scholarly journals A comprehensive annotation and differential expression analysis of short and long non-coding RNAs in 16 bat genomes

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Nelly F Mostajo ◽  
Marie Lataretu ◽  
Sebastian Krautwurst ◽  
Florian Mock ◽  
Daniel Desirò ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Viral Infections ◽  
Gene Expression Regulation ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Regulatory Processes ◽  
Genome Annotations ◽  
Host Virus Interactions

Abstract Although bats are increasingly becoming the focus of scientific studies due to their unique properties, these exceptional animals are still among the least studied mammals. Assembly quality and completeness of bat genomes vary a lot and especially non-coding RNA (ncRNA) annotations are incomplete or simply missing. Accordingly, standard bioinformatics pipelines for gene expression analysis often ignore ncRNAs such as microRNAs or long antisense RNAs. The main cause of this problem is the use of incomplete genome annotations. We present a complete screening for ncRNAs within 16 bat genomes. NcRNAs affect a remarkable variety of vital biological functions, including gene expression regulation, RNA processing, RNA interference and, as recently described, regulatory processes in viral infections. Within all investigated bat assemblies, we annotated 667 ncRNA families including 162 snoRNAs and 193 miRNAs as well as rRNAs, tRNAs, several snRNAs and lncRNAs, and other structural ncRNA elements. We validated our ncRNA candidates by six RNA-Seq data sets and show significant expression patterns that have never been described before in a bat species on such a large scale. Our annotations will be usable as a resource (rna.uni-jena.de/supplements/bats) for deeper studying of bat evolution, ncRNAs repertoire, gene expression and regulation, ecology and important host–virus interactions.

scholarly journals A comprehensive annotation and differential expression analysis of short and long non-coding RNAs in 16 bat genomes

2019 ◽  
Author(s):  
Nelly Mostajo Berrospi ◽  
Marie Lataretu ◽  
Sebastian Krautwurst ◽  
Florian Mock ◽  
Daniel Desirò ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Viral Infections ◽  
Gene Expression Regulation ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Open Science ◽  
Supplementary Information ◽  
Link Type

ABSTRACTAlthough bats are increasingly becoming the focus of scientific studies due to their unique properties, these exceptional animals are still among the least studied mammals. Assembly quality and completeness of bat genomes vary a lot and especially non-coding RNA (ncRNA) annotations are incomplete or simply missing. Accordingly, standard bioinformatics pipelines for gene expression analysis often ignore ncRNAs such as microRNAs or long antisense RNAs. The main cause of this problem is the use of incomplete genome annotations. We present a complete screening for ncRNAs within 16 bat genomes. NcRNAs affect a remarkable variety of vital biological functions, including gene expression regulation, RNA processing, RNA interference and, as recently described, regulatory processes in viral infections. Within all investigated bat assemblies we annotated 667 ncRNA families including 162 snoRNAs and 193 miRNAs as well as rRNAs, tRNAs, several snRNAs and IncRNAs, and other structural ncRNA elements. We validated our ncRNA candidates by six RNA-Seq data sets and show significant expression patterns that have never been described before in a bat species on such a large scale. Our annotations will be usable as a resource (Electronic Supplement) for deeper studying of bat evolution, ncRNAs repertoire, gene expression and regulation, ecology, and important host-virus interactions.Supplementary informationis available at rna.uni-jena.de/supplements/bats, the Open Science Framework (doi.org/10.17605/OSF.IO/4CMDN), and GitHub (github.com/rnajena/bats_ncrna).

scholarly journals Prognostic value and co-expression patterns of metabolic pathways in cancers

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Dan Zhang ◽  
Yan Guo ◽  
Ni Xie
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Metabolic Pathway ◽  
Metabolic Pathways ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Cancer Prognosis ◽  
Gene Expressions ◽  
Pathway Gene ◽  
Pathway Expression

Abstract Background Abnormal metabolic pathways have been considered as one of the hallmarks of cancer. While numerous metabolic pathways have been studied in various cancers, the direct link between metabolic pathway gene expression and cancer prognosis has not been established. Results Using two recently developed bioinformatics analysis methods, we evaluated the prognosis potential of metabolic pathway expression and tumor-vs-normal dysregulations for up to 29 metabolic pathways in 33 cancer types. Results show that increased metabolic gene expression within tumors corresponds to poor cancer prognosis. Meta differential co-expression analysis identified four metabolic pathways with significant global co-expression network disturbance between tumor and normal samples. Differential expression analysis of metabolic pathways also demonstrated strong gene expression disturbance between paired tumor and normal samples. Conclusion Taken together, these results strongly suggested that metabolic pathway gene expressions are disturbed after tumorigenesis. Within tumors, many metabolic pathways are upregulated for tumor cells to activate corresponding metabolisms to sustain the required energy for cell division.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Primary Cultures ◽  
Gene Expression Profiles ◽  
Filter Method ◽  
Tissue Destruction ◽  
Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

scholarly journals SOMDE: A scalable method for identifying spatially variable genes with self-organizing map

