Lukas Windhager LFE Bioinformatik, Institut für Informatik Ludwig-Maximilians-Universität München Coverage variability in NGS Data

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1 Lukas Windhager LFE Bioinformatik, Institut für Informatik Ludwig-Maximilians-Universität München Coverage variability in NGS Data Short talk

2 Reproducible pattern SOLiD reads mapped to rrna precursor (Doelken data) 2

3 Reproducible pattern Correlation of coverage across six genes encoding for K+/NA+ channel proteins See Harismendy et al. (Genome Biology 2009) Reproducible coverage pattern, depends on: platform (SOLiD, Illumina, 454) local sequence (repeating elements, non-unique reads) experimental protocol (priming and fragmenting, selection) 3

4 Reproducible pattern corr =

5 Coverage variability in NGS Data repeating elements non-unique reads priming and fragmenting selection 5

6 Repeating elements Illumina: approx 2-fold coverage of SINEs, Alus, simple repeats very low coverage especially in AT-rich regions SOLiD: approx. ½-fold coverage of all repeating elements very low coverage especially in AT-rich regions 454: approx 1.25-fold coverage of LINEs Overall tendency: repeating elements influence coverage unexpectedly coverage decreases with increasing AT content See Harismendy et al. (Genome Biology 2009) 6

7 Coverage variability in NGS Data repeating elements non-unique reads priming and fragmenting selection 7

8 Non-unique reads Reads may align to multiple genes/isoforms (multi-reads): Use statistical model to distribute multi-reads OR Ignore reads and adjust transcript length see Lee, Seo, Lim et al. (NAR 2010) 8

9 Non-unique reads Expected unique mapable area 9

10 Non-unique reads see Lee, Seo, Lim et al. (NAR 2010) 10

11 Coverage variability in NGS Data repeating elements non-unique reads priming and fragmenting selection 11

12 Priming, fragmenting Illumina Nucleotide frequencies at positions relative to read start Hexamer priming DNase I fragmentation See Hansen et al. (NAR 2010) 12

13 Priming, fragmenting Nucleotide frequencies at positions relative to read start (T=0) Red:T, green:a, blue:c, black:g Illumina SOLiD See Li, Jiang and Wong (Genome Biology 2010) 13

14 Priming, fragmenting Use non-linear model See Li, Jiang and Wong (Genome Biology 2010) R 2 = percentage of explained non-uniformity Rank correlation (compared to microarray) increases only slightly 14

15 Coverage variability in NGS Data repeating elements non-unique reads priming and fragmenting selection 15

16 Selection Non-uniformity due to 5 3 bias (or other position dependent biases) Caused by e.g. RNA decay from 5 and 3 end + Poly-A selection Other causes possible, not well understood Weight read-count of exons See Wu, Wang and Zhang (Bioinformatics 2011) 16

17 Selection Bias curves for gene-length quartiles See Wu, Wang and Zhang (Bioinformatics 2011) SUPPLEMENT 17

18 Bachelorarbeit Jonas Zierer Bachelorarbeit Jonas Zierer Develop software to handle/decrease coverage variability Integrate and combine available methods (Hansen et al. 2010, Li et al, 2010, Wu et al. 2011, Lee et al (2010)) Investigate influence of SNPs and InDels to coverage Application rrna processing Doelken SOLiD data: DG75 egfp, 10/12 KSHV cell lines Adjust coverage of rrna precursor Develop model for rrna processing based on half-lifes of features 18

19 References 1. Reproducible patterns, repeating elements : Harismendy O, Ng P, Strausberg R, et al. Evaluation of next generation sequencing platforms for population targeted sequencing studies. Genome Biology. 2009;10(3):R Non-unique reads: Lee S, Seo CH, Lim B, et al. Accurate quantification of transcriptome from RNA-Seq data by effective length normalization. Nucleic Acids Research Position specific nucleotide frequencies: Li J, Jiang H, Wong WH. Modeling non-uniformity in short-read rates in RNA-Seq data. Genome Biol. 2010;11(5):R50-R50. Hansen KD, Brenner SE, Dudoit S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Research. 2010;38(12):e Non-uniform read distribution (5'-3 bias): Wu Z, Wang X, Zhang X. Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq. Bioinformatics. 2011;27(4):