forked from MarineOmics/marineomics.github.io
-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathPOP-04-WGS_intro.html
687 lines (594 loc) · 21.1 KB
/
POP-04-WGS_intro.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<title>Whole Genome Resequencing for Population Genomics</title>
<script src="site_libs/header-attrs-2.20/header-attrs.js"></script>
<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/cosmo.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script src="site_libs/jqueryui-1.11.4/jquery-ui.min.js"></script>
<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />
<script src="site_libs/tocify-1.9.1/jquery.tocify.js"></script>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/default.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type="text/css">
/* for pandoc --citeproc since 2.11 */
div.csl-bib-body { }
div.csl-entry {
clear: both;
}
.hanging div.csl-entry {
margin-left:2em;
text-indent:-2em;
}
div.csl-left-margin {
min-width:2em;
float:left;
}
div.csl-right-inline {
margin-left:2em;
padding-left:1em;
}
div.csl-indent {
margin-left: 2em;
}
</style>
<link rel="stylesheet" href="tutorial.css" type="text/css" />
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
details > summary > p:only-child {
display: inline;
}
pre code {
padding: 0;
}
</style>
<style type="text/css">
.dropdown-submenu {
position: relative;
}
.dropdown-submenu>.dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
border-radius: 0 6px 6px 6px;
}
.dropdown-submenu:hover>.dropdown-menu {
display: block;
}
.dropdown-submenu>a:after {
display: block;
content: " ";
float: right;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
border-width: 5px 0 5px 5px;
border-left-color: #cccccc;
margin-top: 5px;
margin-right: -10px;
}
.dropdown-submenu:hover>a:after {
border-left-color: #adb5bd;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left>.dropdown-menu {
left: -100%;
margin-left: 10px;
border-radius: 6px 0 6px 6px;
}
</style>
<script type="text/javascript">
// manage active state of menu based on current page
$(document).ready(function () {
// active menu anchor
href = window.location.pathname
href = href.substr(href.lastIndexOf('/') + 1)
if (href === "")
href = "index.html";
var menuAnchor = $('a[href="' + href + '"]');
// mark the anchor link active (and if it's in a dropdown, also mark that active)
var dropdown = menuAnchor.closest('li.dropdown');
if (window.bootstrap) { // Bootstrap 4+
menuAnchor.addClass('active');
dropdown.find('> .dropdown-toggle').addClass('active');
} else { // Bootstrap 3
menuAnchor.parent().addClass('active');
dropdown.addClass('active');
}
// Navbar adjustments
var navHeight = $(".navbar").first().height() + 15;
var style = document.createElement('style');
var pt = "padding-top: " + navHeight + "px; ";
var mt = "margin-top: -" + navHeight + "px; ";
var css = "";
// offset scroll position for anchor links (for fixed navbar)
for (var i = 1; i <= 6; i++) {
css += ".section h" + i + "{ " + pt + mt + "}\n";
}
style.innerHTML = "body {" + pt + "padding-bottom: 40px; }\n" + css;
document.head.appendChild(style);
});
</script>
<!-- tabsets -->
<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "\e259";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "\e258";
font-family: 'Glyphicons Halflings';
border: none;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>
<!-- code folding -->
<style type="text/css">
#TOC {
margin: 25px 0px 20px 0px;
}
@media (max-width: 768px) {
#TOC {
position: relative;
width: 100%;
}
}
@media print {
.toc-content {
/* see https://github.com/w3c/csswg-drafts/issues/4434 */
float: right;
}
}
.toc-content {
padding-left: 30px;
padding-right: 40px;
}
div.main-container {
max-width: 1200px;
}
div.tocify {
width: 20%;
max-width: 260px;
max-height: 85%;
}
@media (min-width: 768px) and (max-width: 991px) {
div.tocify {
width: 25%;
}
}
@media (max-width: 767px) {
div.tocify {
width: 100%;
max-width: none;
}
}
.tocify ul, .tocify li {
line-height: 20px;
}
.tocify-subheader .tocify-item {
font-size: 0.90em;
}
.tocify .list-group-item {
border-radius: 0px;
}
.tocify-subheader {
display: inline;
}
.tocify-subheader .tocify-item {
font-size: 0.95em;
}
</style>
</head>
<body>
<div class="container-fluid main-container">
<!-- setup 3col/9col grid for toc_float and main content -->
<div class="row">
<div class="col-xs-12 col-sm-4 col-md-3">
<div id="TOC" class="tocify">
</div>
</div>
<div class="toc-content col-xs-12 col-sm-8 col-md-9">
