Investigating Allelic Bias in Personal Genomes
This study delves into allelic bias in personal genomes, examining the influence of various factors such as sequencing datasets, removal of reads with allelic bias, and the impact on allele-specific single nucleotide variants (AS SNVs). The revised AlleleDB pipeline proposed includes steps for constructing personal genomes, assessing overdispersion, pooling datasets, and analyzing allelic bias through AlleleSeq and AS calling. The findings shed light on potential challenges and implications in identifying AS SNVs, particularly post-read removal.
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Looking at allelic bias in personal genomes Jieming 19thOct 2015 Allele
What went in Personal Genome: NA12878 GATK BP3 high coverage 1. CTCF encode set 1 2. CTCF encode set 2 3. RNAseq set 1 (encode) 4. RNAseq set 2 (kasowski)
Unaligned (%) Multi (%) Remove reads #SNVs affected (%) auto Remove SNVs with 5% allelic bias (%) auto 3145498 Mf2P:1726 (0.05%) 3145302 Pf2M:1689 (0.05%) 95/557 (17%) 212/557 (38%) Mf2P:54333 (1.73%) POL2 pooled Pf2M:53881 (1.71%) 152873 [~10 min] Mf2P:83 (0.05%) 152726 Pf2M:125 (0.08%) 6/58 (10.3%) + 4 new ones 11/58 (19%) Mf2P:2255 (1.48%) CTCF encode set 1 6c (CSHL) Pf2M:2202 (1.44%) 195235 [~2 hours] Mf2P:127 (0.07%) 195372 Pf2M:140 (0.07%) 3/19 (15.8%) 4/19 (21%) Mf2P:2618 (1.34%) CTCF encode set 2 6a (Broad) Pf2M:2575 (1.32%) 1157920 [~10 hours] Mf2P:3500 (0.30%) 1163694 Pf2M:3142 (0.27%) 6/369 (1.6%) + 7 new ones 10/369 (2.7%) Mf2P:7653 (0.66%) RNAseq set 1 (kasowski) 6d Pf2M:8359 (0.72%) 2137620 [>24hours] Mf2P:3928 (0.18%) 2094342 Pf2M:3251 (0.16%) B:169/10486 (1.6%) BB: 15/607 (2.5%) B:307/10486 (3%) BB:21/607 (3.5%) Mf2P:19789 (0.93%) RNAseq set 2 (encode) 6b Pf2M:25899 (1.24%) ** all auto betabinom results; ChIP-seq no intersection with peaks, just plain results; denominator is original-alleleseq
Examples NA12878 CTCF combined pool Before cA 0 135 19 0 After cA 0 8 7 0 cC 20 0 0 0 cG 0 148 0 15 cT 44 0 2 0 cC 15 0 0 0 cG 0 104 0 0 cT 0 0 2 0 nonAS > AS AS > nonAS
Removal of reads with allelic bias 1. Removal of AS SNVs after removal of reads were due to --reads from original AS SNVs removed partially or wholly --removal of reads does not necessarily remove AS SNVs (often does not) 2. Introduction of AS SNVs after removal of reads --reads removed can cause SNVs originally not AS to be now AS --FDR simulation can also change that can make the FDR p value cutoff shift and many borderline cases can get included (or excluded)
Proposing a revised AlleleDB pipeline 1. Construct personal genomes [DONE] 2. Calculate overdispersion for each dataset > remove datasets with too much overdispersion [DONE] 3. Pool datasets [DONE] 4. Allelic bias check for each personal genome (map [DONE], ref, flip, remap) > remove reads with allelic bias in either haplotype 5. Run AlleleSeq using filtered fastq file 6. AS call using betabinomial (incl. cnv filter <=0.5;>=1.5) > peak filter (for ChIP-seq)
