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bioRxiv · 10.1101/776807

ConnectedReads: machine-learning optimized long-range genome analysis workflow for next-generation sequencing

Su, C.-T.·Weng, S.·Li, Y.-L.·Chang, M.-T.
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Abstract

Current human genome sequencing assays in both clinical and research settings primarily utilize short-read sequencing and apply resequencing pipelines to detect genetic variants. However, theses mapping-based data analysis pipelines remains a considerable challenge due to an incomplete reference genome, mapping errors and high sequence divergence. To overcome this challenge, we propose an efficient and effective whole-read assembly workflow with unsupervised graph mining algorithms on an Apache Spark large-scale data processing platform called ConnectedReads. By fully utilizing short-read data information, ConnectedReads is able to generate assembled contigs and then benefit downstream pipelines to provide higher-resolution SV discovery than that provided by other methods, especially in high diversity against reference and N-gap regions of reference. Furthermore, we demonstrate a cost-effective approach by leveraging ConnectedReads to investigate all spectra of genetic changes in population-scale studies.

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Su, C.-T., Weng, S., Li, Y.-L., Chang, M.-T.. 2019-09-20. ConnectedReads: machine-learning optimized long-range genome analysis workflow for next-generation sequencing. https://doi.org/10.1101/776807

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