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tutorial_pairddRAD.ipynb
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"Basic Tutorial: paired-end ddRAD analysis"
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"----------------------------- \n",
"\n",
"###__Topics__: \n",
"\n",
" + Setup of params file for paired-end ddRAD\n",
" + Visualize ideal results on simulated data\n",
" + To see more options for filtering empirical paired ddRAD data see the Advanced tutorials\n",
" \n",
"\n",
"This tutorial assumes you have already read the [_general use pyRAD_ tutorial](http://goo.gl/Crmia4). \n",
"\n",
"----------------------------- "
]
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"source": [
"What is ddRAD?"
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"The double digest library preparation method was developed and described by [Peterson et al. 2012](http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0037135). Here I will be talking about __ _paired-end ddRAD_ __ data and describe my recommendations for how to setup a params file to analyze them in _pyRAD_. "
]
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"A ddRAD library is prepared by cutting genomic DNA with two different restriction enzymes and selecting the intervening fragments that are within a certain size window. These will contain overhangs from the respective cut sites on either side. One side will have a barcode+illumina adapter ligated, and the other end will have only the reverse Illumina adapter ligated. The first reads may come in one or multiple files with \"\\_R1\\_\" in the name, and the second reads are in a separate file/s with \"\\_R2\\_\". Second read files will contain the corresponding pair from the first read files in the exact same order.\n",
"\n",
"-------------------"
]
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"language": "python",
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"--------------------- \n",
"\n",
"Your data will likely come to you non-demultiplexed (meaning not sorted by which individual the reads belong to). You can demultiplex the reads according to their barcodes using _pyRAD_ or separate software. For simplicity, I suggest using _pyRAD_, but for data with combinatorial barcodes you will need to use a more advanced de-multiplexing software. \n",
"\n",
"A number of programs are available to check for overlap of paired end reads, and you can run your data through these programs before being input to _pyRAD_. However, as of _pyRAD_ v.2.0 I now include a robust built-in overlap detection method utilizing _USEARCH_. This can be used to separate merged from non-merged reads to be analyzed separately in _pyRAD_. This method is illustrated in the Advanced tutorial. \n"
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"</g></g></svg>"
],
"text": [
"<IPython.core.display.SVG at 0x2be1d10>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this tutorial I simulated paired-end ddRAD loci on a 12 taxon species tree, shown above. You can download the data using the script below and assemble these data by following along with all of the instructions below: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%bash\n",
"wget http://goo.gl/cGUuWd 2&> /dev/null\n",
"gunzip cGUuWd\n",
"rm cGUuWd"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------------------- \n",
"\n",
"You can simply download the data, but it is also worth describing how the data were generated. I simulated ddRAD-like data using the egglib coalescent simulator with the following options. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"indels = 0.005 ## low rate of indels (prob. mutation is indel)\n",
"dropout = 0 ## if 1, mutations to restriction site can cause locus dropout.\n",
"nloci = 1000 ## Total Nloci simulated (less if locus dropout or merged reads) \n",
"ninds = 1 ## sampled individuals per tip taxon\n",
"shortfrag = 300 ## smallest size of digested fragments\n",
"longfrag = 600 ## largest size of digested fragments"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----------\n",
"Because I simulate sequences at 20X coverage, and the reads are read from both ends (paired-end), the total number of reads is: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reads = 12*ninds*nloci*20*2\n",
"print \"%s sequenced reads\" % reads"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"480000 sequenced reads\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------------ \n",
"\n",
"Here I execute the simulation script (available here) to simulate the data, and write it to a file called simpairddRADs.fastq.gz. If you simply downloaded the data you do not need to do this step, I show it only for those interested. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## you will need the Python package egglib installed to run the simRRLs script.\n",
"! python ../simscripts/simRRLs.py 0.005 0 1000 1 300,600 pairddrad simpairddRADs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\tsimulating pairddrad data\r\n",
"\tsimulating 1000 loci at 20X coverage across 12 tip taxa with 1 samples per taxon\r\n",
"\tindels arise at frequency of 0.005000 per mutation\r\n",
"\tmutations in restriction site = False\r\n",
"\tsequencing error rate = 0.0005\r\n",
"\ttheta=4Nu= 0.0014\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\tcreating new barcode map\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
".\r\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We simulated 1000 loci per sample. Because we simulated no sequence overlap (min frag size = 300), and no mutations to restriction sites we should recover all 1000 loci shared across our entire data set. \n",
"\n",
