scholarly journals Gene Sets Analysis using Network Patterns

2019 ◽  
Author(s):  
Gregory Linkowski ◽  
Charles Blatti ◽  
Krishna Kalari ◽  
Saurabh Sinha ◽  
Shobha Vasudevan
Keyword(s):  
Learning Algorithm ◽  
Gene Families ◽  
Response To Treatment ◽  
Experimental Conditions ◽  
Functional Interpretation ◽  
Gene Set ◽  
Gene Sets ◽  
Breast Cancer Study ◽  
Network Patterns ◽  
First Time

ABSTRACTHigh throughput assays allow researchers to identify sets of genes related to experimental conditions or phenotypes of interest. These gene sets are frequently subjected to functional interpretation using databases of gene annotations. Recent approaches have extended this approach to also consider networks of gene-gene relationships and interactions when attempting to characterize properties of a gene set. We present here a supervised learning algorithm for gene set analysis, called ‘GeneSet MAPR’, that for the first time explicitly considers the patterns of direct as well as indirect relationships present in the network to quantify gene-gene similarities and then report shared properties of the gene set. Our extensive evaluations show that GeneSet MAPR performs better than other network-based methods for the task of identifying genes related to a given gene set, enabling more reliable functional characterizations of the gene set. When applied to the set of response-associated genes from a triple negative breast cancer study, GeneSet MAPR uncovers gene families such as claudins, kallikreins, and collagen type alpha chains related to patient’s response to treatment, and which are not uncovered with traditional analysis.

scholarly journals Association between a Prognostic Gene Signature and Functional Gene Sets

2008 ◽  
Vol 2 ◽  
pp. BBI.S1018 ◽  
Author(s):  
Manuela Hummel ◽  
Klaus H. Metzeler ◽  
Christian Buske ◽  
Stefan K. Bohlander ◽  
Ulrich Mansmann
Keyword(s):  
Gene Ontology ◽  
Risk Score ◽  
Gene Signature ◽  
Functional Gene ◽  
Gene Set Enrichment ◽  
Functional Interpretation ◽  
Data Set ◽  
Gene Set ◽  
Signature Genes ◽  
Gene Sets

Background The development of expression-based gene signatures for predicting prognosis or class membership is a popular and challenging task. Besides their stringent validation, signatures need a functional interpretation and must be placed in a biological context. Popular tools such as Gene Set Enrichment have drawbacks because they are restricted to annotated genes and are unable to capture the information hidden in the signature's non-annotated genes. Methodology We propose concepts to relate a signature with functional gene sets like pathways or Gene Ontology categories. The connection between single signature genes and a specific pathway is explored by hierarchical variable selection and gene association networks. The risk score derived from an individual patient's signature is related to expression patterns of pathways and Gene Ontology categories. Global tests are useful for these tasks, and they adjust for other factors. GlobalAncova is used to explore the effect on gene expression in specific functional groups from the interaction of the score and selected mutations in the patient's genome. Results We apply the proposed methods to an expression data set and a corresponding gene signature for predicting survival in Acute Myeloid Leukemia (AML). The example demonstrates strong relations between the signature and cancer-related pathways. The signature-based risk score was found to be associated with development-related biological processes. Conclusions Many authors interpret the functional aspects of a gene signature by linking signature genes to pathways or relevant functional gene groups. The method of gene set enrichment is preferred to annotating signature genes to specific Gene Ontology categories. The strategies proposed in this paper go beyond the restriction of annotation and deepen the insights into the biological mechanisms reflected in the information given by a signature.

Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets

2013 ◽  
Vol 29 (16) ◽  
pp. 2024-2031 ◽  
Author(s):  
Xi Zhou ◽  
Pengcheng Chen ◽  
Qiang Wei ◽  
Xueling Shen ◽  
Xin Chen
Keyword(s):  
Linkage Analysis ◽  
Human Interactome ◽  
Functional Interpretation ◽  
Gene Set ◽  
Gene Sets

scholarly journals MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chih-Yi Chien ◽  
Ching-Wei Chang ◽  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Enrichment Analysis ◽  
R Package ◽  
Ordinary Least Squares ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Analysis ◽  
Gene Set Enrichment ◽  
Experimental Conditions ◽  
Gene Set ◽  
Gene Sets ◽  
Significant Difference

