scholarly journals Global investigation of estrogen-responsive genes regulating lipid metabolism in the liver of laying hens

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Junxiao Ren ◽  
Weihua Tian ◽  
Keren Jiang ◽  
Zhang Wang ◽  
Dandan Wang ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Chicken Liver ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Chip Sequencing ◽  
Functional Sites ◽  
Protein Coding Genes ◽  
A Genome

Abstract Background Estrogen plays an essential role in female development and reproductive function. In chickens, estrogen is critical for lipid metabolism in the liver. The regulatory molecular network of estrogen in chicken liver is poorly understood. To identify estrogen-responsive genes and estrogen functional sites on a genome-wide scale, we determined expression profiles of mRNAs, lncRNAs, and miRNAs in estrogen-treated ((17β-estradiol)) and control chicken livers using RNA-Sequencing (RNA-Seq) and studied the estrogen receptor α binding sites by ChIP-Sequencing (ChIP-Seq). Results We identified a total of 990 estrogen-responsive genes, including 962 protein-coding genes, 11 miRNAs, and 17 lncRNAs. Functional enrichment analyses showed that the estrogen-responsive genes were highly enriched in lipid metabolism and biological processes. Integrated analysis of the data of RNA-Seq and ChIP-Seq, identified 191 genes directly targeted by estrogen, including 185 protein-coding genes, 4 miRNAs, and 2 lncRNAs. In vivo and in vitro experiments showed that estrogen decreased the mRNA expression of PPARGC1B, which had been reported to be linked with lipid metabolism, by directly increasing the expression of miR-144-3p. Conclusions These results increase our understanding of the functional network of estrogen in chicken liver and also reveal aspects of the molecular mechanism of estrogen-related lipid metabolism.

scholarly journals Spatio-Temporal Transcriptional Dynamics of Maize Long Non-Coding RNAs Responsive to Drought Stress

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 138 ◽  
Author(s):  
Junling Pang ◽  
Xia Zhang ◽  
Xuhui Ma ◽  
Jun Zhao
Keyword(s):  
Drought Stress ◽  
Developmental Stages ◽  
Expression Patterns ◽  
Functional Enrichment ◽  
Transcriptional Analysis ◽  
Protein Coding ◽  
Altered Expression ◽  
Protein Coding Genes ◽  
A Genome ◽  
Non Coding Rnas

Long non-coding RNAs (lncRNAs) have emerged as important regulators in plant stress response. Here, we report a genome-wide lncRNA transcriptional analysis in response to drought stress using an expanded series of maize samples collected from three distinct tissues spanning four developmental stages. In total, 3488 high-confidence lncRNAs were identified, among which 1535 were characterized as drought responsive. By characterizing the genomic structure and expression pattern, we found that lncRNA structures were less complex than protein-coding genes, showing shorter transcripts and fewer exons. Moreover, drought-responsive lncRNAs exhibited higher tissue- and development-specificity than protein-coding genes. By exploring the temporal expression patterns of drought-responsive lncRNAs at different developmental stages, we discovered that the reproductive stage R1 was the most sensitive growth stage with more lncRNAs showing altered expression upon drought stress. Furthermore, lncRNA target prediction revealed 653 potential lncRNA-messenger RNA (mRNA) pairs, among which 124 pairs function in cis-acting mode and 529 in trans. Functional enrichment analysis showed that the targets were significantly enriched in molecular functions related to oxidoreductase activity, water binding, and electron carrier activity. Multiple promising targets of drought-responsive lncRNAs were discovered, including the V-ATPase encoding gene, vpp4. These findings extend our knowledge of lncRNAs as important regulators in maize drought response.

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 The genome sequence of the European peacock butterfly, Aglais io (Linnaeus, 1758)

2021 ◽  
Vol 6 ◽  
pp. 258
Author(s):  
Konrad Lohse ◽  
Alexander Mackintosh ◽  
Roger Vila ◽  
◽  
◽  
...  
Keyword(s):  
Genome Sequence ◽  
Genome Assembly ◽  
Sex Chromosome ◽  
Gene Annotation ◽  
Protein Coding ◽  
Individual Male ◽  
Protein Coding Genes ◽  
A Genome ◽  
Inachis Io

We present a genome assembly from an individual male Aglais io (also known as Inachis io and Nymphalis io) (the European peacock; Arthropoda; Insecta; Lepidoptera; Nymphalidae). The genome sequence is 384 megabases in span. The majority (99.91%) of the assembly is scaffolded into 31 chromosomal pseudomolecules, with the Z sex chromosome assembled. Gene annotation of this assembly on Ensembl has identified 11,420 protein coding genes.

scholarly journals The Absence of Universally-Conserved Protein-coding Genes

2019 ◽  
Author(s):  
Change Laura Tan
Keyword(s):  
Public Access ◽  
Orphan Genes ◽  
Protein Coding ◽  
Great Opportunity ◽  
Protein Coding Genes ◽  
Phylogenetic Profiles ◽  
Gene Count ◽  
A Genome ◽  
Wide Scale ◽  
Specific Species

AbstractPublic access to thousands of completely sequenced and annotated genomes provides a great opportunity to address the relationships of different organisms, at the molecular level and on a genome-wide scale. Via comparing the phylogenetic profiles of all protein-coding genes in 317 model species described in the OrthoInspector3.0 database, we found that approximately 29.8% of the total protein-coding genes were orphan genes (genes unique to a specific species) while < 0.01% were universal genes (genes with homologs in each of the 317 species analyzed). When weighted by potential birth event, the orphan genes comprised 82% of the total, while the universal genes accounted for less than 0.00008%. Strikingly, as the analyzed genomes increased, the sum total of universal and nearly-universal genes plateaued while that of orphan and nearly-orphan genes grew continuously. When the compared species increased to the inclusion of 3863 bacteria, 711 eukaryotes, and 179 archaea, not one of the universal genes remained. The results speak to a previously unappreciated degree of genetic biodiversity, which we propose to quantify using the birth-event-weighted gene count method.

