Sam:LabNotes/Microbiome-new/2010-12-10: Difference between revisions

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>Sam Chiang
(Created page with '='''Data analysis- HC-MDA-Ecoli-SAGs-Ind1to8(SE - down-sampled reads) - Coverage-vs-input reads plot'''= ==Background== *Construct Genome coverage-vs-input reads plot using uniq…')
 
>Sam Chiang
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**Y-axis is total genomic regions covered (bps)
**Y-axis is total genomic regions covered (bps)
**X-axis is the total amount of sequences generated (or mapped).
**X-axis is the total amount of sequences generated (or mapped).
*An example of this plot could be found in Rax's paper
*An example of this plot could be found in Rax's paper  
**[[Media:2009_PlosOne_Whole genome amplification and de novo assembly of single bacterial cells.pdf]]
 





Revision as of 21:58, 14 December 2010

Data analysis- HC-MDA-Ecoli-SAGs-Ind1to8(SE - down-sampled reads) - Coverage-vs-input reads plot

Background







In order to compare the amplification bias between short-amplified and long amplified MDA, we can compare genome-coverage to assess amplification bias.

    • Hypothesis: Given a same number of unique mappable reads, the higher genome coverage means less (position)bias.
  • We need to use the ratio of unique mappable read/total reads to decided how much total read we should down-sample to get
  • The number of unique mappable reads was decided on 1,000,000 reads (since the lowest number of unique mappable reads is 1,192,051 in ID-4 library.
  • The required total reads for getting 1,000,000 unique mappable reads:
File:Sam120810-number of need to downsampled reads.jpg
  • Using the down-sampling script to get desired number of raw reads that meet 1,000,000 unique mappable reads).
    • Script: Data-sampler.pl (= randomizor by CW)
    • Data resource: HC-MDA-Ecoli-SAGs-Ind1to8 (FASTQ format)
      • NOTE: Actually this script only split the data from the beginning but not randomly sampling.
    • Data output:
 /media/disk-2/samchiang/Raw-Read-backup/101123_HL083/HC-MDA-Ecoli-SAGs-Ind1to8/
 s_4_ID1_cut_2262955.txt   s_4_ID2_cut_1929385.txt
 s_4_ID3_cut_1150351.txt   s_4_ID4_cut_1478415.txt
 s_4_ID5_cut_2042067.txt   s_4_ID6_cut_1680672.txt
 s_4_ID7_cut_1898254.txt   s_4_ID8_cut_1422273.txt
  • Perform down-sampled reads alignment using Bowtie, followed by genome coverage calculation.
    • Script: Read2coverage-kz-3
    • Data resource: down-sampled raw reads (meet 1,000,000 unique mappalbe reads)
    • Template:E.coli-k12-mg1655 template
    • Bowtie setting: -k 1 -l 28 -n 2 -m 1 --best --best --phred64-quals
    • Downstream: Data was used for genome coverage calculation.

Results

File:Sam121410-genome coverage on down-sampled reads-3.jpg

Discussion

  • Under a fair comparison using same number of unique mappable reads, short-amplified SAGs showed higher (2~3 fold) genome coverage than their long-amplified SAG mates (ID1 vs ID3, ID2 vs ID4, ID5 vs ID7, ID6 vs ID8).
  • Higher genome coverage suggested a less overlapped of sequencing reads, which also suggested a less amplification bias in short-amplified MDA products.