Spatial Partitioning of miRNAs Is Related to Sequence Similarity in Overall Transcriptome

Seffens, William; Abebe, Fisseha; Evans, Chad; Wang, Xiaoqian

doi:10.3390/ijms17060830

Cited by 3 publications

(12 citation statements)

References 19 publications

(26 reference statements)

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“…These raw counts can be multiplied by the frequencies of repetitive words in W to yield tWord ¼ tCount * tFreq and rcWord ¼ rcCount * rcFreq. As shown by Seffens [17], miRNAs with greater similarity to the transcriptome, i.e., greater tCount and tWord, are suggested to diffuse differentially based on spatial partitioning. In addition, greater intramolecular RNA-RNA interaction would be expected to hinder diffusion.…”

Section: Spatial Transcriptome Information Cloud (Stic) Model Construmentioning

confidence: 92%

“…There k-mer words of miRNA functional size were added to a dictionary from sliding windows of transcript sequences S. A prediction from this cloud model is that anomalous diffusion can occur if random-walk transcripts interact with their surrounding scaffold as a stochastic semantic cloud, and if the cloud relaxation time is a longer time frame than transit [16]. We showed that RNAs with sequences similar to the whole transcriptome exhibit modified or enhanced transport compared to RNA sequences without similar sequences [17]. Thus, RNAs were found to partition into different cellular compartments based on a semantic similarity of word compositions within W k .…”

Section: Spatial Transcriptome Information Cloud (Stic) Model Construmentioning

confidence: 99%

“…We could also compare to ranCount, number of words in common with a randomized W k . Reverse complement measure (RCM) factors word frequencies to assess transcript-cloud interactions that correlate to measured compartmentalization of individual transcripts [17].…”

Section: Accessibility Of An Rna Sequence Wordmentioning

confidence: 99%

“…The intermediate output from program TIC-generator (for transcriptome information cloud generator, described in Ref. [17]) listed all k-mer words contained in each transcript, together with their frequency of occurrence. These lists from the eight rRNA and tRNA transcripts were combined, and then duplicate words resolved to form dictionary W k 1 .…”

Section: Transcriptome Model 0 (W 0 )mentioning

confidence: 99%

“…These lists from the eight rRNA and tRNA transcripts were combined, and then duplicate words resolved to form dictionary W k 1 . With the total possible number of words of length k ¼ 4 k , the fraction of all the words actually present in W k 1 decreased for increasing word size [17].…”

Section: Transcriptome Model 0 (W 0 )mentioning

confidence: 99%

See 4 more Smart Citations

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

Seffens¹

2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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Resilience is a network property of systems responding under stress, which for biomedicine correlates to chronic or acute insults. Current need exists for models and algorithms to study whole transcriptome differences between tissues and disease states to understand resilience. Goal of this effort is to interpret cellular transcription in a dynamic system biology framework of RNA molecules forming an information structure with regulatory properties acting on individual transcripts. We develop and evaluate a bioinformatics framework based on information theory that utilizes RNA expression data to create a whole transcriptome model of interaction that could lead to the discovery of new biological control mechanisms. This addresses a fundamental question as to why transcription yields such a small fraction of protein products. We focus on a transformative concept that individual transcripts collectively form an "information cloud" of sequence words, which for some genes may have significant regulatory impact. Extending the concept of cis-and trans-regulation, we propose to search for RNAs that are modulated by interactions with the transcriptome cloud and calling such examples nebula regulation. This framework has implications as a paradigm change for RNA regulation and provides a deeper understanding of nucleotide sequence structure and -omic language meaning.

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Section: Spatial Transcriptome Information Cloud (Stic) Model Construmentioning

confidence: 92%

Section: Spatial Transcriptome Information Cloud (Stic) Model Construmentioning

confidence: 99%

Section: Accessibility Of An Rna Sequence Wordmentioning

confidence: 99%

Section: Transcriptome Model 0 (W 0 )mentioning

confidence: 99%

Section: Transcriptome Model 0 (W 0 )mentioning

confidence: 99%

See 3 more Smart Citations

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

Seffens¹

2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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Electrochemical biosensor based on efficient target-trigger T-structure recycling with dual strand displacement amplification for sensing miRNA-155

Zhang

Zhang²,

Ding³

et al. 2023

Analytica Chimica Acta

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Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience

Seffens

2017

Computation

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Much of biology-inspired computer science is based on the Central Dogma, as implemented with genetic algorithms or evolutionary computation. That 60-year-old biological principle based on the genome, transcriptome and proteasome is becoming overshadowed by a new paradigm of complex ordered associations and connections between layers of biological entities, such as interactomes, metabolomics, etc. We define a new hierarchical concept as the "Connectosome", and propose new venues of computational data structures based on a conceptual framework called "Grand Ensemble" which contains the Central Dogma as a subset. Connectedness and communication within and between living or biology-inspired systems comprise ensembles from which a physical computing system can be conceived. In this framework the delivery of messages is filtered by size and a simple and rapid semantic analysis of their content. This work aims to initiate discussion on the Grand Ensemble in network biology as a representation of a Persistent Turing Machine. This framework adding interaction and persistency to the classic Turing-machine model uses metrics based on resilience that has application to dynamic optimization problem solving in Genetic Programming.

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Spatial Partitioning of miRNAs Is Related to Sequence Similarity in Overall Transcriptome

Cited by 3 publications

References 19 publications

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

Electrochemical biosensor based on efficient target-trigger T-structure recycling with dual strand displacement amplification for sensing miRNA-155

Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience

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