-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy path01_annotate_lib.html
380 lines (286 loc) · 38 KB
/
01_annotate_lib.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
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>Step 01 - Annotating Library Member Data Frame</title>
<base target="_blank"/>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
}
pre code {
display: block; padding: 0.5em;
}
code.r {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- Styles for R syntax highlighter -->
<style type="text/css">
pre .operator,
pre .paren {
color: rgb(104, 118, 135)
}
pre .literal {
color: rgb(88, 72, 246)
}
pre .number {
color: rgb(0, 0, 205);
}
pre .comment {
color: rgb(76, 136, 107);
}
pre .keyword {
color: rgb(0, 0, 255);
}
pre .identifier {
color: rgb(0, 0, 0);
}
pre .string {
color: rgb(3, 106, 7);
}
</style>
<!-- R syntax highlighter -->
<script type="text/javascript">
var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/</gm,"<")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};
hljs.initHighlightingOnLoad();
</script>
</head>
<body>
<p><link href="http://kevinburke.bitbucket.org/markdowncss/markdown.css" rel="stylesheet"></link></p>
<h1>Step 01 - Annotating Library Member Data Frame</h1>
<h2>Motivation</h2>
<p>Here we are loading all the library member sequences into a DataFrame called <code>lib_seqs</code> and annotating them based on their promoter, RBS, CDS, gene, etc. At the end we check to make sure that the library has every item, the sequences all check out, and that the sequences are all the same length.</p>
<h2>Splitting</h2>
<p>We want to take <code>203.norestrict.fa</code> and split it on the upper/lowercase boundaries. First, lets look at the UC/LC situation for each of the <br/>
sequence types in 203 and also the 202 library.</p>
<h3>Comparison of Library Sequences between 203 and 202</h3>
<p>The 203 library consists of a promoter, a RBS, an ATG start codon, and then exactly 10 more codons (30 bp) of the sequence in question. So I want to cut at the lc/uc boundary, then cut again at the ATG.</p>
<p>Things are different for the natural UTR, like this:</p>
<pre><code>>BBaJ23100-ribF-1
ttgacggctagctcagtcctaggtacagtgctagcTTAATTTCACTGTTTTGAGCCAGACATGAAGCTGATACG...
</code></pre>
<p>Compared to the designed UTR, like this:</p>
<pre><code>>BBaJ23100-ribF-2
ttgacggctagctcagtcctaggtacagtgctagcTTAATATTAAAGAGGAGAAAtactagATGAAGCTGATAC...
</code></pre>
<p>Here is an example of that same promo/RBS pair in 202:</p>
<pre><code class="console">$ grep -B1 -i TTAATATTAAAGAGGAGAAA 202.norestrict.fa
>BBa_J23100--B0030_RBS
TTGACGGCTAGCTCAGTCCTAGGTACAGTGCTAGCTTAATattaaagaggagaaatta
</code></pre>
<h3>Aligning them by hand:</h3>
<pre><code>wt ttgacggctagctcagtcctaggtacagtgctagcTTAAT TTCACTGTTTTGAGCCAGACATGAAGCTGA...
bbbbb.rrrrrrrrrrrrrrrrrrrr MET
rbs ttgacggctagctcagtcctaggtacagtgctagcTTAATATTAAAGAGGAGAAAtactagATGAAGCTGA...
bbbbbrrrrrrrrrrrrrrrrrrrXXXXXXMET
202 TTGACGGCTAGCTCAGTCCTAGGTACAGTGCTAGCTTAATattaaagaggagaaattaCATATG <GFP>
bbbbbrrrrrrrrrrrrrrrrrrrXXX MET
</code></pre>
<p><code>b</code> for barcode, <code>r</code> for RBS, and <code>X</code> for variable rbs/cds spacer, and <code>MET</code>for start codon.</p>
<p>So, take home messages:</p>
<ul>
<li>WT promo/rbs combos in 203 have no spacer between the WT RBS and the MET codon. </li>
<li><code>BBaJ23100/BBaJ23108</code> RBSes have <code>TACATG</code> separating the RBS sequence and the MET. This is different than the same RBSs for 202, which necessarily end in <code>CATATG</code>. So they are not equivalent, strictly speaking. Which might complicate comparison of 202 and 203 libraries. </li>
<li>The equivalent 202 RBSes (<code>BBa_J23100/BBa_J23108</code>) have the barcode on the promoter, not the RBS (which is actually more correct.)</li>
</ul>
<p>So first we need to annotate the library sequences and split them up using this information. </p>
<p>For the 203 sequences, we split into </p>
<ul>
<li><em>Promoter</em> - all LC seq + 5 bases (for barcode)</li>
<li><em>RBS</em> - middle of this sequence</li>
<li><em>CDS</em> - last 33 bases (ATG + 10 aa)</li>
</ul>
<h3>Regex Prep for reaidng FASTA library sequences into R</h3>
<p>I reformatted <code>203.norestrict.fa</code> into a tab-delimited file with perl regexp. I also added column headers. </p>
<pre><code class="bash">echo -en "Name\tPromoter\tGene\tCDS.num\tPromoter.seq\tRBS.seq\tCDS.seq" \
> 203.norestrict.txt
