Brandon:LabNotes/Project1/2012-7-20
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Update to 2012/6/29, data analysis after sequencing[edit]
- Libraries look like they were successfully created after cloning/transformation etc. Now continuing data analysis after the sequencing run
bioinformatic analysis[edit]
- samples
sample # | description | barcode | file |
1 | 6 ng pure DNA | Index 49 | s_5_1_Indx49.unique.bed |
2 | 600 pg pure DNA | Index 50 | s_5_1_Indx50.unique.bed |
3 | 60 pg pure DNA | Index 51 | s_5_1_Indx51.unique.bed |
4 | 10000 Cells | Index 52 | s_5_1_Indx52.unique.bed |
5 | 1000 Cells | Index 53 | s_5_1_Indx53.unique.bed |
6 | 100 Cells | Index 54 | s_5_1_Indx54.unique.bed |
scripts run[edit]
- perl script for mapping etc.
#!/usr/bin/perl -w use strict; my $in_file = $ARGV[0]; my $split = $in_file; $split =~ s/.txt//; #substitution my $unmappable = $split . ".unmappable.txt"; my $samfile = $split . ".bowtie.sam"; my $bamfile = $split . ".bam"; my $sortedBam = $split . ".sorted"; my $sortedBam2 = $split . ".sorted.bam"; my $uniqueBam = $split . ".unique.bam"; my $bed = $split . ".unique.bed"; #print "\n$split\n"; #print "$unmappable\n"; #print "$samfile\n\n"; my $cmd = "bowtie -n1 -k1 --best --chunkmbs 10240 --strata -l32 -m1 -p4 --nomaqround --solexa1.3-quals --sam --un $unmappable /GenomeDB/HsGenome/hg19/hg19 $in_file $samfile"; print "\n$cmd\n"; system($cmd); $cmd = "samtools view -S -b -o $bamfile $samfile"; print "\n$cmd\n"; system($cmd); $cmd = "samtools sort $bamfile $sortedBam"; print "\n$cmd\n"; system($cmd); #$cmd = "samtools index Index1_sorted.bam"; #print "$cmd\n"; #system($cmd); $cmd = "samtools rmdup -s $sortedBam2 $uniqueBam"; print "\n$cmd\n"; system($cmd); #$cmd = "genomeCoverageBed -bg -ibam $uniqueBam -g /home/bsos/software/BEDTools-Version-2.12.0/genomes/human.hg19.genome > $bed"; #print "\n$cmd\n\n"; #system($cmd);
- shell script for multiple samples
for f in s_5_1_Indx*.txt do perl /media/Ext12T/BS_Scratch2/120712_HL125/perltest2.pl $f done;
- parameters used for generate .bed files for genome mapping
genomeCoverageBed -bg -ibam s_5_1_Indx49.unique.bam -g /home/bsos/software/BEDTools-Version-2.12.0/genomes/human.hg19.genome > s_5_1_Indx49.unique.bed
- library complexity, clonal reads, and unique reads statistics for tagmentation/IVT generated library.
' | sample | total reads | one alignment | one alignment % | failed to map | fail map % | supressed | supressed % | clonal reads | clonal read% | unique reads |
Index49 | 6 ng pure DNA | 8,153,450 | 2,988,691 | 36.66% | 4,782,904 | 58.66% | 370,803 | 4.55% | 63,703 | 2.131% | 2,924,988 |
Index50 | 600 pure pg | 3,438,219 | 396,617 | 11.54% | 2,988,275 | 86.91% | 53,327 | 1.55% | 4,596 | 1.159% | 392,021 |
Index51 | 60 pure pg | 2,457,611 | 21,791 | 0.89% | 2,432,690 | 98.99% | 3,130 | 0.13% | 102 | 0.468% | 21,689 |
Index52 | 10000 cells | 3,691,004 | 1,139,024 | 30.86% | 2,418,176 | 65.52% | 133,804 | 3.63% | 22,580 | 1.982% | 1,116,444 |
Index53 | 1000 cells | 6,941,784 | 1,619,425 | 23.33% | 5,129,288 | 73.89% | 193,071 | 2.78% | 67,628 | 4.176% | 1,551,797 |
Index54 | 100 cells | 4,899,774 | 353,895 | 7.22% | 4,498,074 | 91.80% | 47,805 | 0.98% | 21,793 | 6.158% | 332,102 |
index53 | 1k cells downsampled | 3,854,691 | 899,400 | 23.33% | 2,847,985 | 73.88% | 107,306 | 2.78% | 28,016 | 3.115% | 871,384 |
UCSCRep1 | 69,585,103 | 46,179,576 | 66.36% | 2,258,954 | 3.25% | 21,146,573 | 30.39% | 22,721,422 | 49.202% | 23,458,154 | |
UCSCRep3 | 144,500,455 | 90,721,068 | 62.78% | 15,251,224 | 10.55% | 38,528,163 | 26.66% | 28,285,232 | 31.178% | 62,435,836 | |
- file size of .bed files
sample | ' | unique reads | file size | file | ' |
6 ng pure DNA | 2,924,988 | 79M | s_5_1_Indx49.unique.bed | ||
600 pure pg | 392,021 | 9.9M | s_5_1_Indx50.unique.bed | ||
60 pure pg | 21,689 | 551K | s_5_1_Indx51.unique.bed | ||
10000 cells | 1,116,444 | 29M | s_5_1_Indx52.unique.bed | ||
1000 cells | 1,551,797 | 41M | s_5_1_Indx53.unique.bed | ||
100 cells | 332,102 | 8.3M | s_5_1_Indx54.unique.bed | ||
- data generated from performing tagmentation only for DNA accessibility, analysis/sample prep is present here, data analysis tagmentation only, protease versus no protease
- data analysis from running through my own pipeline earlier so could be comparable with UCSC genome browser data.
