OverviewTeaching: 30 min
Exercises: 15 minQuestions
How can I know to which taxa my sequences belong?Objectives
Understand how taxonomic assignment works.
Use Kraken to assign taxonomies to reads and contigs.
Visualize taxonomic assignations in graphics.
What is a taxonomic assignment?
A taxonomic assignment is a process of assigning an Operational Taxonomic Unit (OTU, that is, groups of related individuals) to sequences that can be reads or contigs. Sequences are compared against a database constructed using complete genomes. When a sequence finds a good enough match in the database, it is assigned to the corresponding OTU. The comparison can be made in different ways.
Strategies for taxonomic assignment
There are many programs for doing taxonomic mapping, and almost all of them follow one of the following strategies:
BLAST: Using BLAST or DIAMOND, these mappers search for the most likely hit for each sequence within a database of genomes (i.e., mapping). This strategy is slow.
Markers: They look for markers of a database made a priori in the sequences to be classified and assigned the taxonomy depending on the hits obtained.
K-mers: A genome database is broken into pieces of length k to be able to search for unique pieces by taxonomic group, from a lowest common ancestor (LCA), passing through phylum to species. Then, the algorithm breaks the query sequence (reads/contigs) into pieces of length k, looks for where these are placed within the tree and make the classification with the most probable position.
When you do the taxonomic assignment of metagenomes, a key result is the abundance of each taxon or OTU in your sample. The absolute abundance of a taxon is the number of sequences (reads or contigs, depending on what you did) assigned to it. Moreover, its relative abundance is the proportion of sequences assigned to it. It is essential to be aware of the many biases that can skew the abundances along the metagenomics workflow, shown in the figure, and that because of them, we may not be obtaining the actual abundance of the organisms in the sample.
Discussion: Taxonomic level of assignment
What do you think is harder to assign, a species (like E. coli) or a phylum (like Proteobacteria)?
Using Kraken 2
Kraken 2 is the newest version of Kraken,
a taxonomic classification system using exact k-mer matches to achieve
high accuracy and fast classification speeds.
kraken2 is already installed in the metagenomics
environment, let us have a look at
$ kraken2 --help
Need to specify input filenames! Usage: kraken2 [options] <filename(s)> Options: --db NAME Name for Kraken 2 DB (default: none) --threads NUM Number of threads (default: 1) --quick Quick operation (use first hit or hits) --unclassified-out FILENAME Print unclassified sequences to filename --classified-out FILENAME Print classified sequences to filename --output FILENAME Print output to filename (default: stdout); "-" will suppress normal output --confidence FLOAT Confidence score threshold (default: 0.0); must be in [0, 1]. --minimum-base-quality NUM Minimum base quality used in classification (def: 0, only effective with FASTQ input). --report FILENAME Print a report with aggregate counts/clade to file --use-mpa-style With --report, format report output like Kraken 1's kraken-mpa-report --report-zero-counts With --report, report counts for ALL taxa, even if counts are zero --report-minimizer-data With --report, report minimizer, and distinct minimizer count information in addition to normal Kraken report --memory-mapping Avoids loading database into RAM --paired The filenames provided have paired-end reads --use-names Print scientific names instead of just taxids --gzip-compressed Input files are compressed with gzip --bzip2-compressed Input files are compressed with bzip2 --minimum-hit-groups NUM Minimum number of hit groups (overlapping k-mers sharing the same minimizer) needed to make a call (default: 2) --help Print this message --version Print version information If none of the *-compressed flags are specified, and the filename provided is a regular file, automatic format detection is attempted.
In the help, we can see that in addition to our input files, we also need a database to compare them. The database you use will determine the result you get for your data. Imagine you are searching for a recently discovered lineage that is not part of the available databases. Would you find it?
There are several databases compatible to be used with kraken2 in the taxonomical assignment process.
Unfortunately, even the smallest Kraken database Minikraken, which needs 8Gb of free RAM, is not small enough to be run by the machines we are using, so we will not be able to run
kraken2. We can check our available RAM with
free -hto be sure of this.
$ free -h
total used free shared buff/cache available Mem: 3.9G 272M 3.3G 48M 251M 3.3G Swap: 0B 0B 0B
Taxonomic assignment of metagenomic reads
As we have learned, taxonomic assignments can be attempted before the assembly.