<div class="navbar navbar-inverse navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-bs-toggle="collapse" data-target="#navbar" data-bs-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">MarineOmics</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="contributions.html">Contributions</a>
</li>
<li>
<a href="panels.html">Panel Seminars</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
Population Genomics
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu">
<li>
<a href="choosing-population-genetics.html">Choosing a Population Genomics Approach</a>
</li>
<li>
<a href="WGS_intro.html">Whole Genome Resequencing</a>
</li>
<li>
<a href="RADseq.html">Reduced Representation Sequencing</a>
</li>
<li>
<a href="poolseq.html">Poolseq</a>
</li>
<li>
<a href="RDAtraitPredictionTutorial.html">Redundancy Analysis (RDA) Trait Prediction</a>
</li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
Functional Genomics
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu">
<li>
<a href="DGE_comparison_v2.html">Mutifactorial RNAseq</a>
</li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
Genome-Phenome
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu">
<li class="dropdown-header">coming soon!</li>
</ul>
</li>
<li>
<a href="https://github.com/MarineOmics/marineomics.github.io/discussions">Discussion Forum</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
ga('create', 'G-53GH9PV49T', 'auto');
ga('send', 'pageview');
</script>
<div id="header">
<h1 class="title toc-ignore">Whole Genome Resequencing for Population
Genomics</h1>
<h3 class="subtitle"><em>Katherine Silliman, Nicolas Lou</em></h3>
</div>
<p><br></p>
<div id="introduction" class="section level1">
<h1>Introduction</h1>
<p>For the purpose of this guide, we use “whole genome (re)sequencing
(WGS)) to refer to methods where a reference genome already exists
(whether for the focal species or a related species), and uniquely
barcoded samples are sequenced and then mapped to most or all of the
reference genome. This method can provide high density genetic variants
(e.g., SNPs) and structural information for population genomic analyses,
while also facilitating functional insights if the reference genome is
functionally annotated. This is separate from whole-genome <em>de
novo</em> sequencing, which aims to produce a reference genome by
sequencing and assembling a complete genome for a species for the first
time.</p>
<p>An excellent and thorough review of the methodology and consideration
for WGS approaches, especially as they apply to non-model organisms, is
<a href="https://doi.org/10.1111/mec.14264"><span
class="citation">(Fuentes-Pardo and Ruzzante 2017)</span></a>. But
briefly, current WGS approaches can be broadly categorized into two
types: high-to-moderate-coverage WGS, and low-coverage WGS. Their major
distinction is that with high-to-moderate-coverage WGS, each individual
is sequenced at a depth with which genotype can be confidently called at
most sites, whereas with low-coverage WGS, each individual is sequenced
at a depth too low to call genotype with, and downstream analyses should
take such genotype uncertainties into acount.</p>
<p>However, the line between high-to-moderate-coverage WGS and
low-coverage WGS is not always as clear-cut as presented above. For
example, with moderate-coverage WGS (e.g. 5-20x), many sites within an
individual can still have low coverage due to random sampling, resulting
in unreliable genotype calls and/or missing data that can become
problematic in downstream analysis. Therefore, it could be preferrable
to avoid hard-calling genotypes with moderate-coverage WGS in certain
applications. On the other hand, in populations with high levels of
linkage disequilibrium (LD), it could be possible to leverage LD to
carry out genotype imputation, making genotype calling a lot more
accurate with low-coverage WGS data. Imputation is more likely to be
successful when a high-quality reference panel exists in the system (<a
href="https://www.science.org/doi/10.1126/science.aba4674"><span
class="citation">Fuller et al. (2020)</span></a>, <a
href="https://www.nature.com/articles/s41588-020-00756-0"><span
class="citation">Rubinacci et al. (2021)</span></a>), but methods for
imputation without such reference panels have also been developed (in
which case a very large sample size would be required, <a
href="https://www.nature.com/articles/ng.3594"><span
class="citation">Davies et al. (2016)</span></a>).</p>
<p>This said, here are the main differences between these two approaches
in practice: with same sample size, high-to-moderate-coverage WGS tends
