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

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.

SUBMITTER: Moutsopoulos I

PROVIDER: S-EPMC8373073 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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

Moutsopoulos Ilias I Maischak Lukas L Lauzikaite Elze E Vasquez Urbina Sergio A SA Williams Eleanor C EC Drost Hajk-Georg HG Mohorianu Irina I II

Nucleic acids research 20210801 14

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 ...[more]

PMID: 34076236

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Project description:MotivationAnalysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks.ResultsWe present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals.Availability and implementationDRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR.Contactblatti@illinois.eduSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

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

Publications

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

Similar Datasets

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