scholarly journals An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Frédéric Jehl ◽  
Kévin Muret ◽  
Maria Bernard ◽  
Morgane Boutin ◽  
Laetitia Lagoutte ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Biological Processes ◽  
Rna Seq ◽  
Protein Coding ◽  
Tissue Specific ◽  
Protein Coding Genes ◽  
Intergenic Regions ◽  
Immune Tissues ◽  
Non Coding Rnas

AbstractLong non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC (http://www.fragencode.org/), built from the Ensembl reference extended using novel LNC modelled here from 364 RNA-seq and LNC from four public databases. The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.

scholarly journals Dynamic Expression of Long Non-Coding RNAs Throughout Parasite Sexual and Neural Maturation in Schistosoma Japonicum

2020 ◽  
Vol 6 (2) ◽  
pp. 15 ◽  
Author(s):  
Lucas Maciel ◽  
David Morales-Vicente ◽  
Sergio Verjovski-Almeida
Keyword(s):  
Schistosoma Japonicum ◽  
Sexual Maturation ◽  
System Development ◽  
Sequence Similarity ◽  
Nervous System Development ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Synteny Conservation ◽  
Non Coding Rnas

Schistosoma japonicum is a flatworm that causes schistosomiasis, a neglected tropical disease. S. japonicum RNA-Seq analyses has been previously reported in the literature on females and males obtained during sexual maturation from 14 to 28 days post-infection in mouse, resulting in the identification of protein-coding genes and pathways, whose expression levels were related to sexual development. However, this work did not include an analysis of long non-coding RNAs (lncRNAs). Here, we applied a pipeline to identify and annotate lncRNAs in 66 S. japonicum RNA-Seq publicly available libraries, from different life-cycle stages. We also performed co-expression analyses to find stage-specific lncRNAs possibly related to sexual maturation. We identified 12,291 S. japonicum expressed lncRNAs. Sequence similarity search and synteny conservation indicated that some 14% of S. japonicum intergenic lncRNAs have synteny conservation with S. mansoni intergenic lncRNAs. Co-expression analyses showed that lncRNAs and protein-coding genes in S. japonicum males and females have a dynamic co-expression throughout sexual maturation, showing differential expression between the sexes; the protein-coding genes were related to the nervous system development, lipid and drug metabolism, and overall parasite survival. Co-expression pattern suggests that lncRNAs possibly regulate these processes or are regulated by the same activation program as that of protein-coding genes.

scholarly journals Integrated analysis of differentially expressed genes and construction of a competing endogenous RNA network in human Huntington neural progenitor cells

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaoping Tan ◽  
Yang Liu ◽  
Taiming Zhang ◽  
Shuyan Cong
Keyword(s):  
Transcription Factors ◽  
Progenitor Cells ◽  
Neural Progenitor Cells ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Competing Endogenous Rna ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Cerna Network ◽  
Non Coding Rnas

Abstract Background Huntington's disease (HD) is one of the most common polyglutamine disorders, leading to progressive dyskinesia, cognitive impairment, and neuropsychological problems. Besides the dysregulation of many protein-coding genes in HD, previous studies have revealed a variety of non-coding RNAs that are also dysregulated in HD, including several long non-coding RNAs (lncRNAs). However, an integrated analysis of differentially expressed (DE) genes based on a competing endogenous RNA (ceRNA) network is still currently lacking. Methods In this study, we have systematically analyzed the gene expression profile data of neural progenitor cells (NPCs) derived from patients with HD and controls (healthy controls and the isogenic controls of HD patient cell lines corrected using a CRISPR-Cas9 approach at the HTT locus) to screen out DE mRNAs and DE lncRNAs and create a ceRNA network. To learn more about the possible functions of lncRNAs in the ceRNA regulatory network in HD, we conducted a functional analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and established a protein–protein interaction (PPI) network for mRNAs interacting with these lncRNAs. Results We identified 490 DE mRNAs and 94 DE lncRNAs, respectively. Of these, 189 mRNAs and 20 lncRNAs were applied to create a ceRNA network. The results showed that the function of DE lncRNAs mainly correlated with transcriptional regulation as demonstrated by GO analysis. Also, KEGG enrichment analysis showed these lncRNAs were involved in tumor necrosis factor, calcium, Wnt, and NF-kappa B signaling pathways. Interestingly, the PPI network revealed that a variety of transcription factors in the ceRNA network interacted with each other, suggesting such lncRNAs may regulate transcription in HD by controlling the expression of such protein-coding genes, especially transcription factors. Conclusions Our research provides new clues for uncovering the mechanisms of lncRNAs in HD and can be used as the focus for further investigation.

