! tip
Click the **Download** button to download these results.
**Bootstrap**
{width="800px" height="400px"}
This menu will display a box plot that indicates the difference in
expression between conditions. The box plots themselves indicate
variation between replicates as estimated by bootstrap sampling of
the reads. A dropbox enables you to select any transcript.
Clicking the "Show genes" will load alternative gene names if
available.
!!! tip
Right-click a graph to download this and other images.
**PCA**
{width="800px" height="400px"}
This graph displays principle components of each of the
conditions/replicates. In general replicates of the same condition
should cluster closely together.
**Volcano Plot**
{width="800px" height="400px"}
This scatter plot displays all transcripts colored by significance
of differential abundance. You may also use menu on the left of
the screen to highlight specific genes/transcripts or previously
set filters from the results menu.
**Loadings**
{width="800px" height="400px"}
This barplot indicates which genes/transcripts explain most of the variance computed in the principle components analysis.
**Heatmap**
{width="800px" height="400px"}
This heatmap gives a measure of the similarity between the possible comparison of the samples and their replicates.
------------------------------------------------------------------------
**Summary**: Together, Kallisto and Sleuth are quick, powerful ways to
analyze RNA-Seq data.
------------------------------------------------------------------------
## Walkthrough of DNA Subway Purple Line (beta testing documentation)
!!! danger "BETA RELEASE"
The Purple line is in beta release. Please send feedback to [DNALC Admin](mailto:dnalcadmin@cshl.edu).
The Purple Line provides the capability for analysis of microbiome and
eDNA (environmental DNA) by implementing a simplified version of the [QIIME 2](https://qiime2.org/){target=_blank}
(pronounced "chime two") workflow. Using the Purple Line, you can
analyze uploaded high throughput sequencing reads to identify species in
microbial or environmental DNA samples.
Metabarcoding uses high-throughput sequencing to analyze hundreds of
thousands of DNA barcodes from complex mixtures of DNA. In a typical
experiment, DNA is isolated from sterile swabs or material taken from
different environmental locations or conditions. PCR is used to amplify
a variable region, such as COI, or 12S or 16S ribosomal RNA genes, and
sequence reads identify the variety and abundance of species from
different samples. The analysis requires specialized software, such as
QIIME 2.
The Purple Line integrates sequence data and metadata imported from
CyVerse's Data Store, demultiplexing of samples, quality control, and
taxonomic identification and quantitation. Once sequences are analyzed,
the results can be visualized to allow comparisons between samples and
different conditions summarized in the metadata.
**Some things to remember about the platform**
- You must be a registered CyVerse user to use Purple Line (register
for a CyVerse account at [user.cyverse.org](https://user.cyverse.org/){target=_blank}).
- The Purple line was designed to make microbiome/eDNA data analysis
"simple". However, we ask that users very carefully and
thoughtfully decide what "jobs" they want to submit.
- A single Purple Line project may take hours to process since HPC
computing is subject to queues which may support hundreds of other
jobs. These systems also undergo regular maintenance and are subject
to periodic disruption.
- DNA Subway implements the [QIIME 2](https://qiime2.org/){target=_blank} software. This software is in continual
development. Our version may not be the most current, and our
documentation and explanation is not meant to replace the full [QIIME 2 documentation](https://docs.qiime2.org/2018.8/){target=_blank}.
- We have made design decisions to create a straightforward
classroom-friendly workflow. While this Subway Line does not have
all possible features of QIIME 2, we purpose to cover important
concepts behind microbiome and eDNA analysis.
- You may work with up to 96 samples (e.g. 192 paired files or 96 single read files) in a Purple line project.
------------------------------------------------------------------------
!!! warning "Sample Data: How to use provided sample data"
In this guide, we will use a microbiome dataset (*"ubiome-test-data"*) collected from various
water sources in Montana (down-sampled and de-identified).Where
appropriate, a note (in this orange colored background) in the
instructions will indicate which options to select to make use of this
provided dataset.
### DNA Subway Purple Line - Metadata file and Sequencing Prerequisites
If you are generating data for a project (i.e. sequencing samples), you
will need to provide the sequencing data (fastq files) as well as a
metadata file that describes the data contained in these sequencing
files. This metadata must conform to strict guidelines, or analyses will
fail. QIIME 2 metadata is stored in a TSV (tab-separated values) file.
