Skip to main content
Donate to the Python Software Foundation or Purchase a PyCharm License to Benefit the PSF! Donate Now

A tool for the analysis of bisulfite-free and base-resolution sequencing data generated with TET Assisted Pyridine borane Sequencing (TAPS), or other modified cytosine to thymine conversion methods (mCtoT). It also has some features for bisulfite sequencing data (unmodified cytosine to thymine conversion methods, CtoT).

Project description

asTair is a toolchain to process DNA modification sequencing data. asTair was designed primarily for handling TET-Assisted Pyridine Borane (TAPS) sequencing output, but also contains functions that are useful for Bisulfite Sequencing (BS) data.

Build status

Basic usage

0. Installation

Installation through pip is the easiest way to get asTair, and it works in python2 and 3:

pip install astair

You should now be able to call astair:

astair --help
Usage: astair [OPTIONS] COMMAND [ARGS]...

  asTair (tools for processing cytosine modification sequencing data)

Options:
  --help  Show this message and exit.

Commands:
  align     Align raw reads in fastq format to a reference genome.
  call      Call modified cytosines from a bam or cram file.
  filter    Look for sequencing reads with more than N CpH modifications.
  find      Output positions of Cs from fasta file per context.
  mbias     Generate modification per read length information (Mbias).
  phred     Calculate per base (A, C, T, G) Phred scores for each strand.
  simulate  Simulate TAPS/BS conversion on top of an existing bam/cram file.

  __________________________________About__________________________________
  asTair was written by Gergana V. Velikova and Benjamin Schuster-Boeckler.
  This code is made available under the GNU General Public License, see
  LICENSE.txt for more details.
                                                           Version: 3.x.x

In general, you can use --help on all astair sub-commands to get detailed instructions on the available options.

(If for some reason pip is not an option, check our FAQ for further ways to install asTair.)

All of the examples in the main part of the current tutorial are based on the assumption that the input sequencing data are TAPS pair-end sequencing reads, however, asTair analyses can be run in single-end mode (--se). Also, asTair enables you to run analyses on WGBS data, which requires a running installation of bwa-meth for the alignment step. For more information on WGBS analyses you may check the section Analysis of WGBS data (or other unmodified cytosine to thymine conversion methods).

1. Align reads

We will assume that you have generated paired-end sequencing data, which is stored in two fastq files. For this brief tutorial, we assume the files are called lambda.phage_test_sample_R1.fq.gz and lambda.phage_test_sample_R2.fq.gz. If you want to follow this tutorial, you can download the files here:

# Or use curl -O if wget is not available
wget https://zenodo.org/record/2582855/files/lambda.phage_test_sample_1.fq.gz
wget https://zenodo.org/record/2582855/files/lambda.phage_test_sample_2.fq.gz

The raw reads need to be aligned. asTair contains a command to help with this. It assumes that bwa and samtools are available on your system. (If you prefer to use a different aligner, skip to step 2.)

You will also need an indexed reference genome to align to, which can be given as a gzip compressed file. For this example we are using the lambda phage genome, which you can download with

wget https://zenodo.org/record/2582855/files/lambda_phage.fa
wget https://zenodo.org/record/2582855/files/lambda_phage.fa.fai

Now, you are ready to align:

mkdir -p output_dir
astair align -f lambda_phage.fa -1 lambda.phage_test_sample_1.fq.gz -2 lambda.phage_test_sample_2.fq.gz -d output_dir

2. Call methylation

Once your fastq files are aligned and sorted (done automatically by astair align), you can run astair call to create a list of putative modified positions:

astair call -i output_dir/lambda.phage_test_sample_mCtoT.cram -f lambda_phage.fa --context CpG --minimum_base_quality 13 -d output_dir/

3. Interpret results

After calling methylation, you will find two additional files in output_dir:

  1. lambda.phage_test_sample_mCtoT_mCtoT_CpG.stats
  2. lambda.phage_test_sample_mCtoT_mCtoT_CpG.mods

The .stats file contains global statistics on the modification rate in different sequence contexts. You can use this to get an idea of the overall level of modification in your sample. Here you will find information about how many cytosine positions of certain context are in the reference, how many of them were covered, and how many reads were modified/unmodified at the covered positions on the relevant strand assuming directionality. In our example here, we used a 1:1 mixture of in-vitro modified and unmodified lambda phage, so the results show a methylation rate of approximately 50% :

CONTEXT SPECIFIC_CONTEXT MEAN_MODIFICATION_RATE_PERCENT TOTAL_POSITIONS COVERED_POSITIONS MODIFIED UNMODIFIED
CpG * 48.225 6225 6225 356153 382377
* CGA 44.647 1210 1210 64160 79545
* CGC 48.595 1730 1730 97842 103499
* CGG 48.936 1847 1847 108283 112991
* CGT 49.862 1438 1438 85868 86342

The .mods file contains per-position information on your sample:

CHROM START END MOD_LEVEL MOD UNMOD REF ALT SPECIFIC_CONTEXT CONTEXT SNV TOTAL_DEPTH
lambda 3 4 1.0 23 0 C T CGG CpG No 57
lambda 4 5 0.0 0 34 G A CGC CpG No 71
lambda 6 7 1.0 38 0 C T CGA CpG No 104
lambda 7 8 1.0 58 0 G A CGC CpG No 127
lambda 12 13 1.0 88 0 C T CGC CpG No 240
lambda 13 14 0.0 0 139 G A CGA CpG No 250

The header should be mostly self-explanatory. MOD and UNMOD refer to the number of reads covering that base that showed evidence of modification/no modification, and were of the right orientation to be meaningful for modification calling. The total coverage, including reads that were oriented in a way that no modification information can be extracted, is shown in TOTAL_DEPTH. SNV gives a heuristic indication whether the position is indeed a modified base, or a genetic C to T variant in the genome of the sample.

Other useful information

Recommendations for data pre-processing

  1. Do quality control of the sequencing reads and do quality trimming before mapping and dispose of very short reads, using FastQC, trimgalore or similar tools.
  2. In most cases, it will be best to remove PCR duplicates before running the modification caller, unless your reads are non-randomly fragmented (e.g. enzymatically digested).
  3. Check the fragment (insert) size distribution and decide on an overlap removal method for paired-end reads. The simplest option is the default removal of overlaps handled by astair call, which will randomly select one of two overlapping reads. This behaviour can be disabled by the -sc option, in case you are using a more sophisticated overlap-clipping tool.
  4. For speed and convenience we recommend using the --per_chromosome option, if possible, in order to run multiple processes in parallel. This also reduces the memory requirement when asTair is run on a desktop machine.

Analysis of WGBS data (or other unmodified cytosine to thymine conversion methods)

The analysis pipeline for bisulfite sequencing data does follows the same steps as TAPS data analysis, but requires different options. We again start from fastq files. To avoid Bismark-style double-alignments, we prefer to use bwa meth, which can be used directly through astair align when you choose the --method CtoT option.

mkdir -p output_dir
astair align -f lambda_phage.fa -1 lambda.phage_test_sample_BS_1.fastq.gz -2 lambda.phage_test_sample_BS_2.fastq.gz --method CtoT -d output_dir/

You can now use astair call with --method CtoT for the modifcation calling:

astair call -i output_dir/lambda.phage_test_sample_BS_CtoT.cram -f lambda_phage.fa --method CtoT --context CpG --minimum_base_quality 13 -d output_dir/

Further information

License

This software is made available under the terms of the GNU General Public License v3.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
asTair-3.2.1-py2.py3-none-any.whl (54.5 kB) Copy SHA256 hash SHA256 Wheel py2.py3
asTair-3.2.1.tar.gz (34.0 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page