2020 ◽  
Author(s):  
Minsheng Hao ◽  
Kui Hua ◽  
Xuegong Zhang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Spatial Expression ◽  
Variable Expression ◽  
Sequencing Technologies ◽  
Physical Context ◽  
Variable Genes

AbstractRecent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue micro-environments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data.We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses selforganizing map (SOM) to cluster neighboring cells into nodes, and then uses a Gaussian Process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ~5 minutes in large datasets of more than 20,000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde.

scholarly journals Large-Scale Metadata Analysis of Ovary Based Multi-Omics Datasets for Understanding the Genes Regulating Litter Size

2020 ◽  
Author(s):  
Ayyappa Kumar Sista Kameshwar ◽  
Julang Li
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Litter Size ◽  
Large Scale ◽  
Expression Patterns ◽  
Genetic Selection ◽  
Follicular Development ◽  
Oocyte Quality ◽  
Differentially Expressed ◽  
Metadata Analysis

Abstract Background : Litter size is a very important production index in the livestock industry, which is controlled by various complex physiological processes. To understand and reveal the common gene expression patterns involved in controlling prolificacy, we have performed a large-scale metadata analysis of five genome-wide transcriptome datasets of pig and sheep ovary samples obtained from high and low litter groups, respectively. We analyzed separately each transcriptome dataset using GeneSpring v14.8 software by implementing standard, generic analysis pipelines and further compared the list of most significant and differentially expressed genes obtained from each dataset to identify genes that are found to be common and significant across all the studies. Results : We have observed a total of 62 differentially expressed genes common among more than two gene expression datasets. The KEGG pathway analysis of most significant genes has shown that they are involved in metabolism, the biosynthesis of lipids, cholesterol and steroid hormones, immune system, cell growth and death, cancer-related pathways and signal transduction pathways. Of these 62 genes, we further narrowed the list to the 25 most significant genes by focusing on the ones with fold change >1.5 and p<0.05. These genes are CYP11A1, HSD17B2, STAR, SCARB1, IGSF8, MSMB, SERPINA1 , FAM46C, HEXA, PTTG1, TIMP1, FAM167B, CCNG1, FAXDC2, HMGCS1, L2HGDH, Lipin1, MME, MSMO1, PARM1, PTGFR, SLC22A4, SLC35F5, CCNA2, CENPU, CEP55, RASSF2, and SLC16A3 . Conclusions : Interestingly, comparing the list of genes with the list of genes obtained from our literature search analysis, we found only three genes in common. These genes are HEXA, PTTG1, and TIMP1. Our finding points to the potential of a few genes that may be important for ovarian follicular development and oocyte quality. Future studies revealing the function of these genes will further our understanding of how litter size is controlled in the ovary while also providing insight on genetic selection of high litter gilts.

scholarly journals Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular distribution of RNA transcripts

2020 ◽  
Author(s):  
Ammar Zaghlool ◽  
Adnan Niazi ◽  
Åsa K. Björklund ◽  
Jakub Orzechowski Westholm ◽  
Adam Ameur ◽  
...  
Keyword(s):  
Gene Expression ◽  
Subcellular Localization ◽  
Human Brain ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Adult Brain ◽  
Sequencing Data ◽  
Human Brain Tissue ◽  
Protein Coding ◽  
The Impact

AbstractTranscriptome analysis has mainly relied on analyzing RNA sequencing data from whole cells, overlooking the impact of subcellular RNA localization and its influence on our understanding of gene function, and interpretation of gene expression signatures in cells. Here, we performed a comprehensive analysis of cytosolic and nuclear transcriptomes in human fetal and adult brain samples. We show significant differences in RNA expression for protein-coding and lncRNA genes between cytosol and nucleus. Transcripts displaying differential subcellular localization belong to particular functional categories and display tissue-specific localization patterns. We also show that transcripts encoding the nuclear-encoded mitochondrial proteins are significantly enriched in the cytosol compared to the rest of protein-coding genes. Further investigation of the use of the cytosolic or the nuclear transcriptome for differential gene expression analysis indicates important differences in results depending on the cellular compartment. These differences were manifested at the level of transcript types and the number of differentially expressed genes. Our data provide a resource of RNA subcellular localization in the human brain and highlight differences in using the cytosolic or the nuclear transcriptomes for differential expression analysis.

