Nanopolish

Software package for signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more (see Nanopolish modules, below).

Dependencies

libhdf5 is automatically downloaded and compiled when running make but this can be disabled with: HDF5=nofetch make. The nanopolish binary will link libhdf5.a statically.

eigen is also automatically downloaded and included when compiling with make.

biopython is required to run the helpers in scripts/.

htslib is included as a submodule and compiled automatically.

A compiler that supports C++11 is needed to build the sources. Development of the code is performed using gcc-4.8.

Installation instructions

You will need to run git clone --recursive https://github.com/jts/nanopolish.git to get the source code and submodules. You can then compile nanopolish by running:

make

This will automatically download and install libhdf5 and eigen.

Nanopolish modules

The main subprograms of nanopolish are:

nanopolish extract: extract reads in FASTA or FASTQ format from a directory of FAST5 files
nanopolish call-methylation: predict genomic bases that may be methylated
nanopolish variants: detect SNPs and indels with respect to a reference genome
nanopolish variants --consensus: calculate an improved consensus sequence for a draft genome assembly
nanopolish eventalign: align signal-level events to k-mers of a reference genome

Analysis workflow examples

Computing a new consensus sequence for a draft assembly

The original purpose of nanopolish was to compute an improved consensus sequence for a draft genome assembly produced by a long-read assembly like canu. This section describes how to do this, starting with your draft assembly which should have megabase-sized contigs.

First we prepare the data by extracting the reads from the FAST5 files, and aligning the basecalls to our draft assembly (draft.fa).

# Extract the QC-passed reads from a directory of FAST5 files
nanopolish extract --type [2d|template] directory/pass/ > reads.fa

# Index the draft genome
bwa index draft.fa

# Align the basecalled reads to the draft sequence
bwa mem -x ont2d -t 8 draft.fa reads.fa | samtools sort -o reads.sorted.bam -T reads.tmp -
samtools index reads.sorted.bam

Now, we use nanopolish to compute the consensus sequence (the genome is polished in 50kb blocks and there will be one output file per block). We'll run this in parallel:

python nanopolish_makerange.py draft.fa | parallel --results nanopolish.results -P 8 \
    nanopolish variants --consensus polished.{1}.fa -w {1} -r reads.fa -b reads.sorted.bam -g draft.fa -t 4 --min-candidate-frequency 0.1

This command will run the consensus algorithm on eight 50kbp segments of the genome at a time, using 4 threads each. Change the -P and --threads options as appropriate for the machines you have available.

After all polishing jobs are complete, you can merge the individual 50kb segments together back into the final assembly:

python nanopolish_merge.py polished.*.fa > polished_genome.fa

Calling Methylation

nanopolish can use the signal-level information measured by the sequencer to detect 5-mC as described here. Here's how you run it:

# Extract all reads from a directory of FAST5 files
nanopolish extract -r --type template directory/ > reads.fa

# Align the basecalled reads to a reference genome
bwa mem -x ont2d -t 8 reference.fa reads.fa | samtools sort -o reads.sorted.bam -T reads.tmp -
samtools index reads.sorted.bam

# Run the methylation caller
nanopolish call-methylation -t 8 -r reads.fa -g reference.fa -b reads.sorted.bam > methylation.tsv

The output of call-methylation is a tab-separated file containing per-read log-likelihood ratios (positive values indicate more evidence for 5-mC, negative values indicate more evidence for C). Each line contains the name of the read that covered the CpG site, which allows methylation calls to be phased. We have provided a script to calculate per-site methylation frequencies using call-methylation's output:

python /path/to/nanopolish/scripts/calculate_methylation_frequency -c 2.5 -i methylation.tsv > frequencies.tsv

The output of this script is a tab-seperated file containing the genomic position of the CpG site, the number of reads that covered the site, and the percentage of those reads that were predicted to be methylated. The -c 2.5 option requires the absolute value of the log-likelihood ratio to be at least 2.5 to make a call, otherwise the read will be ignored. This helps reduce calling errors as only sites with sufficient evidence will be included in the calculation.

To run using docker

First build the image from the dockerfile:

docker build .

Note the uuid given upon successful build. Then you can run nanopolish from the image:

docker run -v /path/to/local/data/data/:/data/ -it :image_id  ./nanopolish eventalign -r /data/reads.fa -b /data/alignments.sorted.bam -g /data/ref.fa

Credits and Thanks

The fast table-driven logsum implementation was provided by Sean Eddy as public domain code. This code was originally part of hmmer3.