title: MAC: identifying and correcting annotation for multi-nucleotide variations.
abstract: BACKGROUND
  Next-Generation Sequencing (NGS) technologies have rapidly advanced our understanding of human variation in cancer. To accurately translate the raw sequencing data into practical knowledge, annotation tools, algorithms and pipelines must be developed that keep pace with the rapidly evolving technology. Currently, a challenge exists in accurately annotating multi-nucleotide variants (MNVs). These tandem substitutions, when affecting multiple nucleotides within a single protein codon of a gene, result in a translated amino acid involving all nucleotides in that codon. Most existing variant callers report a MNV as individual single-nucleotide variants (SNVs), often resulting in multiple triplet codon sequences and incorrect amino acid predictions. To correct potentially misannotated MNVs among reported SNVs, a primary challenge resides in haplotype phasing which is to determine whether the neighboring SNVs are co-located on the same chromosome.
  
  
  RESULTS
  Here we describe MAC (Multi-Nucleotide Variant Annotation Corrector), an integrative pipeline developed to correct potentially mis-annotated MNVs. MAC was designed as an application that only requires a SNV file and the matching BAM file as data inputs. Using an example data set containing 3024 SNVs and the corresponding whole-genome sequencing BAM files, we show that MAC identified eight potentially mis-annotated SNVs, and accurately updated the amino acid predictions for seven of the variant calls.
  
  
  CONCLUSIONS
  MAC can identify and correct amino acid predictions that result from MNVs affecting multiple nucleotides within a single protein codon, which cannot be handled by most existing SNV-based variant pipelines. The MAC software is freely available and represents a useful tool for the accurate translation of genomic sequence to protein function.
authors.affiliation: Department of Biostatistics & Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA. Lei.Wei@RoswellPark.org.
authors.forename: Lei
authors.initials: L
authors.lastname: Wei
authors.affiliation: Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA. Lu.Liu@RoswellPark.org.
authors.forename: Lu T
authors.initials: LT
authors.lastname: Liu
authors.affiliation: Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA. jrc63@duke.edu.
authors.forename: Jacob R
authors.initials: JR
authors.lastname: Conroy
authors.affiliation: Department of Biostatistics & Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA. Qiang.Hu@RoswellPark.org.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Hu
authors.affiliation: Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA. Jeffrey.Conroy@RoswellPark.org.
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Conroy
authors.affiliation: Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA. Carl.Morrison@RoswellPark.org.
authors.forename: Carl D
authors.initials: CD
authors.lastname: Morrison
authors.affiliation: Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, NY, USA. Candace.Johnson@RoswellPark.org.
authors.forename: Candace S
authors.initials: CS
authors.lastname: Johnson
authors.affiliation: Department of Biostatistics & Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA. Jianmin.Wang@RoswellPark.org.
authors.forename: Jianmin
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Biostatistics & Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA. Song.Liu@RoswellPark.org.
authors.forename: Song
authors.initials: S
authors.lastname: Liu
mesh_terms: D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D058977:Molecular Sequence Annotation; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*