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Taxonomic investigation

Preface

We want to investigate if there are sequences of other species in our collection of sequenced DNA pieces. We hope that most of them are from our species that we try to study, i.e. the DNA that we have extracted and amplified. This might be a way of quality control, e.g. have the samples been contaminated? Lets investigate if we find sequences from other species in our sequence set.

We will use the tool to assign taxonomic classifications to our sequence reads. Let us see if we can id some sequences from other species.

Note

You will encounter some To-do sections at times. Write the solutions and answers into a text-file.

Overview

The part of the workflow we will work on in this section can be viewed in fig-workflow-taxa.

The part of the workflow we will work on in this section marked in red.

Before we start

Lets see how our directory structure looks so far:

$ cd ~/analysis
$ ls -1F
assembly/
data/
mappings/
multiqc_data
trimmed/
trimmed-fastqc/

Attention

If you have not run the previous section ngs-mapping, you can download the unmapped sequencing data needed for this section here: downloads. Download the file to the ~/analysis directory and decompress. Alternatively on the CLI try:

cd ~/analysis
wget -O mappings.tar.gz https://osf.io/g5at8/download
tar xvzf mappings.tar.gz

Kraken2

We will be using a tool called [WOOD2014]. This tool uses k-mers to assign a taxonomic labels in form of to the sequence (if possible). The taxonomic label is assigned based on similar k-mer content of the sequence in question to the k-mer content of reference genome sequence. The result is a classification of the sequence in question to the most likely taxonomic label. If the k-mer content is not similar to any genomic sequence in the database used, it will not assign any taxonomic label.

Installation

Use conda in the same fashion as before to install . However, we are going to install kraken into its own environment:

$ conda create --yes -n kraken kraken2 bracken
$ conda activate kraken

Now we create a directory where we are going to do the analysis and we will change into that directory too.

# make sure you are in your analysis root folder
$ cd ~/analysis

# create dir
$ mkdir kraken
$ cd kraken

Now we need to create or download a database that can be used to assign the taxonomic labels to sequences. We opt for downloading the pre-build "minikraken2" database from the website:

$ curl -O ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken2_v2_8GB_201904_UPDATE.tgz

# alternatively we can use wget
$ wget ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken2_v2_8GB_201904_UPDATE.tgz

# once the download is finished, we need to extract the archive content:
$ tar -xvzf minikraken2_v2_8GB_201904_UPDATE.tgz

Attention

Should the download fail. Please find links to alternative locations on the downloads page.

Note

The "minikraken2" database was created from bacteria, viral and archaea sequences. What are the implications for us when we are trying to classify our sequences?

Usage

Now that we have installed and downloaded and extracted the minikraken2 database, we can attempt to investigate the sequences we got back from the sequencing provider for other species as the one it should contain. We call the tool and specify the database and fasta-file with the sequences it should use. The general command structure looks like this:

$ kraken2 --use-names --threads 4 --db PATH_TO_DB_DIR --report example.report.txt example.fa > example.kraken

However, we may have fastq-files, so we need to use --fastq-input which tells that it is dealing with fastq-formated files. The --gzip-compressed flag specifies tat te input-files are compressed. In addition, we are dealing with paired-end data, which we can tell with the switch --paired. Here, we are investigating one of the unmapped paired-end read files of the evolved line.

$ kraken2 --use-names --threads 4 --db minikraken2_v2_8GB_201904_UPDATE --fastq-input --report evol1 --gzip-compressed --paired ../mappings/evol1.sorted.unmapped.R1.fastq.gz ../mappings/evol1.sorted.unmapped.R2.fastq.gz > evol1.kraken

This classification may take a while, depending on how many sequences we are going to classify. The resulting content of the file evol1.kraken looks similar to the following example:

Each sequence classified by results in a single line of output. Output lines contain five tab-delimited fields; from left to right, they are:

  1. C/U: one letter code indicating that the sequence was either classified or unclassified.
  2. The sequence ID, obtained from the FASTA/FASTQ header.
  3. The taxonomy ID used to label the sequence; this is 0 if the sequence is unclassified and otherwise should be the identifier.
  4. The length of the sequence in bp.
  5. A space-delimited list indicating the lowest common ancestor (in the taxonomic tree) mapping of each k-mer in the sequence. For example, 562:13 561:4 A:31 0:1 562:3 would indicate that:
    • the first 13 k-mers mapped to taxonomy ID #562
    • the next 4 k-mers mapped to taxonomy ID #561
    • the next 31 k-mers contained an ambiguous nucleotide
    • the next k-mer was not in the database
    • the last 3 k-mers mapped to taxonomy ID #562

Note

The manual can be accessed here.