Abstract 545: Differential mRNA Expression is Influenced by Apolipoprotein A-I in Order to Promote Foam Cell Regression

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elisa C Maruko ◽  
Hao Xu ◽  
Sushma Kaul ◽  
Brian J Capaldo ◽  
Nathalie Pamir ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Immune Cell ◽  
Foam Cell ◽  
Differential Expression Analysis ◽  
Ldl Receptor ◽  
Gene Set Enrichment Analysis ◽  
Control Group ◽  
Apolipoprotein A

Atherosclerosis is a disease of both lipids and inflammatory immune cells. More specifically, elevated plasma levels of low-density lipoproteins (LDL) leads to migration of circulating monocytes into the artery wall. Lipid loaded monocyte cells subsequently proliferate in the arterial walls becoming macrophage foam cells; a hallmark of atherosclerotic lesions. A proposed mechanism of the protective effects of high-density lipoprotein (HDL) is apolipoprotein A-I (apo A-I) acting as a mediator of cholesterol efflux and subsequent foam cell regression. To better understand the biological changes stimulated by apo A-I treatment, differential expression analysis of microarray data was performed on spleen cells from apo A-I treated mice. LDL receptor null (LDLr -/- ) and LDL receptor and apo A-I null (LDLr -/- , apoA-I -/- ) mice were fed a western diet consisting of 0.2% cholesterol and 42% of calories as fat for 12 weeks. After 6 weeks of diet, a subset of mice for each genotype was subcutaneously injected with 200 micrograms of apo A-I 3 times a week for the remaining 6 weeks. The control group mice were subcutaneously injected with 200 micrograms of saline or BSA. Spleen cell RNA was isolated, purified, and analyzed for differential expression analysis using Illumina BeadArray Microarray Technology Analysis. Individual gene expression analysis for LDLr -/- , apoA-I -/- apo A-I treated mice showed 281 significantly differentially expressed genes compared to BSA treated mice. LDLr -/- A-I treated mice had 1502. Of the significant genes, 189 intersected across both genotypes. LDLr -/- , apoA-I -/- A-I mice showed 73 up-regulated and 116 down-regulated genes. Similarly, LDLr -/- A-I mice had 71 up-regulated and 118 down-regulated. One-directional Gene Set Enrichment Analysis (GSEA) of LDLr -/- , apoA-I -/- A-I mice revealed 49 significant pathways while a total of 63 were found for LDLr -/- . Of these pathways, 21 were up-regulated and 13 were down-regulated in both genotypes. Eight of the top 10 most significant up-regulated pathways in both genotypes were immune cell related. Their functions involve receptor, adhesion, and chemokine signaling. Overall, preliminary analysis suggests A-I treatment induces similar gene expression changes across different genotypes.

scholarly journals Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer

2019 ◽  
Vol 104 (11) ◽  
pp. 5225-5237 ◽  
Author(s):  
Mariam Haffa ◽  
Andreana N Holowatyj ◽  
Mario Kratz ◽  
Reka Toth ◽  
Axel Benner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Adipose Tissue ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Human Study ◽  
Subcutaneous Fat ◽  
Specific Gene ◽  
Adipose Tissue Depots

Abstract Context Adipose tissue inflammation and dysregulated energy homeostasis are key mechanisms linking obesity and cancer. Distinct adipose tissue depots strongly differ in their metabolic profiles; however, comprehensive studies of depot-specific perturbations among patients with cancer are lacking. Objective We compared transcriptome profiles of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) from patients with colorectal cancer and assessed the associations of different anthropometric measures with depot-specific gene expression. Design Whole transcriptomes of VAT and SAT were measured in 233 patients from the ColoCare Study, and visceral and subcutaneous fat area were quantified via CT. Results VAT compared with SAT showed elevated gene expression of cytokines, cell adhesion molecules, and key regulators of metabolic homeostasis. Increased fat area was associated with downregulated lipid and small molecule metabolism and upregulated inflammatory pathways in both compartments. Comparing these patterns between depots proved specific and more pronounced gene expression alterations in SAT and identified unique associations of integrins and lipid metabolism–related enzymes. VAT gene expression patterns that were associated with visceral fat area poorly overlapped with patterns associated with self-reported body mass index (BMI). However, subcutaneous fat area and BMI showed similar associations with SAT gene expression. Conclusions This large-scale human study demonstrates pronounced disparities between distinct adipose tissue depots and reveals that BMI poorly correlates with fat mass–associated changes in VAT. Taken together, these results provide crucial evidence for the necessity to differentiate between distinct adipose tissue depots for a correct characterization of gene expression profiles that may affect metabolic health of patients with colorectal cancer.

scholarly journals TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

2020 ◽  
Vol 49 (D1) ◽  
pp. D1420-D1430
Author(s):  
Dongqing Sun ◽  
Jin Wang ◽  
Ya Han ◽  
Xin Dong ◽  
Jun Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Large Scale ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Multiple Cancer ◽  
Web Resource ◽  
Cancer Types

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

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