Sample | total reads | uniquely mapped | unique map % | failed to map | fail map % | not uniquely mapped | not uniquly mapped % | clonal reads | clonal read % | unique reads |
gentle-lysis-500-cell | 13,030,577 | 7,999,292 | 61.39% | 2876464 | 22.07% | 2,154,821 | 16.54% | 7,136,718 | 89.22% | 862,574 |
gentle-lysis-200-cell | 4,720,341 | 2,722,830 | 57.68% | 1181536 | 25.03% | 815,975 | 17.29% | 2,432,561 | 89.34% | 290,269 |
gentle-lysis-100-cell | 4,017,439 | 1,911,153 | 47.57% | 1458224 | 36.30% | 648,062 | 16.13% | 1,710,288 | 89.49% | 200,865 |
lyse+protease-500-cell | 5,220,947 | 3,715,710 | 71.17% | 520931 | 9.98% | 984,306 | 18.85% | 207,382 | 5.58% | 3,508,328 |
lyse+protease-200-cell | 4,492,276 | 3,077,980 | 68.52% | 534772 | 11.90% | 879,524 | 19.58% | 165,647 | 5.38% | 2,912,333 |
lyse+protease-100-cell | 3,323,185 | 2,154,267 | 64.83% | 416699 | 12.54% | 752,219 | 22.64% | 159,548 | 7.41% | 1,994,719 |
- Comparison between data from when tagmentation was performed that added orange and blue ends, versus my method using amplifcation with IVT.
- comparing IVT method versus tagmentation only, more reads were obtained from tagmentation only data, but 89% were clonal reads. with IVT amplification method, less clonal reads obtained, but most were unmappable due to inefficiencies during the procedure. Still more reads were obtained with the 100 cell sample using IVT than either the 100 or 200 cell sample using only tagmentation.
- looking at the the IVT method, many more unique reads were obtained in the 1000 cell sample than in pure DNA, though could be possible that there were more cells used when doing the DNA accessibility assay, versus comparable amount of DNA from the pure DNA sample.
- in IVT method massive amount of unmappable reads. as [DNA] and cell # decreases. Though still got more reads for DNA accessibility samples that was achieved through tagmentation only.
- in IVT method, alot less reads suppressed (repeats etc), but correlated with amount of reads that failed to map so isn't an issue. similar in both pure DNA and cell samples.
UCSC genome browser validation, comparision with tagmentation data[edit]
- seems to be working just as well as when only tagmentation was performed on the samples.
- cases where tagementation and IVT does not work as well as when only tagmentation was done, and cases where it works better than when only tagmentation was done.
- also hard to tell since when comparing to only tagmentation different cell lines were used. Pure DNA sample is also a IPS cell line so harder to make comparisons.
- chromosome 1, 91 Mb, good match
- samples prefixed with old are from sample where only tagmentation only was performed.
- samples with 100*cell_cc and 6* pure DNA are the new samples with IVT amplification.
- can see it works as expected with pure DNA having less tags than accessible DNA for the same region.
File:Chr1 91 tagmentation versus IVT 100 cells comparison.png
- chromosoem 7, 61 mb.
- read counts are higher in controls though.
File:Chr7 61 tagmentation versus IVT 100 cells comparison.png
- chromosome 21, 9.8 mb
- in IVT amplified sample definetly more accessible than control
- not the case with older tagmentation only samples.
File:Chr21 9.8 tagementation versus IVT 100 cells comparison good alignment.png
MACS analysis[edit]
- looked at analysis of peak calling programs (for CHIP-seq) again and MACS is best at calling more peaks with datasets that have fewer reads.
- shown in figure 3 where NRSF data set has 2 million reads. My data sets have 200K to a couple million reads
- previous MACS and peak calling programs analysis
MACS
macs14 -t s_5_1_Indx54.unique.bam -c s_5_1_Indx50.unique.bam -n 100cell_with_control600pg
- after running MACS program, alot of the FDRs were messed up, with them being 100, probably since there are not enough control reads in the control sample used.
- Not sure why 10,000 cell sample didnt work as well.
