sismonr: simulation of in silico multi-omic networks with adjustable ploidy and post-transcriptional regulation in R

2020 ◽  
Vol 36 (9) ◽  
pp. 2938-2940
Author(s):  
Olivia Angelin-Bonnet ◽  
Patrick J Biggs ◽  
Samantha Baldwin ◽  
Susan Thomson ◽  
Matthieu Vignes
Keyword(s):  
In Silico ◽  
Regulatory Networks ◽  
Anthocyanin Biosynthesis ◽  
Expression Profiles ◽  
R Package ◽  
Supplementary Information ◽  
Protein Coding ◽  
Stochastic Simulation Algorithms ◽  
Post Transcriptional Regulation ◽  
Biosynthesis Regulation

Abstract Summary We present sismonr, an R package for an integral generation and simulation of in silico biological systems. The package generates gene regulatory networks, which include protein-coding and non-coding genes along with different transcriptional and post-transcriptional regulations. The effect of genetic mutations on the system behaviour is accounted for via the simulation of genetically different in silico individuals. The ploidy of the system is not restricted to the usual haploid or diploid situations but can be defined by the user to higher ploidies. A choice of stochastic simulation algorithms allows us to simulate the expression profiles of the genes in the in silico system. We illustrate the use of sismonr by simulating the anthocyanin biosynthesis regulation pathway for three genetically distinct in silico plants. Availability and implementation The sismonr package is implemented in R and Julia and is publicly available on the CRAN repository (https://CRAN.R-project.org/package=sismonr). A detailed tutorial is available from GitHub at https://oliviaab.github.io/sismonr/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals iMIRAGE: an R package to impute microRNA expression using protein-coding genes

2019 ◽  
Vol 36 (8) ◽  
pp. 2608-2610
Author(s):  
Aritro Nath ◽  
Jeremy Chang ◽  
R Stephanie Huang
Keyword(s):  
Gene Expression ◽  
Small Rnas ◽  
Transcriptional Regulators ◽  
R Package ◽  
Machine Learning Algorithms ◽  
Supplementary Information ◽  
Expression Data ◽  
Protein Coding ◽  
Altered Protein ◽  
Independent Test

Abstract Summary MicroRNAs (miRNAs) are critical post-transcriptional regulators of gene expression. Due to challenges in accurate profiling of small RNAs, a vast majority of public transcriptome datasets lack reliable miRNA profiles. However, the biological consequence of miRNA activity in the form of altered protein-coding gene (PCG) expression can be captured using machine-learning algorithms. Here, we present iMIRAGE (imputed miRNA activity from gene expression), a convenient tool to predict miRNA expression using PCG expression of the test datasets. The iMIRAGE package provides an integrated workflow for normalization and transformation of miRNA and PCG expression data, along with the option to utilize predicted miRNA targets to impute miRNA activity from independent test PCG datasets. Availability and implementation The iMIRAGE package for R, along with package documentation and vignette, is available at https://aritronath.github.io/iMIRAGE/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

2019 ◽  
Author(s):  
Deepank R Korandla ◽  
Jacob M Wozniak ◽  
Anaamika Campeau ◽  
David J Gonzalez ◽  
Erik S Wright
Keyword(s):  
R Package ◽  
Evolutionary Conservation ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Gene Finding ◽  
Proteomics Data ◽  
Protein Coding ◽  
New Approach ◽  
Protein Coding Genes ◽  
Clear Winner

Abstract Motivation A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy. Results Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88–95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites. Availability and implementation AssessORF is available as an R package via the Bioconductor package repository. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification and analysis of lncRNA, microRNA and mRNA expression profiles and construction of ceRNA network in Talaromyces marneffei-infected THP-1 macrophage

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10529
Author(s):  
Yueqi Li ◽  
Wudi Wei ◽  
Sanqi An ◽  
Junjun Jiang ◽  
Jinhao He ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
R Package ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Next Generation Sequencing Technology ◽  
Talaromyces Marneffei ◽  
Kegg Analysis ◽  
Cerna Network

