Daniel:Notebook/GenomeMiner/2013-8-28
HL155[edit]
Slim Indexing Analysis (Started 8/27/2013)[edit]
Counted Errors Using Modified Perl Script from Matt
Script: File:Imp count mismatch.txt
Raw Error Counts and Read Error Percentages
Total read counts and numbers of reads with errors
Total reads | Filtered reads | Perfect match | 1 ins/del | 1 sub | 2 ins/del | 2 sub | 1 ins/del & 1 sub | 3+ | |
Number | 20924455 | 15399065 | 4801948 | 3829283 | 2242835 | 1891019 | 634644 | 1506743 | 492593 |
Percent | NA | 100 | 31.18 | 24.87 | 14.56 | 12.28 | 4.12 | 9.78 | 3.20 |
Error Percentages
Percent errors by base (ex 2% error means 1 in 50 bp will have an error)
Mycroarray Slim Error % | |
Error Rate | 2.03 |
Insertions | 0.11 |
Deletions | 1.18 |
Substitutions | 0.75 |
Counting Results From Index 10-13[edit]
Print Only 3rd Column
awk '{print $3}' hrcp_sortfilt_slimindex_eq.sam > readlist.txt
Perl Script to Count Total Probe Number
perl script: File:Probecount.txt
perl probecount.plx > probecount_idx10to13.txt
Counts[edit]
Compare with full sequencing index
Histograms
Probe counts. Normalization for x axis is based on the expected number of reads, calculated by dividing the total number of reads for a given set by the total number of probes in that set. One indicates the probes were amplified more or less linearly, <1 means they were underamplified, and >1 means the probe was overamplifed. File:Probehist HL155 idx10to13.png
Pie Chart Statistics
Pie chart showing probe count statistics. Probe counts are divided into 3 classes: 0 reads, over-amplified (described as >5x the expected count), and normally amplified reads.
File:V6ProbeCounts Idx10to13.png|File:V4ProbeCounts Idx10to13.pngDiscussion[edit]
At first I was a bit confused by these results, especially the pie charts. There are now more zeros and over-amplifications present, although there are fewer reads used. But I remembered I used the phred 64 flag, which is more stringent, so that probably threw out some data. Since the errors were probably only present one or a few times, they wouldn't have ended up in the zeros category (which had to be equal to zero) or in the over-amplification category (which required ~1000 reads), so they would have been in the normal read count category, which would have reduced the number of normal reads, decreasing normal read percent and increasing the other two.