PMID- 28407084
OWN - NLM
STAT- MEDLINE
DCOM- 20190409
LR  - 20221207
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Print)
IS  - 1467-5463 (Linking)
VI  - 19
IP  - 5
DP  - 2018 Sep 28
TI  - Comparative analysis of de novo assemblers for variation discovery in personal 
      genomes.
PG  - 893-904
LID - 10.1093/bib/bbx037 [doi]
AB  - Current variant discovery approaches often rely on an initial read mapping to the 
      reference sequence. Their effectiveness is limited by the presence of gaps, 
      potential misassemblies, regions of duplicates with a high-sequence similarity 
      and regions of high-sequence divergence in the reference. Also, mapping-based 
      approaches are less sensitive to large INDELs and complex variations and provide 
      little phase information in personal genomes. A few de novo assemblers have been 
      developed to identify variants through direct variant calling from the assembly 
      graph, micro-assembly and whole-genome assembly, but mainly for whole-genome 
      sequencing (WGS) data. We developed SGVar, a de novo assembly workflow for 
      haplotype-based variant discovery from whole-exome sequencing (WES) data. Using 
      simulated human exome data, we compared SGVar with five variation-aware de novo 
      assemblers and with BWA-MEM together with three haplotype- or local de novo 
      assembly-based callers. SGVar outperforms the other assemblers in sensitivity and 
      tolerance of sequencing errors. We recapitulated the findings on whole-genome and 
      exome data from a Utah residents with Northern and Western European ancestry 
      (CEU) trio, showing that SGVar had high sensitivity both in the highly divergent 
      human leukocyte antigen (HLA) region and in non-HLA regions of chromosome 6. In 
      particular, SGVar is robust to sequencing error, k-mer selection, divergence 
      level and coverage depth. Unlike mapping-based approaches, SGVar is capable of 
      resolving long-range phase and identifying large INDELs from WES, more 
      prominently from WGS. We conclude that SGVar represents an ideal platform for 
      WES-based variant discovery in highly divergent regions and across the whole 
      genome.
FAU - Tian, Shulan
AU  - Tian S
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, Rochester, MN, USA.
FAU - Yan, Huihuang
AU  - Yan H
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, Rochester, MN, USA.
FAU - Klee, Eric W
AU  - Klee EW
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, Rochester, MN, USA.
AD  - Center for Individualized Medicine Bioinformatics Program, Mayo Clinic, USA.
FAU - Kalmbach, Michael
AU  - Kalmbach M
AD  - Division of Information Management and Analytics, Department of Information 
      Technology, Mayo Clinic, USA.
FAU - Slager, Susan L
AU  - Slager SL
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, Rochester, MN, USA.
LA  - eng
GR  - U01 CA118444/CA/NCI NIH HHS/United States
GR  - UL1 TR000135/TR/NCATS NIH HHS/United States
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - England
TA  - Brief Bioinform
JT  - Briefings in bioinformatics
JID - 100912837
RN  - 0 (HLA Antigens)
SB  - IM
MH  - Chromosome Mapping/methods/statistics & numerical data
MH  - Chromosomes, Human, Pair 6/genetics
MH  - Computational Biology/methods
MH  - Computer Simulation
MH  - Female
MH  - *Genetic Variation
MH  - Genome, Human
MH  - HLA Antigens/genetics
MH  - Haplotypes
MH  - Humans
MH  - INDEL Mutation
MH  - Polymorphism, Single Nucleotide
MH  - Pregnancy
MH  - Exome Sequencing/*methods/statistics & numerical data
MH  - Whole Genome Sequencing/methods/statistics & numerical data
PMC - PMC6169673
EDAT- 2017/04/14 06:00
MHDA- 2019/04/10 06:00
PMCR- 2017/04/11
CRDT- 2017/04/14 06:00
PHST- 2016/01/12 00:00 [received]
PHST- 2017/03/08 00:00 [accepted]
PHST- 2017/04/14 06:00 [pubmed]
PHST- 2019/04/10 06:00 [medline]
PHST- 2017/04/14 06:00 [entrez]
PHST- 2017/04/11 00:00 [pmc-release]
AID - 3603524 [pii]
AID - bbx037 [pii]
AID - 10.1093/bib/bbx037 [doi]
PST - ppublish
SO  - Brief Bioinform. 2018 Sep 28;19(5):893-904. doi: 10.1093/bib/bbx037.