Gene set analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes thePvalues and FDR (false discovery rate)q-value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.

scholarly journals Combining multiple tools outperforms individual methods in gene set enrichment analyses

2016 ◽  
Author(s):  
Monther Alhamdoosh ◽  
Milica Ng ◽  
Nicholas J. Wilson ◽  
Julie M. Sheridan ◽  
Huy Huynh ◽  
...  
Keyword(s):  
Simulated Data ◽  
R Package ◽  
Sequencing Data ◽  
Gene Set Enrichment ◽  
Experimental Conditions ◽  
Data Set ◽  
Gene Set ◽  
Link Type ◽  
Analysis Methods ◽  
Gene Sets

AbstractGene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular data set. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions.Results: The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. redEGSEA’s gene set database contains around 25,000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse data sets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes.Availability and Implementation: EGSEA is available as an R package at http://www.bioconductor.org/packages/EGSEA/. The gene sets collections are available in the R package EGSEAdata from http://www.bioconductor.org/packages/EGSEA/.

scholarly journals Comprehensive exploration of the genetic contribution of the dopaminergic and serotonergic pathways to psychiatric disorders

2020 ◽  
Author(s):  
Judit Cabana-Domínguez ◽  
Bàrbara Torrico ◽  
Andreas Reif ◽  
Noèlia Fernàndez-Castillo ◽  
Bru Cormand
Keyword(s):  
Psychiatric Disorders ◽  
Meta Analysis ◽  
Genetic Contribution ◽  
Gene Set ◽  
Kegg Pathways ◽  
The Core ◽  
Gene Sets ◽  
Genetic Overlap ◽  
Gene Level ◽  
First Time

ABSTRACTPsychiatric disorders are highly prevalent and display considerable clinical and genetic overlap. Dopaminergic and serotonergic neurotransmission have been shown to have an important role in many psychiatric disorders. Here we aim to assess the genetic contribution of these systems to eight psychiatric disorders (ADHD, ANO, ASD, BIP, MD, OCD, SCZ and TS) using publicly available GWAS analyses performed by the Psychiatric Genomics Consortium. To do so, we elaborated four different gene sets using the Gene Ontology and KEGG pathways tools: two ‘wide’ selections for dopamine (DA) and for serotonin (SERT), and two ‘core’ selections for the same systems. At the gene level, we found 67 genes from the DA and/or SERT gene sets significantly associated with one of the studied disorders, and 12 of them were associated with two different disorders. Gene-set analysis revealed significant associations for ADHD and ASD with the wide DA gene set, for BIP with the wide SERT gene set, and for MD with both the core DA set and the core SERT set. Interestingly, interrogation of the cross-disorder GWAS meta-analysis displayed association with the wide DA gene set. To our knowledge, this is the first time that these two neurotransmitter systems have systematically been inspected in these disorders. Our results support a cross-disorder contribution of dopaminergic and serotonergic systems in several psychiatric conditions.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Sequencing of Organellar Genomes of Nowellia curvifolia (Cephaloziaceae Jungermanniales) Revealed the Smallest Plastome with Complete Gene Set and High Intraspecific Variation Suggesting Cryptic Speciation

Diversity ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 81
Author(s):  
Jakub Sawicki ◽  
Katarzyna Krawczyk ◽  
Monika Ślipiko ◽  
Monika Szczecińska
Keyword(s):  
Cryptic Speciation ◽  
Editing Event ◽  
Protein Coding ◽  
Gene Set ◽  
Protein Coding Genes ◽  
Organellar Genomes ◽  
The Family ◽  
Leafy Liverwort ◽  
Aneura Mirabilis ◽  
First Time

