aKmerBroom: Ancient oral DNA decontamination using Bloom filters on k-mer sets

This tool identifies ancient reads, given a file of known ancient kmers. It does so in the following steps:

1. Build an ancient_kmers.bloom filter from an ancient kmers text file (if such a Bloom filter does not yet exist).
2. For a set of input reads:
   1. Save those reads which have 2 consecutive kmer matches against ancient_kmers.bloom
   2. Kmerize the saved reads to generate a new set of ancient kmers, called "anchor kmers"
3. For the same set of input reads, identify matches against anchor kmers and classify each read with >50% matches as an ancient read.

Usage

# Use the ancient kmers bloom filter provided
python akmerbroom.py --ancient_bloom

or    

# Use an ancient kmers text file 
python akmerbroom.py --ancient_kmers_set

Input

The data/ folder should contain the following input files:

ancient_kmers.bloom : a bloom filter with ancient kmers
unknown_reads.fastq : a file with reads which we want to classify as ancient or not
[optional] ancient_kmers : a text file where each row is a known ancient kmer

Output

The output/ folder should contain the following output files:

annotated_reads.fastq                     # intermediate output
annotated_reads_with_anchor_kmers.fastq   # final output

The final output file has the following 4 fields in each record header:

SeqId, ReadLen, isConsecutiveMatchFound, AnchorProportion

By default, reads with AnchorProportion >= 0.5 (ie. 50%) are chosen as ancient reads.

Dependencies

pip install biopython
pip install cython
pip install pybloomfiltermmap3

Testing

The tests/ folder contains a test dataset consisting of aOral data @SRR13355797 mixed with non aOral data @ERR671934. To run a test, use the following steps:

First, link the test dataset in the input data/ folder:

cd data/
ln -sf ../tests/unknown_reads.fastq .

Next, download the Bloom Filter into the data/ folder from the following Google Drive link. Note that it could take a few minutes (file size = 3Gb). This can be done from the command line using the gdown utility.

cd data/             # if you are not already in the data/ directory 
pip install gdown
gdown --id 16-7N6l_FwxCG5UDdR8cP7tvVhjG55mtf

Finally, run aKmerBroom

cd ../              # if you are not already in the main directory
python akmerbroom.py --ancient_bloom

The ancient reads file will be written to output/annotated_reads_with_anchor_kmers.fastq. The majority of output reads should be from the aOral sample @SRR13355797, with a few false positives from non aOral @ERR671934.