These files typically have a .tsv or .txt file extension, though it
doesn't matter to QIIME 2 what file extension is used. TSV files are
simple text files used to store tabular data, and the format is
supported by many types of software, such as editing, importing, and
exporting from spreadsheet programs and databases. Thus, it's usually
straightforward to manipulate QIIME 2 metadata using the software of
your choosing. If in doubt, we recommend using a spreadsheet program
such as Microsoft Excel or Google Sheets to edit and export your
metadata files.
**Handling Project Metadata**
Before you create your project, you will have generated metadata (as
described above) for your project. You have two options for preparing
this metadata to ensure that it conforms to the required QIIME2
parameters. The file must be validated (which you can do on your own or
using Subway). If there are errors in your file (this is common), they
must be fixed.
!!! tip "Formatting Your Metadata"
**Leading and trailing whitespace characters**
If any cell in the metadata contains leading or trailing whitespace
characters (e.g. spaces, tabs), those characters will be ignored when
the file is loaded. Thus, leading and trailing whitespace characters
are not significant, so cells containing the values 'gut' and ' gut' are equivalent. This rule is applied before any other rules
described below
**ID column**
The first column MUST be the ID column name (i.e. ID header) and the
first line of this column should be #SampleID or one of a few
alternative.
- Case-insensitive: id; sampleid; sample id; sample-id; featureid;
feature id; feature-id.
- Case-sensitive: #SampleID; #Sample ID; #OTUID; #OTU ID;
sample_name
**Sample IDs**
For the sample IDs, there are some simple rules to comply with QIIME 2
requirements:
- IDs may consist of any Unicode characters, with the exception
that IDs must not start with the pound sign (#), as those rows
would be interpreted as comments and ignored. IDs cannot be
empty (i.e. they must consist of at least one character).
- IDs must be unique (exact string matching is performed to detect
duplicates).
- At least one ID must be present in the file.
- IDs cannot use any of the reserved ID column names (the sample
ID names, above).
- The ID column can optionally be followed by additional columns
defining metadata associated with each sample or feature ID.
Metadata files are not required to have additional metadata
columns, so a file containing only an ID column is a valid QIIME
2 metadata file.
**Column names**
- May consist of any Unicode characters.
- Cannot be empty (i.e. column names must consist of at least one
character).
- Must be unique (exact string matching is performed to detect
duplicates).
- Column names cannot use any of the reserved ID column names.
**Column values**
- May consist of any Unicode characters.
- Empty cells represent missing data. Note that cells consisting
solely of whitespace characters are also interpreted as missing
data.
QIIME 2 currently supports categorical and numeric metadata columns.
By default, QIIME 2 will attempt to infer the type of each metadata
column: if the column consists only of numbers or missing data, the
column is inferred to be numeric. Otherwise, if the column contains
any non-numeric values, the column is inferred to be categorical.
Missing data (i.e. empty cells) are supported in categorical columns
as well as numeric columns. For more details, and for how to define
the nature of the data when needed, see the [QIIME 2 metadata documentation](https://docs.qiime2.org/2019.10/tutorials/metadata/){target=_blank}.
**Working with an existing metadata file**
!!! tip
If you have your own metadata file, it will still need to be validated
once uploaded to DNA Subway.
Using a spreadsheet editor, create a metadata sheet that provides
descriptions of the sequencing files used in your experiment. Export
this file as a tab-delimited **.txt** or **.tsv** file. following
the QIIME 2 metadata documentation](https://docs.qiime2.org/2019.10/tutorials/metadata/) recommendations. (Optional: if you using your own metadata file you can validate it using DNA Subway and or online QIIME2 validator [Keemei](https://keemei.qiime2.org/){target=_blank}).
!!! tip
See an example metadata file used for our sample data here: [metadata file](http://datacommons.cyverse.org/browse/iplant/home/shared/cyverse_training/platform_guides/dna_subway/purple_line/mappingfile.xlsx){target=_blank}. Click the **Download** button on the
linked page to download and examine the file. (**Note**: This is an
Excel version of the metadata file, you must save Excel files as .TSV
(tab-separated) to be compatible with the QIIME 2 workflow.)
