Calculating Kolmogorov Complexity from the Transcriptome Data

Seekaki, Panpaki; Ogata, Naoya

doi:10.1007/978-3-319-63312-1_46

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…All short reads were mapped to the CHO-K1 RefSeq assembly (22,516 sequences; RefSeq Assembly ID: GCF_000223135.1) and CHO-K1 mitochondrial DNA (1 sequence; RefSeq Assembly ID: GCF_000055695.1) using Bowtie2 (version 2.3.4.1; Langmead & Salzberg, 2012 ) and quantified using RSEM (version 1.2.31, Li & Dewey, 2011 ). Shannon’s information entropy, which can be used to assess changes in transcriptome diversity ( Ogata, Yokoyama & Iwabuchi, 2012 ; Ogata et al, 2015 ; Seekaki & Ogata, 2017 ; Kannan et al, 2020 ; Liu et al, 2020 ) was calculated based on TPM (transcripts per million). Principal component analysis (PCA) based on TPM was performed with the prcomp function with scale = TRUE in the R Stats package (version 3.3.3; R Development Core Team, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…Shannon's information entropy, which can be used to assess changes in transcriptome diversity(Ogata, Yokoyama & Iwabuchi, 2012;Ogata et al, 2015;Seekaki & Ogata, 2017;Kannan et al, 2020;Liu et al, 2020) was calculated based on TPM (transcripts per million). Principal component analysis (PCA) based on TPM was performed with the prcomp function with scale = TRUE in the R Stats package (version 3.3.3; R Development Core Team, 2016).…”

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confidence: 99%

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Importance of experimental information (metadata) for archived sequence data: case of specific gene bias due to lag time between sample harvest and RNA protection in RNA sequencing

Matsuda¹

2021

PeerJ

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Large volumes of high-throughput sequencing data have been submitted to the Sequencing Read Archive (SRA). The lack of experimental metadata associated with the data makes reuse and understanding data quality very difficult. In the case of RNA sequencing (RNA-Seq), which reveals the presence and quantity of RNA in a biological sample at any moment, it is necessary to consider that gene expression responds over a short time interval (several seconds to a few minutes) in many organisms. Therefore, to isolate RNA that accurately reflects the transcriptome at the point of harvest, raw biological samples should be processed by freezing in liquid nitrogen, immersing in RNA stabilization reagent or lysing and homogenizing in RNA lysis buffer containing guanidine thiocyanate as soon as possible. As the number of samples handled simultaneously increases, the time until the RNA is protected can increase. Here, to evaluate the effect of different lag times in RNA protection on RNA-Seq data, we harvested CHO-S cells after 3, 5, 6, and 7 days of cultivation, added RNA lysis buffer in a time course of 15, 30, 45, and 60 min after harvest, and conducted RNA-Seq. These RNA samples showed high RNA integrity number (RIN) values indicating non-degraded RNA, and sequence data from libraries prepared with these RNA samples was of high quality according to FastQC. We observed that, at the same cultivation day, global trends of gene expression were similar across the time course of addition of RNA lysis buffer; however, the expression of some genes was significantly different between the time-course samples of the same cultivation day; most of these differentially expressed genes were related to apoptosis. We conclude that the time lag between sample harvest and RNA protection influences gene expression of specific genes. It is, therefore, necessary to know not only RIN values of RNA and the quality of the sequence data but also how the experiment was performed when acquiring RNA-Seq data from the database.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Importance of experimental information (metadata) for archived sequence data: case of specific gene bias due to lag time between sample harvest and RNA protection in RNA sequencing

Matsuda¹

2021

PeerJ

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“…MetPlast has been initially developed using MS data sets. Missing or negative values should be treated using either standard or more sophisticated methodologies as described in (Seekaki and Ogata, 2017).…”

Section: Package Structure and Usagementioning

confidence: 99%

MetPlast: an R package to evaluate Metabolic Plasticity using Information Theory statistical framework

D'Andrea

Souza

Fernie

et al. 2023

Preprint

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MetPlast, is an R package that provides the functionalities necessary to analyze biological samples metabolic plasticity. This package takes advantage of the statistical framework provided by the Information Theory to quantify and defined metabolic plasticity parameters. Using previous implemented formula based on Shannon entropy we automatize the calculation and visualization of a set of metabolic plasticity indexes including metabolic diversity, metabolome specialization, and metabolite specialization. We use a publicly available metabolic data set on tomato domestication to demonstrate the power of the present tool to evaluate changes in metabolic plasticity parameters. Thus, MetPlast represents a new and invaluable member of the computational metabolomics toolbox, that will certainly help scientists to unveil hidden information in cell metabolomic landscape.

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Synergic Interaction between Permethrin and Bt toxins discovered using RNA-seq

Sakamoto

Kozaki

Ogata³

2019

Preprint

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Acting against the development of resistance to antibiotics and insecticides, involving negatively correlated cross-resistance (NCR) is an alternative to use-and-discard approach. It is termed NCR that toxic chemicals interact with each other and resistance of target organisms to one chemical is sometimes associated with increased susceptibility to a second chemical when; an allele confers resistance to one toxic chemical and hyper-susceptibility to another, NCR occurs. However, only 11 toxin pairs have been revealed to cause NCR in insects. Finding novel NCRs is needed for integrated pest management. We analyzed permethrin, an insecticide, induced transcriptomes of cultured fat bodies of the silkworm Bombyx mori, a lepidopteran model insect. Differentially expressed gene analyses suggested Bacillus thuringiensis (Bt) toxin was an NCR toxin of permethrin. NCR to permethrin and Bt toxins inThysanoplusia intermixta, the agricultural pest moth, was examined; the children of permethrin survivor T. intermixta had increased susceptibility to Bt toxin. A novel NCR toxin pair, permethrin and Bt toxin, was discovered. The screening and developmental method for negatively correlated cross-resistance toxins established in this study was effective, in vitro screening using model organisms and in vivo verification using agricultural pests.

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Calculating Kolmogorov Complexity from the Transcriptome Data

Cited by 6 publications

References 8 publications

Importance of experimental information (metadata) for archived sequence data: case of specific gene bias due to lag time between sample harvest and RNA protection in RNA sequencing

Importance of experimental information (metadata) for archived sequence data: case of specific gene bias due to lag time between sample harvest and RNA protection in RNA sequencing

MetPlast: an R package to evaluate Metabolic Plasticity using Information Theory statistical framework

Synergic Interaction between Permethrin and Bt toxins discovered using RNA-seq

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