scholarly journals Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mikhail Pomaznoy ◽  
Ashu Sethi ◽  
Jason Greenbaum ◽  
Bjoern Peters
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Library Preparation ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Machine Learning Model ◽  
Specific Manner ◽  
Library Preparation Protocol

Abstract RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount.

scholarly journals Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jeanne Wilbrandt ◽  
Bernhard Misof ◽  
Kristen A. Panfilio ◽  
Oliver Niehuis
Keyword(s):  
Structural Properties ◽  
Gene Structure ◽  
Comparative Studies ◽  
Gene Repertoire ◽  
Manual Annotation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Gene Sets ◽  
A Genome ◽  
Gene Models

Abstract Background The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be automatically predicted by gene annotation algorithms. These predictions are often used for comparative studies on gene structure, gene repertoires, and genome evolution. However, automatic annotation algorithms do not yet correctly identify all genes within a genome, and manual annotation is often necessary to obtain accurate gene models and gene sets. As manual annotation is time-consuming, only a fraction of the gene models in a genome is typically manually annotated, and this fraction often differs between species. To assess the impact of manual annotation efforts on genome-wide analyses of gene structural properties, we compared the structural properties of protein-coding genes in seven diverse insect species sequenced by the i5k initiative. Results Our results show that the subset of genes chosen for manual annotation by a research community (3.5–7% of gene models) may have structural properties (e.g., lengths and exon counts) that are not necessarily representative for a species’ gene set as a whole. Nonetheless, the structural properties of automatically generated gene models are only altered marginally (if at all) through manual annotation. Major correlative trends, for example a negative correlation between genome size and exonic proportion, can be inferred from either the automatically predicted or manually annotated gene models alike. Vice versa, some previously reported trends did not appear in either the automatic or manually annotated gene sets, pointing towards insect-specific gene structural peculiarities. Conclusions In our analysis of gene structural properties, automatically predicted gene models proved to be sufficiently reliable to recover the same gene-repertoire-wide correlative trends that we found when focusing on manually annotated gene models only. We acknowledge that analyses on the individual gene level clearly benefit from manual curation. However, as genome sequencing and annotation projects often differ in the extent of their manual annotation and curation efforts, our results indicate that comparative studies analyzing gene structural properties in these genomes can nonetheless be justifiable and informative.

scholarly journals Draft Genome Sequence of a Novel Bacterium,Pseudomonassp. Strain MR 02, Capable of Pyomelanin Production, Isolated from the Mahananda River at Siliguri, West Bengal, India

2018 ◽  
Vol 6 (3) ◽  
pp. e01443-17 ◽  
Author(s):  
Vivek Kumar Ranjan ◽  
Tilak Saha ◽  
Shriparna Mukherjee ◽  
Ranadhir Chakraborty
Keyword(s):  
Genome Sequence ◽  
West Bengal ◽  
Draft Genome ◽  
Homogentisic Acid ◽  
Draft Genome Sequence ◽  
Gene Length ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Novel Bacterium ◽  
A Genome

ABSTRACTThe draft genome sequence of a novel strain,Pseudomonassp. MR 02, a pyomelanin-producing bacterium isolated from the Mahananda River at Siliguri, West Bengal, India, is reported here. This strain has a genome size of 5.94 Mb, with an overall G+C content of 62.6%. The draft genome reports 5,799 genes (mean gene length, 923 bp), among which 5,503 are protein-coding genes, including the genes required for the catabolism of tyrosine or phenylalanine for the characteristic production of homogentisic acid (HGA). Excess HGA, on excretion, auto-oxidizes and polymerizes to form pyomelanin.

scholarly journals The genome sequence of the Glanville fritillary, Melitaea cinxia (Linnaeus, 1758)

2021 ◽  
Vol 6 ◽  
pp. 266
Author(s):  
Roger Vila ◽  
Alex Hayward ◽  
Konrad Lohse ◽  
Charlotte Wright ◽  
◽  
...  
Keyword(s):  
Genome Sequence ◽  
Genome Assembly ◽  
Sex Chromosome ◽  
Gene Annotation ◽  
Melitaea Cinxia ◽  
Protein Coding ◽  
Individual Male ◽  
Protein Coding Genes ◽  
A Genome

We present a genome assembly from an individual male Melitaea cinxia (the Glanville fritillary; Arthropoda; Insecta; Lepidoptera; Nymphalidae). The genome sequence is 499 megabases in span. The complete assembly is scaffolded into 31 chromosomal pseudomolecules, with the Z sex chromosome assembled. Gene annotation of this assembly on Ensembl has identified 13,666 protein coding genes.

scholarly journals TSEBRA: transcript selector for BRAKER

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lars Gabriel ◽  
Katharina J. Hoff ◽  
Tomáš Brůna ◽  
Mark Borodovsky ◽  
Mario Stanke
Keyword(s):  
Statistical Models ◽  
Gene Prediction ◽  
Software Tool ◽  
Genome Project ◽  
Rna Seq ◽  
Protein Coding ◽  
Homologous Protein ◽  
Protein Coding Genes ◽  
Overlapping Transcripts ◽  
Eukaryotic Genomes

Abstract Background BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. Results We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. Conclusion TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

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