Exome Sequencing: Current and Future Perspectives

Warr, Amanda; Robert, Christelle; Hume, David; Archibald, Alan; Deeb, Nader; Watson, Michael

doi:10.1534/g3.115.018564

Cited by 186 publications

(139 citation statements)

References 79 publications

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“…These strategies usually rely on addressing the gene space, but many of them, such as RNA-Seq (Wang et al, 2010) or exome capture (Warr et al, 2015), are beyond reach of scientists working on crops of lesser economical importance, due to high cost involved or because an extensive preexisting genome sequence knowledge is required. Diversity Arrays Technology (Jaccoud et al, 2001) is an affordable, sequence independent alternative, which delivers several thousand genetic markers in a single assay.…”

Section: Discussionmentioning

confidence: 99%

DArT Markers Effectively Target Gene Space in the Rye Genome

et al. 2016

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Large genome size and complexity hamper considerably the genomics research in relevant species. Rye (Secale cereale L.) has one of the largest genomes among cereal crops and repetitive sequences account for over 90% of its length. Diversity Arrays Technology is a high-throughput genotyping method, in which a preferential sampling of gene-rich regions is achieved through the use of methylation sensitive restriction enzymes. We obtained sequences of 6,177 rye DArT markers and following a redundancy analysis assembled them into 3,737 non-redundant sequences, which were then used in homology searches against five Pooideae sequence sets. In total 515 DArT sequences could be incorporated into publicly available rye genome zippers providing a starting point for the integration of DArT- and transcript-based genomics resources in rye. Using Blast2Go pipeline we attributed putative gene functions to 1101 (29.4%) of the non-redundant DArT marker sequences, including 132 sequences with putative disease resistance-related functions, which were found to be preferentially located in the 4RL and 6RL chromosomes. Comparative analysis based on the DArT sequences revealed obvious inconsistencies between two recently published high density consensus maps of rye. Furthermore we demonstrated that DArT marker sequences can be a source of SSR polymorphisms. Obtained data demonstrate that DArT markers effectively target gene space in the large, complex, and repetitive rye genome. Through the annotation of putative gene functions and the alignment of DArT sequences relative to reference genomes we obtained information, that will complement the results of the studies, where DArT genotyping was deployed, by simplifying the gene ontology and microcolinearity based identification of candidate genes.

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Section: Discussionmentioning

confidence: 99%

DArT Markers Effectively Target Gene Space in the Rye Genome

et al. 2016

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“…Whereas whole genome sequencing does not require any a priori knowledge, for exome capture it is necessary to have some level of knowledge of the intron boundaries and gene content of the organism of interest. Because well‐annotated genomes improve probe design, high‐quality reference genomes and transcriptomes reduce the risk of false positives or missing important variants in the generated data set (Chamala et al., 2015; Warr et al., 2015). …”

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confidence: 99%

Capturing variation in Lens (Fabaceae): Development and utility of an exome capture array for lentil

et al. 2018

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Premise of the StudyLentil is an important legume crop with reduced genetic diversity caused by domestication bottlenecks. Due to its large and complex genome, tools for reduced representation sequencing are needed. We developed an exome capture array for use in various genetic diversity studies.MethodsBased on the CDC Redberry draft genome, we developed an exome capture array using multiple sources of transcript resources. The probes were designed to target not only the cultivated lentil, but also wild species. We assessed the utility of the developed method by applying the generated data set to population structure and phylogenetic analyses.ResultsThe data set includes 16 wild lentils and 22 cultivar accessions of lentil. Alignment rates were over 90%, and the genic regions were well represented in the capture array. After stringent filtering, 6.5 million high‐quality variants were called, and the data set was used to assess the interspecific relationships within the genus Lens.DiscussionThe developed exome capture array provides large amounts of genomic data to be used in many downstream analyses. The method will have useful applications in marker‐assisted breeding programs aiming to improve the quality of cultivated lentil.

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“…1,2 In recessive conditions, two of such variants have forcibly to be present in the same gene to cause disease, lowering the number of candidate genes associated with the pathology to only 5-10, genome-wide. 1,3,4 In contrast, any gene harboring one of these 400 variants in a heterozygous state represents potentially a gene associated with a dominant disorder, making it difficult to identify the cause of this class of genetic conditions ( Figure 1A). As a consequence, NGS-based studies appear to be almost 10-fold more efficient in detecting genes associated to recessive disorders as compared to dominant ones.…”

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confidence: 99%

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

Quinodoz

Royer‐Bertrand²,

Cisarova

et al. 2017

The American Journal of Human Genetics

109

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In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a 400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that are the properties of genes, rather than of variants. It uses a machine-learning approach to extract discriminant information from a broad array of features (N ¼ 432), including: genomic data, intra-, and interspecies conservation, gene expression, protein-protein interactions, protein structure, etc. DOMINO's iterative architecture includes a training process on 985 genes with well-established inheritance patterns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power. When validated on 99 newly-discovered genes with pathogenic mutations, the algorithm displays an excellent final performance, with an area under the curve (AUC) of 0.92. Furthermore, unsupervised analysis by DOMINO of real sets of NGS data from individuals with intellectual disability or epilepsy correctly recognizes known genes and predicts 9 new candidates, with very high confidence. In summary, DOMINO is a robust and reliable tool that can infer dominance of candidate genes with high sensitivity and specificity, making it a useful complement to any NGS pipeline dealing with the analysis of the morbid human genome.

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Exome Sequencing: Current and Future Perspectives

Cited by 186 publications

References 79 publications

DArT Markers Effectively Target Gene Space in the Rye Genome

DArT Markers Effectively Target Gene Space in the Rye Genome

Capturing variation in Lens (Fabaceae): Development and utility of an exome capture array for lentil

DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

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