"------------------------ \n"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"_PyRAD_ Analysis - the params file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first create an empty params.txt input file for the _pyRAD_ analysis. \n",
"The following command will create a template which we will fill in with all relevant parameter settings for the analysis."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n",
"\tnew params.txt file created\r\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------- \n",
"\n",
"Take a look at the default options. Each line designates a parameter, and contains a \"##\" symbol after which comments can be added, and which includes a description of the parameter. The affected step for each parameter is shown in parentheses. The first 14 parameters are required. Numbers 15-35 are optional."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! cat params.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"==== parameter inputs for pyRAD version 2.00 ============================ affected step ==\r\n",
"./ ## 1. Working directory (all)\r\n",
"./*.fastq.gz ## 2. Loc. of non-demultiplexed files (if not line 18) (s1)\r\n",
"./*.barcodes ## 3. Loc. of barcode file (if not line 18) (s1)\r\n",
"usearch7.0.1090_i86linux32 ## 4. command (or path) to call usearch v.7 (s3,s6)\r\n",
"muscle ## 5. command (or path) to call muscle (s3,s7)\r\n",
"TGCAG ## 6. restriction overhang (e.g., C|TGCAG -> TGCAG) (s1,s2)\r\n",
"2 ## 7. N processors to use in parallel (all)\r\n",
"6 ## 8. Mindepth: minimum dpth of coverage for a cluster (s4,s5)\r\n",
"4 ## 9. NQual: max # sites with qual < minQ (line 20) (s2)\r\n",
".90 ## 10. Wclust: clustering threshold as a decimal (s3,s6)\r\n",
"rad ## 11. Datatype: rad,gbs,ddrad,pairgbs,pairddrad,merge (all)\r\n",
"4 ## 12. MinCov: min samples in a final locus (s7)\r\n",
"3 ## 13. MaxSH: max inds with shared hetero site (s7)\r\n",
"c90d6m4p3 ## 14. prefix name for final output (no spaces) (s7)\r\n",
"==== optional params below this line =================================== affected step ==\r\n",
" ## 15.opt.: select subset (list or use prefix*) (s2-s7)\r\n",
" ## 16.opt.: add-on (outgroup) taxa (list or prefix*) (s6,s7)\r\n",
" ## 17.opt.: exclude taxa (list or prefix*) (s7)\r\n",
" ## 18.opt.: Loc. of de-multiplexed data (s2)\r\n",
" ## 19.opt.: maxM: N mismatches in barcodes (def. 1) (s1)\r\n",
" ## 20.opt.: MinQ: min Quality score after offset (def. 20) (s2)\r\n",
" ## 21.opt.: Filter: 0=NQual 1=NQual+adapters. 2=1+strict (s2)\r\n",
" ## 22.opt.: a priori E,H (def. 0.001,0.01, if not estimated) (s4)\r\n",
" ## 23.opt.: maxN: Ns in a consensus seq (def. 4) (s5)\r\n",
" ## 24.opt.: maxH: hetero. sites in consensus seq (def. 4) (s5)\r\n",
" ## 25.opt.: diploid: limit 2 alleles in a consensus (def. 1) (s5)\r\n",
" ## 26.opt.: maxSNPs: step 7. (def=100). Paired (def=100,100) (s7)\r\n",
" ## 27.opt.: maxIndels: within-clust,across-clust (def. 3,99) (s3,s7)\r\n",
" ## 28.opt.: random number seed (def. 112233) (s3,s6)\r\n",
" ## 29.opt.: trim overhang left,right on final loci, def(0,0) (s7)\r\n",
" ## 30.opt.: add output formats: a,n,s,u (see documentation) (s7)\r\n",
" ## 31.opt.: call maj. consens if dpth < stat. limit (def. 0) (s5)\r\n",
" ## 32.opt.: merge/remove paired overlap (def 1), 0=nocheck (s2)\r\n",
" ## 33.opt.: keep trimmed reads (def=0). Enter min length. (s2)\r\n",
" ## 34.opt.: max stack size (int), def= max(500,mean+2*SD) (s3)\r\n",
" ## 35.opt.: minDerep: exclude dereps with <= N copies, def=0 (s3)\r\n",
"==== list hierachical cluster groups below this line =====================================\r\n",
"\r\n",
"\r\n",
"\r\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Edit the params file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"I will use the script below to substitute new values, but you should simply __use any text editor__ to make changes. For this analysis I made the following changes from the defaults: \n",
"\n",
"-------------------- \n",
"\n",
" 5. set the two restriction enzymes used to generate the ddRAD data\n",
" 10. lowered clustering threshold to .85\n",
" 11. set datatype to pairddrad\n",
" 14. changed the output name prefix\n",
" 19. mismatches for demulitiplexing set to 0, exact match.\n",
" 24. Raised maxH. Lower is better for filtering paralogs.\n",
" 30. added additional output formats (e.g., nexus and SNPs)\n",
"\n",
"-------------------- \n",
" "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%bash\n",
"sed -i '/## 6. /c\\TGCAG,AATT ## 6. cutsites... ' ./params.txt\n",
"sed -i '/## 10. /c\\.85 ## 10. lowered clust thresh' ./params.txt\n",
"sed -i '/## 11. /c\\pairddrad ## 11. datatype... ' ./params.txt\n",
"sed -i '/## 14. /c\\c85d6m4p3 ## 13. prefix name ' ./params.txt\n",
"sed -i '/## 19./c\\0 ## 19. errbarcode... ' ./params.txt\n",
"sed -i '/## 24./c\\10 ## 24. maxH... ' ./params.txt\n",
"sed -i '/## 30./c\\a,s,u,n ## 30. outformats... ' ./params.txt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----------------- \n",
"\n",
"Let's take a look at the edited params.txt file\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! cat params.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"==== parameter inputs for pyRAD version 2.00 ============================ affected step ==\r\n",
"./ ## 1. Working directory (all)\r\n",
"./*.fastq.gz ## 2. Loc. of non-demultiplexed files (if not line 18) (s1)\r\n",
"./*.barcodes ## 3. Loc. of barcode file (if not line 18) (s1)\r\n",
"usearch7.0.1090_i86linux32 ## 4. command (or path) to call usearch v.7 (s3,s6)\r\n",
"muscle ## 5. command (or path) to call muscle (s3,s7)\r\n",
"TGCAG,AATT ## 6. cutsites... \r\n",
"2 ## 7. N processors to use in parallel (all)\r\n",
"6 ## 8. Mindepth: minimum dpth of coverage for a cluster (s4,s5)\r\n",
