scholarly journals Fast detection of differential chromatin domains with SCIDDO

2020 ◽  
Author(s):  
Peter Ebert ◽  
Marcel H Schulz
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Chromatin State ◽  
Supplementary Information ◽  
Differential Analysis ◽  
Chromatin Domains ◽  
Chromatin Dynamics ◽  
Histone Marks ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Downstream Analysis

Abstract Motivation The generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing (ChIP-seq) is a standard approach to dissect the complexity of the epigenome. Interpretation and differential analysis of histone datasets remains challenging due to regulatory meaningful co-occurrences of histone marks and their difference in genomic spread. To ease interpretation, chromatin state segmentation maps are a commonly employed abstraction combining individual histone marks. We developed the tool SCIDDO as a fast, flexible, and statistically sound method for the differential analysis of chromatin state segmentation maps. Results We demonstrate the utility of SCIDDO in a comparative analysis that identifies differential chromatin domains (DCD) in various regulatory contexts and with only moderate computational resources. We show that the identified DCDs correlate well with observed changes in gene expression and can recover a substantial number of differentially expressed genes. We showcase SCIDDO’s ability to directly interrogate chromatin dynamics such as enhancer switches in downstream analysis, which simplifies exploring specific questions about regulatory changes in chromatin. By comparing SCIDDO to competing methods, we provide evidence that SCIDDO’s performance in identifying differentially expressed genes (DEG) via differential chromatin marking is more stable across a range of cell-type comparisons and parameter cut-offs. Availability The SCIDDO source code is openly available under github.com/ptrebert/sciddo Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fast Detection of Differential Chromatin Domains with SCIDDO

2018 ◽  
Author(s):  
Peter Ebert ◽  
Marcel H. Schulz
Keyword(s):  
Histone Modifications ◽  
Chromatin Immunoprecipitation ◽  
Regulatory Elements ◽  
Differential Analysis ◽  
Chromatin Domains ◽  
Chromatin Dynamics ◽  
Fast Detection ◽  
Genome Wide ◽  
Chromatin Immunoprecipitation Sequencing

AbstractThe generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing (ChIP-seq) is a common approach to dissect the complexity of the epigenome. However, interpretation and differential analysis of histone ChIP-seq datasets remains challenging due to the genomic co-occurrence of several marks and their difference in genomic spread. Here we present SCIDDO, a fast statistical method for the detection of differential chromatin domains (DCDs) from chromatin state maps. DCD detection simplifies relevant tasks such as the characterization of chromatin changes in differentially expressed genes or the examination of chromatin dynamics at regulatory elements. SCIDDO is available at github.com/ptrebert/sciddo

scholarly journals Identification of prognostic biomarkers related to the tumor microenvironment in thyroid carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun-wei Du ◽  
Guo-quan Li ◽  
Yang-sen Li ◽  
Xin-guang Qiu
Keyword(s):  
Tumor Microenvironment ◽  
Thyroid Carcinoma ◽  
Differentially Expressed Genes ◽  
Essential Role ◽  
Endocrine Tumor ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Additional Information ◽  
Cerna Network ◽  
Immune Score

AbstractThyroid Carcinoma (THCA) is the most common endocrine tumor that is mainly treated using surgery and radiotherapy. In addition, immunotherapy is a recently developed treatment option that has played an essential role in the management of several types of tumors. However, few reports exist on the use of immunotherapy to treat THCA. The study downloaded the miRNA, mRNA and lncRNA data for THCA patients from the TCGA database (https://portal.gdc.cancer.gov/). Thereafter, the tumor samples were divided into cold and hot tumors, based on the immune score of the tumor microenvironment. Moreover, the differentially expressed lncRNAs and miRNAs were obtained. Finally, the study jointly constructed a ceRNA network through differential analysis of the mRNA data for cold and hot tumors. The study first assessed the level of immune infiltration in the THCA tumor microenvironment then divided the samples into cold and hot tumors, based on the immune score. Additionally, a total of 568 up-regulated and 412 down-regulated DEGs were screened by analyzing the differences between hot and cold tumors. Thereafter, the study examined the differentially expressed genes for lncRNA and miRNA. The results revealed 629 differentially expressed genes related to lncRNA and 114 associated with miRNA. Finally, a ceRNA network of the differentially expressed genes was constructed. The results showed a five-miRNA hubnet, i.e., hsa-mir-204, hsa-mir-128, hsa-mir-214, hsa-mir-150 and hsa-mir-338. The present study identified the immune-related mRNA, lncRNA and miRNA in THCA then constructed a ceRNA network. These results are therefore important as they provide more insights on the immune mechanisms in THCA. The findings also provides additional information for possible THCA immunotherapy.

