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Ab Initio training

This walk-through describes an alternative entry point for training models with lighter input requirements that for the full walk through. The models obtained will not achieve the same accuracy as the full training process, but is a useful starting point for basecalling and mapping reads as preparation for more rigorous training.

The input for ab initio training is a set of signal-sequence pairs:

  • Fixed length chunks from reads
  • A reference sequence trimmed for each chunk.

Three sets of input data are provided by way of example:

  • R9.4.1 DNA, 1497098 chunks of 2000 samples
    • r941_dna/chunks.hdf5
    • r941_dna/chunks.fa
  • R9.4.1 RNA, 44735 chunks of 10000 samples
    • r941_rna/chunks.hdf5
    • r941_rna/chunks.fa
  • R10 DNA, 498224 chunks of 2000 samples
    • r10_dna/chunks.hdf5
    • r10_dna/chunks.fa

Warning

R10 data is from a prelease version. The exact pore and chemistry may change before commercial release

Obtaining data

Sample chunk files are stored on Amazon S3 and can be easily downloaded like, for example:

wget  https://s3-eu-west-1.amazonaws.com/ont-research/taiyaki_abinitio.tar.gz
# On some platforms, curl may be installed instead of wget
# curl -O https://s3-eu-west-1.amazonaws.com/ont-research/taiyaki_abinitio.tar.gz
tar zxvf taiyaki_abinitio.tar.gz
cd taiyaki_abinitio

Unpacking the taiyaki_abinitio.tar.gz archive creates a directory taiyaki_abinitio containing the files needed for this walk through. An additional directory taiyaki_abinitio/intermediate_files contains examples of the outputs that will be created.

Obtain and install Taiyaki

Download the Taiyaki software and install into a Python virtual environment. For further information, see https://github.com/nanoporetech/taiyaki

git clone https://github.com/nanoporetech/taiyaki
(cd taiyaki && make install)
source taiyaki/venv/activate

The remainder of this walk-through assumes that the working directory is taiyaki_abinitio, containing the data to train from, and that the taiyaki virtual environment is activated.

Training

Training is as simple as:

train_abinitio.py --device 0 mGru_flipflop.py signal_chunks.hdf5 references.fa
--device Run training on GPU 0
mGru_flipflop.py Model description file, see taiyaki/models
signal_chunks.hdf5 Signal chunk file, formatted as described in Chunk format.
references.fa Per-chunk reference sequence

A Makefile is provided to demonstrate training for the example data sets provided.

#  Run all examples
make all
#  Run single example.  Possible examples r941_dna, r941_rna, or r10_dna
make r941_dna/training

Chunk format

Chunks are stored in a HDF5 file as a single 2D array, chunks x samples.

For example, the training file for the R941 DNA consists of 1497098 chunks of 2000 samples.

h5ls -r r941_dna/chunks.hdf5
/                        Group
/chunks                  Dataset {1497098, 2000}

Creating this file, and the corresponding read reference file, is left up to the user and is the primary difficulty with the ab initio route.

Scaling issues

For compatibilty with ONT's basecallers and the default tool-chain, it is recommended that each read (not chunk) is scaled as follows:

signal_scaled = signal - median(signal)
                -----------------------
                   1.4826 mad(signal)

where the 'MAD' (median absolute deviation) has additional multiplicative factor of 1.4826 to scale it consistently with standard deviation.

Other scaling methods could be used if the user is will to create a pre-read parameter file for future training (see file formats).

Reference format

The references are stored in a fasta format, one reference for each chunk trimmed to that chunk. The name of each reference should be the index of its respective chunk.

For example, the training file for the R941 DNA consists of 1497098 chunks of 2000 samples.

>0
AGACAGCGAGGTTTATCCAATATTTTACAAGACACAAGAACTTCATGTCCATGCTTCAGG
AACAGGACGTCAGATAGCAAACAATGGGAAGTATATTTTTATAACCGAGCAACATCTCTA
CGGAACAGCGTTATCGGTATACAAGTACTCTATATCTTTCAAACGGTGGCTGTTCGTGGG
CTACTCAGACATTAGGGCCAAATACGGTATA
>1
GTATAAGGAGTGTCAAAGATCTCTTTGTTGGTAACTGTCCCTCTGTAAATAGCCCAGTGC
TGACAATTCTTACTGATGACAATAACATTCAAACAATTCTTCTTAAATAAAGGTTAAGGA
AATGTAAATAAAAAAATAACAGTGACATTAATTTGTATATATCTCAACTTCTTCACTTTA
ACCTGTCTGAGCTGTTTGGTTTTGAACTG

RNA

The motor for sequencing RNA translocates along the strand from 3' to 5', so it is observed reversed compared to its natural orientation. For training a basecaller, RNA training sequences must be reversed (but not complemented).

Modified bases

Ab initio training does not yet support our modified base models. While a model could be trained treating each modified base as an additional canonical base, the recommended proceedure is to train a canonical model using the ab initio process and then use this as the 'pre-trained' model in the modbase walk through.