"4 ## 9. NQual: max # sites with qual < minQ (line 20) (s2)\r\n",
".85 ## 10. lowered clust thresh\r\n",
"pairddrad ## 11. datatype... \r\n",
"4 ## 12. MinCov: min samples in a final locus (s7)\r\n",
"3 ## 13. MaxSH: max inds with shared hetero site (s7)\r\n",
"c85d6m5p3 ## 13. prefix name \r\n",
"==== optional params below this line =================================== affected step ==\r\n",
" ## 15.opt.: select subset (list or use prefix*) (s2-s7)\r\n",
" ## 16.opt.: add-on (outgroup) taxa (list or prefix*) (s6,s7)\r\n",
" ## 17.opt.: exclude taxa (list or prefix*) (s7)\r\n",
" ## 18.opt.: Loc. of de-multiplexed data (s2)\r\n",
"0 ## 19. errbarcode... \r\n",
" ## 20.opt.: MinQ: min Quality score after offset (def. 20) (s2)\r\n",
" ## 21.opt.: Filter: 0=NQual 1=NQual+adapters. 2=1+strict (s2)\r\n",
" ## 22.opt.: a priori E,H (def. 0.001,0.01, if not estimated) (s4)\r\n",
" ## 23.opt.: maxN: Ns in a consensus seq (def. 4) (s5)\r\n",
"10 ## 24. maxH... \r\n",
" ## 25.opt.: diploid: limit 2 alleles in a consensus (def. 1) (s5)\r\n",
" ## 26.opt.: maxSNPs: step 7. (def=100). Paired (def=100,100) (s7)\r\n",
" ## 27.opt.: maxIndels: within-clust,across-clust (def. 3,99) (s3,s7)\r\n",
" ## 28.opt.: random number seed (def. 112233) (s3,s6)\r\n",
" ## 29.opt.: trim overhang left,right on final loci, def(0,0) (s7)\r\n",
"a,s,u,n ## 30. outformats... \r\n",
" ## 31.opt.: call maj. consens if dpth < stat. limit (def. 0) (s5)\r\n",
" ## 32.opt.: merge/remove paired overlap (def 1), 0=nocheck (s2)\r\n",
" ## 33.opt.: keep trimmed reads (def=0). Enter min length. (s2)\r\n",
" ## 34.opt.: max stack size (int), def= max(500,mean+2*SD) (s3)\r\n",
" ## 35.opt.: minDerep: exclude dereps with <= N copies, def=0 (s3)\r\n",
"==== list hierachical cluster groups below this line =====================================\r\n",
"\r\n",
"\r\n",
"\r\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"PyRAD Analysis"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step 1: De-multiplexing the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------------- \n",
" \n",
"Four examples of acceptable input file name formats for paired-end data: \n",
"\n",
" 1. xxxx_R1_001.fastq xxxx_R2_001.fastq\n",
" 2. xxxx_R1_001.fastq.gz xxxx_R2_001.fastq.gz\n",
" 3. xxxx_R1_100.fq.gz xxxx_R2_100.fq.gz\n",
" 4. xxxx_R1_.fq xxxx_R2_.fq\n",
"\n",
"The file ending can be .fastq, .fq, or .gz. \n",
"There should be a unique name or number shared by each pair and the characters \\_R1\\_ and \\_R2\\_. \n",
"For every file name with \\_R1\\_ there should be a corresponding \\_R2\\_ file. \n",
"\n",
"If your data are _already_ demultiplexed skip step 1 and see step 2 below. \n",
"\n",
"----------------- \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -p params.txt -s 1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n",
"\r\n",
"\tstep 1: sorting reads by barcode\r\n",
" "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can look at the stats output for this below:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! cat stats/s1.sorting.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"file \tNreads\tcut_found\tbar_matched\r\n",
"simpairddRADs_001.fastq.gz\t240000\t240000\t240000\r\n",
"\r\n",
"\r\n",
"sample\ttrue_bar\tobs_bars\tN_obs\r\n",
"1C0 \tAGATAA \tAGATAA\t20000 \r\n",
"2F0 \tAGTGAT \tAGTGAT\t20000 \r\n",
"1B0 \tATGTGA \tATGTGA\t20000 \r\n",
"3I0 \tATGTTG \tATGTTG\t20000 \r\n",
"3K0 \tATTAGG \tATTAGG\t20000 \r\n",
"2E0 \tGAGTTA \tGAGTTA\t20000 \r\n",
"1D0 \tGGATAT \tGGATAT\t20000 \r\n",
"3J0 \tTAGGAG \tTAGGAG\t20000 \r\n",
"2H0 \tTATAAT \tTATAAT\t20000 \r\n",
"3L0 \tTGAAGG \tTGAAGG\t20000 \r\n",
"1A0 \tTGATGG \tTGATGG\t20000 \r\n",
"2G0 \tTTTTTA \tTTTTTA\t20000 \r\n",
"\r\n",
"nomatch \t_ \t0\r\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The de-multiplexed reads are written to a new file for each individual in a new directory created within your working directory called fastq/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------------\n",
"\n",
"The reads were sorted into a separate file for the first (R1) and second (R2) reads for each individual. \n",
"\n",
"__If your data were previously de-multiplexed__ you need the following things before step 2: \n",
"\n",
"+ your sorted file names should be formatted similar to above, but with sample names substituted for 1A0, 1A1, etc. \n",
"+ the files can be zipped (.gz) or not (.fq or .fastq). \n",
"+ the barcode should be removed (not on left side of reads) \n",
"+ the restriction site should _not_ be removed, but if it is, enter a '@' symbol before the location of your sorted data.\n",
"\n",
"+ __Enter on line 18 of the params file the location of your sorted data.__\n",
"\n",
"-------------------- \n"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step 2: Quality filtering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------- \n",
"\n",
"Next we apply the quality filtering. \n",
"\n",
"+ We left the filter setting (line 21) at its default of 0, meaning it will apply only a filter based on quality scores. Low quality sites are converted to Ns, and any locus with more than X number of Ns is discarded, where X is the number set on line 9 of the params file. \n",
"\n",
"\n",
"+ We could also check for overlap of paired-end reads and filter for the presence of Illumina adapters in this step, however we assume here that the data were properly size selected and do not contain any of these problems. To see those filters applied see the Advanced tutorials. \n",
"\n",
"\n",
"---------------------- \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -p params.txt -s 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n",
"\r\n",
"\tstep 2: quality filtering \r\n",