In this case, we would use FASTQ files as inputs, which would be
JP4D_R2.trim.fastq.gz. And the outputs would be two files: the report
JP4D.report and the kraken file
To run kraken2, we would use a command like this:
No need to run this
$ mkdir TAXONOMY_READS $ kraken2 --db kraken-db --threads 8 --paired JP4D_R1.trim.fastq.gz JP4D_R2.trim.fastq.gz --output TAXONOMY_READS/JP4D.kraken --report TAXONOMY_READS/JP4D.report
Since we cannot run
kraken2 here, we precomputed its results in a server, i.e., a more powerful machine.
In the server we ran
kraken2 and obtained
Let us look at the precomputed outputs of
kraken2 for our JP4D reads.
U MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:19691:2037 0 250|251 0:216 |:| 0:217 U MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:14127:2052 0 250|238 0:216 |:| 0:204 U MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:14766:2063 0 251|251 0:217 |:| 0:217 C MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:15697:2078 2219696 250|120 0:28 350054:5 1224:2 0:1 2:5 0:77 2219696:5 0:93 |:| 379:4 0:82 U MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:15529:2080 0 250|149 0:216 |:| 0:115 U MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:14172:2086 0 251|250 0:217 |:| 0:216 U MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:17552:2088 0 251|249 0:217 |:| 0:215 U MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:14217:2104 0 251|227 0:217 |:| 0:193 C MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:15110:2108 2109625 136|169 0:51 31989:5 2109625:7 0:39 |:| 0:5 74033:2 31989:5 1077935:1 31989:7 0:7 60890:2 0:105 2109625:1 C MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:19558:2111 119045 251|133 0:18 1224:9 2:5 119045:4 0:181 |:| 0:99
This information may need to be clarified. Let us take out our cheatsheet to understand some of its components:
|C||Classified or unclassified|
|MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:15697:2078||FASTA header of the sequence|
|0:28 350054:5 1224:2 0:1 2:5 0:77 2219696:5 0:93 379:4 0:82||kmers hit to a taxonomic ID e.g., tax ID 350054 has five hits, tax ID 1224 has two hits, etc.|
The Kraken file could be more readable. So let us look at the report file:
78.13 587119 587119 U 0 unclassified 21.87 164308 1166 R 1 root 21.64 162584 0 R1 131567 cellular organisms 21.64 162584 3225 D 2 Bacteria 18.21 136871 3411 P 1224 Proteobacteria 14.21 106746 3663 C 28211 Alphaproteobacteria 7.71 57950 21 O 204455 Rhodobacterales 7.66 57527 6551 F 31989 Rhodobacteraceae 1.23 9235 420 G 1060 Rhodobacter 0.76 5733 4446 S 1063 Rhodobacter sphaeroides
|78.13||Percentage of reads covered by the clade rooted at this taxon|
|587119||Number of reads covered by the clade rooted at this taxon|
|587119||Number of reads assigned directly to this taxon|
|U||A rank code, indicating (U)nclassified, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, or (S)pecies. All other ranks are simply ‘-‘.|
|0||NCBI taxonomy ID|
|unclassified||Indented scientific name|
Taxonomic assignment of the contigs of a MAG
We now have the taxonomic identity of the reads of the whole metagenome, but we need to know to which taxon our MAGs correspond. For this, we have to make the taxonomic assignment with their contigs instead of its reads because we do not have the reads corresponding to a MAG separated from the reads of the entire sample.
For this, the
kraken2 is a little bit different; here, we can look at the command for the
No need to run this
$ mkdir TAXONOMY_MAG $ kraken2 --db kraken-db --threads 12 -input JP4D.001.fasta --output TAXONOMY_MAG/JP4D.001.kraken --report TAXONOMY_MAG/JP4D.001.report
The results of this are pre-computed in the
$ cd ~/dc_workshop/taxonomy/mags_taxonomy $ ls
50.96 955 955 U 0 unclassified 49.04 919 1 R 1 root 48.83 915 0 R1 131567 cellular organisms 48.83 915 16 D 2 Bacteria 44.40 832 52 P 1224 Proteobacteria 19.37 363 16 C 28216 Betaproteobacteria 16.22 304 17 O 80840 Burkholderiales 5.66 106 12 F 506 Alcaligenaceae 2.72 51 3 G 517 Bordetella 1.12 21 21 S 2163011 Bordetella sp. HZ20 . . .