scholarly journals Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

2021 ◽  
Author(s):  
Chengang Guo ◽  
Zhimin wei ◽  
Wei Lyu ◽  
Yanlou Geng
Keyword(s):  
Differentially Expressed Genes ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Hierarchical Cluster ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

scholarly journals Integrative molecular analysis of metastatic hepatocellular carcinoma

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Dongfang Wang ◽  
Yanjing Zhu ◽  
Jing Tang ◽  
Qiuyu Lian ◽  
Guijuan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Differentially Expressed Genes ◽  
Primary Tumor ◽  
Integrative Analysis ◽  
Differentially Expressed ◽  
Focal Region ◽  
Differential Analysis ◽  
Gene Expressions ◽  
Molecular Alterations ◽  
Matched Primary Tumor

Abstract Background Hepatocellular carcinoma (HCC) is the major type of primary liver cancer. Intrahepatic metastasis, such as portal vein tumor thrombosis (PVTT), strongly indicates poor prognosis of HCC. But now, there are limited understandings of the molecular features and mechanisms of those metastatic HCCs. Methods To characterize the molecular alterations of the metastatic HCCs, we implemented an integrative analysis of the copy number variations (CNVs), DNA methylations and transcriptomes of matched adjacent normal, primary tumor and PVTT samples from 19 HCC patients. Results CNV analysis identified a frequently amplified focal region chr11q13.3 and a novel deletion peak chr19q13.41 containing three miRNAs. The integrative analysis with RNA-seq data suggests that CNVs and differential promoter methylations regulate distinct oncogenic processes. Then, we used individualized differential analysis to identify the differentially expressed genes between matched primary tumor and PVTT of each patient. Results show that 5 out of 19 studied patients acquire evidential progressive alterations of gene expressions (more than 1000 differentially expressed genes were identified in each patient). While, another subset of eight patients have nearly identical gene expressions between the corresponding matched primary tumor and PVTT. Twenty genes were found to be recurrently and progressively differentially expressed in multiple patients. These genes are mainly associated with focal adhesion, xenobiotics metabolism by cytochrome P450 and amino acid metabolism. For several differentially expressed genes in metabolic pathways, their expressions are significantly associated with overall survivals and vascular invasions of HCC patients. The following transwell assay experiments validate that they can regulate invasive phenotypes of HCC cells. Conclusions The metastatic HCCs with PVTTs have significant molecular alterations comparing with adjacent normal tissues. The recurrent alteration patterns are similar to several previously published general HCC cohorts, but usually with higher severity. By an individualized differential analysis strategy, the progressively differentially expressed genes between the primary tumor and PVTT were identified for each patient. A few patients aquire evidential progressive alterations of gene expressions. And, experiments show that several recurrently differentially expressed genes can strongly regulate HCC cell invasions.

scholarly journals Performance of gene expression analyses using de novo assembled transcripts in polyploid species

2018 ◽  
Author(s):  
Ling-Yun Chen ◽  
Diego F. Morales-Briones ◽  
Courtney N. Passow ◽  
Ya Yang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Reference Genes ◽  
De Novo ◽  
Differentially Expressed ◽  
Supplementary Information ◽  
Network Analyses ◽  
Gene Expression Analyses ◽  
Polyploidy Event ◽  
Differential Gene

AbstractMotivationQuality of gene expression analyses using de novo assembled transcripts in species experienced recent polyploidization is yet unexplored.ResultsFive plant species with various polyploidy history were used for differential gene expression (DGE) analyses. DGE analyses using putative genes inferred by Trinity performed similar to or better than Corset and Grouper in precision, but lower in sensitivity. In species that lack polyploidy event in the past few million years, DGE analyses using de novo assembled transcriptome identified 50–76% of the differentially expressed genes recovered by mapping reads to the reference genes. However, in species with more recent polyploidy event, the percentage decreased to 7–30%. In addition, 7–89% of differentially expressed genes from de novo assembly are contaminations. Gene co-expression network analyses using de novo assemblies vs. mapping to the reference genes recovered the same module that significantly correlated with treatment in one of the five species tested.Availability and ImplementationCommands and scripts used in this study are available at https://bitbucket.org/lychen83/chen_et_al_2018_benchmark_dge/; Analysis files are available at Dryad doi: [email protected] informationSupplementary data are available at Bioinformatics online

scholarly journals Defining chromatin state transitions predicts a network that modulates cell wall remodeling in phosphate-starved rice shoots