"\t"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------- \n",
"\n",
"Statistics for the number of reads that passed filtering can be found in the stats/ directory. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! cat stats/s2.rawedit.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"sample\tNreads\texclude\ttrimmed\tpassed\r\n",
"2H0\t20000\t0\t0\t20000\r\n",
"3I0\t20000\t0\t0\t20000\r\n",
"3L0\t20000\t0\t0\t20000\r\n",
"3J0\t20000\t0\t0\t20000\r\n",
"1C0\t20000\t0\t0\t20000\r\n",
"3K0\t20000\t0\t0\t20000\r\n",
"1D0\t20000\t0\t0\t20000\r\n",
"1B0\t20000\t0\t0\t20000\r\n",
"2G0\t20000\t0\t0\t20000\r\n",
"1A0\t20000\t0\t0\t20000\r\n",
"2F0\t20000\t0\t0\t20000\r\n",
"2E0\t20000\t0\t0\t20000\r\n",
"\r\n",
" Nreads = total number of reads for a sample\r\n",
" exclude = reads that were excluded\r\n",
" trimmed = reads that had adapter trimmed but were kept\r\n",
" passed = total kept reads\r\n",
"\r\n",
" note: you can set minimum length of kept trimmed reads on line 35 the params file \r\n",
" kept trimmed (fragment) reads written to /home/deren/Dropbox/Public/PYRAD_TUTORIALS/tutorial_pairddRAD/mergedreads/\r\n",
"\r\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------\n",
"The filtered data files are converted to fasta format and written to a directory called edits/. I show this below:\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ls -l edits/"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"total 50064\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 1A0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 1B0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 1C0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 1D0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 2E0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 2F0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 2G0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 2H0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 3I0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 3J0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 3K0.edit\r\n",
"-rw-rw-r-- 1 deren deren 4268890 Mar 9 18:57 3L0.edit\r\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------ \n",
"\n",
"Here you can see that the first and second reads have been concatenated into a single read separated by 'xxxx' in these files, and the read quality scores have now been discarded. \n",
"\n",
"---------- \n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! head -n 8 edits/1A0.edit"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
">1A0_0_pair\r\n",
"TGCAGATCTAAGTCTTTTAGGTTACACGAACGAATGAGCGCCCCCCTGTGTGTCGTTCGTTCACTGTCCGTCTTGTACCCTACCTCGAACCTAxxxxGAACCCCATTACCACCTGCGGGAAATGGCCGGAGGTTACATTTCGAAAGTGATTCTTCTTGGCACATGAATGACAAAGGTTCTCCTTCGCTGCTGCAATT\r\n",
">1A0_1_pair\r\n",
"TGCAGATCTAAGTCTTTTAGGTTACACGAACGAATGAGCGCCCCCCTGTGTGTCGTTCGTTCACTGTCCCTCTTGTACCCTACCTCGAACCTAxxxxGAACCCCATTACCACCTGCGGGAAATGGCCGGAGGTTACATTTCGAAAGTGATTCTTCTTGGCACATGAATGACAAAGGTTCTCCTTCGCTGCTGCAATT\r\n",
">1A0_2_pair\r\n",
"TGCAGATCTAAGTCTTTTAGGTTACACGAACGAATGAGCGCCCCCCTGTGTGTCGTTCGTTCACTGTCCGTCTTGTACCCTACCTCGAACCTAxxxxGAACCCCATTACCACCTGCGGGAAATGGCCGGAGGTTACATTTCGAAAGTGATTCTTCTTGGCACATGAATGACAAAGGTTCTCCTTCGCTGCTGCAATT\r\n",
">1A0_3_pair\r\n",
"TGCAGATCTAAGTCTTTTAGGTTACACGAACGAATGAGCGCCCCCCTGTGTGTCGTTCGTTCACTGTCCGTCTTGTACCCTACCTCGAACCTAxxxxGAACCCCATTACCACCTGCGGGAAATGGCCGGAGGTTACATTTCGAAAGTGATTCTTCTTGGCACATGAATGACAAAGGTTCTCCTTCGCTGCTGCAATT\r\n"
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"Step 3: Within-sample clustering"
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"From here you have two options for how to proceed. The first is to treat these concatenated reads like single end ddRAD sequences and cluster them as concatenated reads. To do this change the datatype option in the params folder from \"pairddrad\" to \"ddrad\", and proceed with steps 3-7. It will detect and remove the 'xxxx' seperator and cluster the long reads. \n",
"\n",
"The more complex option, and the one I prefer, is to cluster the reads separately, which I call the \"split clustering\" method. This will do the following: \n",
"\n",
"+ de-replicate concatenated reads \n",
"+ split them and cluster first reads only \n",
"+ match second reads back to firsts\n",
"+ align second read clusters separately from first-read clusters\n",
"+ discard loci for which second reads align poorly (incomplete digestion or paralogs)\n",
"\n",
"This is illustrated graphically below:"
]
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" <rect height=\"3.2758915\" id=\"rect4156\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"563.46991\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4158\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"545.51428\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4160\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"554.492\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4162\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"508.53638\"/>\n",
" <text id=\"text3973-1\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"468.72534\"><tspan id=\"tspan3975-9\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"468.72534\">20x</tspan></text>\n",
" <text id=\"text4185\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"477.23877\"><tspan id=\"tspan4187\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"477.23877\">20x</tspan></text>\n",
" <text id=\"text4189\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"486.39072\"><tspan id=\"tspan4191\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"486.39072\">2x</tspan></text>\n",
" <text id=\"text4193\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"495.117\"><tspan id=\"tspan4195\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"495.117\">2x</tspan></text>\n",
