Browsing by Author "Tang, J."

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Open Access
Discovery of large genomic inversions using long range information
(BioMed Central Ltd., 2017) Rasekh, M. E.; Chiatante, G.; Miroballo, M.; Tang, J.; Ventura M.; Amemiya, C. T.; Eichler, E. E.; Antonacci, F.; Alkan C.
Although many algorithms are now available that aim to characterize different classes of structural variation, discovery of balanced rearrangements such as inversions remains an open problem. This is mainly due to the fact that breakpoints of such events typically lie within segmental duplications or common repeats, which reduces the mappability of short reads. The algorithms developed within the 1000 Genomes Project to identify inversions are limited to relatively short inversions, and there are currently no available algorithms to discover large inversions using high throughput sequencing technologies. Results: Here we propose a novel algorithm, Valor, to discover large inversions using new sequencing methods that provide long range information such as 10X Genomics linked-read sequencing, pooled clone sequencing, or other similar technologies that we commonly refer to as long range sequencing. We demonstrate the utility of Valor using both pooled clone sequencing and 10X Genomics linked-read sequencing generated from the genome of an individual from the HapMap project (NA12878). We also provide a comprehensive comparison of Valor against several state-of-the-art structural variation discovery algorithms that use whole genome shotgun sequencing data. Conclusions: In this paper, we show that Valor is able to accurately discover all previously identified and experimentally validated large inversions in the same genome with a low false discovery rate. Using Valor, we also predicted a novel inversion, which we validated using fluorescent in situ hybridization. Valor is available at https://github.com/BilkentCompGen/Valor. © 2017 The Author(s).
Open Access
Univariate X̄ control charts for individual characteristics in a multinormal model
(Taylor & Francis, 2000) Serel, D. A.; Moskowitz, H.; Tang, J.
The early work on multivariate statistical process control was built upon Hotelling's T2 control chart which was developed to simultaneously monitor the means of correlated quality variables. This chart, however, has a drawback, namely, the problem of identifying the responsible variable(s) when an out-of-control signal occurs. One alternative is to use a separate X̄ control chart for each individual characteristic with equal risks, based on Bonferroni inequality. In this study, we show that, from an economic perspective, it may be desirable to have unequal type I risks for the individual charts, because of different inspection and restoration costs associated with each variable. We obtain their risk ratios, which are measures of relative importance of the variables monitored. Then, based on these risk ratios, we develop computer algorithms for finding the exact control limits for individual variables from a multinormal distribution, in the sense that the overall type I risk of the charts is equal to the desired value. Numerical studies show that the proposed methods give optimal or near-optimal results from an economic as well as statistical point of view.