perl -ne 'chomp; s/^>((\w+)-(\w+)-(\d+))/\n$1\t$2\t$3\t$4/;
s/^([atgc]+[ATGC]{5})([ATGCatgc]*?)(ATG[ATGCatgc]{30})$/\t$1\t$2\t$3/;
print $_;' 203.norestrict.fa >> 203.norestrict.txt
</code></pre>
<h2>Reading Into R</h2>
<p>Now we have to read <code>203.norestrict.txt</code> into R as a tab-delimited text file. </p>
<pre><code class="r">lib_seqs <- read.table(file = paste(getwd(), "/data/203.norestrict.txt",
sep = ""), sep = "\t", header = T)
</code></pre>
<h3>Annotating <code>lib_seqs</code> DataFrame</h3>
<p>Now we want to add the library info to each sequence.</p>
<p>Using the leader peptide number (<code>CDS.num</code>) we can assign the predetermined attributes to each sequence, like RBS identity, codon usage, and secondary structure. </p>
<pre><code class="r">lib_seqs$CDS.num <- as.integer(lib_seqs$CDS.num)
# split Leader into RBS identities
lib_seqs$RBS = NA
lib_seqs$RBS[which(lib_seqs$CDS.num %in% c(1, 5, 9, 13:22))] <- "WT"
lib_seqs$RBS[which(lib_seqs$CDS.num %in% c(2, 6, 10, 23:32))] <- "BB0030"
lib_seqs$RBS[which(lib_seqs$CDS.num %in% c(3, 7, 11, 33:42))] <- "BB0032"
lib_seqs$RBS[which(lib_seqs$CDS.num %in% c(4, 8, 12, 43:52))] <- "BB0034"
lib_seqs$RBS <- factor(lib_seqs$RBS, rev(c("BB0030", "BB0034", "BB0032",
"WT")))
# split leader into CDS types (WT, min/max rare codons, secondary
# structure)
lib_seqs$CDS.type <- (as.integer(lib_seqs$CDS.num) - 3)%%10
lib_seqs$CDS.type[which(lib_seqs$CDS.num %in% c(1:4))] <- "WT"
lib_seqs$CDS.type[which(lib_seqs$CDS.num %in% c(5:8))] <- "Min Rare"
lib_seqs$CDS.type[which(lib_seqs$CDS.num %in% c(9:12))] <- "Max Rare"
lib_seqs$CDS.type <- factor(lib_seqs$CDS.type, rev(c("WT", "Min Rare",
"Max Rare", 0:9)), rev(c("WT", "Min Rare", "Max Rare", paste("∆G", c(1:10),
sep = " "))))
# function to get the length of any factored string field and add RBS
# length to each sequence, since this varies, unlike promoter and CDS
get_len <- function(df, field) {
seq_field <- paste(field, "seq", sep = ".")
len_field <- paste(field, "len", sep = ".")
df[, len_field] <- nchar(as.character(df[1, seq_field]))
return(df)
}
require(plyr)
lib_seqs <- ddply(lib_seqs, .(RBS.seq), get_len, "RBS")
</code></pre>
<h2>Checking <code>lib_seqs</code> DataFrame for completeness</h2>
<p>First we should check that all the identities are equally distributed:</p>
<pre><code class="r"># Promoter
table(lib_seqs$Promoter)
</code></pre>
<pre><code>##
## BBaJ23100 BBaJ23108
## 7124 7124
</code></pre>
<pre><code class="r"># RBS Type
table(lib_seqs$RBS)
</code></pre>
<pre><code>##
## WT BB0032 BB0034 BB0030
## 3562 3562 3562 3562
</code></pre>
<pre><code class="r"># CDS Type
table(lib_seqs$CDS.type)
</code></pre>
<pre><code>##
## ∆G 10 ∆G 9 ∆G 8 ∆G 7 ∆G 6 ∆G 5 ∆G 4 ∆G 3
## 1096 1096 1096 1096 1096 1096 1096 1096
## ∆G 2 ∆G 1 Max Rare Min Rare WT
## 1096 1096 1096 1096 1096
</code></pre>
<pre><code class="r"># Gene
table(lib_seqs$Gene)
</code></pre>
<pre><code>##
## accA accD acpP acpS alaS asnS aspS bamA bamD can coaE csrA cysS dapA dapB
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## dapD dapE der dnaE dnaX dxr dxs eno era erpA fabA fabB fabD fabG fabI
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## fbaA ffh fldA folA folC folD folE folK ftsA ftsB ftsI ftsL ftsQ ftsW ftsZ
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## gapA gltX grpE gyrA hemA hemB hemH hemL holA holB ileS infA ispA ispD ispE
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## ispF ispG ispH kdsA kdsB lepB leuS lgt ligA lnt lolA lolB lolC lolD lolE
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## lptD lpxB lpxC lpxD lpxK map metG metK mnmA mraY mrdA mrdB msbA mukB mukE
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## mukF murC murD murF murG murJ nadD nadE nadK nrdA nrdB pgk pgsA pheS pheT
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## plsC prfA prfB prmC proS pssA pyrG pyrH ribA ribC ribE ribF rne rplS rpsA
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## rpsB rseP secA serS suhB thiL thrS tilS tmk topA trmD tsf tyrS yeaZ yejM
## 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104
## yqgF zipA
## 104 104
</code></pre>
<p>Now we can see why there are differences in the RBS lengths:</p>
<pre><code class="r">ggplot(lib_seqs, aes(x = RBS, y = RBS.len)) + geom_point()
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk 1.04-plot_rbs_len"/> </p>
<p>WT sequences are all 20 bp, and the three designed RBSs are 18, 19, and 21 bp each. Good to know.</p>
</body>
</html>