**Creating a metadata file using DNA Subway**
See [DNA Subway Purple Line - Metadata and QC](#dna-subway-purple-line-metadata-and-qc) section C.
### DNA Subway Purple Line - Create a Microbiome Analysis Project
**A. Create a project in Subway**
1. Log-in to DNA Subway (unregistered users may NOT use Purple Line,
register for a CyVerse account at [user.cyverse.org](https://user.cyverse.org/){target=_blank}.
2. Click the purple square ("Microbiome Analysis") to begin a
project.
3. For 'Select Project Type' select either **Single End Reads** or
**Paired End Reads**
!!! warning "Sample Data"
*"ubiome-test-data"* dataset: Select **Single End Reads**
4. For 'Select File Format' select the format the corresponds to
your sequence metadata.
!!! warning "Sample Data"
*"ubiome-test-data"* dataset: Select **Illumina Casava 1.8**
!!! tip
Typically, microbiome/eDNA will be in the form of multiplexed FastQ sequences. We support the following formats:
- [Illumina Casava 1.8](http://illumina.bioinfo.ucr.edu/ht/documentation/data-analysis-docs/CASAVA-FASTQ.pdf/at_download/file){target=_blank}
5. Enter a project title, and description; click **Continue**.
**B. Upload read data to CyVerse Data Store**
The sequence read files used in these experiments are too large to
upload using the Subway interface. You must upload your files (**Note**: Only `.fastq.gz` files are accepted) directly to the CyVerse Data Store:
1. Upload your
- FASTQ sequence reads; **Note**: Only `.fastq.gz` files are accepted.
- Sample metadata file (.tsv or .txt formatted according to [QIIME 2 Metadata documentation](https://docs.qiime2.org/2019.10/tutorials/metadata/){target=_blank}) to
the CyVerse Data Store using Cyberduck. See instructions: [CyVerse Data Store Guide](https://unm-carc.github.io/cyverse/data-store/sftp/cyberduck/).
(Optional: You can edit and change metadata using the Subway
interface in the [Manage data]step once the project is
created.)
------------------------------------------------------------------------
### DNA Subway Purple Line - Metadata and QC
**A. Select files using Manage Data**
1. Click on the 'Manage data stop: this opens a window where you can
add your FASTQ (up to 192 paired files or 96 single read files) and metadata files. Click
**+Add from CyVerse** to add the
FASTQ files uploaded to the CyVerse Data Store. Select your files
and then click **Add selected files** or **Add all FASTQ files in this directory** as appropriate. **Note**: Only `.fastq.gz` files are accepted.
!!! warning "Sample Data"
*"ubiome-test-data"* dataset: Navigate to: Shared Data > SEPA_microbiome_2016 > **ubiome-test-data** and click **Add all FASTQ files in this directory**
2\. To add your metadata file you may use one of three options:
- *Add from CyVerse*: Add a metadata file you have uploaded to CyVerse Data store
- *Upload locally*: Directly upload a metadata file from your local computer
- *Create New*: Create a new metadata file using DNA Subway
!!! tip "Creating a metadata file using DNA Subway"
You can create a metadata file using DNA Subway. Creating the file
step-by-step will help you to avoid metadata errors. Be sure you
have consulted the [QIIME 2 documentation](https://docs.qiime2.org/2019.10/tutorials/metadata/){target=_blank} so you can anticipate what the required fields
are. To use this feature under in the 'Manage data' step under
'Metadata Files' click **Create new**
**Sample IDs and adding/removing samples**
These are unique IDs for each of your samples.
All metadata files must have a column called **#SampleID**. Click
**+Add samples** to add additional
rows. In the Subway form, these will be unique, arbitrary names
(roughly corresponding to well-positions on a 96-well microplate).
You can change these (including pasting in sample names from an
existing spreadsheet).
{width="450px" height="250px"}
Right-clicking on a row number allows you to remove or insert rows.
{width="450px" height="250px"}
**Adding columns, managing sample descriptions and data types**
The very **last** column must be a sample description. You can click
the arrow on the right of this column to add a new column (which
will be added to the left). Column names must be unique, must not be
empty, cannot contain whitespace, can contain a maximum of 32
characters, cannot match a reserved column name. Notice that when
you click on a column name it is colored -pink for columns that have
numeric data (e.g. measurements) and cyan for everything else (e.g.
categorical descriptions in the form of words (i.e. strings)).