to provide higher resolution data that are more versatile and less
susceptible to technical artefacts (especially those caused by
sequencing errors) when compared with low-coverage WGS, but it could be
a lot more costly. Low-coverage WGS, in contrast, can be used to achieve
higher sample size with a fixed budget, which can then contribute to
higher-resolution population-level inferences, but it does require a
different computational toolbox that takes genotype uncertainties into
account, and is thus constrained by limitations of the current toolbox
(see Section 6 in <a
href="https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.16077"><span
class="citation">(Lou et al. 2021)</span></a> for a more detailed
discussion on this).</p>
<p><br></p>
</div>
<div id="tutorials" class="section level1">
<h1>Tutorials</h1>
<p>Here we provide a couple different tutorials by our working group
members, as well as links to other detailed tutorials on the web. The
goal of these MarineOmics tutorials are to provide extensive details on
“why” certain parameters are chosen, and some guidance on how to
evaluate different parameter options to fit your data.</p>
<p><br></p>
<div id="high-to-moderate-coverage-wgs" class="section level2">
<h2>High to moderate coverage WGS</h2>
<ul>
<li><p><a href="fastq2vcf_june2022_update.html">Fastq-to-VCF SnakeMake
pipeline</a>: in-depth explanation of an automated short-read mapping
and variant calling pipeline maintained by <a
href="https://github.com/harvardinformatics/shortRead_mapping_variantCalling">Harvard
Informatics</a>. Useful if you plan to adopt this pre-packaged automated
and parallelizable pipeline, and would like to understand its different
components, but not necessarily change it substantially.</p></li>
<li><p><a href="WGS-Full-Walkthrough.html">Fastq-to-VCF workflow</a>:
detailed walkthrough of processing 15x sequencing depth WGS data for
cod, from raw reads to a VCF. Useful if you would like to run each
component of the pipeline yourself and potentially tweak some of them
for your own purpose. In other words, you can more easily add, skip, or
change parts of this pipeline, but will lose the convenience offered by
an automated pipeline.</p></li>
</ul>
<p><br></p>
</div>
<div id="low-coverage-wgs" class="section level2">
<h2>Low coverage WGS</h2>
<p>The quality control and read alignment part of the pipeline for
high-to-moderate-coverage WGS also applies for low-coverage WGS.
Therefore, the two tutorials for high-to-moderate-coverage WGS are also
useful for low-coverage WGS until the point where variants and genotypes
are called. In addition to these, here are some resources specifically
designed for low-coverage WGS.</p>
<ul>
<li><p><a
href="https://github.com/nt246/lcwgs-guide-tutorial">Low-coverage WGS
tutorial</a>: a tutorial for the processing and analysis of low-coverage
WGS data (i.e. from raw fastq files to population genomic inference),
with example datasets and hands-on exercises. It is associated with the
paper <a
href="https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.16077"><span
class="citation">(Lou et al. 2021)</span></a>.</p></li>
<li><p><a
href="https://github.com/therkildsen-lab/batch-effect/blob/main/tutorial/tutorial.md">Detection
and mitigation of batch effects</a>: a tutorial for the detection and
mitigation of batch effects with low-coverage WGS data, with example
datasets and hands-on exercises. It is associated with the paper <a
href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13559"><span
class="citation">Lou and Therkildsen (2021)</span></a>.</p></li>
<li><p><a
href="https://github.com/therkildsen-lab/genomic-data-analysis/blob/master/lcwgs_data_analysis.md">Low-coverage
WGS data analysis pipeline</a>: a collection of scripts for the
efficient and reproducible analysis of low-coverage WGS data (i.e. from
bam to population genomic inference).</p></li>
<li><p><a
href="https://github.com/therkildsen-lab/data-processing/blob/master/lcwgs_data_processing.md">Low-coverage
WGS data processing pipeline</a>: a collection of script for the
efficient and reproducible processing of low-coverage WGS data
(i.e. from raw fastq to bam). This pipeline should also be compatible
with high-to-moderate-coverage WGS data.</p></li>
</ul>
<p><br></p>
</div>
</div>
<div id="references" class="section level1 unnumbered">
<h1 class="unnumbered">References</h1>
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-Davies2016-rq" class="csl-entry">
Davies, Robert W, Jonathan Flint, Simon Myers, and Richard Mott. 2016.