" <text id=\"text4197\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"504.26895\"><tspan id=\"tspan4199\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"504.26895\">1x</tspan></text>\n",
" <text id=\"text4201\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"513.63373\"><tspan id=\"tspan4203\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"513.63373\">1x</tspan></text>\n",
" <rect height=\"3.8310485\" id=\"rect4205\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"279.66653\" y=\"472.341\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4207\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"279.66653\" y=\"481.36459\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4209\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"279.66653\" y=\"499.24283\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4211\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"204.10974\" y=\"499.24283\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4213\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"312.4433\" y=\"481.36459\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4215\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"332.66272\" y=\"490.30371\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4217\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"358.62869\" y=\"508.18195\"/>\n",
" <text id=\"text4219\" sodipodi:linespacing=\"125%\" style=\"font-size:20px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"309.08554\" xml:space=\"preserve\" y=\"434.30441\"><tspan id=\"tspan4221\" sodipodi:role=\"line\" x=\"309.08554\" y=\"434.30441\">cluster first reads</tspan></text>\n",
" <text id=\"text4223\" sodipodi:linespacing=\"125%\" style=\"font-size:20px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"56.236336\" xml:space=\"preserve\" y=\"488.79044\"><tspan id=\"tspan4225\" sodipodi:role=\"line\" style=\"font-size:14px\" x=\"56.236336\" y=\"488.79044\">locus 1</tspan></text>\n",
" <text id=\"text4227\" sodipodi:linespacing=\"125%\" style=\"font-size:20px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"56.236336\" xml:space=\"preserve\" y=\"571.37085\"><tspan id=\"tspan4229\" sodipodi:role=\"line\" style=\"font-size:14px\" x=\"56.236336\" y=\"571.37085\">locus 2</tspan></text>\n",
" <text id=\"text4231\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"549.60303\"><tspan id=\"tspan4233\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"549.60303\">10x</tspan></text>\n",
" <text id=\"text4235\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"558.11646\"><tspan id=\"tspan4237\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"558.11646\">8x</tspan></text>\n",
" <text id=\"text4239\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"567.26843\"><tspan id=\"tspan4241\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"567.26843\">1x</tspan></text>\n",
" <text id=\"text4243\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"575.99475\"><tspan id=\"tspan4245\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"575.99475\">1x</tspan></text>\n",
" <rect height=\"3.8310485\" id=\"rect4255\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"209.83556\" y=\"553.78613\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4257\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"209.83556\" y=\"563.23541\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4259\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"292.60934\" y=\"572.0838\"/>\n",
" <text id=\"text4261\" sodipodi:linespacing=\"125%\" style=\"font-size:20px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"246.08607\" xml:space=\"preserve\" y=\"619.04614\"><tspan id=\"tspan4263\" sodipodi:role=\"line\" x=\"246.08607\" y=\"619.04614\">pair second reads back to first reads</tspan></text>\n",
" <rect height=\"3.2758915\" id=\"rect4265\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"656.05115\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4267\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"665.02899\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4269\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"674.00677\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4271\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"682.98456\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4273\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"691.9624\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4275\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"764.85138\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4277\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"755.87366\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4279\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"737.91803\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4281\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"746.89581\"/>\n",
" <rect height=\"3.2758915\" id=\"rect4283\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#000080;fill-opacity:1;stroke:#000000;stroke-width:1.47682858;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4;stroke-opacity:1;stroke-dasharray:none;stroke-dashoffset:0\" width=\"264.19531\" x=\"111.86298\" y=\"700.94019\"/>\n",
" <text id=\"text4285\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"661.12915\"><tspan id=\"tspan4287\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"661.12915\">20x</tspan></text>\n",
" <text id=\"text4289\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"669.64258\"><tspan id=\"tspan4291\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"669.64258\">20x</tspan></text>\n",
" <text id=\"text4293\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"678.79449\"><tspan id=\"tspan4295\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"678.79449\">2x</tspan></text>\n",
