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update tools
1 parent 0322fa5 commit 3b530a2

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Lines changed: 38 additions & 167 deletions

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_includes/software-content.html

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python/software.py

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@@ -1,7 +1,7 @@
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#!/usr/bin/env python
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'''
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Generate and update the software-content.html file for birollab.ca
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3-column layout with improved styling, lighter headers, tighter spacing.
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3-column layout with aligned install blocks and improved styling.
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'''
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import glob
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import os
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P2 = ' <a href="'
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P3 = '"><img class="git" src="assets/githubicon.svg"></a></span>'
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# Smaller, compact install block
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DIV_START = '<div style="text-align:center; margin-top:4px;"><div class="downloadinfo" style="font-size:12px; font-family:monospace; white-space:nowrap;">'
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LINUXBREW_DIV = '<br>{% include linuxbrew-icon.html%}'
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CONDA_DIV = '<br>{% include bioconda-icon.html%}'
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DOCKER_DIV = '<br>{% include docker-icon.html%}'
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DIV_END = '</div></div>'
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# Install block styling (key fix here)
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DIV_START = '<div style="text-align:center; margin-top:4px;">'
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LINE_STYLE = 'style="font-size:12px; font-family:monospace; height:18px; line-height:18px; white-space:nowrap;"'
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P4 = '<p style="padding-top:4px; margin:0;">'
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END = '</p></td>'
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LINUXBREW_ICON = '{% include linuxbrew-icon.html%}'
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CONDA_ICON = '{% include bioconda-icon.html%}'
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DOCKER_ICON = '{% include docker-icon.html%}'
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def downloads(lin, bio, dock):
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"""Format string with installation options, preserving alignment"""
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return_string = DIV_START
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if lin:
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return_string += (LINUXBREW_DIV + lin)
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else:
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return_string += "<br>&nbsp;"
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DIV_END = '</div>'
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if bio:
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return_string += (CONDA_DIV + bio)
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else:
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return_string += "<br>&nbsp;"
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P4 = '<p style="padding-top:4px; margin:0;">'
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END = '</p></td>'
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if dock:
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return_string += (DOCKER_DIV + dock)
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def line(icon, text):
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"""Return a fixed-height line"""
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if text:
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return f'<div {LINE_STYLE}>{icon} {text}</div>'
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else:
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return_string += "<br>&nbsp;"
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return f'<div {LINE_STYLE}>&nbsp;</div>'
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return return_string + DIV_END
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def downloads(lin, bio, dock):
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"""Aligned 3-line install block"""
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return (
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DIV_START +
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line(LINUXBREW_ICON, lin) +
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line(CONDA_ICON, bio) +
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line(DOCKER_ICON, dock) +
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DIV_END
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)
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def write_html(softwareblurbs_path):
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"""Write the software-content.html file with improved layout"""
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"""Write the software-content.html file with aligned layout"""
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categories = defaultdict(list)
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# Read tools
@@ -66,11 +65,10 @@ def write_html(softwareblurbs_path):
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# Write HTML
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with open(os.path.join(softwareblurbs_path, "../_includes/software-content.html"), "w+") as software:
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# Tight table layout (removes spacing between cells)
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software.write('<table style="width:100%; table-layout:fixed; border-collapse:collapse;">\n')
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for category in sorted(categories.keys()):
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# Lighter header, larger font, black text
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# Header
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software.write(
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'<tr>'
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'<td colspan="3" style="background-color:#DCEEFF; '
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i += 1
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# Pad final row
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# Pad last row
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if i % 3 != 0:
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remaining = 3 - (i % 3)
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for _ in range(remaining):

python/softwareNEW.py

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This file was deleted.

softwareblurbs/amplify.txt

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@@ -4,5 +4,5 @@ https://github.com/birollab/AMPlify
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conda install -c bioconda amplify
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docker run quay.io/biocontainers/amplify
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Attentive deep learning model for antimicrobial peptide prediction
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Deep learning model for AMP prediction
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Antimicrobial Peptide (AMP) Discovery

softwareblurbs/ampseek.txt

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Anti-microbial property, protein structure, toxicity detection
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AMP prediction, 3D structure, toxicity detection
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Antimicrobial Peptide (AMP) Discovery

softwareblurbs/chopstitch.txt

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De novo exon prediction and splice graph construction
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De novo exon prediction and splice graph
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Genome Analysis

softwareblurbs/dida.txt

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brew install brewsci/bio/dida
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Sequence alignment pipeline to distribute indexing/alignments
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Distribution of sequence indexing/alignments
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Genome Analysis

softwareblurbs/ntsynt.txt

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conda install -c bioconda ntsynt
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docker run quay.io/biocontainers/ntsynt
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Multi-genome synteny detection using minimizer graph mapping
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Multi-genome synteny detection w/minimizers
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Comparative Genomics

softwareblurbs/tamper.txt

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Toxicity prediction with 3D structure-aware deep learning
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Toxicity prediction w/3D structure-aware deep learning
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Antimicrobial Peptide (AMP) Discovery

softwareblurbs/triamph.txt

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Heterogeneous graph attention networks in AMP predictions
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Heterogeneous graph networks in AMP predictions
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Antimicrobial Peptide (AMP) Discovery

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