Anglerfish, a tool to demultiplex Illumina libraries from ONT data
Project description
Anglerfish
Introduction
Anglerfish is a tool designed to demultiplex Illumina libraries sequenced on Oxford Nanopore flowcells. The primary purpose for this would be to do QC, i.e. to check pool balancing, assess contamination, library insert sizes and so on.
For more information on how this can be used, please see this poster.
Installation
Requirements
- Python3 (3.7)
Python modules:
- biopython v. 1.70
- python-levenshtein v. 0.12.0
- numpy v. 1.19.2
- pyyaml v. 6.0
Software:
- minimap2 v. 2.20
From PyPi
pip install bio-anglerfish
From Bioconda
conda install -c bioconda anglerfish
Manually with Conda
First install miniconda, then:
git clone https://github.com/remiolsen/anglerfish.git
cd anglerfish
# Create a the anglerfish conda environment
conda env create -f environment.yml
# Install anglerfish
pip install -e .
Development version
pip install --upgrade --force-reinstall git+https://github.com/remiolsen/anglerfish.git
Usage
Anglerfish requires two files to run.
- A basecalled FASTQ file from for instance Guppy (
/path/to/ONTreads.fastq.gz
) - A samplesheet containing the sample names and indices expected to be found in the sequencing run. (
/path/to/samples.csv
)
Example of a samplesheet file:
P12864_201,truseq_dual,TAATGCGC-CAGGACGT,/path/to/ONTreads.fastq.gz
P12864_202,truseq_dual,TAATGCGC-GTACTGAC,/path/to/ONTreads.fastq.gz
P9712_101, truseq_dual,ATTACTCG-TATAGCCT,/path/to/ONTreads.fastq.gz
P9712_102, truseq_dual,ATTACTCG-ATAGAGGC,/path/to/ONTreads.fastq.gz
P9712_103, truseq_dual,ATTACTCG-CCTATCCT,/path/to/ONTreads.fastq.gz
P9712_104, truseq_dual,ATTACTCG-GGCTCTGA,/path/to/ONTreads.fastq.gz
P9712_105, truseq_dual,ATTACTCG-AGGCGAAG,/path/to/ONTreads.fastq.gz
P9712_106, truseq_dual,ATTACTCG-TAATCTTA,/path/to/ONTreads.fastq.gz
Or using single index (note samplesheet supports wildcard *
use):
P12345_101,truseq,CAGGACGT,/path/to/*.fastq.gz
Then run:
anglerfish -s /path/to/samples.csv
Options
Common
--out_fastq OUT_FASTQ, -o OUT_FASTQ
Analysis output folder (default: Current dir)
--samplesheet SAMPLESHEET, -s SAMPLESHEET
CSV formatted list of samples and barcodes
--threads THREADS, -t THREADS
Number of threads to use (default: 4)
--skip_demux, -c Only do BC counting and not demuxing
--max-distance MAX_DISTANCE, -m MAX_DISTANCE
Manually set maximum edit distance for BC matching, automatically set this is set to either 1 or 2
--run_name RUN_NAME, -r RUN_NAME
Name of the run (default: anglerfish)
--debug, -d Extra commandline output
--version, -v Print version and quit
--max-unknowns / -u
Anglerfish will try to recover indices which are not specified in the samplesheet but follow the specified adaptor setup(s). This is analogous to undetermined indices
as reported by Illumina demultiplexing. --max-unknowns
will set the number of such indices reported.
--lenient / -l
This will consider both orientations of the I5 barcode and will use the reverse complement (of what was inputted in the samplesheet) only if significantly more reads were matched. This should be used with with extreme care, but the reason for this is that Anglerfish will try to guess which version of the Illumina samplesheet these indices were derived from. See this guide for when i5 should be reverse complemented and not.
--ont_barcodes / -n
This is an ONT barcode aware mode. Which means each ONT barcode will be mapped and treated separately. A use case for this might be to put one Illumina pool per ONT barcode to spot potential index collisions you don't know of if you want to later make a pool of pools for sequencing in the same lane. This mode requires the fastq files to be placed in folders named barcode01
, barcode02
, etc. as is the default for MinKNOW (23.04). Example of such an anglerfish samplesheet:
P12345_101,truseq,CAGGACGT,/path/to/barcode01/*.fastq.gz
P54321_101,truseq,ATTACTCG,/path/to/barcode02/*.fastq.gz
Output files
In folder anglerfish_????_??_??_?????/
*.fastq.gz
Demultiplexed reads (if any)anglerfish_stats.txt
Barcode statistics from anglerfish runanglerfish_stats.json
Machine readable anglerfish statistics
Credits
The Anglerfish code was written by @remiolsen but it would not exist without the contributions of @FranBonath, @taborsak, @ssjunnebo and Carl Rubin. Also, the Anglerfish logo was designed by @FranBonath.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file bio-anglerfish-0.6.0.tar.gz
.
File metadata
- Download URL: bio-anglerfish-0.6.0.tar.gz
- Upload date:
- Size: 16.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b67a0c77680f85ebdf88622382bb271655e807c60dc3bdcd01997d336e6574fa |
|
MD5 | a3b8efe54a50caea6bce57d1c9602f3a |
|
BLAKE2b-256 | 324f23f6b895bf4e0a99184c4e3ef5df78f72cced4a1d16a1a51b8d8e8a48979 |
File details
Details for the file bio_anglerfish-0.6.0-py3-none-any.whl
.
File metadata
- Download URL: bio_anglerfish-0.6.0-py3-none-any.whl
- Upload date:
- Size: 16.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 298ecf9d9c3beb8ece003158c344b1be5e31cf7bbb4a7f8ff189c2860af3d675 |
|
MD5 | 54448225101c8140ba2168e01ba5be07 |
|
BLAKE2b-256 | 62860e2d137bd1f3f582b364368207c017012c0764bd4c34422056f2980b9045 |