scholarly journals Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and widespread in trans effects on the protein regulatory network

2020 ◽  
Author(s):  
Laura Natalia Balarezo-Cisneros ◽  
Steven Parker ◽  
Marcin G Fraczek ◽  
Soukaina Timouma ◽  
Ping Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Environmental Conditions ◽  
Mode Of Action ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Genome Wide ◽  
In Trans ◽  
Non Coding Rnas ◽  
The Impact

AbstractNon-coding RNAs (ncRNAs), including the more recently identified Stable Unannotated Transcripts (SUTs) and Cryptic Unstable Transcripts (CUTs), are increasingly being shown to play pivotal roles in the transcriptional and post-transcriptional regulation of genes in eukaryotes. Here, we carried out a large-scale screening of ncRNAs in Saccharomyces cerevisiae, and provide evidence for SUT and CUT function. Phenotypic data on 372 ncRNA deletion strains in 23 different growth conditions were collected, identifying ncRNAs responsible for significant cellular fitness changes. Transcriptome profiles were assembled for 18 haploid ncRNA deletion mutants and 2 essential ncRNA heterozygous deletants. Guided by the resulting RNA-seq data we analysed the genome-wide dysregulation of protein coding genes and non-coding transcripts. Novel functional ncRNAs, SUT125, SUT126, SUT035 and SUT532 that act in trans by modulating transcription factors were identified. Furthermore, we described the impact of SUTs and CUTs in modulating coding gene expression in response of different environmental conditions, regulating important biological process such as respiration (SUT125, SUT126, SUT035, SUT432), steroid biosynthesis (CUT494, SUT530, SUT468) or rRNA processing (SUT075 and snR30). Overall, this data captures and integrates the regulatory and phenotypic network of ncRNAs and protein coding genes, providing genome-wide evidence of the impact of ncRNAs on cellular homeostasis.Author SummaryThe yeast genome contains 25% of non-coding RNA molecules (ncRNAs), which do not translate into proteins but are involved in regulation of gene expression. ncRNAs can affect nearby genes by physically interfering with their transcription (cis mode of action), or they interact with DNA, proteins or others RNAs to regulate the expression of distant genes (trans mode of action). Examples of cis-acting ncRNAs have been broadly described, however genome-wide studies to identify functional trans-acting ncRNAs involved in global gene regulation are still lacking. Here, we used the ncRNA yeast deletion collection to score their impact on cellular function in different environmental conditions. A group of 20 ncRNAs mutants with broad fitness diversity were selected to investigate their effect on the protein and ncRNA expression network. We showed a high correlation between altered phenotypes and global transcriptional changes, in an environmental dependent manner. We confirmed the widespread trans acting expressional regulation of ncRNAs in the genome and their role in affecting transcription factors. These findings support the notion of the involvement on ncRNAs in fine tuning the cellular expression via regulations of TFs, as an advantageous RNA-mediated mechanism that can be fast and cost-effective for the cells.

scholarly journals Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

2019 ◽  
Vol 21 (2) ◽  
pp. 637-648 ◽  
Author(s):  
Aritro Nath ◽  
Paul Geeleher ◽  
R Stephanie Huang
Keyword(s):  
Expression Patterns ◽  
Imputation Accuracy ◽  
High Quality ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Cancer Tissues ◽  
Long Non Coding Rna

Abstract Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

scholarly journals Integrated Analysis of Differentially Expressed Genes and Construction of a Competing Endogenous RNA Network in Human Huntington Neural Progenitor Cells

2020 ◽  
Author(s):  
Xiaoping Tan ◽  
Shuyan Cong ◽  
Yang Liu ◽  
Taiming Zhang
Keyword(s):  
Transcription Factors ◽  
Progenitor Cells ◽  
Neural Progenitor Cells ◽  
Integrated Analysis ◽  
Ppi Network ◽  
Competing Endogenous Rna ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Cerna Network ◽  
Non Coding Rnas

