Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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')
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。