" <text id=\"text4297\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"687.52081\"><tspan id=\"tspan4299\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"687.52081\">2x</tspan></text>\n",
" <text id=\"text4301\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"696.67273\"><tspan id=\"tspan4303\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"696.67273\">1x</tspan></text>\n",
" <text id=\"text4305\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"706.03754\"><tspan id=\"tspan4307\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"706.03754\">1x</tspan></text>\n",
" <rect height=\"3.8310485\" id=\"rect4309\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"279.66653\" y=\"664.74481\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4311\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"279.66653\" y=\"673.76837\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4313\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"279.66653\" y=\"691.64661\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4315\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"204.10974\" y=\"691.64661\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4317\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"312.4433\" y=\"673.76837\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4319\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"332.66272\" y=\"682.70752\"/>\n",
" <rect height=\"3.8310485\" id=\"rect4321\" ry=\"1.6379458\" style=\"opacity:0.51655626;fill:#ffff00;fill-opacity:1;stroke:none\" width=\"8.0877695\" x=\"358.62869\" y=\"700.58575\"/>\n",
" <text id=\"text4323\" sodipodi:linespacing=\"125%\" style=\"font-size:20px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"56.236336\" xml:space=\"preserve\" y=\"681.19421\"><tspan id=\"tspan4325\" sodipodi:role=\"line\" style=\"font-size:14px\" x=\"56.236336\" y=\"681.19421\">locus 1</tspan></text>\n",
" <text id=\"text4327\" sodipodi:linespacing=\"125%\" style=\"font-size:20px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"56.236336\" xml:space=\"preserve\" y=\"763.7746\"><tspan id=\"tspan4329\" sodipodi:role=\"line\" style=\"font-size:14px\" x=\"56.236336\" y=\"763.7746\">locus 2</tspan></text>\n",
" <text id=\"text4331\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"742.00684\"><tspan id=\"tspan4333\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"742.00684\">10x</tspan></text>\n",
" <text id=\"text4335\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"750.52026\"><tspan id=\"tspan4337\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"750.52026\">8x</tspan></text>\n",
" <text id=\"text4339\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"759.67218\"><tspan id=\"tspan4341\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"759.67218\">1x</tspan></text>\n",
" <text id=\"text4343\" sodipodi:linespacing=\"125%\" style=\"font-size:14px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"379.89218\" xml:space=\"preserve\" y=\"768.3985\"><tspan id=\"tspan4345\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"379.89218\" y=\"768.3985\">1x</tspan></text>\n",
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" <text id=\"text4415\" sodipodi:linespacing=\"120%\" style=\"font-size:20px;font-style:normal;font-weight:normal;line-height:120.00000477%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Times New Roman;-inkscape-font-specification:Times New Roman\" x=\"395.7243\" xml:space=\"preserve\" y=\"814.09131\"><tspan id=\"tspan4417\" sodipodi:role=\"line\" style=\"text-align:center;line-height:120.00000477%;text-anchor:middle\" x=\"395.7243\" y=\"814.09131\">align second reads separate from first read cluster.</tspan><tspan id=\"tspan4419\" sodipodi:role=\"line\" style=\"text-align:center;line-height:120.00000477%;text-anchor:middle\" x=\"395.7243\" y=\"838.09131\">above: locus 1 is good, locus 2 has poor alignment, discard as paralog.</tspan><tspan id=\"tspan4421\" sodipodi:role=\"line\" style=\"text-align:center;line-height:120.00000477%;text-anchor:middle\" x=\"395.7243\" y=\"862.09131\">call consensus sequences.</tspan></text>\n",
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}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"The screen captures below show split clustering performed on an empirical paired ddRAD data set: \n"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"The good aligned clusters are written to a .clustS.gz file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(\"https://dl.dropboxusercontent.com/u/2538935/PYRAD_TUTORIALS/figures/goodpairs.png\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
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"output_type": "pyout",
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eRE0t4Lqc1et3OnMPclvGoH9nGxSYYPE5jXP+TWFTXNdPsbRE99DDeDbiGjRt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\nY82kC5HYPwmX/C4d3wNAdbew8hjJYealyk6ER9T6g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hypOAmR4yg8VIFTVgs0L2me82JF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\nWDsMMe3bIzz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"prompt_number": 17,
"text": [
"<IPython.core.display.Image at 0x2be1dd0>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"And the poor aligned pairs which are excluded from further analysis are written to a separate file ending with .badpairs.gz."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(\"https://dl.dropboxusercontent.com/u/2538935/PYRAD_TUTORIALS/figures/badpairs.png\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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Lrqx64w+CgM4ybs1RSf7XFeJQzv9T51Zxj8im7FRZOqKzBNTKO7DHg7Ijb5NM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\nnF7unlbUGZ2zETuO1LdOf+u0bNHqu16db7bUf8yMKGe2Ti/e5kFvOe0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QUI6ij0Yitk0b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\nT0BxmvWfjn4wrXuT+8GjbUxYG6fqeAmw3836tsl5LsXyXBridDVPv9u8LjV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"prompt_number": 18,