Clicking a column name will allow you to change its type.
{width="450px" height="250px"}
**Handling errors**
If you violate one of the rules for metadata formatting, the entry
will turn red. Consult the help and or the [QIIME 2 documentation](https://docs.qiime2.org/2019.10/tutorials/metadata/){target=_blank} to correct the error.
{width="450px" height="250px"}
Click **Save** to save your
metadata file, and close the window.
!!! warning "Sample Data"
*"ubiome-test-data"* dataset:
Click **Add from CyVerse**Navigate to: Shared Data > SEPA_microbiome_2016 > **ubiome-test-data**
Select the **mappingfile_MT_corrected.tsv** and then click **Add selected files**.
3\. As needed, you can edit or rename your metadata file. Before
proceeding, you must validate your metadata file. To validate,
click the "validate" link to the right of the metadata file you
wish to check. Once the validation completed, click `Run` to proceed. If you have
errors, you will be presented with an `Edit` button so that you can return to the file and
edit.
**B. Demultiplex reads**
At this step, reads will be grouped according to the sample metadata.
This includes separating reads according to their index sequences if
this was not done prior to running the Purple Line. For demultiplexing
based on index sequences, the index sequences must be defined in the
metadata file.
!!! note
Even if your files were previously demultimplexed (as will generally
be the case with Illumina data) you must still complete this step to
have your sequence read files appropriately associated with metadata.
1\. Click the 'Demultiplex reads' and choose a number of reads to sample. When the job has completed click *Demultiplexing Summary* to view your results. In 'Random sequences to sample for QC', enter a value (1000 is recommended),
!!! warning "Sample Data"
*"ubiome-test-data"* dataset: Use the default of 1000 sequences
2\. When demultiplexing is complete, you will generate a file (.qzv) click this link to view a visualization and statistics on the sequence and metadata for this project.
!!! tip
Several jobs on Purple Line will take several minutes to an hour
to complete. Each time you launch one of these steps you will get
a Job ID. You can click the **View job info** button to see a detailed status and
diagnostic/error messages. If needed There is a *stop this job* link at the bottom of the info page to cancel a
job.
!!! note
**QIIME2 Visualizations**
One of the features of QIIME 2 are the variety of visualizations
provided at several analysis steps. Although this guide will not
cover every feature of every visualization, here are some
important points to note.
**QIIME2 View**: DNA Subway uses the QIIME 2 View plugin to
display visualizations. Like the standalone QIIME 2
software, you can navigate menus, and interact with several
visualizations. Importantly, many files and visualizations
can be directly download for your use outside of DNA Subway,
including in report generation, or in your custom QIIME 2
analyses. You can view downloaded .qza or .qzv files at [view.qiime2.org](https://view.qiime2.org/){target=_blank}.
!!! tip "Quality Graphs Explained"
After demultiplexing, you will be presented with a visualization
that displays the following tables and graphs:
**Overview Tab**
- *Demultiplexed sequence counts summary*: For each of the
fastq files (each of which may generally correspond to a
single sample), you are presented with comparative
statistics on the number of sequences present. This is
followed by a histogram that plots number of sequences by
the number of samples.
- *Per-sample sequence counts*: These are the actual counts
of sequences per sample as indicated by the sample names
you provided in your metadata sheet.
{width="450px" height="250px"}
**Interactive Quality Plot**
This is an interactive plot that gives you an average quality
(y-axis) by the position along the read (x-axis). This box plot
is derived from a random sampling of a subset of sequences. The
number of sequences sampled will be indicated in the plot
caption. You can use your mouse drag and zoom in to regions on
the plot. Double-click your mouse to zoom out.
{width="450px" height="250px"}
3\. Click the "Interactive Quality Plot" tab to view a histogram of sequence quality. Use this plot at the tip below to determine a location to trim.
!!! tip "Tips on trimming for sequence quality"
On the Interactive Quality Plot you are shown an histogram, plotting
the average quality (x axis) [Phred score](https://en.wikipedia.org/wiki/Phred_quality_score){target=_blank} vs. the position on the read (y axis) in base pairs for a **subsample** of reads.
