Taxonomic Classification
Sequencing Data: Choose the type of input data: single-end, paired-end or interleaved paired-end reads If paired-end is selected, two files per sample are required and the file pattern has to be provided.
Reads, Contigs, or Genes: Select files that contain the desired input data. Kraken was designed to work with short reads, but works reliably with assembled sequences or genes.
Paired-end configuration: When working with paired-end libraries, a so-called pattern has to be established to help the software distinguish between upstream and downstream read files. Per default, we assume the following pattern:
upstream: SampleA_1.fastq
downstream: SampleA_2.fastq
Note:
For example, if the upstream file is named SRR037717_1.fastq and the downstream one SRR037717_2.fastq, you should establish "_1" as the upstream pattern and "_2" as the downstream pattern.
Figure 1.Taxonomic Classification Wizard: input page
The second wizard page contains information about the Kraken version and how to cite it.
Figure 2. Taxonomic Classification Wizard: configuration page.
Using a Custom Kraken2 Database
This section describes how to upload and use a custom Kraken2 database for taxonomic classification in OmicsBox.
Custom Database Requirements
The custom database is in Kraken2 format consisting of three files: hash.k2d, taxo.k2d and opts.k2d
The custom database has been created from nucleotide sequence data. Kraken in OmicsBox does not accept protein-based databases.
The custom database is built based on the NCBI taxonomy. Other taxonomies e.g. Silva, Green Genes, or GTDB are not supported in OmicsBox.
Please have a look at this page if you want to download alternative pre-formatted Kraken2 databases:
If you want to create your own Kraken2 Database from a fasta file please follow the instructions provided by the official Kraken documentation below. This is only recommended if you know some shell scripting, know how to handle big amounts of data, and have access to a powerful Linux machine (50+GB of RAM).
https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown#custom-databases
https://software.cqls.oregonstate.edu/updates/docs/kraken2/MANUAL.html
How to upload a custom database
Access the Cloud Files Tab within OmicsBox: On the left-hand side of the OmicsBox interface, you will find a panel with different tabs. Click on the "Cloud Files" tab to access your personal cloud storage.
Create a New Folder: Right-click in any location of the "Cloud Files" tab to create a new folder with a significant name at the desired location. The folder name will determine the database name in the OmicsBox user interface.
Upload the Kraken2 Database Files: Drag and drop all three database files (hash.k2d, taxo.k2d, opts.k2d) from your local computer into the newly created folder within the "Cloud Files" tab.
How to use a Custom Database
To classify taxa using your custom Kraken2 database in OmicsBox, follow these steps:
Open the Taxonomic Classification dialog under the Metagenomics menu.
In the analysis settings, choose your custom Kraken2 database from the "Database" dropdown.
Adjust any required parameters and settings for your classification analysis.
Continue with your taxonomic classification analysis in OmicsBox as usual.
Results
The results of the taxonomic classification with Kraken are:
The main result table shows all identified OTUs for each provided sample.
PDF Report that shows overall input and carried-out analysis information.
Stacked bar chart to compare samples at specific taxonomic levels.
Radial cladogram in as a Krona chart to study OTU abundances in a sample.
Rarefaction curves to assess the sequencing depth.
Chao1 (species) diversity curve to evaluate the (species) diversity in the whole data set.
Principal Coordinates Analysis plot to get an overview and to identify outliers.
Figure 4. Taxonomic Classification results table.
Main result table
The result table (Figure 4) shows for each analyzed sample the number of OTUs found and their confidence scores. OTU stands for operational taxonomic unit. A count of 0 means the OTU is not present in the current sample. The evidence scores vary from 0 to 1, where 1 is best. The counts shown are cumulative, meaning that they do not only show direct hits for a certain OTU, but also sum up OTUs that converge into them in the taxonomic tree i.e. the taxonomic tree's hierarchy is taken into account. We use the taxonomic hierarchy from the NCBI in a simplified form and it consists of 8 main levels instead of 33, i.e. the many levels are summarized as shown in Figure 5.
The result table can be used to filter for certain taxonomic levels via the column header filter function, e.g. showing only the species or phylum level OTUs.
