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Home.py
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Home.py
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import streamlit as st
from PIL import Image
import yaml
st.set_page_config(page_title="mBARq App", layout='wide',
page_icon=Image.open("images/image.png")
)
st.image("images/mbarq-logo.png")
def home_page():
config_file = "scripts/config.yaml"
with open(config_file, 'r') as cf:
config = yaml.load(cf, Loader=yaml.SafeLoader)
repo_url = config['links']['mbarq_repo']
paper_url = config['links']['mbarq_paper']
docs_url = config['links']['docs_url']
string_url = config['links']['string_database']
kegg_url = config['links']['kegg_database']
st.info(f"""
DNA barcoding has become a powerful tool for assessing the fitness of strains in a variety of studies, including random transposon mutagenesis screens, attenuation of site-directed mutants, and population dynamics of isogenic strain pools. For example, adding a random DNA barcode sequence into each transposon has significantly increased the experimental throughput of random transposon mutagenesis screens and allowed in-depth characterization of gene fitness across multiple bacterial species. To facilitate the data analysis of such screens, we provide mBARq (pronounced: ‘embark’), a versatile and user-friendly framework for the analysis and interpretation of RB-TnSeq and other barcoded sequencing data. [Our command line tool]({repo_url}) allows mapping, counting, and statistical analysis of RB-TnSeq data. To learn more about mBARq please consult [the publication]({paper_url}). This companion web app enables customized quality control, visualization of the results, and exploratory data analysis via integration with the [STRING]({string_url}) and [KEGG]({kegg_url}) databases. To learn more about the analysis of barcoded sequencing data using mBARq, please read the [documentation]({docs_url}).
""")
map_url = 'https://mbarq.readthedocs.io/en/latest/mapping.html'
st.markdown(f"""
## Pages:
Below is a quick summary of each of the pages and you can visit each of the pages to browse the example data set. Our example dataset was produced by re-analysing RB-TnSeq data from [this Salmonella pathogenesis study](https://doi.org/10.1016/j.chom.2020.04.013) with `mbarq`.
### ⬆️ Data Upload
- On this page, you can upload all the data tables produced by the `mbarq` command line tool.
***
### 📍 Library Map
- This page allows you to visualize the insertions found in your library and provides some basic summary statistics.
- Requires a `csv` file generated by the [`mbarq map`]({map_url}) command. This file lists the insertion site of each barcode in your mutant library.
***
### 📈 Exploratory Data Analysis
- This page generates an interactive PCA plot and barcode abundance plots for genes of interest.
- Requires a `csv` file with barcode counts for each sample. This file is generated by `mbarq count` and `mbarq merge` commands.
- Also requires a `csv` file describing the experimental design (sample data file).
***
### 📊 Differential Abundance
- This page allows you to look at the 'hits' and create heatmaps of LFCs for genes of interest.
- Requires a `csv` file produced by the `mbarq analyze` command. This file lists log fold changes (LFC) and false discover rates (FDRs) for each gene in the library.
***
### 🧶 STRING
- This page allows you to perform functional analysis via STRING-db.
- Requires a `csv` file produced by the `mbarq analyze` command. This file lists log fold changes (LFC) and false discover rates (FDRs) for each gene in the library.
***
### 🥚 KEGG
- This page allows you to visualize the results in the context of KEGG metabolic maps.
- Requires a `csv` file produced by the `mbarq analyze` command. This file lists log fold changes (LFC) and false discover rates (FDRs) for each gene in the library.
""")
home_page()