Background Competitive endogenous RNA (ceRNA) reveals new mechanisms for interactions between RNAs, which have been considered to play a significant role in pathogen-host innate immune response. However, knowledge of ceRNA regulatory networks in Talaromyces marneffei (TM)-macrophages is still limited. Methods Next-generation sequencing technology (NGS) was used to obtain mRNA, miRNA and lncRNA expression profiles in TM-infected macrophages. The R package DESeq2 was used to identify differentially expressed lncRNA, miRNA and mRNA. The R package GOseq was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and the ceRNA network of lncRNA–miRNA–mRNA interaction was constructed in Cytoscape. Similarly, functional enrichment analysis on mRNA in the ceRNA network. Finally, two mRNAs and four lncRNAs in the ceRNA network were randomly selected to verify the expression using qRT-PCR. Results In total, 119 lncRNAs, 28 miRNAs and 208 mRNAs were identified as differentially expressed RNAs in TM-infected macrophages. The constructed ceRNA network contains 38 lncRNAs, 10 miRNAs and 45 mRNAs. GO and KEGG analysis of mRNA in the ceRNA network indicated that activated pathways in TM-infected macrophages were related to immunity, inflammation and metabolism. The quantitative validation of the expression of four randomly selected differentially expressed lncRNAs, AC006252.1, AC090197.1, IL6R-AS1, LINC02009 and two mRNAs, CSF1, NR4A3 showed that the expression levels were consistent with those in the RNA-sequencing. Conclusions The ceRNA network related to immunity, inflammation and metabolism plays an important role in TM-macrophage interaction. This study may provide effective and novel insights for further understanding the underlying mechanism of TM infection.

scholarly journals Genome-wide identification and comparison of differentially expressed profiles of miRNAs and lncRNAs with associated ceRNA networks in the gonads of Chinese soft-shelled turtle, Pelodiscus sinensis

2019 ◽  
Author(s):  
Xiao Ma ◽  
Shuangshuang Cen ◽  
Luming Wang ◽  
Chao Zhang ◽  
Limin Wu ◽  
...  
Keyword(s):  
Messenger Rna ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Profiles ◽  
Integrated Analysis ◽  
Regulation Mechanism ◽  
Pelodiscus Sinensis ◽  
Specific Expression ◽  
Protein Coding ◽  
Protein Coding Genes

Abstract Abstract Background: Gonad is the major factor affecting the animal reproduction. The regulation mechanism of protein coding genes expression involved reproduction is still remains to be elucidated. Increasing evidence has shown that ncRNAs play key regulatory roles in gene expression in many life processes. The roles of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in reproduction had been investigated in some species. However, the regulation patterns of miRNA and lncRNA in sex biased expression of protein coding genes remains to be elucidated. In this study, we performed an integrated analysis of miRNA, messenger RNA (mRNA), and lncRNA expression profiles to explore their regulatory patterns in the female ovary and male testis of the soft-shelled turtle, Pelodiscus sinensis. Results: We identified 10 796 mature miRNAs, 44 678 mRNAs, and 58 923 lncRNAs in the testis and ovary. A total of 16 817 target genes were identified for miRNAs. Of these, 11 319 mRNAs, 10 495 lncRNAs, and 633 miRNAs were expressed differently. The predicted target genes of these differential expression (DE) miRNAs and lncRNAs included genes related to reproduction regulation. Furthermore, we found that 5 408 DElncRNAs and 186 DE miRNAs showed sex-specific expression. Of these, 3 miRNAs and 917 lncRNAs were testis specific and 186 DEmiRNAs and 4 491 DElncRNAs were ovary specific. We constructed compete endogenous lncRNA-miRNA-mRNA networks using bioinformatics, including 273 DEmRNAs, 5 730 DEmiRNAs, and 2 945 DElncRNAs. The target genes for the different expressed of miRNAs and lncRNAs included Wt1, Creb3l2, Gata4, Wnt2, Nr5a1, Hsd17, Igf2r, H2afz, Lin52, Trim71, Zar1, and Jazf1, etc. Conclusions: In animals, miRNA and lncRNA regulate the reproduction process, including the regulation of oocyte maturation and spermatogenesis. Considering their importance, the identified miRNAs, lncRNAs, and their targets in P. sinensis might be useful for genome editing to produce higher quality aquaculture animals. A thorough understanding of ncRNA-based cellular regulatory networks will aid in the improvement of P. sinensis reproduction traits for aquaculture.