**Zooming to determine 3' trim location**
Click and drag your mouse around a collection of base pair positions
you wish to examine. Clicking on a given histogram bar will also
generate a text report and metrics in the table below the chart. Using
these metrics, you can choose a position to trim on the right side
(e.g. 3' end of the sequence read). The 5' (left trim) is specific
to your choice of primers and sequencing adaptors (e.g. the sum of the
adaptor sequence you expect to be attached to the 5' end of the
read). Poor quality metrics will generate a table colored in red, and
those base positions will also be colored red in the histogram.
Double-clicking will return the histogram to its original level of
zoom.
**Example plots**
It is important to maximize the length of the reads while minimizing
the use of low quality base calls. To this end, a good guideline is to
trim the right end of reads to a length where the 25th percentile is
at a quality score of 25 or more. However, the length of trimming will
depend on the quality of the sequence, so you may have to use a lower
quality threshold to retain enough sequence for informative sequence
searches and alignments. This may require multiple runs of the
analysis to find the optimal trim length for your data.
*Quality drops significantly at base 35*
{width="400px" height="250px"}
*Improved quality sequence*
{width="400px" height="250px"}
**C. Use DADA2 for Trimming and Error-correction of Reads**
It is important to only work with high quality data. This step will
generate a sequence quality histogram which can be used to determine
parameter for trimming.
1. Click 'DADA2' and choose the metadata file corresponding to the
samples you wish to analyze. Then choose values for trimming of
the reads. For "trimLeft" (the position starting from the left
you wish to trim) and "TruncLen" (this is the position where
reads should be trimmed, truncating the 3' end of the read. Reads
shorter than this length will be discarded). Finally, click
**Trim reads**.
!!! warning "Sample Data"
*"ubiome-test-data"* dataset: Based on the histogram for our sample, we recommend the following parameters:
- **trimLeft: 17** (this is specific to primers and adaptors in
this experiment)
- **TruncLen: 200** (this is where low quality sequence begins, in
this case because our sequence length is lower than the expected
read length)
**D. Check Results of Trimming** Once trimming is complete, the
following outputs are expected:
1. Click on DADA2 and then click on the links in the *Results* table
to examine results.
- **Trim Table** (*Metric summary*, *Frequency per sample*,
*Frequency per feature*): Summarizes the dataset post-trimming
including the number of samples and the number of features per
sample. The "Interactive Sample Detail" tab contains a sampling
depth tool that will be used in computation of the core matrix.
!!! note
**You will use the maximum frequency value for the Alpha
rarefaction step** So you may wish to record this value now for
the DNA Subway 'Clustering sequences' step.
- **Stats**: Sequencing statistics for each of the sample IDs
described in the original metadata file.
- **Representative Sequences** (*Sequence Length Statistics*,
*Seven-Number Summary of Sequence Lengths*, *Sequence Table*):
This table contains a listing of features observed in the sequence
data, as well as the DNA sequence that defines a feature. Clicking
on the DNA sequence will submit that sequence for BLAST at NCBI in
a separate browser tab.
The feature table contains two columns output by DADA2. DADA2
(Divisive Amplicon Denoising Algorithm 2) determines what sequences
are in the samples. DADA2 filters the sequences and identifies
probable amplification or sequencing errors, filters out chimeric
reads, and can pair forward and reverse reads to create the best
representation of the sequences actually found in the samples and
eliminating erroneous sequences.
- **Feature ID**: A unique identifier for sequences.
- **Sequence**: A DNA Sequence associated with each identifier.
Clicking on any given sequence will initiate at BLAST search on the
NCBI website. Click "View report" on the BLAST search that opens in
a new web browser tab to obtain your results. Keep in mind that if
your sequences are short (due to read length or trimming) many BLAST
searches may not return significant results.
!!! tip
Although the term "feature" can (unfortunately) [have many meanings](https://forum.qiime2.org/t/what-is-a-feature-exactly/2201){target=_blank} as used by the
QIIME2 documentation, unless otherwise noted in this documentation
it can be thought of as an OTU ([Operational Taxonomic Unit](https://en.wikipedia.org/wiki/Operational_taxonomic_unit){target=_blank}); another substitution for the word
species. OTU is a convenient and common terminology for referring to
an unclassified or undetermined species. Ultimately, we are
attempting to identify an organism from a sample of DNA which may
not be informative enough to reach a definitive conclusion.
