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Journal Article

noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise

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Maischak,  L
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

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Vasquez Urbina,  SA
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

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Drost,  H-G
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

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Citation

Moutsopoulos, I., Maischak, L., Lauzikaite, E., Vasquez Urbina, S., Williams, E., Drost, H.-G., et al. (2021). noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise. Nucleic Acids Research, 49(14): e83. doi:10.1093/nar/gkab433.


Cite as: https://hdl.handle.net/21.11116/0000-000A-4C7F-4
Abstract
High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.