title: Targeted alignment and end repair elimination increase alignment and methylation measure accuracy for reduced representation bisulfite sequencing data.
abstract: BACKGROUND
  DNA methylation is an important epigenetic modification involved in many biological processes. Reduced representation bisulfite sequencing (RRBS) is a cost-effective method for studying DNA methylation at single base resolution. Although several tools are available for RRBS data processing and analysis, it is not clear which strategy performs the best and there has not been much attention to the contamination issue from artificial cytosines incorporated during the end repair step of library preparation. To address these issues, we describe a new method, Targeted Alignment and Artificial Cytosine Elimination for RRBS (TRACE-RRBS), which aligns bisulfite sequence reads to MSP1 digitally digested reference and specifically removes the end repair cytosines. We compared this approach on a simulated and a real dataset with 7 other RRBS analysis tools and Illumina 450 K microarray platform.
  
  
  RESULTS
  TRACE-RRBS aligns sequence reads to a small fraction of the genome where RRBS protocol targets on and was demonstrated as the fastest, most sensitive and specific tool for the simulated dataset. For the real dataset, TRACE-RRBS took about the same time as RRBSMAP, a third to a sixth of time needed for BISMARK and NOVOALIGN. TRACE-RRBS aligned more reads uniquely than other tools and achieved the highest correlation with 450 k microarray data. The end repair artificial cytosine removal increased correlation between nearby CpGs and accuracy of methylation quantification.
  
  
  CONCLUSIONS
  TRACE-RRBS is fast and more accurate tool for RRBS data analysis. It is freely available for academic use at http://bioinformaticstools.mayo.edu/.
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. baheti.saurabh@mayo.edu.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Baheti
authors.affiliation: Department of Medical Oncology, Mayo Clinic, Rochester, MN, 55905, USA. kanwar.rahul@mayo.edu.
authors.forename: Rahul
authors.initials: R
authors.lastname: Kanwar
authors.affiliation: Department of Medical Oncology, Mayo Clinic, Rochester, MN, 55905, USA. meike.goe@gmail.com.|Charité - Universitaetsmedizin Berlin, Berlin, Germany. meike.goe@gmail.com.
authors.forename: Meike
authors.initials: M
authors.lastname: Goelzenleuchter
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. kocher.jeanpierre@mayo.edu.
authors.forename: Jean-Pierre A
authors.initials: JP
authors.lastname: Kocher
authors.affiliation: Department of Medical Oncology, Mayo Clinic, Rochester, MN, 55905, USA. beutler.andreas@mayo.edu.
authors.forename: Andreas S
authors.initials: AS
authors.lastname: Beutler
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. sun.zhifu@mayo.edu.
authors.forename: Zhifu
authors.initials: Z
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D018899:CpG Islands; D003596:Cytosine; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D023281:Genomics / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D003596:Cytosine