Abstract Background Huntington's disease (HD) is one of the most common polyglutamine disorders, leading to progressive dyskinesia, cognitive impairment, and neuropsychological problems. Besides the dysregulation of many protein-coding genes in HD, previous studies have revealed a variety of non-coding RNAs that are dysregulated in HD, including several long non-coding RNAs (lncRNAs). However, an integrated analysis of differentially expressed (DE) genes based on a competing endogenous RNA (ceRNA) network is still currently lacking. Results Here, we have systematically analyzed the gene expression profile data of neural progenitor cells (NPCs) derived from patients with HD and controls (healthy controls and the isogenic controls of HD patient cell lines corrected using CRISPR-Cas9 approach at the HTT locus, and we identified 490 DE mRNAs and 94 DE lncRNAs, respectively. Of these, 189 mRNAs and 20 lncRNAs were applied to create a ceRNA network. To learn more about the possible functions of lncRNAs in the ceRNA regulatory network in HD, we conducted a functional analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and established a protein-protein interaction (PPI) network for mRNAs interacting with these lncRNAs. It is suggested that the function of DE lncRNAs mainly correlated with transcriptional regulation demonstrated by GO analysis. Also, KEGG enrichment analysis showed these lncRNAs were involved in tumor necrosis factor, calcium, Wnt, and NF-kappa B signaling pathways. Interestingly, the PPI network revealed that a variety of transcription factors in the ceRNA network interacted with each other, suggesting such lncRNAs may regulate transcription in HD by controlling the expression of such protein-coding genes, especially transcription factors. Conclusions Our research provides new clues for uncovering the mechanism of lncRNAs in HD and can be used as the focus for further investigation.

Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer

2019 ◽  
Vol 35 (21) ◽  
pp. 4344-4349 ◽  
Author(s):  
Yuwei Zhang ◽  
Yang Tao ◽  
Huihui Ji ◽  
Wei Li ◽  
Xingli Guo ◽  
...  
Keyword(s):  
Network Analysis ◽  
Regulatory Network ◽  
Essential Genes ◽  
Supplementary Information ◽  
Published Data ◽  
Biological Processes ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Non Coding Rnas ◽  
Pan Cancer

Abstract Motivation Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer. Methods CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predict additional essential genes on broader scale. Finally, the expression status and potential roles of these genes were explored based on TCGA portal and regulatory network analysis. Results We collected 926 samples from 10 CRISPR-based screening studies involving 33 different types of cancer to identify cancer-essential genes, which consists of 799 protein-coding genes (PCGs) and 97 long non-coding RNAs (lncRNAs). Then, we constructed a ‘bi-colored’ network with both PCGs and lncRNAs and applied it to predict additional essential genes including 495 PCGs and 280 lncRNAs on a broader scale using hypergeometrics test and RWR. After obtaining all essential genes, we further investigated their potential roles in cancer and found that essential genes have higher and more stable expression levels, and are associated with multiple cancer-associated biological processes and survival time. The regulatory network analysis detected two intriguing modules of essential genes participating in the regulation of cell cycle and ribosome biogenesis in cancer. Availability and implementation   Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Regulatory Potential of Long Non-Coding RNAs (lncRNAs) in Boar Spermatozoa with Good and Poor Freezability

Life ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 300
Author(s):  
Leyland Fraser ◽  
Łukasz Paukszto ◽  
Anna Mańkowska ◽  
Paweł Brym ◽  
Przemysław Gilun ◽  
...  
Keyword(s):  
Target Genes ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Biological Processes ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Potential Target ◽  
Non Coding Rnas

Long non-coding RNAs (lncRNAs) are suggested to play an important role in the sperm biological processes. We performed de novo transcriptome assembly to characterize lncRNAs in spermatozoa, and to investigate the role of the potential target genes of the differentially expressed lncRNAs (DElncRNAs) in sperm freezability. We detected approximately 4007 DElncRNAs, which were differentially expressed in spermatozoa from boars classified as having good and poor semen freezability (GSF and PSF, respectively). Most of the DElncRNAs were upregulated in boars of the PSF group and appeared to significantly affect the sperm’s response to the cryopreservation conditions. Furthermore, we predicted that the potential target genes were regulated by DElncRNAs in cis or trans. It was found that DElncRNAs of both freezability groups had potential cis- and trans-regulatory effects on different protein-coding genes, such as COX7A2L, TXNDC8 and SOX-7. Gene Ontology (GO) enrichment revealed that the DElncRNA target genes are associated with numerous biological processes, including signal transduction, response to stress, cell death (apoptosis), motility and embryo development. Significant differences in the de novo assembled transcriptome expression profiles of the DElncRNAs between the freezability groups were confirmed by quantitative real-time PCR analysis. This study reveals the potential effects of protein-coding genes of DElncRNAs on sperm functions, which could contribute to further research on their relevance in semen freezability.

scholarly journals Identification of sheep lncRNAs related to the immune response to vaccines and aluminium adjuvants

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Martin Bilbao-Arribas ◽  
Endika Varela-Martínez ◽  
Naiara Abendaño ◽  
Damián de Andrés ◽  
Lluís Luján ◽  
...  
Keyword(s):  
Immune Response ◽  
Expression Analysis ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Livestock Species ◽  
Non Coding Rnas ◽  
Aluminium Adjuvant