scholarly journals ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets

2018 ◽  
Author(s):  
Hong-Dong Li ◽  
Yunpei Xu ◽  
Xiaoshu Zhu ◽  
Quan Liu ◽  
Gilbert S. Omenn ◽  
...  
Keyword(s):  
Expression Profiles ◽  
R Package ◽  
Biological Data ◽  
Supplementary Information ◽  
Consensus Clustering ◽  
Cluster Membership ◽  
Link Type ◽  
Novel Approach ◽  
Gene Sets ◽  
Biological Interpretation

ABSTRACTMotivationClustering analysis is essential for understanding complex biological data. In widely used methods such as hierarchical clustering (HC) and consensus clustering (CC), expression profiles of all genes are often used to assess similarity between samples for clustering. These methods output sample clusters, but are not able to provide information about which gene sets (functions) contribute most to the clustering. So interpretability of their results is limited. We hypothesized that integrating prior knowledge of annotated biological processes would not only achieve satisfying clustering performance but also, more importantly, enable potential biological interpretation of clusters.ResultsHere we report ClusterMine, a novel approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets, e.g., in Gene Ontology. In addition to outputting cluster membership of each sample as conventional approaches do, it outputs gene sets that are most likely to contribute to the clustering, a feature facilitating biological interpretation. Using three cancer datasets, two single cell RNA-sequencing based cell differentiation datasets, one cell cycle dataset and two datasets of cells of different tissue origins, we found that ClusterMine achieved similar or better clustering performance and that top-scored gene sets prioritized by ClusterMine are biologically relevant.Implementation and availabilityClusterMine is implemented as an R package and is freely available at: www.genemine.org/[email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals debCAM: a bioconductor R package for fully unsupervised deconvolution of complex tissues

2020 ◽  
Vol 36 (12) ◽  
pp. 3927-3929 ◽  
Author(s):  
Lulu Chen ◽  
Chiung-Ting Wu ◽  
Niya Wang ◽  
David M Herrington ◽  
Robert Clarke ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Software Tool ◽  
R Package ◽  
Supplementary Information ◽  
Tissue Cell ◽  
Deconvolution Method ◽  
Imaging Data ◽  
Specific Expression ◽  
Knowledge Incorporation

Abstract Summary We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts. Availability and implementation http://bioconductor.org/packages/debCAM. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Gene Expression Profiles and microRNA Regulation Networks in Tiller Primordia, Stem Tips, and Young Spikes of Wheat Guomai 301

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 686 ◽  
Author(s):  
Li ◽  
Jiao ◽  
He ◽  
Sun ◽  
Xu ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Triticum Aestivum L ◽  
Differentially Expressed ◽  
High Yield ◽  
Differentially Expressed Mirnas ◽  
Key Events ◽  
Post Transcriptional Regulation

Tillering and spike differentiation are two key events for wheat (Triticum aestivum L.). A study on the transcriptomes and microRNA group profiles of wheat at the two key developmental stages will bring insight into the molecular regulation mechanisms. Guomai 301 is a representative excellent new high yield wheat cultivar in the Henan province in China. The transcriptomes and microRNA (miRNA) groups of tiller primordia (TPs), stem tips (STs), and young spikes (YSs) in Guomai 301 were compared to each other. A total of 1741 tillering specifically expressed and 281 early spikes differentiating specifically expressed differentially expressed genes (DEGs) were identified. Six major expression profile clusters of tissue-specific DEGs for the three tissues were classified by gene co-expression analysis using K-means cluster. The ribosome (ko03010), photosynthesis-antenna proteins (ko00196), and plant hormone signal transduction (ko04075) were the main metabolic pathways in TPs, STs, and YSs, respectively. Similarly, 67 TP specifically expressed and 19 YS specifically expressed differentially expressed miRNAs were identified, 65 of them were novel. The roles of 3 well known miRNAs, tae-miR156, tae-miR164, and tae-miR167a, in post-transcriptional regulation were similar to that of other researches. There were 651 significant negative miRNA–mRNA interaction pairs in TPs and YSs, involving 63 differentially expressed miRNAs (fold change > 4) and 416 differentially expressed mRNAs. Among them 12 key known miRNAs and 16 novel miRNAs were further analyzed, and miRNA–mRNA regulatory networks during tillering and early spike differentiating were established.