Right-clicking an OTU shows the table context menu to create statistics and id lists and provides extra functionality. The Extract Sequences option allows exporting the read names and actual reads of the currently selected OTUs. This makes it possible to export all reads that have been classified as e.g. bacteria and thus reduce the dataset size for further gene finding and functional annotation. Many other scenarios and use cases exist.
Figure 5. NCBI taxonomic hierarchy.
Add, Remove, and Rename Samples
It is possible to combine taxonomic classification results by selecting Add Samples from the side panel. This will open a dialog to browse taxonomic classification results and which samples to add. A new object with the desired samples will be created.
Samples can also be removed or renamed by right-clicking the desired column/samples.
All side panel actions such as the report, bar chart, etc have to be created anew to reflect these changes.
Figure 6a. Add, Remove, and Rename Samples.
Stacked Bar Chart
The Stacked bar chart (Figure 6b) is a combined view for inter-sample comparison, separated in the 7 main taxonomic levels. Average OTUs are ordered by abundance from high to low. Only the 500 biggest OTUs are shown for each sample, the remaining are gathered into an extra group called Others. These low frequent OTUs can be analyzed in detail with the Krona Pie Chart. The button Hide Unclassified in the top-right corner shows how the percentages change when only taking into account the data that could be classified by Kraken.
The graphic can be exported as a PNG image by clicking the corresponding icon in the top-right corner.
Figure 6b. Stacked bar chart.
Krona Pie Chart
This graphic (Figure 7) shows a slightly modified Krona chart with various options in the side panel. Again, the counts are cumulative and grouped into roughly 8 main levels. However, all direct counts are shown as well, which is helpful when looking at the "below species" level, which includes subspecies and strains.
The currently visualized sample is selected from a list in the side panel, the All Combined entry shows all samples together in one chart. Furthermore, text sizes can be adjusted and OTUs can be searched. Coloring by average Kraken evidence scores is also possible.
The graphic can be exported as a PNG image and PDF by clicking the corresponding icons in the top-right corner.
Summary Report
A summary report which shows basic statistics and alpha-diversity indices for each analyzed sample. It also gives information about the percentages of reads that were classified. In addition, for each of the 7 main taxonomic levels (Superkingdom, Phylum, Class, Order, Family, Genus, and Species), the top 10 OTUs per sample are listed.
The graphic can be exported as PDF by clicking the corresponding icon in the top-right corner.
Figure 7. Krona pie chart.
In addition, more charts and statistics can be generated to offer a global visualisation of the taxonomic classification results. These charts can be found in the side panel of the taxonomic classification results.
Rarefaction Curves
Rarefaction is a technique widely used in ecology which is applied to OTU analysis. A rarefaction graph (Figure 8) represents the number of expected OTUs (Y-axis) found in n NGS reads (X-axis).
The goal of rarefaction is to determine whether sequencing coverage is deep enough to get a good estimate of the total number of the OTUs present in a specific sample.
If the rarefaction curve still presents a growing trend at its end, it means that the coverage is not enough to adequately represent the real microbial diversity of the sample. By contrast, if the curve shows a horizontal asymptote, it means that a good estimation of diversity was obtained.
Note that the results of the rarefaction technique give us a suggestion about the coverage, but they are not conclusive, i.e. rare OTUs that are present in the sample could not be yet observed even if the curve presents an asymptotic trend.
Figure 8. Rarefaction curves.
Diversity Curve
An accumulation or diversity curve (Figure 9) plots the cumulative number of distinct OTUs discovered as a function of the number of samples examined. I.e. The minimum, average, and maximum number of OTUs, when looking at 1, 2, ... N samples of the current dataset.
This curve can be used to evaluate the benefits in microbial diversity of including additional samples to the dataset or to compare this diversity across datasets with similar sampling efforts.
Figure 9. Diversity curve.
Principal Coordinate Analysis (PCoA Plot)
PCoA (Figure 10) is a two-dimensional plot in which the Bray-Curtis distances between samples are drawn. You can select an experimental condition to color the points from an experimental design, and the taxonomy level from which the distances will be calculated.
Figure 10. PCoA plot.