Abstract Background Long non-coding RNAs (lncRNAs) are involved in several immune processes, including the immune response to vaccination, but most of them remain uncharacterised in livestock species. The mechanism of action of aluminium adjuvants as vaccine components is neither not fully understood. Results We built a transcriptome from sheep PBMCs RNA-seq data in order to identify unannotated lncRNAs and analysed their expression patterns along protein coding genes. We found 2284 novel lncRNAs and assessed their conservation in terms of sequence and synteny. Differential expression analysis performed between animals inoculated with commercial vaccines or aluminium adjuvant alone and the co-expression analysis revealed lncRNAs related to the immune response to vaccines and adjuvants. A group of co-expressed genes enriched in cytokine signalling and production highlighted the differences between different treatments. A number of differentially expressed lncRNAs were correlated with a divergently located protein-coding gene, such as the OSM cytokine. Other lncRNAs were predicted to act as sponges of miRNAs involved in immune response regulation. Conclusions This work enlarges the lncRNA catalogue in sheep and puts an accent on their involvement in the immune response to repetitive vaccination, providing a basis for further characterisation of the non-coding sheep transcriptome within different immune cells.

scholarly journals Predicting the Functions of Long Noncoding RNAs Using RNA-Seq Based on Bayesian Network

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Yun Xiao ◽  
Yanling Lv ◽  
Hongying Zhao ◽  
Yonghui Gong ◽  
Jing Hu ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Regulatory Network ◽  
Noncoding Rnas ◽  
Nervous System Development ◽  
Long Noncoding Rnas ◽  
Functional Enrichment ◽  
Biological Processes ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes

Long noncoding RNAs (lncRNAs) have been shown to play key roles in various biological processes. However, functions of most lncRNAs are poorly characterized. Here, we represent a framework to predict functions of lncRNAs through construction of a regulatory network between lncRNAs and protein-coding genes. Using RNA-seq data, the transcript profiles of lncRNAs and protein-coding genes are constructed. Using the Bayesian network method, a regulatory network, which implies dependency relations between lncRNAs and protein-coding genes, was built. In combining protein interaction network, highly connected coding genes linked by a given lncRNA were subsequently used to predict functions of the lncRNA through functional enrichment. Application of our method to prostate RNA-seq data showed that 762 lncRNAs in the constructed regulatory network were assigned functions. We found that lncRNAs are involved in diverse biological processes, such as tissue development or embryo development (e.g., nervous system development and mesoderm development). By comparison with functions inferred using the neighboring gene-based method and functions determined using lncRNA knockdown experiments, our method can provide comparable predicted functions of lncRNAs. Overall, our method can be applied to emerging RNA-seq data, which will help researchers identify complex relations between lncRNAs and coding genes and reveal important functions of lncRNAs.

scholarly journals Limitation of alignment-free tools in total RNA-seq quantification

2018 ◽  
Author(s):  
Douglas C. Wu ◽  
Jun Yao ◽  
Kevin S. Ho ◽  
Alan M. Lambowitz ◽  
Claus O. Wilke
Keyword(s):  
Small Rnas ◽  
State Of The Art ◽  
Rna Seq ◽  
Protein Coding ◽  
Total Rna ◽  
Protein Coding Genes ◽  
Gene Quantification ◽  
Rna Quantification ◽  
Alignment Free ◽  
Non Coding Rnas

AbstractBackgroundAlignment-free RNA quantification tools have significantly increased the speed of RNA-seq analysis. However, it is unclear whether these state-of-the-art RNA-seq analysis pipelines can quantify small RNAs as accurately as they do with long RNAs in the context of total RNA quantification.ResultWe comprehensively tested and compared four RNA-seq pipelines on the accuracies of gene quantification and fold-change estimation on a novel total RNA benchmarking dataset, in which small non-coding RNAs are highly represented along with other long RNAs. The four RNA-seq pipelines were of two commonly-used alignment-free pipelines and two variants of alignment-based pipelines. We found that all pipelines showed high accuracies for quantifying the expressions of long and highly-abundant genes. However, alignment-free pipelines showed systematically poorer performances in quantifying lowly-abundant and small RNAs.ConclusionWe have shown that alignment-free and traditional alignment-based quantification methods performed similarly for common gene targets, such as protein-coding genes. However, we identified a potential pitfall in analyzing and quantifying lowly-expressed genes and small RNAs with alignment-free pipelines, especially when these small RNAs contain mutations.

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