Srivatsan Sridhar

All Publications

• Impact of lossy compression of nanopore raw signal data on basecalling and consensus accuracy Bioinformatics (Oxford, England) Chandak, S. n., Tatwawadi, T. n., Sridhar, S. n., Weissman, T. n. 2020

  Abstract

  Nanopore sequencing provides a real-time and portable solution to genomic sequencing, enabling better assembly, structural variant discovery and modified base detection than second generation technologies. The sequencing process generates a huge amount of data in the form of raw signal contained in fast5 files, which must be compressed to enable efficient storage and transfer. Since the raw data is inherently noisy, lossy compression has potential to significantly reduce space requirements without adversely impacting performance of downstream applications.We explore the use of lossy compression for nanopore raw data using two state-of-the-art lossy time-series compressors, and evaluate the tradeoff between compressed size and basecalling/consensus accuracy. We test several basecallers and consensus tools on a variety of datasets at varying depths of coverage, and conclude that lossy compression can provide 35–50% further reduction in compressed size of raw data over the state-of-the-art lossless compressor with negligible impact on basecalling accuracy (⁠≲0.2% reduction) and consensus accuracy (⁠≲0.002% reduction). In addition, we evaluate the impact of lossy compression on methylation calling accuracy and observe that this impact is minimal for similar reductions in compressed size, although further evaluation with improved benchmark datasets is required for reaching a definite conclusion. The results suggest the possibility of using lossy compression, potentially on the nanopore sequencing device itself, to achieve significant reductions in storage and transmission costs while preserving the accuracy of downstream applications.The code is available at https://github.com/shubhamchandak94/lossy_compression_evaluation.

  View details for DOI 10.1093/bioinformatics/btaa1017

  View details for PubMedID 33325499

• Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares using Random Projections IEEE Journal on Selected Areas in Information Theory Sridhar, S., Pilanci, M., Ozgur, A. 2020