f5c

An optimised re-implementation of the index, call-methylation and eventalign modules in Nanopolish. Given a set of basecalled Nanopore reads and the raw signals, f5c call-methylation detects the methylated cytosine and f5c eventalign aligns raw nanopore signals (events) to the reference k-mers. f5c can optionally utilise NVIDIA graphics cards for acceleration. For best performance and easy usability, it is recommended to use f5c on BLOW5 format. Use slow5tools for FAST5->BLOW5 conversion and blue-crab for POD5->BLOW5 conversion.

First, the reads have to be indexed using f5c index. Then, invoke f5c call-methylation to detect methylated cytosine bases. Finally, you may use f5c meth-freq to obtain methylation frequencies. Alternatively, invoke f5c eventalign to perform event alignment. The results are almost the same as from nanopolish except a few differences due to floating point approximations.

f5c v1.2 onwards support nanopore R10.4.1 chemistry (must specify --pore r10 if FAST5 input, autodetected for S/BLOW5 input).
f5c v1.4 onwards support nanopore RNA004 chemistry (make specify --pore rna004 if FAST5 input, autodetected for S/BLOW5 input).

Full Documentation : https://hasindu2008.github.io/f5c/docs/overview Latest release : https://github.com/hasindu2008/f5c/releases/latest Pre-print : https://doi.org/10.1101/756122 Publication : https://doi.org/10.1186/s12859-020-03697-x Supplementary: nanopore_signal_alignment_supplementary_material.pdf Talk Video : https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31391

Please cite the following when using f5c in your publications:

Gamaarachchi, H., Lam, C.W., Jayatilaka, G. et al. GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis. BMC Bioinformatics 21, 343 (2020). https://doi.org/10.1186/s12859-020-03697-x

@article{gamaarachchi2020gpu,
  title={GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis},
  author={Gamaarachchi, Hasindu and Lam, Chun Wai and Jayatilaka, Gihan and Samarakoon, Hiruna and Simpson, Jared T and Smith, Martin A and Parameswaran, Sri},
  journal={BMC bioinformatics},
  volume={21},
  number={1},
  pages={1--13},
  year={2020},
  publisher={BioMed Central}
}

Quick start

If you are a Linux user and want to quickly try out download the compiled binaries from the latest release. For example:

VERSION=v1.6
wget "https://github.com/hasindu2008/f5c/releases/download/$VERSION/f5c-$VERSION-binaries.tar.gz" && tar xvf f5c-$VERSION-binaries.tar.gz && cd f5c-$VERSION/
./f5c_x86_64_linux        # CPU version
./f5c_x86_64_linux_cuda   # cuda supported version

Binaries should work on most Linux distributions as the only dependency is zlib which is available by default on most distributions. For compiled binaries to work, your processor must support SSSE3 instructions or higher (processors after 2007 have these) and your operating system must have GLIBC 2.17 or higher (Linux distributions from 2014 onwards typically have this).

You can also use conda to install f5c as conda install f5c -c bioconda -c conda-forge.

From f5c v1.6 onwards, experimental binaries for AMD GPUs are also provided under releases.

Building from source

Building a release

Users are recommended to build from the latest release tar ball. You need a compiler that supports C++11. Quick example for Ubuntu :

sudo apt-get install libhdf5-dev zlib1g-dev   #install HDF5 and zlib development libraries
VERSION=v1.6
wget "https://github.com/hasindu2008/f5c/releases/download/$VERSION/f5c-$VERSION-release.tar.gz" && tar xvf f5c-$VERSION-release.tar.gz && cd f5c-$VERSION/
scripts/install-hts.sh  # download and compile the htslib
./configure
make                    # make cuda=1 to enable CUDA support

The commands to install hdf5 (and zlib) development libraries on some popular distributions :

On Debian/Ubuntu : sudo apt-get install libhdf5-dev zlib1g-dev
On Fedora/CentOS : sudo dnf/yum install hdf5-devel zlib-devel
On Arch Linux: sudo pacman -S hdf5
On OS X : brew install hdf5

