pages/User Guide.py

import streamlit as st

def indented_markdown(text, indentation=40, font_size=25):
    indented_text = f'<p style="font-family:Trebuchet MS; margin-left: {indentation}px; font-size: {font_size}px;">{text}</p>'
    st.markdown(indented_text, unsafe_allow_html=True)


original_title = '<p style="font-family:Trebuchet MS; color:#FD7456; font-size: 35px; font-weight:bold">ASCARIS:  Positional Feature Annotation and Protein Structure-Based Representation of Single Amino Acid Variations</p>'
st.markdown(original_title, unsafe_allow_html=True)


st.text('')
st.text('')

body = '<p style="font-family:Trebuchet MS;  font-size: 25px; font-weight:bold">Input Format</p>'
st.markdown(body, unsafe_allow_html=True)


body = '<p style="font-family:Trebuchet MS;  font-size: 20x; font-weight:bold">ASCARIS can be run on single or multiple variation data points.</p>'
st.markdown(body, unsafe_allow_html=True)

text = '<p style="font-family:Trebuchet MS;  font-size: 20x">Run ASCARIS for only one SAV datapoint:</p>'
st.markdown(text, unsafe_allow_html=True)


text = 'Type your variation of interest in the form of the example:'
indented_markdown(text, indentation=40, font_size=16)

text = 'P13637-T-613-M'
indented_markdown(text, indentation=40, font_size=16)

st.text('')
st.text('')


text = '<p style="font-family:Trebuchet MS;  font-size: 25">Run ASCARIS for more than one datapoints:</p>'
st.markdown(text, unsafe_allow_html=True)


text = 'Type your variation of interest in the form of the example:'
indented_markdown(text, indentation=40, font_size=16)
text = 'P13637-T-613-M, Q9Y4W6-N-432-T, Q9Y4W6-N-432-T'
indented_markdown(text, indentation=40, font_size=16)

st.text('')
st.text('')

body = '<p style="font-family:Trebuchet MS;  font-size: 25px; font-weight:bold">Structure Selection</p>'
st.markdown(body, unsafe_allow_html=True)

body = '<p style="font-family:Trebuchet MS;  font-size: 20x">Users may select where to retrieve their structures to map annotations. If the selection is PDB-ModBase-SwissModel, please select option 1; if the selection is AlphaFold, please select 2 from the dropdown menu.</p>'
st.markdown(body, unsafe_allow_html=True)

st.text('')
st.text('')

body = '<p style="font-family:Trebuchet MS;  font-size: 25px; font-weight:bold">Imputation</p>'
st.markdown(body, unsafe_allow_html=True)

body = '<p style="font-family:Trebuchet MS;  font-size: 20x">ASCARIS calculates distance information for annotations and domains, as well as SASA values whenever a structure is available, or variation is found on the selected structure. Otherwise, such values are reported as NaN. ASCARIS feature vector can be created either by imputing NaN values in the dataset, or by viewing them as NaNs. Imputation is done by taking the median value of corresponding column feature. Users can select where they want their output to be imputed or not imputed from the dropdown menu.</p>'
st.markdown(body, unsafe_allow_html=True)