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https://github.com/dieterich-lab/mAFiA


https://github.com/dieterich-lab/mAFiA

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README 

        
![Logo](logo.png "mAFiA")



# mAFiA - (Another) m6A Finding Algorithm



Here we provide a brief walkthrough to run mAFiA, using the example of chromosome X.



## 0. Preliminary

- The following steps are tested with python verion 3.9. Get code and activate virtual environment, e.g.:

```

git clone https://github.com/dieterich-lab/mAFiA.git

cd mAFiA

python3 -m venv mafia-venv

source mafia-venv/bin/activate

```

If you pip version is <21.3, then upgrade it to a newer version:

```

python3 -m pip install --upgrade pip

```

Install package

```

pip install -e .

```

- Download models and data from [here](https://zenodo.org/record/8321727)

    - The folder "models" contains:

        - backbone.torch: [RODAN](https://github.com/biodlab/RODAN)-based neural network for basecalling and feature extraction

        - backbone.config: training configuration for backbone

        - classifiers: pickled logistic regression models

    - The folder "data" contains a subset of input data on chr X:

        - fast5_chrX: dRNA-Seq raw data from HEK293 WT mRNA

        - GRCh38_96.X: genome reference

        - GLORI_chrX.bed: query modification sites in bed format. This file specifically corresponds to those listed in [GLORI](https://www.nature.com/articles/s41587-022-01487-9).

- Assume that data and model are unzipped to ${data} and ${model} respectively. Your output directory is ${output}

```

backbone="${models}/backbone.torch"

classifiers="${models}/classifiers"

fast5dir="${data}/fast5_chrX"

ref="${data}/GRCh38_96.X.fa"

mod="${data}/GLORI_chrX.bed"



mkdir -p "${output}"

basecall="${output}/rodan.fasta"

bam="${output}/minimap.q50.bam"

```



## 1. Basecalling

The basecalling script is adapted from the [RODAN](https://github.com/biodlab/RODAN) repository. Assume that ${mafia} is your code directory.

```

python3 ${mafia}/RODAN/basecall.py \

--fast5dir ${fast5dir} \

--model ${backbone} \

--batchsize 4096 \

--outdir ${output}

```

On a reasonably modern GPU machine, this step should take less than 30 mins.



## 2. Alignment

Align basecalling results to reference genome. Filter, sort, and index BAM file.

```

minimap2 --secondary=no -ax splice -uf -k14 -t 36 --cs ${ref} ${basecall} \

| samtools view -bST ${ref} -q50 - \

| samtools sort - > ${bam}



samtools index ${bam}

```



## 3. mAFiA

After the standard procedures, we can now measure m6A stoichiometry of the sites specified in ${mod}.

```

test_mAFiA \

--bam_file ${bam} \

--fast5_dir ${fast5dir} \

--ref_file ${ref} \

--mod_file ${mod} \

--min_coverage 50 \

--max_num_reads 1000 \

--backbone_model_path ${backbone} \

--classifier_model_dir ${classifiers} \

--mod_prob_thresh 0.5 \

--out_dir ${output}

```

The last step should take less than 1 hour. We are currently working on integrating the feature extraction step directly into basecalling. The run-time should then be significantly reduced.



In your ${output} directory, you should now see two files:

- "mAFiA.sites.bed": List of sites with coverage above minimum threshold (default 50). The column "modRatio" lists the site's stoichiometry.

- "mAFiA.reads.bam": Aligned reads identical to those in the input BAM file ${bam}, but with additional MM and ML tags that mark the location and modification probability in each individual read. The results can be visualized with, eg, [IGV](https://software.broadinstitute.org/software/igv/).



The complete HEK293 WT dataset for all chromosomes can be downloaded from [zenodo](https://zenodo.org/record/8319583).