Challenges in measuring and understanding biological noise

Eling, Nils; Morgan, Michael D.; Marioni, John C.

doi:10.1038/s41576-019-0130-6

Cited by 179 publications

(150 citation statements)

References 140 publications

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“…But if we captured the sex information when analyzing these samples, we would observe less variability. If we could capture in the annotations all conditions impacting gene expression, what would be left to observe would only be gene expression noise and stochasticity 81 , but we are far from that perspective.…”

Section: Providing This Clear Gene-level Expression Information Allowmentioning

confidence: 98%

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bastian

Roux

Niknejad³

et al. 2020

Preprint

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Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced from multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as "healthy" or not). Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Several tools are available, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

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Section: Providing This Clear Gene-level Expression Information Allowmentioning

confidence: 98%

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Bastian

Roux

Niknejad³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…smORF247301 has translation evidence through ribosomal profiling (Carvunis et al, 2012). One possibility is that the transcription and translation of smORF247301 is "noise" (Eling et al, 2019) YPL223C. Another possibility is that smORF247301 is a young, notyet-annotated gene that is "piggybacking" on the expression apparatus of YPL223C.…”

Section: Exploring Gene Functionmentioning

confidence: 99%

Landscape of the Dark Transcriptome Revealed through Re-mining Massive RNA-Seq Data

Jing

Singh

Arendsee

et al. 2019

Preprint

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The “dark transcriptome” can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated ORFs in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 48% of the transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins (“orphan-ORFs”); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph, a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally-testable hypotheses about candidate genes.

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“…Kalmar et al , ) and homogeneity of dynamic cellular behaviours across the population, both of which may deviate from reality, as recently shown in Manning et al (). Even with improved methods of incorporating prior factors of interest in such methods (Campbell & Yau, ), biological noise analysis is by necessity restricted to quantifying the variability in the data (Eling et al , ) and cannot provide information on the dynamic properties of noise.…”

Section: Introductionmentioning

confidence: 99%

Dynamic properties of noise and Her6 levels are optimized by miR‐9, allowing the decoding of the Her6 oscillator

et al. 2020

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Noise is prevalent in biology and has been widely quantified using snapshot measurements. This static view obscures our understanding of dynamic noise properties and how these affect gene expression and cell state transitions. Using a CRISPR/Cas9 Zebrafish her6::Venus reporter combined with mathematical and in vivo experimentation, we explore how noise affects the protein dynamics of Her6, a basic helix‐loop‐helix transcriptional repressor. During neurogenesis, Her6 expression transitions from fluctuating to oscillatory at single‐cell level. We identify that absence of miR‐9 input generates high‐frequency noise in Her6 traces, inhibits the transition to oscillatory protein expression and prevents the downregulation of Her6. Together, these impair the upregulation of downstream targets and cells accumulate in a normally transitory state where progenitor and early differentiation markers are co‐expressed. Computational modelling and double smFISH of her6 and the early neurogenesis marker, elavl3, suggest that the change in Her6 dynamics precedes the downregulation in Her6 levels. This sheds light onto the order of events at the moment of cell state transition and how this is influenced by the dynamic properties of noise. Our results suggest that Her/Hes oscillations, facilitated by dynamic noise optimization by miR‐9, endow progenitor cells with the ability to make a cell state transition.

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Challenges in measuring and understanding biological noise

Cited by 179 publications

References 140 publications

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Landscape of the Dark Transcriptome Revealed through Re-mining Massive RNA-Seq Data

Dynamic properties of noise and Her6 levels are optimized by miR‐9, allowing the decoding of the Her6 oscillator

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