- when comparing old 500 cell tagmentation sample to 1000 cell tagmentation then IVT sample it seems more efficient. More RNA was generated in the 1000 cell sample too.
sample | total reads acc | unique rds acc | unique rds con | total peak calls | bp called sig | minux X | minus X |
IVT 100 cell, 6 ng control | 4,899,774 | 332,102 | 2,924,988 | 197 | 101,875 | 48,909 | minus 4 count tags |
IVT 100 cell, 600 pg control | 4,899,774 | 332,102 | 392,021 | 135 | 66,910 | 31,073 | minus 4 count tags |
IVT 100 cell, 60 pg control | 4,899,774 | 332,102 | 21,689 | 152 | 74,489 | 29,674 | minus 4 count tags |
old 100 cell | 4,017,439 | 200,865 | 1,994,719 | 1579 | 637,575 | 143,891 | minus 3 count tags |
old 200 cell | 4,720,341 | 290,269 | 2,912,333 | 477 | 248,256 | 104,743 | minus 4 count tags |
old 500 cell | 13,030,577 | 862,574 | 3,508,328 | 901 | 539,451 | 307,184 | minus 5 count tags |
IVT 1000 cell dwnsmpled, 6 ng con | 3,854,691 | 871,384 | 2,924,988 | 1179 | 644,539 | 354,740 | minus 5 count tags |
IVT 1000 cell, 6 ng control | 6,941,784 | 1,551,797 | 2,924,988 | 5339 | 3,070,305 | 1,910,345 | minus 5 count tags |
IVT 1000 cell, 600 pg control | 6,941,784 | 1,551,797 | 392,021 | 4381 | 2,521,586 | 1,576,574 | minus 5 count tags |
IVT 1000 cell, 60 pg control | 6,941,784 | 1,551,797 | 21,689 | 5325 | 3,040,347 | 1,808,772 | minus 5 count tags |
IVT 10000 cell, 6 ng control | 3,691,004 | 1,116,444 | 2,924,988 | 657 | 364,017 | 158,185 | minus 5 count tags |
- to directly compare 1000 cell sample to the 500 cell old tagmentation sample, the 1000 cell sample was downsampled so the same amount of unique reads were obtained after analysis. The original demultiplexed text file was downsampled, and then run through the same computational pipeline as the other files in the original analysis
- Can see even though a similar amount of unique reads were obtained in the 500 cell sample and in the 1000 cell IVT downsampled sample, more peaks were called and bp covered in the genome. That is with 9.1 million fewer total reads in the IVT sample.
sample | total reads acc | unique rds acc | unique rds con | total peak calls | bp called sig | minux X | minus X |
old 500 cell | 13,030,577 | 862,574 | 3,508,328 | 901 | 539,451 | 307,184 | minus 5 count tags |
IVT 1000 cell dwnsmpled, 6 ng con | 3,854,691 | 871,384 | 2,924,988 | 1179 | 644,539 | 354,740 | minus 5 count tags |
genome coverage to see reporducability
coverageBed -a 1000cell_with_control600pg_peaks.bed -b 1000cell_with_control60pg_peaks.bed > 1000cell600pg_onto_1000cell60pg_coverage.txt
sample | bases covered | total length | % covered | ' |
100 cell 600 pg onto 100 cell 60 pg | 60,084 | 74,489 | 0.806615742 | |
100 cell 60 pg onto 100 cell 600 pg | 60,084 | 66,910 | 0.897982364 | Thus less peaks called in 100 cell 600 pg samples (makes sense) |
100cell60pg_onto_1000cell6ng_coverage.txt | 31,365 | 3,070,305 | 0.010215597 | |
1000cell6ng_onto_100cell60pg_coverage.txt | 31,365 | 74,489 | 0.421068883 | expect more of smaller smaple to be covered by larger sample? |
100cell600pg_onto_1000cell6ng_coverage.txt | 31,383 | 3,070,305 | 0.01022146 | |
1000cell6ng_onto_100cell600pg_coverage | 31,383 | 66,910 | 0.469033029 | expect the same here. |
1000cell600pg_onto_1000cell60pg | 2,313,603 | 3,040,347 | 0.760966758 | 24ish% loss in reprodability when using different control |
1000cell600pg_onto_1000cell6ng | 2,371,737 | 3,070,305 | 0.772476024 | 24ish% loss in reprodability when using different control |
1000cell6ng_onto_10000cell6ng | 58262 | 364017 | 0.160052965 | same type of coverage stats as in tagmentation only |
10000cell6ng_onto_1000cell6ng | 58262 | 644539 | 0.090393289 | same type of coverage stats as in tagmentation only |
see comparison after MACS analysis for old data | ||||
used control 500 cell and control 100 cell for input into macs | ||||
500cell_onto_100cell_coverage | 124,211 | 878,558 | 0.141380535 | same type of coverage as in tagmentation IVT samples |
100cell_onto_500cell_coverage | 85,136 | 539,451 | 0.157819709 | same type of coverage as in tagmentation IVT samples |
Future[edit]
- repeat procedure but use DNase after IVT, should hopefully solve alot of the unmappable reads issues.
- instead of using pure DNA as the control, just use protease treated cells from the same sample. Then won't have the issue of possible differences in the amount of DNA tagmentation is being performed on.