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White, Joseph

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White

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Joseph

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White, Joseph
Author's Publications: search.filters.isAuthorOfPublication.6db45e01-bfaa-442b-9875-54ccab43a3bb

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    Transcriptome analyses of the human retina identify unprecedented transcript diversity and 3.5 Mb of novel transcribed sequence via significant alternative splicing and novel genes
    (BioMed Central, 2013) Farkas, Michael; Grant, Gregory R; White, Joseph; Sousa, Maria E; Consugar, Mark Bryant; Pierce, Eric
    Background: The retina is a complex tissue comprised of multiple cell types that is affected by a diverse set of diseases that are important causes of vision loss. Characterizing the transcripts, both annotated and novel, that are expressed in a given tissue has become vital for understanding the mechanisms underlying the pathology of disease. Results: We sequenced RNA prepared from three normal human retinas and characterized the retinal transcriptome at an unprecedented level due to the increased depth of sampling provided by the RNA-seq approach. We used a non-redundant reference transcriptome from all of the empirically-determined human reference tracks to identify annotated and novel sequences expressed in the retina. We detected 79,915 novel alternative splicing events, including 29,887 novel exons, 21,757 3′ and 5′ alternate splice sites, and 28,271 exon skipping events. We also identified 116 potential novel genes. These data represent a significant addition to the annotated human transcriptome. For example, the novel exons detected increase the number of identified exons by 3%. Using a high-throughput RNA capture approach to validate 14,696 of these novel transcriptome features we found that 99% of the putative novel events can be reproducibly detected. Further, 15-36% of the novel splicing events maintain an open reading frame, suggesting they produce novel protein products. Conclusions: To our knowledge, this is the first application of RNA capture to perform large-scale validation of novel transcriptome features. In total, these analyses provide extensive detail about a previously uncharacterized level of transcript diversity in the human retina.
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    Efficient In Silico Identification of a Common Insertion in the MAK Gene which Causes Retinitis Pigmentosa
    (Public Library of Science, 2015) Bujakowska, Kinga; White, Joseph; Place, Emily; Consugar, Mark; Comander, Jason
    Background: Next generation sequencing (NGS) offers a rapid and comprehensive method of screening for mutations associated with retinitis pigmentosa and related disorders. However, certain sequence alterations such as large insertions or deletions may remain undetected using standard NGS pipelines. One such mutation is a recently-identified Alu insertion into the Male Germ Cell-Associated Kinase (MAK) gene, which is missed by standard NGS-based variant callers. Here, we developed an in silico method of searching NGS raw sequence reads to detect this mutation, without the need to recalculate sequence alignments or to screen every sample by PCR. Methods: The Linux program grep was used to search for a 23 bp “probe” sequence containing the known junction sequence of the insert. A corresponding search was performed with the wildtype sequence. The matching reads were counted and further compared to the known sequences of the full wildtype and mutant genomic loci. (See https://github.com/MEEIBioinformaticsCenter/grepsearch.) Results: In a test sample set consisting of eleven previously published homozygous mutants, detection of the MAK-Alu insertion was validated with 100% sensitivity and specificity. As a discovery cohort, raw NGS reads from 1,847 samples (including custom and whole exome selective capture) were searched in ~1 hour on a local computer cluster, yielding an additional five samples with MAK-Alu insertions and solving two previously unsolved pedigrees. Of these, one patient was homozygous for the insertion, one compound heterozygous with a missense change on the other allele (c. 46G>A; p.Gly16Arg), and three were heterozygous carriers. Conclusions: Using the MAK-Alu grep program proved to be a rapid and effective method of finding a known, disease-causing Alu insertion in a large cohort of patients with NGS data. This simple approach avoids wet-lab assays or computationally expensive algorithms, and could also be used for other known disease-causing insertions and deletions.