scholarly journals deTS: tissue-specific enrichment analysis to decode tissue specificity

2019 ◽  
Vol 35 (19) ◽  
pp. 3842-3845 ◽  
Author(s):  
Guangsheng Pei ◽  
Yulin Dai ◽  
Zhongming Zhao ◽  
Peilin Jia
Keyword(s):  
Expression Profiles ◽  
Association Studies ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide ◽  
Specific Regulation

Abstract Motivation Diseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles. Results We present deTS, an R package, to conduct tissue-specific enrichment analysis with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association studies data and cancer expression data showed that deTS could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies. Availability and implementation https://github.com/bsml320/deTS and CRAN https://cran.r-project.org/web/packages/deTS/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals miRTil: An Extensive Repository for Nile Tilapia microRNA Next Generation Sequencing Data

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1752
Author(s):  
Luiz Augusto Bovolenta ◽  
Danillo Pinhal ◽  
Marcio Luis Acencio ◽  
Arthur Casulli de Oliveira ◽  
Simon Moxon ◽  
...  
Keyword(s):  
Nile Tilapia ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Interaction Network ◽  
Mature Mirnas ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Genomic Context ◽  
Protein Coding ◽  
Post Transcriptional Regulation

Nile tilapia is the third most cultivated fish worldwide and a novel model species for evolutionary studies. Aiming to improve productivity and contribute to the selection of traits of economic impact, biotechnological approaches have been intensively applied to species enhancement. In this sense, recent studies have focused on the multiple roles played by microRNAs (miRNAs) in the post-transcriptional regulation of protein-coding genes involved in the emergence of phenotypes with relevance for aquaculture. However, there is still a growing demand for a reference resource dedicated to integrating Nile Tilapia miRNA information, obtained from both experimental and in silico approaches, and facilitating the analysis and interpretation of RNA sequencing data. Here, we present an open repository dedicated to Nile Tilapia miRNAs: the “miRTil database”. The database stores data on 734 mature miRNAs identified in 11 distinct tissues and five key developmental stages. The database provides detailed information about miRNA structure, genomic context, predicted targets, expression profiles, and relative 5p/3p arm usage. Additionally, miRTil also includes a comprehensive pre-computed miRNA-target interaction network containing 4936 targets and 19,580 interactions.

scholarly journals utr.annotation: a tool for annotating genomic variants that could influence post-transcriptional regulation

2021 ◽  
Author(s):  
Yating Liu ◽  
Joseph D Dougherty
Keyword(s):  
R Package ◽  
Open Reading Frames ◽  
Supplementary Information ◽  
Translation Start ◽  
Upstream Open Reading Frames ◽  
Mouse Species ◽  
Translational Regulators ◽  
Post Transcriptional Regulation ◽  
Annotated Translation ◽  
Reading Frames

Abstract Summary Whole genome sequencing of patient populations is identifying thousands of new variants in UnTranslated Regions(UTRs). While the consequences of UTR mutations are not as easily predicted from primary sequence as coding mutations are, there are some known features of UTRs that modulate their function. utr.annotation is an R package that can be used to annotate potential deleterious variants in the UTR regions for both human and mouse species. Given a CSV or VCF format variant file, utr.annotation provides information of each variant on whether and how it alters known translational regulators including: upstream Open Reading Frames (uORFs), upstream Kozak sequences, polyA signals, Kozak sequences at the annotated translation start site, start codons, and stop codons, conservation scores in the variant position, and whether and how it changes ribosome loading based on a model derived from empirical data. Availability utr.annotation is freely available on Bitbucket (https://bitbucket.org/jdlabteam/utr.annotation/src/master/) and CRAN (https://cran.r-project.org/web/packages/utr.annotation/index.html) Supplementary information Supplementary data are available at https://wustl.box.com/s/yye99bryfin89nav45gv91l5k35fxo7z.

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