Browsing by Author "Rasekh, M. E."

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    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).
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    VALOR2: characterization of large-scale structural variants using linked-reads
    (BioMed Central Ltd., 2020-03) Karaoğlanoğlu, Fatih; Ricketts, C.; Ebren, Ezgi; Rasekh, M. E.; Hajirasouliha, I.; Alkan, Can
    Most existing methods for structural variant detection focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced structural variants with no gain or loss of genomic segments, for example, inversions and translocations, is a particularly challenging task. Furthermore, there are very few algorithms to predict the insertion locus of large interspersed segmental duplications and characterize translocations. Here, we propose novel algorithms to characterize large interspersed segmental duplications, inversions, deletions, and translocations using linked-read sequencing data. We redesign our earlier algorithm, VALOR, and implement our new algorithms in a new software package, called VALOR2.