Efficient detection and assembly of non-reference DNA sequences with synthetic long reads |
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Authors: | Dmitry Meleshko Rui Yang Patrick Marks Stephen Williams Iman Hajirasouliha |
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Affiliation: | Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, NY 10021, USA;Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, NY 10021, USA;10x Genomics Inc., Stoneridge Mall Road, Pleasanton, CA 94566, USA;Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, NY 10021, USA |
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Abstract: | Recent pan-genome studies have revealed an abundance of DNA sequences in human genomes that are not present in the reference genome. A lion’s share of these non-reference sequences (NRSs) cannot be reliably assembled or placed on the reference genome. Improvements in long-read and synthetic long-read (aka linked-read) technologies have great potential for the characterization of NRSs. While synthetic long reads require less input DNA than long-read datasets, they are algorithmically more challenging to use. Except for computationally expensive whole-genome assembly methods, there is no synthetic long-read method for NRS detection. We propose a novel integrated alignment-based and local assembly-based algorithm, Novel-X, that uses the barcode information encoded in synthetic long reads to improve the detection of such events without a whole-genome de novo assembly. Our evaluations demonstrate that Novel-X finds many non-reference sequences that cannot be found by state-of-the-art short-read methods. We applied Novel-X to a diverse set of 68 samples from the Polaris HiSeq 4000 PGx cohort. Novel-X discovered 16 691 NRS insertions of size > 300 bp (total length 18.2 Mb). Many of them are population specific or may have a functional impact. |
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