!!! tip
If you want to redo the DADA2 step with different parameters, click
the "New Job" tab on the upper left of a DADA2 window to submit a
new job. New jobs appear as tabs on Subway steps that are typically
run several times. You can go back an see these jobs which are
labeled with a job number.
{width="450px" height="250px"}
------------------------------------------------------------------------
### DNA Subway Purple Line - Alpha Rarefaction/Clustering Sequences
**A. Alpha rarefaction**
At this step, you can visualize summaries of the data. A feature table
will generate summary statistics, including how many sequences are
associated with each sample. **Note that sample depth is limited to 100,000.**
1. Click on 'Alpha rarefaction'. Select "run" and designate the
minimum and maximum rarefaction depth. A minimum value should be
set at 1. The maximum value is specific to your data set. The
maximum value is specific to your data set. To determine what the
maximum value should be set to, open the "Trim Table" from the
"DADA2" step. You may not choose a value that is greater than
the maximum frequency per sample. In general, choosing a value
that is somewhere around the median frequency seems to work well,
but you may want to increase that value if the lines in the
resulting rarefaction plot don't appear to be leveling out, or
decrease that value if you seem to be losing many of your samples
due to low total frequencies closer to the minimum sampling depth
than the maximum sampling depth. Identify the maximum Sequence
Count value and enter that number as the maximum value. Click
**Submit Job**.
!!! note
Since you may want to try Alpha rarefaction using different
combinations of results from DADA2 trimming and your choice of
rarefaction depths, your trim (DADA2) jobs are displayed on the
left, and each new Alpha rarefaction setting will appear as a tab on
the top.
{width="450px" height="250px"}
!!! warning "Sample Data"
*"ubiome-test-data"* dataset:
We recommend the following parameters:
- **Min. rarefaction depth**: 1
- **Max. rarefaction depth**: 2938
2. Under 'Results' click on **Alpha Rarefaction Plot** to view the results.
??? tip "Navigating Alpha Rarefaction graphs"
**Alpha rarefaction** generates an interactive plot of species
diversity by sampling depth by the categorical samplings described
in your sample metadata. You can use dropdown menus to change
metrics/conditions displayed and also export data as a CSV file.
{width="450px" height="250px"}
------------------------------------------------------------------------
### DNA Subway Purple Line - Calculate Core Metrics/Alpha and Beta Diversity
At this stop, you will examine *Alpha Diversity* (the diversity of
species/taxa present within a single sample) and *Beta Diversity* (a
comparison of species/taxa diversity between two or more samples).
- Alpha diversity answers the question - "How many species are in a
sample?"
- Beta diversity answer the question - "What are the differences in
species between samples?"
**A. Calculate core metrics**
1. Click on 'Core metrics' and then click the "run" link. Choose
a sampling depth based upon the "Sampling depth" tool (described
in Section D Step 1, in the *Trim Table* output; *Interactive
Sample Detail* tab). Choose an appropriate classifier (see
comments in the tip below) and click **Submit job**.
!!! tip "Choosing Core metrics parameters"
*Sampling Depth*
In downstream steps, you will need to choose a sampling depth for
your sample comparisons. You can choose by examining the table
generated at the **Trim reads** step. In the *Trim Table* output,
*Interactive Sample Detail* tab, use the "Sampling depth" tool to
explore how many sequences can be sampled during the Core matrix
computation. As you slide the bar to the right, more sequences are
sampled, but samples that do not have this many sequences will be
removed during analysis. The sampling depth affects the number of
sequences that will be analyzed for taxonomy in later steps: as the
sampling depth increases, a greater representation of the sequences
will be analyzed. However, high sampling depth could exclude
important samples, so a balance between depth and retaining samples
in the analysis must be found.
*Classifier*
Choose a classifier pertaining to your experiment type.
