Skip to content

Latest commit

 

History

History
58 lines (33 loc) · 3.18 KB

README.md

File metadata and controls

58 lines (33 loc) · 3.18 KB
Raw

There are three scripts provided here.

1. smkalign.py

This is a Snakemake file that can be used to process the .fastq.gz files and align against a reference sequence. For this to work, concat R1 and R2 reads for each sample and put them in a folder. The method uses bwa-mem2 based alignment. Hence, the reference sequence needs to be indexed. Also requires a config.yaml file (Template is provided).

Command: It is a good idea to run DAG (Directed Acyclic Graph) before executing the snakemake program to ensure everything is in place. (This step is optional). Also gives a visualisation of the expected process.

snakemake --snakefile smkalign.py --configfile config.yaml --dag | dot -Tpng > dag.png

For executing snakemake

snakemake --snakefile smkalign.py --configfile config.yaml --cores n

Depending on the system configuration you are using, filesystems might cache the status of files. Further, there can be a delay between the completion of a job and the visibility of its output files. This can cause Snakemake to prematurely fail, thereby wrongly assuming the files were not generated. Adding a latency wait gives the filesystem time to update and make the new files visible. In such cases Use --latency-wait sec

$PATH = "/path/to/working_dir"

$PATH/results containing all the outputs will be created in the working directory for verification and further processing if needed.

Unfortunately, samtools consensus calling creates a header as >reference in each fasta file. This leads to issues in downstream processing if you run cat *.fasta > Samples.fasta

Hence it is recommended to change headers in all .fasta files using the following command structure before further processing.

sed -i 's/^>.*/>file/' file.fasta

Dependencies:

  1. Snakemake (https://snakemake.readthedocs.io/en/stable/)
  2. Cutadapt ( https://github.com/marcelm/cutadapt )
  3. samtools (https://github.com/samtools/samtools/releases/)
  4. bwa-mem2 (https://github.com/bwa-mem2/bwa-mem2)
  5. If running DAG, requires Graphviz (https://github.com/graphp/graphviz)

.bai file can be viewed using Tablet Alignment viewer (https://ics.hutton.ac.uk/tablet/)

2. alignstats.sh

Sometimes it is useful to have a deeper look into the Alignment statistics. The bash script looks into .bam and file_depth.txt files and generates alignment_info.csv file containing data on Number of Reads, Aligned Coverage (%), Minimum Depth, Maximum Depth and Average Depth

Command: ./alignstats.sh $PATH/results

3. smkscrub.py

Sometimes it is useful to scrub the host reads before aligning the reads to the target reference sequence. This is especially useful if dealing with a complex mixture of microbes in samples. This is a Snakemake file that can be used to process the .fastq.gz files against a host reference sequence. Once the scrubbed reads are obtained, it can be further processed using smkalign.py

scrubbing uses minimap2 and needs to be installed in system, if not available (https://github.com/lh3/minimap2)

Rules of usage are similar to smkalign.py

Command: snakemake --snakefile smkscrub.py --configfile config.yaml --cores

Note: The pipelines are for working with Illumina Short-reads only (Uses bwa-mem2 and cutadapt with Illumina adaptors).