Other building options

Building from the Github repository additionally requires invoking autoreconf --install to generate the configure script. autoreconf can be installed on Ubuntu using sudo apt-get install autoconf automake.
If you want only S/BLOW5 support you can disable HDF5 by invoking ./configure --disable-hdf5 && make.
If you skip scripts/install-hts.sh and ./configure, hdf5 will be compiled locally. It is a good option if you cannot install hdf5 library system wide. However, building hdf5 takes ages.
f5c from version 0.8.0 onwards by default requires vector instructions (SSSE3 or higher for Intel/AMD and neon for ARM) for builtin slow5lib. If your processor is an ancient processor with no such vector instructions, invoke make as make no_simd=1.
You can optionally enable zstd support for builtin slow5lib when building f5c by invoking make zstd=1. This requires zstd 1.3 development libraries installed on your system (libzstd1-dev package for apt, libzstd-devel for yum/dnf and zstd for homebrew).
On Mac M1 or in any system if ./configure cannot find the hdf5 libraries installed through the package manager, you can specify the location as LDFLAGS=-L/path/to/shared/lib/ CPPFLAGS=-I/path/to/headers/. For example on Mac M1:
```
./configure LDFLAGS=-L/opt/homebrew/lib/ CPPFLAGS=-I/opt/homebrew/include/
make
```
Instructions to build a docker image and conda installation are detailed here.
Other uncommon building options are detailed here.
An SIMD accelerated version contributed by @dkhyland is available in the simd branch.

NVIDIA CUDA support

To build for the GPU, you need to have the CUDA toolkit installed. Make sure nvcc (NVIDIA C Compiler) is in your PATH.

The building instructions are the same as above except that you should call make as :

make cuda=1

Optionally you can provide the CUDA architecture as :

make cuda=1 CUDA_ARCH=-arch=sm_xy

If your CUDA library is not in the default location /usr/local/cuda/lib64, point to the correct location as:

make cuda=1 CUDA_LIB=/path/to/cuda/library/

Visit here for troubleshooting CUDA related problems.

AMD ROCM support

From f5c v1.6, AMD GPUs are supported. To build for such GPUs, you need to have the ROCM toolkit installed.

The building instructions are the same as above for the CPU, except that you should call make as :

make rocm=1

Optionally you can provide the ROCM architecture as :

make rocm=1 ROCM_ARCH=--offload-arch=gfxnnn

If your ROCM library is not in the default location /opt/rocm, point to the correct location as:

make rocm=1 ROCM_LIB=/path/to/rocm/library/

Usage

# indexing #

f5c index -d [fast5_folder] [read.fastq|fasta] 		# for FAST5
f5c index --slow5 [slow5_file] [read.fastq|fasta]	# for S/BLOW5

# methylation calling #

# for S/BLOW5 (R9.4 or R10.4 DNA data)
f5c call-methylation -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta] --slow5 [slow5_file] > [meth.tsv]
# for FAST5, R9.4 DNA data
f5c call-methylation -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta] > [meth.tsv]
# for FAST5, R10.4 DNA data
f5c call-methylation -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta] --pore r10 > [meth.tsv]
# methylation frequency
f5c meth-freq -i [meth.tsv] > [freq.tsv]

# event align #

# for S/BLOW5 (R9.4 DNA/RNA or R10.4 DNA data)
f5c eventalign -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta] --slow5 [slow5_file] > [events.tsv]
# for FAST5 (R9.4 DNA data)
f5c eventalign -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta]  > [events.tsv]
# for FAST5 (R9.4 RNA data)
f5c eventalign -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta] --rna > [events.tsv]
# for FAST5 (R10.4 DNA data)
f5c eventalign -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta] --pore r10 > [events.tsv]
# for FAST5 (RNA004 RNA data)
f5c eventalign -b [reads.sorted.bam] -g [ref.fa] -r [reads.fastq|fasta] --pore rna004 --rna > [events.tsv]

Visit the man page for all the commands and options. S

F5c

Install / Use

README

f5c