Investigate taxa

We can use the webpage NCBI TaxIdentifier to quickly get the names to the taxonomy identifier. However, this is impractical as we are dealing potentially with many sequences. has some scripts that help us understand our results better.

Because we used the switch --report FILE, we have got also a sample-wide report of all taxa found. This is much better to get an overview what was found.

The first few lines of an example report are shown below.

The output of kraken-report is tab-delimited, with one line per taxon. The fields of the output, from left-to-right, are as follows:

  1. Percentage of reads covered by the clade rooted at this taxon
  2. Number of reads covered by the clade rooted at this taxon
  3. Number of reads assigned directly to this taxon
  4. 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 "-".
  5. ID
  6. The indented scientific name

Note

If you want to compare the taxa content of different samples to another, one can create a report whose structure is always the same for all samples, disregarding which taxa are found (obviously the percentages and numbers will be different).

We can cerate such a report using the option --report-zero-counts which will print out all taxa (instead of only those found). We then sort the taxa according to taxa-ids (column 5), e.g. sort -n -k5.

The report is not ordered according to taxa ids and contains all taxa in the database, even if they have not been found in our sample and are thus zero. The columns are the same as in the former report, however, we have more rows and they are now differently sorted, according to the id.

Bracken

stands for Bayesian Re-estimation of Abundance with KrakEN, and is a statistical method that computes the abundance of species in DNA sequences from a metagenomics sample [LU2017]. uses the taxonomy labels assigned by (see above) to estimate the number of reads originating from each species present in a sample. classifies reads to the best matching location in the taxonomic tree, but does not estimate abundances of species. Combined with the Kraken classifier, will produces more accurate species- and genus-level abundance estimates than alone.

The use of subsequent to is optional but might improve on the results.

Installation

We installed already together with above, so it should be ready to be used. We also downloaded the files together with the minikraken2 database above, so we are good to go.

Usage

Now, we can use on the results to improve them.

The general structure of the command look like this:

$ bracken -d PATH_TO_DB_DIR -i kraken2.report -o bracken.species.txt -l S
  • -l S: denotes the level we want to look at. S stands for species but other levels are available.
  • -d PATH_TO_DB_DIR: specifies the path to the database that should be used.

Let us apply to the example above:

$ bracken -d minikraken2_v2_8GB_201904_UPDATE -i evol1.kraken -l S -o evol1.bracken

The species-focused result-table looks similar to this:

The important column is the new_est_reads, which gives the newly estimated reads.

Centrifuge

We can also use another tool by the same group called [KIM2017]. This tool uses a novel indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem to assign a taxonomic labels in form of to the sequence (if possible). The result is a classification of the sequence in question to the most likely taxonomic label. If the search sequence is not similar to any genomic sequence in the database used, it will not assign any taxonomic label.

Note

I would normally use and only prefer if memory and/or speed are an issue .

Installation

Use conda in the same fashion as before to install :

$ conda create --yes -n centrifuge centrifuge
$ conda activate centrifuge

Now we create a directory where we are going to do the analysis and we will change into that directory too.

# make sure you are in your analysis root folder
$ cd ~/analysis

# create dir
$ mkdir centrifuge
$ cd centrifuge

Now we need to create or download a database that can be used to assign the taxonomic labels to sequences. We opt for downloading the pre-build database from the website:

$ curl -O ftp://ftp.ccb.jhu.edu/pub/infphilo/centrifuge/data/p_compressed+h+v.tar.gz

$ # alternatively we can use wget
$ wget ftp://ftp.ccb.jhu.edu/pub/infphilo/centrifuge/data/p_compressed+h+v.tar.gz

# once the download is finished, we need to extract the archive content
# It will extract a few files from the archive and may take a moment to finish.
$ tar -xvzf p_compressed+h+v.tar.gz

Attention

Should the download fail. Please find links to alternative locations on the downloads page.

Note

The database we will be using was created from bacteria and archaea sequences only. What are the implications for us when we are trying to classify our sequences?

Usage

Now that we have installed and downloaded and extracted the pre-build database, we can attempt to investigate the sequences we got back from the sequencing provider for other species as the one it should contain. We call the tool and specify the database and fastq-files with the sequences it should use. The general command structure looks like this:

$ centrifuge -x p_compressed+h+v -1 example.1.fq -2 example.2.fq -U single.fq --report-file report.txt -S results.txt

Here, we are investigating paired-end read files of the evolved line.