"text": [
"<IPython.core.display.Image at 0x2b43890>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------- \n",
"\n",
"The benefit of this method over clustering concatenated first+second reads is that second reads can be retained that are either more divergent than the clustering threshold, or which contain many low quality base calls (Ns), thus potentially yielding more SNPs. Also, for very large data sets it is _much_ faster to cluster shorter length reads. The drawbacks are that it discards sequences for which the first and second reads do not appear to be from the same DNA fragment, whereas the concatenated clustering method _might_ cluster them separately. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The split clustering method filters the alignment of second reads based on the number of indels in the alignment. This number can be set on line 27 of the params file. The default values are 3,6,99,99. Meaning for within-sample clusters we allow 3 indels in first read clusters and 6 in second read clusters. For across-sample clusters we allow 99 in first read clusters and 99 in second read clusters. If only two numbers are set (e.g., 3,10) this is equivalent to 3,3,10,10. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -p params.txt -s 3 "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n",
"\r\n",
"\tde-replicating files for clustering...\r\n",
"\r\n",
"\tstep 3: within-sample clustering of 12 samples at \r\n",
"\t '.85' similarity using up to 2 processors\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t1A0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t1D0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t2E0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t2G0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t2H0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t3I0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t2F0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t3L0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t1B0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t3K0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t3J0.edit finished, 1000loci\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t1C0.edit finished, 1000loci\r\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! cat stats/s3.clusters.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"taxa\ttotal\tdpt.me\tdpt.sd\td>5.tot\td>5.me\td>5.sd\tbadpairs\r\n",
"1A0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"1D0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"2E0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"2G0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"2H0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"3I0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"2F0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"3L0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"1B0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"3K0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"3J0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"1C0\t1000\t20.0\t0.0\t1000\t20.0\t0.0\t0\r\n",
"\r\n",
" ## total = total number of clusters, including singletons\r\n",
" ## dpt.me = mean depth of clusters\r\n",
" ## dpt.sd = standard deviation of cluster depth\r\n",
" ## >N.tot = number of clusters with depth greater than N\r\n",
" ## >N.me = mean depth of clusters with depth greater than N\r\n",
" ## >N.sd = standard deviation of cluster depth for clusters with depth greater than N\r\n",
" ## badpairs = mismatched 1st & 2nd reads (only for paired ddRAD data)\r\n",
" \r\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Steps 4 & 5: Consensus base calling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We next make consensus base calls for each cluster within each individual. First we estimate the error rate and heterozygosity within each sample:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -p params.txt -s 4"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n",
"\r\n",
"\tstep 4: estimating error rate and heterozygosity\r\n",
"\t"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling consensus sequences applies a number of filters, as listed in the params file, to remove potential paralogs or highly repetitive markers from the data set. For paired-end data the maxN filter only applies to first reads, since we don't mind allowing low quality base calls in second reads. The maxH filter applies to both reads together. The diploid filter (only allow 2 haplotypes in a consensus sequence) also applies to the two reads together."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -p params.txt -s 5"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n",
"\r\n",
"\tstep 5: created consensus seqs for 12 samples, using E=0.00051 H=0.00137\r\n",
"\t"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! cat stats/s5.consens.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"taxon \tnloci\tf1loci\tf2loci\tnsites\tnpoly\tpoly\r\n",
"3I0.clustS.gz \t1000\t1000\t1000\t183005\t246\t0.0013442\r\n",
"2H0.clustS.gz \t1000\t1000\t1000\t183002\t260\t0.0014207\r\n",
"1C0.clustS.gz \t1000\t1000\t1000\t183010\t265\t0.001448\r\n",
"1D0.clustS.gz \t1000\t1000\t1000\t183000\t256\t0.0013989\r\n",
"3L0.clustS.gz \t1000\t1000\t1000\t183007\t261\t0.0014262\r\n",
"2G0.clustS.gz \t1000\t1000\t1000\t183002\t260\t0.0014207\r\n",
