Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses.
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See how to installReview the instructions the agent will receive before you install.
Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Export figures for publication" → Save plots as high-resolution PDF/SVG/TIFF with journal-required DPI and dimensions.
fig.savefig('fig.pdf', dpi=300, bbox_inches='tight')ggsave('fig.pdf', width=7, height=5, units='in')import matplotlib.pyplot as plt
# Set publication defaults
plt.rcParams.update({
'font.size': 8,
'font.family': 'Arial',
'axes.linewidth': 0.5,
'lines.linewidth': 1,
'figure.dpi': 300
})
fig, ax = plt.subplots(figsize=(3.5, 3)) # Single column width
# ... create plot ...
# Save in multiple formats
fig.savefig('figure1.pdf', bbox_inches='tight', dpi=300)
fig.savefig('figure1.png', bbox_inches='tight', dpi=300)
fig.savefig('figure1.svg', bbox_inches='tight')
library(ggplot2)
p <- ggplot(data, aes(x, y)) + geom_point() +
theme_classic(base_size = 8) +
theme(text = element_text(family = 'Arial'))
# PDF for vector graphics
ggsave('figure1.pdf', p, width = 3.5, height = 3, units = 'in')
# High-res PNG
ggsave('figure1.png', p, width = 3.5, height = 3, units = 'in', dpi = 300)
# TIFF (some journals require)
ggsave('figure1.tiff', p, width = 3.5, height = 3, units = 'in',
dpi = 300, compression = 'lzw')
| Journal Type | Format | Resolution | Width |
|---|---|---|---|
| Most journals | PDF/EPS | Vector | 3.5" (1-col), 7" (2-col) |
| Online-only | PNG | 300 DPI | Variable |
| TIFF | 300-600 DPI | Column width |
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(7, 5)) # Two-column width
gs = GridSpec(2, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1:])
ax3 = fig.add_subplot(gs[1, :])
# Add panel labels
for ax, label in zip([ax1, ax2, ax3], ['A', 'B', 'C']):
ax.text(-0.1, 1.1, label, transform=ax.transAxes,
fontsize=10, fontweight='bold')
fig.savefig('figure_multipanel.pdf', bbox_inches='tight')
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
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AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
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