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Requirements:
- bash
- conda 4.7.12
- python 3.6.9
- tensorflow (>=2.1.0)
- django 2.2.5
- R 3.6.2
- dada2 1.14.1
https://www.anaconda.com/distribution/
wget https://github.com/USFOneHealthCodeathon2020/projectZer0/raw/master/projectZer0.tar.gz
tar -vxzf projectZer0.tar.gz
cd projectZer0 && conda create -n Zer0 python=3.6.9 --file requirements.txt
conda activate Zer0
python manage.py runserver
Use a web browser* to access Zer0 locally at http://localhost:8000 or http://127.0.0.1:8000
*google chrome preferred
The purpose of this part of the task is to analyze the raw microbiome sequencing data and acquire the bacterial counts in each sample.
The requirements:
- Paired-end fastq files for each sample
Data format for each sample:
- Forward fastq: xxxx_R1_001.fastq
- Reverse fastq: xxxx_R2_001.fastq
The way the files should be kept:
- All the fastq files should be demultiplexed and stored in one folder
- The folder may be uploaded to the server in zipped format
Users can subsample a certain number of reads from each sample to reduce the data volume They can do it using seqtk tool which is available here: https://github.com/lh3/seqtk
Does it need anything else? No, everything else is already in the server
Generate bacterial counts table: The pre-loaded scripts can give the following results:
- How many sequences in each sample had at the beginning and how many remained at each of the processing step
- The sequences of each Amplicon sequence variants (ASVs) in fasta format
- The bacterial count table for all the samples (ASV counts in rows and samples in the columns)
- The taxonomies (upto genus or species level) of each ASV based on Silva Database v.132
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If you use projectZero in your research, please cite:
*Manuscript in Preperation