PMID- 27716037
OWN - NLM
STAT- MEDLINE
DCOM- 20170718
LR  - 20181202
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 17
IP  - 1
DP  - 2016 Oct 3
TI  - Impact of post-alignment processing in variant discovery from whole exome data.
PG  - 403
LID - 403
AB  - BACKGROUND: GATK Best Practices workflows are widely used in large-scale 
      sequencing projects and recommend post-alignment processing before variant 
      calling. Two key post-processing steps include the computationally intensive 
      local realignment around known INDELs and base quality score recalibration 
      (BQSR). Both have been shown to reduce erroneous calls; however, the findings are 
      mainly supported by the analytical pipeline that incorporates BWA and GATK 
      UnifiedGenotyper. It is not known whether there is any benefit of post-processing 
      and to what extent the benefit might be for pipelines implementing other methods, 
      especially given that both mappers and callers are typically updated. Moreover, 
      because sequencing platforms are upgraded regularly and the new platforms provide 
      better estimations of read quality scores, the need for post-processing is also 
      unknown. Finally, some regions in the human genome show high sequence divergence 
      from the reference genome; it is unclear whether there is benefit from 
      post-processing in these regions. RESULTS: We used both simulated and NA12878 
      exome data to comprehensively assess the impact of post-processing for five or 
      six popular mappers together with five callers. Focusing on chromosome 6p21.3, 
      which is a region of high sequence divergence harboring the human leukocyte 
      antigen (HLA) system, we found that local realignment had little or no impact on 
      SNP calling, but increased sensitivity was observed in INDEL calling for the 
      Stampy + GATK UnifiedGenotyper pipeline. No or only a modest effect of local 
      realignment was detected on the three haplotype-based callers and no evidence of 
      effect on Novoalign. BQSR had virtually negligible effect on INDEL calling and 
      generally reduced sensitivity for SNP calling that depended on caller, coverage 
      and level of divergence. Specifically, for SAMtools and FreeBayes calling in the 
      regions with low divergence, BQSR reduced the SNP calling sensitivity but 
      improved the precision when the coverage is insufficient. However, in regions of 
      high divergence (e.g., the HLA region), BQSR reduced the sensitivity of both 
      callers with little gain in precision rate. For the other three callers, BQSR 
      reduced the sensitivity without increasing the precision rate regardless of 
      coverage and divergence level. CONCLUSIONS: We demonstrated that the gain from 
      post-processing is not universal; rather, it depends on mapper and caller 
      combination, and the benefit is influenced further by sequencing depth and 
      divergence level. Our analysis highlights the importance of considering these key 
      factors in deciding to apply the computationally intensive post-processing to 
      Illumina exome data.
FAU - Tian, Shulan
AU  - Tian S
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
FAU - Yan, Huihuang
AU  - Yan H
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
FAU - Kalmbach, Michael
AU  - Kalmbach M
AD  - Division of Research and Education Support Systems, Department of Information 
      Technology Mayo Clinic, Rochester, MN, 55905, USA.
FAU - Slager, Susan L
AU  - Slager SL
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. 
      Slager.Susan@mayo.edu.
LA  - eng
GR  - U01 CA118444/CA/NCI NIH HHS/United States
GR  - UL1 TR000135/TR/NCATS NIH HHS/United States
PT  - Journal Article
DEP - 20161003
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
SB  - IM
MH  - Computational Biology/*methods/*standards
MH  - Exome/*genetics
MH  - Genome, Human
MH  - High-Throughput Nucleotide Sequencing/methods
MH  - Humans
MH  - Mutation/genetics
MH  - Polymorphism, Single Nucleotide/genetics
MH  - Sequence Alignment/*methods
MH  - *Software
MH  - Workflow
PMC - PMC5048557
OTO - NOTNLM
OT  - Base quality score recalibration
OT  - Human leukocyte antigen
OT  - Local realignment
OT  - Variant calling
OT  - Whole exome sequencing
EDAT- 2016/10/08 06:00
MHDA- 2017/07/19 06:00
PMCR- 2016/10/03
CRDT- 2016/10/08 06:00
PHST- 2016/05/02 00:00 [received]
PHST- 2016/09/26 00:00 [accepted]
PHST- 2016/10/08 06:00 [entrez]
PHST- 2016/10/08 06:00 [pubmed]
PHST- 2017/07/19 06:00 [medline]
PHST- 2016/10/03 00:00 [pmc-release]
AID - 10.1186/s12859-016-1279-z [pii]
AID - 1279 [pii]
AID - 10.1186/s12859-016-1279-z [doi]
PST - epublish
SO  - BMC Bioinformatics. 2016 Oct 3;17(1):403. doi: 10.1186/s12859-016-1279-z.