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Bioinformatics. 2017 May 15;33(10):1437-1446. doi: 10.1093/bioinformatics/btw799.

High-throughput interpretation of gene structure changes in human and nonhuman resequencing data, using ACE.

Author information

Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27710, USA.
Center for Genomic and Computational Biology, Duke University Medical School, Durham, NC 27710, USA.
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT 84112, USA.
USDA ARS NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA.
Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, NC 27710, USA.
USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.



The accurate interpretation of genetic variants is critical for characterizing genotype-phenotype associations. Because the effects of genetic variants can depend strongly on their local genomic context, accurate genome annotations are essential. Furthermore, as some variants have the potential to disrupt or alter gene structure, variant interpretation efforts stand to gain from the use of individualized annotations that account for differences in gene structure between individuals or strains.


We describe a suite of software tools for identifying possible functional changes in gene structure that may result from sequence variants. ACE ('Assessing Changes to Exons') converts phased genotype calls to a collection of explicit haplotype sequences, maps transcript annotations onto them, detects gene-structure changes and their possible repercussions, and identifies several classes of possible loss of function. Novel transcripts predicted by ACE are commonly supported by spliced RNA-seq reads, and can be used to improve read alignment and transcript quantification when an individual-specific genome sequence is available. Using publicly available RNA-seq data, we show that ACE predictions confirm earlier results regarding the quantitative effects of nonsense-mediated decay, and we show that predicted loss-of-function events are highly concordant with patterns of intolerance to mutations across the human population. ACE can be readily applied to diverse species including animals and plants, making it a broadly useful tool for use in eukaryotic population-based resequencing projects, particularly for assessing the joint impact of all variants at a locus.

Availability and Implementation:

ACE is written in open-source C ++ and Perl and is available from

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Supplementary information:

Supplementary information is available at Bioinformatics online.

[Indexed for MEDLINE]
Free PMC Article

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