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This recipe evaluates the accuracy of the Kraken 2 classification for selected markers.

1 result • updated 3.8 years ago by Istvan Albert

This recipe evaluates the accuracy of the Kraken 2 classification for selected markers.

The recipe generates simulated sequencing reads from NCBI accession numbers then performs classification with Kraken2.

In the third step evaluates and reports the accuracy (the percent of reads were classified correctly) of the classification.

The input to the recipe is a list of NCBI accession numbers:

AP012081
AP011279
DQ288268
AP014537

The main output is a file that lists the classification accuracy for each accession number:

accession     expect    actual   percent   taxid      title
AP012081      1000      993      99.3      67547      Margariscus margarita mitochondrial DNA, almost complete genome, except for D-loop
AP011279      1000      996      99.6      90988      Pimephales promelas mitochondrial DNA, complete genome
DQ288268      1000      996      99.6      8022       Oncorhynchus mykiss mitochondrion, complete genome
AP014537      1000      1000    100.0      27706      Micropterus salmoides mitochondrial DNA, almost complete genome

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set -uex

#
# Download the taxonomy for kraken2.
# Needs to be done separately and only once.
#
# mkdir -p ~/refs
#
# (cd ~/refs && kraken2-build --download-taxonomy --db kraken2)
#

set -uex

#
# Download the taxonomy for kraken2.
# Needs to be done separately and only once.
#
# mkdir -p ~/refs
#
# (cd ~/refs && kraken2-build --download-taxonomy --db kraken2)
#

# Input file that contains the accession numbers.
ACC=FILENAME

# How many reads to simulate.
N=1000

# Lenght of reads.
L=100

# The error rate in the simulated reads.
E=0.02

# Will store data in these locations
mkdir -p input output code

# The FASTA file storing the sequence for accession numbers.
FASTA=input/reference.fa

# The FASTQ files that store the read pairs generated from reference.
R1=input/r1.fq
R2=input/r2.fq

# The accesion taxonomy file
TAXONOMY=input/taxonomy.txt

# The Kraken 2 classification report.
REPORT=output/kraken2-report.txt

# The Kraken2 output.
OUTPUT=output/kraken2-output.txt

# The Kraken2 accuracy report.
ACCURACY=accuracy-kraken2.txt

# Copy over the accession file to input folder to aid reproducibility.
cp $ACC input/accessions.txt

# Get sequences from NCBI.
epost -db nuccore -input $ACC | efetch -format fasta > $FASTA

# Identify the taxonomy for each accession number.
epost -db nuccore -input $ACC  | esummary | xtract -pattern DocumentSummary -element Caption,TaxId,Title > $TAXONOMY

# To avoid recomputing the same kraken2 library store with a unique name.
SHA=`shasum $FASTA | cut -c 1-8`

# Store the Kraken2 database under the checksum name.
DB=~/tmp/$SHA

# Create the directory for the kraken2 database.
mkdir -p $DB

# Link the taxonomy.
ln -sf ~/refs/kraken2/taxonomy $DB

# Add marker sequences to the kraken2 library.
kraken2-build --add-to-library $FASTA --db $DB

# Build the marker library.
kraken2-build --build --db $DB

# Install the plac command parser.
pip install plac -q

# Clone the read simulator. Must use wgsim2.
git clone --depth=1 https://github.com/ialbert/wgsim2.git > log.txt

# Compile the read simulator.
(cd wgsim2 && make 2> log.txt)

# Delete the .git folder as it creates too many unneeded files)
(cd wgsim2 && rm -rf .git)

# Simulate reads with errors in fixed N mode.
wgsim2/wgsim2 -f -r 0 -R 0 -N $N -e $E -1 $L -2 $L $FASTA $R1 $R2 > log.txt

# Run kraken2 classifier.
kraken2  -db $DB --paired $R1 $R2 --report-zero-counts --report $REPORT > $OUTPUT

# The location of the code that generates the validation.
URL2=https://raw.githubusercontent.com/biostars/biocode/master/scripts/classify/validate.py

# Get the code.
curl $URL2 > code/validate.py

# Run the validator
python code/validate.py -f $OUTPUT -t $TAXONOMY -c $N > $ACCURACY

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This recipe evaluates the accuracy of the Kraken 2 classification for selected markers.

The recipe generates simulated sequencing reads from NCBI accession numbers then performs classification with Kraken2.

In the third step evaluates and reports the accuracy (the percent of reads were classified correctly) of the classification.

The input to the recipe is a list of NCBI accession numbers:

```
AP012081
AP011279
DQ288268
AP014537
```

The main output is a file that lists the classification accuracy for each accession number:

```
accession     expect    actual   percent   taxid      title
AP012081      1000      993      99.3      67547      Margariscus margarita mitochondrial DNA, almost complete genome, except for D-loop
AP011279      1000      996      99.6      90988      Pimephales promelas mitochondrial DNA, complete genome
DQ288268      1000      996      99.6      8022       Oncorhynchus mykiss mitochondrion, complete genome
AP014537      1000      1000    100.0      27706      Micropterus salmoides mitochondrial DNA, almost complete genome
```

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Kraken2 classification validator

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