Looking at the report, we can see that half of the contigs are unclassified and that a tiny proportion of contigs have been assigned an OTU. This result is weird because we expected only one genome in the bin.
To exemplify how a report of a complete and not contaminated MAG should look like this; let us look at the report of this MAG from another study:
100.00 108 0 R 1 root 100.00 108 0 R1 131567 cellular organisms 100.00 108 0 D 2 Bacteria 100.00 108 0 P 1224 Proteobacteria 100.00 108 0 C 28211 Alphaproteobacteria 100.00 108 0 O 356 Rhizobiales 100.00 108 0 F 41294 Bradyrhizobiaceae 100.00 108 0 G 374 Bradyrhizobium 100.00 108 108 S 2057741 Bradyrhizobium sp. SK17
Visualization of taxonomic assignment results
After we have the taxonomy assignation, what follows is some visualization of our results. Krona is a hierarchical data visualization software. Krona allows data to be explored with zooming and multi-layered pie charts and supports several bioinformatics tools and raw data formats. To use Krona in our results, let us first go into our taxonomy directory, which contains the pre-calculated Kraken outputs.
With Krona, we will explore the taxonomy of the JP4D.001 MAG.
$ cd ~/dc_workshop/taxonomy/mags_taxonomy
Krona is called with the
ktImportTaxonomy command that needs an input and an output file.
In our case, we will create the input file with columns three and four from
$ cut -f2,3 JP4D.001.kraken > JP4D.001.krona.input
Now we call Krona in our
JP4D.001.krona.input file and save results in
$ ktImportTaxonomy JP4D.001.krona.input -o JP4D.001.krona.out.html
Loading taxonomy... Importing JP4D.001.krona.input... [ WARNING ] The following taxonomy IDs were not found in the local database and were set to root (if they were recently added to NCBI, use updateTaxonomy.sh to update the local database): 1804984 2109625 2259134
And finally, open another terminal on your local computer, download the Krona output and open it on a browser.
$ scp firstname.lastname@example.org:~/dc_workshop/taxonomy/JP4D.001.krona.out.html .
You will see a page like this:
Exercise 1: Exploring Krona visualization
Try double-clicking on the pie chart segment representing Bacteria and see what happens. What percentage of bacteria is represented by the genus Paracoccus?
Hint: A search box is in the window’s top left corner.
2% of Bacteria corresponds to the genus Paracoccus in this sample. In the top right of the window, we see little pie charts that change whenever we change the visualization to expand certain taxa.
Pavian is another visualization tool that allows comparison between multiple samples. Pavian should be locally installed and needs R and Shiny, but we can try the Pavian demo WebSite to visualize our results.
First, we need to download the files needed as inputs in Pavian; this time, we will visualize the
assignment of the reads of both samples:
These files correspond to our Kraken reports. Again in our local machine, let us use the
$ scp email@example.com:~/dc_workshop/taxonomy/*report .
We go to the Pavian demo WebSite, click on Browse, and choose our reports. You need to select both reports at the same time.
We click on the Results Overview tab.
We click on the Sample tab.
We can look at the abundance of a specific taxon by clicking on it.
We can look at a comparison of both our samples in the Comparison tab.
Discussion: Unclassified reads
As you can see, a percentage of our data could not be assigned to belong to a specific OTU.
Which factors can affect the taxonomic assignation so that a read is unclassified?
Unclassified reads can be the result of different factors that can go from sequencing errors to problems with the algorithm being used to generate the result. The widely used Next-generation sequencing (NGS) platforms, showed average error rate of 0.24±0.06% per base. Besides the sequencing error, we need to consider the status of the database being used to perform the taxonomic assignation.
All the characterized genomes obtained by different research groups are scattered in different repositories, pages, and banks in the cloud. Some are still unpublished. Incomplete databases can affect the performance of the taxonomic assignation. Imagine that the dominant OTU in your sample belongs to a lineage that has never been characterized and does not have a public genome available to be used as a template for the database. This possibility makes the assignation an impossible task and can promote the generation of false positives because the algorithm will assign a different identity to all those reads.
A database with previously gathered knowledge (genomes) is needed for taxonomic assignment.
Taxonomic assignment can be done using Kraken.
Krona and Pavian are web-based tools to visualize the assigned taxa.