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Merge pull request #67 from EPPIcenter/develop
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Mad4Hatter.wdl

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@@ -46,8 +46,7 @@ workflow MAD4HatTeR {
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String output_directory
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Int dada2_additional_memory = 0
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String? dada2_runtime_size
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# TODO: Pin the specific docker image version here when first release is ready
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String docker_image = "eppicenter/mad4hatter:develop"
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String docker_image = "eppicenter/mad4hatter:v1.0.0"
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}
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# Use sub() with a regular expression to check for valid characters in output_directory

Mad4HatterPostProcessing.wdl

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@@ -18,8 +18,7 @@ workflow Mad4HatterPostProcessing {
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Boolean mask_homopolymers = true
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File? masked_fasta
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File pool_options_json = "/opt/mad4hatter/conf/terra_panel.json" # Optional custom pool options JSON file. Needs to be on docker image.
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# TODO: Pin the specific docker image version here when first release is ready
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String docker_image = "eppicenter/mad4hatter:develop"
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String docker_image = "eppicenter/mad4hatter:v1.0.0"
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}
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# Check that either one of genome or refseq_fasta is provided or nothing is provided (then refseq_fasta is auto-generated)

Mad4HatterQcOnly.wdl

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@@ -14,8 +14,7 @@ workflow Mad4HatterQcOnly {
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Boolean gtrim = false
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Int quality_score = 20
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Int allowed_errors = 0
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# TODO: Pin the specific docker image version here when first release is ready
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String docker_image = "eppicenter/mad4hatter:develop"
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String docker_image = "eppicenter/mad4hatter:v1.0.0"
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}
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File pool_options_json = "/opt/mad4hatter/conf/terra_panel.json" # Optional custom pool options JSON file. Needs to be on docker image.

README.md

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# MAD4HATTER <img src="https://github.com/EPPIcenter/mad4hatter/blob/gh-pages/logo.svg" alt="Mad4hatter logo" height="80">
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# MAD4HATTER <img src="https://github.com/EPPIcenter/wonderland-docs/blob/main/docs/assets/logo.svg" alt="Mad4hatter logo" height="80">
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**Mad4hatter** is a bioinformatics pipeline designed to analyse *Plasmodium* Illumina amplicon sequencing data. It processes raw FASTQ files and produces an allele table, core QC metrics, and drug-resistance information. It was originally developed for the [MAD4HatTeR panel](https://doi.org/10.1038/s41598-025-94716-5) but has since been adapted to support additional panels. While the pipeline can be run on any panel, it was optimised using MAD4HatTeR data; panels with substantially different properties, such as very short amplicon targets, may require additional tuning to achieve optimal performance. Several commonly used panels are preconfigured for convenience, and new panels can be easily added through simple configuration (see below). If you are using a pre-configured panel, simply provide your FASTQ files and the panel name, and you’re ready to run!
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