helen 0.0.13

RNN based assembly HELEN. It works paired with MarginPolish.

H.E.L.E.N.

H.E.L.E.N. (Homopolymer Encoded Long-read Error-corrector for Nanopore)

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Pre-print of a paper describing the methods and overview of a suggested de novo assembly pipeline is now available:

Efficient de novo assembly of eleven human genomes using PromethION sequencing and a novel nanopore toolkit

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Overview

HELEN uses a Recurrent-Neural-Network (RNN) based Multi-Task Learning (MTL) model that can predict a base and a run-length for each genomic position using the weights generated by MarginPolish.

© 2020 Kishwar Shafin, Trevor Pesout, Benedict Paten.
Computational Genomics Lab (CGL), University of California, Santa Cruz.

Why MarginPolish-HELEN ?

• MarginPolish-HELEN outperforms other graph-based and Neural-Network based polishing pipelines.
• Simple installation steps.
• HELEN can use multiple GPUs at the same time.
• Highly optimized pipeline that is faster than any other available polishing tool.
• We have sequenced-assembled-polished 11 samples to ensure robustness, runtime-consistency and cost-efficiency.
• We tested GPU usage on Amazon Web Services (AWS) and Google Cloud Platform (GCP) to ensure scalability.
• Open source (MIT License).

Installation

MarginPolish-HELEN is supported on Ubuntu 16.10/18.04 or any other Linux-based system.

Install prerequisites

sudo apt-get -y install git cmake make gcc g++ autoconf bzip2 lzma-dev zlib1g-dev \
libcurl4-openssl-dev libpthread-stubs0-dev libbz2-dev liblzma-dev libhdf5-dev \
python3-pip python3-virtualenv virtualenv

Method 1: Install MarginPolish-HELEN from GitHub

git clone https://github.com/kishwarshafin/helen.git
cd helen
make install
. ./venv/bin/activate

marginPolish --version
helen --version
helen --help
marginPolish --help

Each time you want to use it, activate the virtualenv:

source <path/to/helen/venv/bin/activate>

Method 2: Install using PyPi

python3 -m pip install helen --user

python3 -m marginpolish --help
python3 -m helen --help

echo 'export PATH="$(python3 -m site --user-base)/bin":$PATH' >> ~/.bashrc
source ~/.bashrc

python3 -m pip install update pip
python3 -m pip install update helen

Usage

MarginPolish requires a draft assembly and a mapping of reads to the draft assembly. We commend using Shasta as the initial assembler and MiniMap2 for the mapping.

Step 1: Generate an initial assembly

Generate an assembly using one of the ONT assemblers:

• Shasta long read assembler.
• Flye assembler
• Canu assembler
• WTDBG2 assembler

Step 2: Create an alignment between reads and shasta assembly

We recommend using MiniMap2 to generate the mapping between the reads and the assembly.

# we recommend using FASTQ as marginPolish uses quality values
# This command can run MiniMap2 with 32 threads, you can change the number as you like.
minimap2 -ax map-ont -t 32 shasta_assembly.fa reads.fq | samtools sort -@ 32 | samtools view -hb -F 0x104 > reads_2_assembly.bam
samtools index -@32 reads_2_assembly.bam

#  the -F 0x104 flag removes unaligned and secondary sequences

Step 3: Generate images using MarginPolish

You can generate images using MarginPolish by running:

marginPolish reads_2_assembly.bam \
Assembly.fa \
</path/to/model_name.json> \
-t <number_of_threads> \
-o <path/to/marginpolish_images> \
-f

You can get the params.json from path/to/marginpolish/params/.

Step 4: Run HELEN

Download Model

helen download_models \
--output_dir <path/to/helen_models/>

Run HELEN

helen polish \
--image_dir </path/to/marginpolish_images/> \
--model_path </path/to/model.pkl> \
--batch_size 256 \
--num_workers 4 \
--threads <num_of_threads> \
--output_dir </path/to/output_dir> \
--output_prefix <output_filename.fa> \
--gpu

If you are using CPUs then remove the --gpu argument.

Help

Please open a github issue if you face any difficulties.

Acknowledgement

We are thankful to Segey Koren and Karen Miga for their help with CHM13 data and evaluation.

We downloaded our data from Telomere-to-telomere consortium to evaluate our pipeline against CHM13.

We acknowledge the work of the developers of these packages:

• Shasta
• pytorch
• ssw library
• hdf5 python (h5py)
• pybind
• hyperband

Fun Fact

The name "HELEN" is inspired from the A.I. created by Tony Stark in the Marvel Comics (Earth-616). HELEN was created to control the city Tony was building named "Troy" making the A.I. "HELEN of Troy".

READ MORE: HELEN

© 2020 Kishwar Shafin, Trevor Pesout, Benedict Paten.