2019 ◽  
Author(s):  
Maryam Foroozani ◽  
Sara Zahraeifard ◽  
Dong-Ha Oh ◽  
Guannan Wang ◽  
Maheshi Dassanayake ◽  
...  
Keyword(s):  
Cell Wall ◽  
Transcription Start Site ◽  
Chromatin Structure ◽  
Differentially Expressed ◽  
Chromatin State ◽  
State Transitions ◽  
Start Site ◽  
Transcription Start ◽  
Chromatin Dynamics ◽  
Combining Data

AbstractPhosphorus (P) is an essential plant macronutrient vital to fundamental metabolic processes. Plant-available P is low in most soils, making it a frequent limiter of growth. Declining P reserves for fertilizer production exasperates this agricultural challenge. Plants modulate complex responses to fluctuating P levels via global transcriptional regulatory networks. Although chromatin structure plays a substantial role in controlling gene expression, the chromatin dynamics involved in regulating P homeostasis have not been determined. Here we define distinct chromatin states across the rice genome by integrating multiple aspects of chromatin structure, including the H2A.Z histone variant, H3K4me3 modification, and nucleosome positioning. In response to P starvation, 40% of all protein-coding genes exhibit a transition from one chromatin state to another at their transcription start site. Several of these transitions are enriched in subsets of genes differentially expressed by P deficiency. The most prominent subset supports the presence of a coordinated signaling network that targets cell wall structure and is regulated in part via a decrease of H3K4me3 at the transcription start site. The P-starvation induced chromatin dynamics and correlated genes identified here will aid in enhancing P-use efficiency in crop plants, benefitting global agriculture.One sentence summaryCombining data for three components of chromatin structure from control and phosphate-starved rice shoots reveals specific chromatin state transitions that correlate with subsets of functionally distinct differentially-expressed genes.

scholarly journals Differential Analysis of Expressed Genes in Diabetic Nephropathy Based on Bioinformatics Technology

2021 ◽  
Author(s):  
Yu Liu ◽  
Jundong Wang ◽  
wencheng Chi ◽  
Jing Xie ◽  
LaiKuan Teh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Biological Effects ◽  
Cytokine Receptor ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Differential Analysis ◽  
Core Genes

Abstract Objective: Bioinformatics technology was used in this study to analyze the expression data of patients with diabetic nephropathy (DN) and normal subjects from the microarray. The purpose of this study was to screen the differentially expressed genes in DN and to explore the pathogenesis and potential therapeutic targets of DN. Methods: The data of gene expression in the gse142153 gene chip was downloaded from the gene expression database (GEO). The up-regulated and down-regulated expressed genes were analyzed by R language. The core genes of differentially expressed genes were analyzed by string database, Cytoscape software and its plug-in. The differentially expressed genes were analyzed by gene ontology and Kyoto Encyclopedia of genes and genomes. Results: A total of 112 differentially expressed genes were screened, including 50 down-regulated genes and 62 up-regulated genes. There are 10 up-regulated core genes including CXCL8, MMP9, IL1B, IL6, IL10, CXCL2, CCL20, ATF3, CXCL3, F3. Their biological effects are mainly concentrated in the IL-17 signaling pathway, rheumatoid arthritis, viral protein interaction with cytokine and cytokine receptor, Amoebiasis, TNF signaling pathway, Legionellosis, Cytokine-cytokine receptor interaction, Lipid, and atherosclerosis, Malaria, NOD-like receptor signaling pathway, etc. Conclusion: Analysis of differentially expressed genes and core genes enhanced the understanding of the pathogenesis of DN and provided a potential train of thought for the treatment of DN.

scholarly journals Dysfunctional network and mutation genes of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Yunwen Cui ◽  
Cheng Liu ◽  
Jian Luo ◽  
Jie Liang
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Ppi Network ◽  
Roc Curve Analysis ◽  
Study Gene Expression