$ centrifuge -x p_compressed+h+v -1 ../mappings/evol1.sorted.unmapped.R1.fastq  -2 ../mappings/evol1.sorted.unmapped.R2.fastq --report-file evol1-report.txt -S evol1-results.txt

This classification may take a moment, depending on how many sequences we are going to classify. The resulting content of the file evol1-results.txt looks similar to the following example:

Each sequence classified by results in a single line of output. Output lines contain eight tab-delimited fields; from left to right, they are according to the website:

  1. The read ID from a raw sequencing read.
  2. The sequence ID of the genomic sequence, where the read is classified.
  3. The taxonomic ID of the genomic sequence in the second column.
  4. The score for the classification, which is the weighted sum of hits.
  5. The score for the next best classification.
  6. A pair of two numbers: (1) an approximate number of base pairs of the read that match the genomic sequence and (2) the length of a read or the combined length of mate pairs.
  7. A pair of two numbers: (1) an approximate number of base pairs of the read that match the genomic sequence and (2) the length of a read or the combined length of mate pairs.
  8. The number of classifications for this read, indicating how many assignments were made.

Investigate taxa

Centrifuge report

The command above creates a report automatically for us. It contains an overview of the identified taxa and their abundances in your supplied sequences (normalised to genomic length):

Each line contains seven tab-delimited fields; from left to right, they are according to the website:

  1. The name of a genome, or the name corresponding to a taxonomic ID (the second column) at a rank higher than the strain.
  2. The taxonomic ID.
  3. The taxonomic rank.
  4. The length of the genome sequence.
  5. The number of reads classified to this genomic sequence including multi-classified reads.
  6. The number of reads uniquely classified to this genomic sequence.
  7. The proportion of this genome normalized by its genomic length.

Kraken-like report

If we would like to generate a report as generated with the former tool , we can do it like this:

$ centrifuge-kreport -x p_compressed+h+v evolved-6-R1-results.txt > evolved-6-R1-kreport.txt

This gives a similar (not the same) report as the tool. The report is tab-delimited, with one line per taxon. The fields of the output, from left-to-right, are as follows:

  1. Percentage of reads covered by the clade rooted at this taxon
  2. Number of reads covered by the clade rooted at this taxon
  3. Number of reads assigned directly to this taxon
  4. 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 “-“.
  5. NCBI Taxonomy ID
  6. The indented scientific name

Visualisation (Krona)

We use the tools to create a nice interactive visualisation of the taxa content of our sample [ONDOV2011]. fig-krona shows an example (albeit an artificial one) snapshot of the visualisation provides. fig-krona is a snapshot of the interactive web-page similar to the one we try to create.

Example of an Krona output webpage.

Installation

Install with:

$ conda create --yes -n krona krona 
$ conda activate krona

First some house-keeping to make the installation work. Do not worry to much about what is happening here.

# we delete a symbolic link that is not correct
$ rm -rf ~/miniconda3/envs/ngs/opt/krona/taxonomy

# we create a directory in our home where the krona database will live
$ mkdir -p ~/krona/taxonomy

# now we make a symbolic link to that directory
$ ln -s ~/krona/taxonomy ~/miniconda3/envs/ngs/opt/krona/taxonomy

Build the taxonomy

We need to build a taxonomy database for . However, if this fails we will skip this step and just download a pre-build one. Lets first try to build one.

$ ktUpdateTaxonomy.sh ~/krona/taxonomy

Attention

Should this fail we can download a pre-build database on the downloads page via a browser.

Once you have downloaded the file, follow these steps:

# we unzip the file
$ gzip -d taxonomy.tab.gz

# we move the unzipped file to our taxonomy directory we specified in the step before.
$ mv taxonomy.tab ~/krona/taxonomy

Visualise

Now, we use the tool ktImportTaxonomy from the tools to create the html web-page. We first need build a two column file (read_id<tab>tax_id) as input to the ktImportTaxonomy tool. We will do this by cutting the columns out of either the or results:

# Kraken2
$ cd kraken
$ cat evol1.kraken | cut -f 2,3 > evol1.kraken.krona
$ ktImportTaxonomy evol1.kraken.krona
$ firefox taxonomy.krona.html

# Centrifuge
$ cd centrifuge
$ cat evol1-results.txt | cut -f 1,3 > evol1-results.krona
$ ktImportTaxonomy evol1-results.krona
$ firefox taxonomy.krona.html

What happens here is that we extract the second and third column from the results. Afterwards, we input these to the script, and open the resulting web-page in a bowser. Done!

html

References

KIM2017

Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 2016 Dec;26(12):1721-1729

LU2017

Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science, 2017, 3:e104, doi:10.7717/peerj-cs.104

ONDOV2011

Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics, 2011, 12(1):385.

WOOD2014

Wood DE and Steven L Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biology, 2014, 15:R46. DOI: 10.1186/gb-2014-15-3-r46.