"1B0.clustS.gz \t1000\t1000\t1000\t183004\t265\t0.0014481\r\n",
"3J0.clustS.gz \t1000\t1000\t1000\t183005\t227\t0.0012404\r\n",
"1A0.clustS.gz \t1000\t1000\t1000\t183010\t242\t0.0013223\r\n",
"3K0.clustS.gz \t1000\t1000\t1000\t183003\t243\t0.0013278\r\n",
"2E0.clustS.gz \t1000\t1000\t1000\t183005\t248\t0.0013552\r\n",
"2F0.clustS.gz \t1000\t1000\t1000\t183002\t259\t0.0014153\r\n",
"\r\n",
" ## nloci = number of loci\r\n",
" ## f1loci = number of loci with >N depth coverage\r\n",
" ## f2loci = number of loci with >N depth and passed paralog filter\r\n",
" ## nsites = number of sites across f loci\r\n",
" ## npoly = number of polymorphic sites in nsites\r\n",
" ## poly = frequency of polymorphic sites\r\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step 6: Across-sample clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This step clusters consensus sequences across samples. It can take a very long time for very large data sets. If run normally it will print its progress to the screen so you can judge how long it might take. Try this first. If it seems too slow (many days or weeks) you can cancel the job and try running the two-step clustering method instead (see the Advanced tutorials). Here I show the results for a data set that runs very quickly. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -p params.txt -s 6"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\tstep 6: clustering across 12 samples at '.85' similarity \r\n",
"\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"usearch v7.0.1090_i86linux32, 4.0Gb RAM (7.9Gb total), 4 cores\r\n",
"(C) Copyright 2013 Robert C. Edgar, all rights reserved.\r\n",
"http://drive5.com/usearch\r\n",
"\r\n",
"Licensed to: daeaton.chicago@gmail.com\r\n",
"\r\n",
"00:00 21Mb 0.1% 0 clusters, max size 0, avg 0.0\r"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"00:01 27Mb 5.8% 489 clusters, max size 4, avg 1.4\r"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"00:02 28Mb 12.7% 800 clusters, max size 6, avg 1.9\r"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"00:03 28Mb 35.2% 994 clusters, max size 10, avg 4.2\r"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"00:03 28Mb 100.0% 1000 clusters, max size 12, avg 12.0\r",
"\r\n",
" \r\n",
" Seqs 12000 (12.0k)\r\n",
" Clusters 1000\r\n",
" Max size 12\r\n",
" Avg size 12.0\r\n",
" Min size 12\r\n",
"Singletons 0, 0.0% of seqs, 0.0% of clusters\r\n",
" Max mem 28Mb\r\n",
" Time 3.00s\r\n",
"Throughput 4000.0 seqs/sec.\r\n",
"\r\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step 7: Statistics and output files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alignment of loci, statistics, and output of various formatted data files."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! ~/pyrad_v.2.0/pyRAD -p params.txt -s 7"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
" ------------------------------------------------------------\r\n",
" pyRAD : RADseq for phylogenetics & introgression analyses\r\n",
" ------------------------------------------------------------\r\n",
"\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\tingroup 1A0,1B0,1C0,1D0,2E0,2F0,2G0,2H0,3I0,3J0,3K0,3L0\r\n",
"\taddon \r\n",
"\texclude \r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\t"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
".."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
".\r\n",
"\tfinal stats written to:\r\n",
"\t /home/deren/Dropbox/Public/PYRAD_TUTORIALS/tutorial_pairddRAD/stats/c85d6m5p3.stats\r\n",
"\toutput files written to:\r\n",
"\t /home/deren/Dropbox/Public/PYRAD_TUTORIALS/tutorial_pairddRAD/outfiles/ directory\r\n",
"\r\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stats for this run. In this case 1000 loci were shared across all 12 samples."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"! cat stats/c85d6m4p3.stats"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
"\r\n",
"1000 ## loci with > minsp containing data\r\n",
"1000 ## loci with > minsp containing data & paralogs removed\r\n",
"1000 ## loci with > minsp containing data & paralogs removed & final filtering\r\n",
"\r\n",
"## number of loci recovered in final data set for each taxon.\r\n",
"taxon\tnloci\r\n",
"1A0\t1000\r\n",
"1B0\t1000\r\n",
"1C0\t1000\r\n",
"1D0\t1000\r\n",
"2E0\t1000\r\n",
"2F0\t1000\r\n",
"2G0\t1000\r\n",
"2H0\t1000\r\n",
"3I0\t1000\r\n",
"3J0\t1000\r\n",
"3K0\t1000\r\n",
"3L0\t1000\r\n",
"\r\n",
"\r\n",
"## nloci = number of loci with data for exactly ntaxa\r\n",
"## ntotal = number of loci for which at least ntaxa have data\r\n",
"ntaxa\tnloci\tsaved\tntotal\r\n",
"1\t-\r\n",
"2\t-\t\t-\r\n",
"3\t-\t\t-\r\n",
"4\t0\t*\t1000\r\n",
"5\t0\t*\t1000\r\n",
"6\t0\t*\t1000\r\n",
"7\t0\t*\t1000\r\n",
"8\t0\t*\t1000\r\n",
"9\t0\t*\t1000\r\n",
"10\t0\t*\t1000\r\n",
"11\t0\t*\t1000\r\n",
"12\t1000\t*\t1000\r\n",
"\r\n",
"\r\n",
"## var = number of loci containing n variable sites.\r\n",
"## pis = number of loci containing n parsimony informative sites.\r\n",
"n\tvar\tPIS\r\n",
"0\t1\t97\r\n",
"1\t237\t210\r\n",
"2\t325\t270\r\n",
"3\t303\t197\r\n",
"4\t269\t122\r\n",
"5\t235\t63\r\n",
"6\t197\t30\r\n",
"7\t131\t8\r\n",
"8\t87\t2\r\n",
"9\t54\t1\r\n",
"10\t30\t0\r\n",
"11\t17\t0\r\n",
"12\t13\t0\r\n",
"13\t3\t0\r\n",
"14\t0\t0\r\n",
"15\t0\t0\r\n",
"16\t1\t0\r\n",
"total var= 8026\r\n",
"total pis= 2405\r\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Examples of the available output formats can be seen in the [general use tutorial](http://goo.gl/Crmia4). "
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Notes on paired ddRAD"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
}
],
"metadata": {}
}
]
}
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