- **Microbiome** choose **Greengenes (515F/806R)** or **Greengenes
(full sequences)** or **Sliva (16S rRNA)** classifier
- **eDNA** experiment with marine fishes you may elect to choose
the **Fish 12S/ecoPrimer** classifier
!!! warning "Sample Data"
*"ubiome-test-data"* dataset:
We recommend the following parameters:
- **Sampling Depth**: 3000
- **Classifier**: Grenegenes (full sequences)
**B. Examine alpha and beta diversity**
2. When core metrics is complete, you should generate several
visualization results. Click each of the following to get access:
- **Alpha Diversity:**
- ***Pielou's Evenness***
- Alpha Correlation: Measure of community evenness using correlation tests
- Group Significance: Analysis of differences between features across group
- ***Faith's Phylogenetic Diversity***
- Alpha Correlation: Faith Phylogenetic Diversity (a measure of community richness) with correlation tests
- Group Significance: Faith Phylogenetic Diversity ( a measure of community richness)
- **Beta Diversity:**
- ***Bray-Curtis Distance***
[Bray-Curtis](https://en.wikipedia.org/wiki/Bray%E2%80%93Curtis_dissimilarity){target=_blank} is a metric for describing the dissimilarity of species in an ecological sampling.
- Bioenv: Bray-Curtis test metrics
- Emperor: Interactive PCoA plot of Bray-Curtis metrics
- ***Jaccard Distance***
- Emperor: Interactive PCoA plot calculated by [Jaccard](https://en.wikipedia.org/wiki/Jaccard_index){target=_blank} similarity index.
- ***Unweighted UniFrac Distance***
[UniFrac](https://en.wikipedia.org/wiki/UniFrac){target=_blank} is a metric for describing the similarity of a biological community, taking into account the relatedness of community members.
- Bioenv: UniFrac test metrics
- Emperor: Unweighted interactive PCoA plot
- ***Weighted UniFrac Distance***
Unweighted UniFrac removes the effect of low-abundance features in the calculation of principal components.
- Emperor: Weighted interactive PCoA plot of UniFrac.
!!! tip
**Emperor Plots**
These plots are all interactive three-dimensional plots of an
analysis using [principal components](https://en.wikipedia.org/wiki/Principal_component_analysis){target=_blank}.
**Customization**
You can customize Emperor plots, including altering the color of
and shape points, axes, and other parameters. You can also export
images from this visualization.
{width="550px" height="300px"}
**Bioenv**
These plots are tables of tests and descriptive metrics.
**C. Taxonomic Diversity:**
Taxonomic diversity is at the heart of many analyses. We suggest
consulting the QIIME [taxonomy overview](https://docs.qiime2.org/2019.1/tutorials/overview/#taxonomy){target=_blank} for a detailed explanation of how QIIME2 calculates taxonomy and additional features of QIIME2 you may wish to
explore beyond the functionalities DNA Subway has included.
- *Bar Plots*
- An interactive stacked bar plot of species diversity.
Dropdown menus allow you to color by seven taxonomic
levels 1) kingdom, 2) phylum,
3) class, 4) order, 5), family, 6) genus, 7) species. Plots
can be further arranged/filtered/sorted accoridng to
characteristics in the sample metadata. You may also
download images and data used to create the barpot
visualization.
{width="550px" height="300px"}
- *Taxonomy*
- A table indicating the identified "features", their taxa,
and an indication of confidence.
You can download and interact with any of the available plots.
**D. Calculate differential abundance**
1. Click on the 'Differential abundance' stop. Then click on the
"Submit new "Differential abundance" job" link. Choose a
metadata category to group by, and a level of taxonomy to
summarize by. Then click **submit job**.
!!! warning "Sample Data"
*"ubiome-test-data"* dataset:
We recommend the following parameters:
- **Group data by**: CollectionMethod
- **Level of taxonomy to summarize**: 5
!!! tip
Download the provided CSV files so that you can generate customized plots.
------------------------------------------------------------------------
### DNA Subway Purple Line - Visualize data with PiCrust and PhyloSeq
**Under Development**
------------------------------------------------------------------------
Adapted from [CyVerse Learning Materials](https://github.com/CyVerse-learning-materials/learning-materials-home/blob/b7392d21be4fd6a67d051847f98c81a18e058a12/docs/edu/tutorials/dna_subway_guide.md){target=_blank} (last source update 2025-03-14), CC BY 4.0. Spotted a problem? [Open an issue](https://github.com/UNM-CARC/cyverse/issues){target=_blank}.