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='''Read distribution analysis (unique mapplabe reads) - HC-MDA-Ecoli-SAGs-Ind1to8(SE - down-sampled reads)'''= ==Background== *DATA source: HL083 (HC-MDA-Ecoli-SAGs(Ind1to8)-Nov15) *Genome coverage is just one indication to assess the amplification bias. *It is more informative to evaluate the mappable reads distribution from each library. A more even distribution (across E.coli genome) of mappable reads should be expected to see on samples with less amplification bias. ==Analysis== *Running bowtie alignment on previous down-sampled datasets and transform the output to sam-format **Using samtools to convert the output from sam-format to bam-format and then to pileup-format. **Script: fastq2pileup-hc2 **Data resource: down-sampled raw reads (meet 1,000,000 unique mappable reads): e.g. s_4_ID1_cut_2262955.txt **Template:E.coli-k12-mg1655 template **Bowtie setting: -k 1 -l 28 -n 2 -m 1 --best --best --phred64-quals **Output the data: e.g. s_4_ID1_cut_2262955.bowtie.pileup *In UNIX, extract data from column 1, 2 and column9 with "^" symbol ("^" read starting sign in pileup-format) from each line **Output the data: e.g. s_4_ID1_cut_2262955.bowtie.pileup.readstart *Using perl to calculate "^" symbol in defined bin size (10,000 bp) **Script: CountReadStart-hc2 **Output the data: e.g. s_4_ID1_cut_2262955.bowtie.pileup.readstart.count *Use excel to plot the Read-start counts vs. bin numbers (genome position) ==Plotting== *Since the bin number data is not continuous (the bin number containing no reads will not be reported in previous step). *I need to create a bin-ladder (from bin 0 to 463) and pool my data set with bin-ladder by matching bin number. **Without doing this step will cause Excel draw the plot with discontinuous bin interval (since Excel regard bin number as a new data set but not X-axis). *I pooled the data set using Excel Add-Ins function "Join (merge)Talbes"(from DigDB). *Take the log10 value of total reads for Y-axis. *'''IMPORTANT:''' Since log10 value can't be taken from no read (count=0/10,000bp), I manually assign the value of '''log10 of "no read data"''' as '''"-1"'''. **The reason to do so is that there are lots of bins containing "0 read" exclusively in long-amplified SAGs' data sets. *'''X-Y scatter plot analysis:''' **Purpose: to compare if libraries made from the same MDA amplicon resource (e.g. ID1-ID3) behave more similar than libraries made from different MDA amplicons (e.g. ID1-ID2). ==Results== [[File:sam121510-read-distribution - ID1 - ID3.jpg|600px]] [[File:sam022011-scattor-plot ID1-ID3-new3.png|400px]] [[File:sam121510-read-distribution - ID2 - ID4.jpg|600px]] [[File:sam022011-scattor-plot ID2-ID4-new3.png|480px]] [[File:sam121510-read-distribution - ID5 - ID7.jpg|600px]] [[File:sam022011-scattor-plot ID5-ID7-new3.png|400px]] [[File:sam121510-read-distribution - ID6 - ID8.jpg|600px]] [[File:sam022011-scattor-plot ID6-ID8-new3.png|400px]] '''Excel files:''' [[Media:Bias-plot-all-pooled ID1-ID3-office2003.xls]] [[Media:Bias-plot-all-pooled ID2-ID4-office2003.xls]] [[Media:Bias-plot-all-pooled ID5-ID7-office2003.xls]] [[Media:Bias-plot-all-pooled ID6-ID8-office2003.xls]] '''*For unknown reason the wikinote doesn't allow me to upload Excel 2007 files(.xlsx), so I saved as Excel 2003 file format(.xls)''' ==Discussion== *Short-amplified and long-amplified libraries showed '''very high overlapped''' on reads distribution. *'''Reads from short-amplified libraries are more evenly distributed''' than that from long-amplified libraries. **Some peaks are missing in long-amplified libraries but still shown in short-amplified libraries (see pooled figure). *'''A significant difference between short and long-amplified libraries was shown at the Y-axis range -1 ~ 0''', where it showed one read(Y=0) in short-amplified libraries but no read (Y=-1) in long amplified libraries at the same bin.
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