Abstract Background Hypertrophic cardiomyopathy (HCM) is a group of heterogeneous diseases that affect the myocardium. It is also a common familial disease. The symptoms are not common and easy to find. Methods In this study, gene expression profiles of 37 samples (GSE130036) were downloaded from GEO database. Differential analysis was used to identify the related dysregulated genes in patients with HCM. Enrichment analysis identified the biological function and signal pathway of these differentially expressed genes. Then, we build PPI network and verify it in GSE36961 dataset. Finally, the gene of single nuclear variants (SNVs) in HCM samples was screened by means of maftools. Results Herein, we obtained 920 differentially expressed genes, and found that these genes are mainly related to metabolic related signaling pathways. 187 interacting genes were identified by PPI network analysis, and the expression trends of C1QB, F13A1, CD163, FCN3, PLA2G2A and CHRDL2 were verified by another dataset. ROC curve analysis showed that they had certain clinical diagnostic ability, and they were the potential key dysfunctional genes of HCM. In addition, we found that PRMT5 mutation was the most frequent in HCM samples, which may affect the pathogenesis of HCM. Conclusions Therefore, the key genes and enrichment results identified by our analysis may provide a reference for the occurrence and development mechanism of HCM. In addition, mutations in PRMT5 may be a useful therapeutic and diagnostic target for HCM.

scholarly journals SCORE: Smart Consensus Of RNA Expression—a consensus tool for detecting differentially expressed genes in bacteria

2020 ◽  
Author(s):  
Silver A Wolf ◽  
Lennard Epping ◽  
Sandro Andreotti ◽  
Knut Reinert ◽  
Torsten Semmler
Keyword(s):  
Differentially Expressed Genes ◽  
Current Method ◽  
Differentially Expressed ◽  
Supplementary Information ◽  
Rna Expression ◽  
Rna Seq ◽  
Bacterial Rna ◽  
Differential Gene ◽  
No Gold Standard ◽  
Selection Of

Abstract Summary RNA-sequencing (RNA-Seq) is the current method of choice for studying bacterial transcriptomes. To date, many computational pipelines have been developed to predict differentially expressed genes from RNA-Seq data, but no gold-standard has been widely accepted. We present the Snakemake-based tool Smart Consensus Of RNA Expression (SCORE) which uses a consensus approach founded on a selection of well-established tools for differential gene expression analysis. This allows SCORE to increase the overall prediction accuracy and to merge varying results into a single, human-readable output. SCORE performs all steps for the analysis of bacterial RNA-Seq data, from read preprocessing to the overrepresentation analysis of significantly associated ontologies. Development of consensus approaches like SCORE will help to streamline future RNA-Seq workflows and will fundamentally contribute to the creation of new gold-standards for the analysis of these types of data. Availability and implementation https://github.com/SiWolf/SCORE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RAD: a web application to identify region associated differentially expressed genes

2021 ◽  
Author(s):  
Yixin Guo ◽  
Ziwei Xue ◽  
Ruihong Yuan ◽  
Jingyi Jessica Li ◽  
William A Pastor ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Web Application ◽  
Distal Region ◽  
Differentially Expressed ◽  
Supplementary Information ◽  
Regulatory Function ◽  
Genome Wide ◽  
Wide Scale ◽  
Genomic Regions ◽  
User Friendly

Abstract Summary With the advance of genomic sequencing techniques, chromatin accessible regions, transcription factor binding sites and epigenetic modifications can be identified at genome-wide scale. Conventional analyses focus on the gene regulation at proximal regions; however, distal regions are usually less focused, largely due to the lack of reliable tools to link these regions to coding genes. In this study, we introduce RAD (Region Associated Differentially expressed genes), a user-friendly web tool to identify both proximal and distal region associated differentially expressed genes (DEGs). With DEGs and genomic regions of interest (gROI) as input, RAD maps the up- and down-regulated genes associated with any gROI and helps researchers to infer the regulatory function of these regions based on the distance of gROI to differentially expressed genes. RAD includes visualization of the results and statistical inference for significance. Availability and implementation RAD is implemented with Python 3.7 and run on a Nginx server. RAD is freely available at https://labw.org/rad as online web service. Supplementary information Supplementary data are available at Bioinformatics online.

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