<span>“Rapid Genotype Imputation from Sequence Without Reference
Panels.”</span> <em>Nat. Genet.</em> 48 (8): 965–69. <a
href="https://doi.org/10.1038/ng.3594">https://doi.org/10.1038/ng.3594</a>.
</div>
<div id="ref-Fuentes-Pardo2017-nu" class="csl-entry">
Fuentes-Pardo, Angela P, and Daniel E Ruzzante. 2017.
<span>“Whole-Genome Sequencing Approaches for Conservation Biology:
Advantages, Limitations and Practical Recommendations.”</span> <em>Mol.
Ecol.</em> 26 (20): 5369–5406. <a
href="https://doi.org/10.1111/mec.14264">https://doi.org/10.1111/mec.14264</a>.
</div>
<div id="ref-Fuller2020" class="csl-entry">
Fuller, Zachary L., Veronique J. L. Mocellin, Luke A. Morris, Neal
Cantin, Jihanne Shepherd, Luke Sarre, Julie Peng, et al. 2020.
<span>“Population Genetics of the Coral <i>acropora
Millepora</i>: Toward Genomic Prediction of Bleaching.”</span>
<em>Science</em> 369 (6501): eaba4674. <a
href="https://doi.org/10.1126/science.aba4674">https://doi.org/10.1126/science.aba4674</a>.
</div>
<div id="ref-Lou2021-me" class="csl-entry">
Lou, Runyang Nicolas, Arne Jacobs, Aryn P Wilder, and Nina Overgaard
Therkildsen. 2021. <span>“A Beginner’s Guide to Low-Coverage Whole
Genome Sequencing for Population Genomics.”</span> <em>Mol. Ecol.</em>,
July. <a
href="https://doi.org/10.1111/mec.16077">https://doi.org/10.1111/mec.16077</a>.
</div>
<div id="ref-Lou2021-ew" class="csl-entry">
Lou, Runyang Nicolas, and Nina Overgaard Therkildsen. 2021. <span>“Batch
Effects in Population Genomic Studies with Low-Coverage Whole Genome
Sequencing Data: Causes, Detection, and Mitigation.”</span> <em>Authorea
Preprints</em>, August. <a
href="https://doi.org/10.22541/au.162791857.78788821/v2">https://doi.org/10.22541/au.162791857.78788821/v2</a>.
</div>
<div id="ref-Rubinacci2021-dm" class="csl-entry">
Rubinacci, Simone, Diogo M Ribeiro, Robin J Hofmeister, and Olivier
Delaneau. 2021. <span>“Efficient Phasing and Imputation of Low-Coverage
Sequencing Data Using Large Reference Panels.”</span> <em>Nat.
Genet.</em> 53 (1): 120–26. <a
href="https://doi.org/10.1038/s41588-020-00756-0">https://doi.org/10.1038/s41588-020-00756-0</a>.
</div>
</div>
</div>
</div>
</div>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
<!-- code folding -->
<script>
$(document).ready(function () {
// temporarily add toc-ignore selector to headers for the consistency with Pandoc
$('.unlisted.unnumbered').addClass('toc-ignore')
// move toc-ignore selectors from section div to header
$('div.section.toc-ignore')
.removeClass('toc-ignore')
.children('h1,h2,h3,h4,h5').addClass('toc-ignore');
// establish options
var options = {
selectors: "h1,h2,h3",
theme: "bootstrap3",
context: '.toc-content',
hashGenerator: function (text) {
return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
},
ignoreSelector: ".toc-ignore",
scrollTo: 0
};
options.showAndHide = false;
options.smoothScroll = true;
// tocify
var toc = $("#TOC").tocify(options).data("toc-tocify");
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>