Amis

Evaluating the impact of assemblers, aligners, sequencer and read length on read-based and assembly-based SV detection.

Analysis workflow

Please check the wiki page for more details about working directory structure, SV detection and benchmarking.

Overview

The major parts involved in the comparison are listed below:

1. Using six long read datasets (details), we assessed the impact of dataset, aligner and assembler on the detection variability.
2. On each dataset, 20 read-based callsets and four assembly-based callsets were compared to assess the impact of aligner and assembler.
3. Based on the analysis of 2, we build high-confident insertions and deletions (insdel) callsets of read and assembly. The high-confident insdel callsets are then compared.
4. Benchmarking 20 read-based and eight assembly-based detection piplines with well curate SVs of HG002 released by GIAB.

Project files at Zenodo

There are 12 separate zip files to download from Zenodo ( ).

1. lra_ont_9kb.zip
2. lra_ont_19kb.zip
3. lra_ont_30kb.zip
4. minimap2_ont_9kb.zip
5. minimap2_ont_19kb.zip
6. minimap2_ont_30kb.zip
7. other_files_under_ONT.zip
8. HiFi dataset calls.zip
9. CMRGs.zip
10. truvari.zip
11. hg19_ref.zip
12. hg19_repeats.zip

Unzip hg19_ref.zip to get Hg19 reference genome hs37d5.fa.

Unzip hg19_repeats.zip to get files listed below that are used in the analysis. Please refer CAMPHOR to get more details for processing the repeat files.

1. Simple repeat file including STR and VNTR (simplerepeat.bed.gz).
2. Segmental duplication file (seg_dup.bed.gz).
3. Repeat masker file including LINE, SINE and etc (rmsk.bed.gz).
4. Hg19 excluded regions (grch37.exclude_regions_cen.bed).

The above files from 1-10 are also available at OneDrive (reproduce_data.zip).

Unzip reproduce_data.zip and you will get all files under the working directory reproduce_data.

Analysis environment

Required tools and packages

## Tools
Jasmine=1.1.4
Samtools=1.9

## Python packages
python=3.6
pandas=1.1.5
numpy=1.19.5
seaborn=0.11.1
pysam=0.15.3
matplotlib_venn=0.11.7
intervaltree=3.1.0

Create environment for data analysis

## Create a python environment
conda create -n py36 python=3.6
conda activate py36

## Install required packages
pip install seaborn==0.11.1
pip install matplotlib-venn==0.11.9
pip install pysam==0.15.3
pip install intervaltree==3.1.0

## Install Jasmine
conda config --add channels bioconda
conda config --add channels conda-forge
conda install jasminesv

Reproducing results

Setup variables

Please assign the absolute path to the following variables in ./Helpers/Constant.py

WORKDIR = '/path/to/reproduce_data'
FIGDIR = '/path/to/reproduce_data/Figures'

HG19REF = '/path/to/hs37d5.fa'
EXREGIONS = '/path/to/hg19_repeats/grch37.exclude_regions_cen.bed'
SIMREP = '/path/to/hg19_repeats/simplerepeat.bed.gz'
RMSK = '/path/to/hg19_repeats/rmsk.bed.gz'
SD = '/path/to/hg19_repeats/seg_dup.bed.gz'

SAMTOOLS = '/path/to/samtools'
JASMINE = '/path/to/jasmine'

NOTE: Please run the scripts by the order listed below.

Figure 2

## Figure 2a
python ./Figure2/Figure2a.py
## Figure 2b and 2c
python ./Figure2/Figure2bc.py
## Figure 2d, 2e, 2f and 2g
python ./Figure2/Figure2defg.py

Figure 3

## Figure 3a, 3b and 3c
python ./Figure3/Figure3abc.py
## Figure 3d, 3e and 3f
python ./Figure3/Figure3def.py

Figure 4

## Figure 4a, 4b, 4c and 4d
python ./Figure4/Figure4.py

Figure 5

## Figure 5a, 5b, 5c, 5d, 5e and 5f
python ./Figure5/Figure5.py

Extended Data Figures

## Extended Data Fig 1
python ./SuppFig/FigS1.py

## Extended Data Fig 2
python ./SuppFig/FigS2.py

## Extended Data Fig 3
python ./SuppFig/FigS3.py

## Extended Data Fig 4
python ./SuppFig/FigS4.py

## Extended Data Fig 5
python ./SuppFig/FigS5.py

## Extended Data Fig 6
python ./SuppFig/FigS6.py

## Extended Data Fig 7
python ./SuppFig/FigS7.py