The leafy liverwort Nowellia curvifolia is a widespread Holarctic species belonging to the family Cephaloziaceae. It is made up of a newly sequenced, assembled and annotated organellar genomes of two European specimens, which revealed the structure typical for liverworts, but also provided new insights into its microevolution. The plastome of N. curvifolia is the second smallest among photosynthetic liverworts, with the shortest known inverted repeats. Moreover, it is the smallest liverwort genome with a complete gene set, since two smaller genomes of Aneura mirabilis and Cololejeunea lanciloba are missing six and four protein-coding genes respectively. The reduction of plastome size in leafy liverworts seems to be mainly impacted by deletion within specific region between psbA and psbD genes. The comparative intraspecific analysis revealed single SNPs difference among European individuals and a low number of 35 mutations differentiating European and North American specimens. However, the genetic resources of Asian specimen enabled to identify 1335 SNPs in plastic protein-coding genes suggesting an advanced cryptic speciation within N. curvifolia or the presence of undescribed morphospecies in Asia. Newly sequenced mitogenomes from European specimens revealed identical gene content and structure to previously published and low intercontinental differentiation limited to one substitution and three indels. The RNA-seq based RNA editing analysis revealed 17 and 127 edited sites in plastome and mitogenome respectively including one non-canonical editing event in plastid chiL gene. The U to C editing is common in non-seed plants, but in liverwort plastome is reported for the first time.

scholarly journals The effect of salinity on enterocytozoon hepatopenaei infection in Penaeus vannamei under experimental conditions

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
L. F. Aranguren Caro ◽  
F. Alghamdi ◽  
K. De Belder ◽  
J. Lin ◽  
H. N. Mai ◽  
...  
Keyword(s):  
Penaeus Monodon ◽  
Oral Route ◽  
Western Hemisphere ◽  
Penaeus Vannamei ◽  
Asian Countries ◽  
Experimental Conditions ◽  
Reliable Source ◽  
Specific Pathogen ◽  
Enterocytozoon Hepatopenaei ◽  
First Time

Abstract Background Enterocytozoon hepatopenaei (EHP) is an enteric pathogen that affects Penaeus vannamei and Penaeus monodon shrimp in many SE Asian countries. In the western hemisphere, EHP was reported for the first time in 2016 in farmed P. vannamei in Venezuela. Anecdotal evidence suggests that EHP is more prevalent in grow-out ponds where the salinity is high (> 15 parts per thousand (ppt)) compared to grow-out ponds with low salinities (< 5 ppt). Considering that P. vannamei is an euryhaline species, we were interested in knowing if EHP can propagate in P. vannamei in low salinities. Results In this study, we described an experimental infection using fecal strings as a source inoculum. Specific Pathogen Free (SPF) P. vannamei were maintained at three different salinities (2 ppt, 15 ppt, and 30 ppt) while continuously challenged using feces from known EHP-infected P. vannamei over a period of 3 weeks. The fecal strings, used as a source of EHP inocula in the challenges, was sufficient to elicit an infection in shrimp maintained at the three salinities. The infectivity of EHP in shrimp reared at 2 ppt, 15 ppt, and 30 ppt salinities was confirmed by PCR and histopathology. The prevalence and the severity of the EHP infection was higher at 30 ppt than at 2 ppt and 15 ppt. Conclusion The data suggests that fecal strings are a reliable source of EHP inoculum to conduct experimental challenges via the fecal-oral route. An EHP infection can occur at a salinity as low as 2 ppt, however, the prevalence and the severity of the EHP infection is higher at a salinity of 30 ppt.

scholarly journals Comparative investigation of lasing and amplification performance in cryogenic Yb:YLF systems

2021 ◽  
Vol 127 (3) ◽  
Author(s):  
Umit Demirbas ◽  
Martin Kellert ◽  
Jelto Thesinga ◽  
Yi Hua ◽  
Simon Reuter ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Estimation Method ◽  
Laser Cavity ◽  
Comparative Investigation ◽  
Limiting Factor ◽  
Experimental Conditions ◽  
Temperature Estimation ◽  
Transverse Temperature ◽  
First Time

AbstractWe present detailed experimental results with cryogenic Yb:YLF gain media in rod-geometry. We have comparatively investigated continuous-wave (cw) lasing and regenerative amplification performance under different experimental conditions. In the cw lasing experiments effect of crystal doping, cw laser cavity geometry and pump wavelength on lasing performance were explored. Regenerative amplification behavior was analyzed and the role of depolarization losses on performance was investigated. A recently developed temperature estimation method was also employed for the first time in estimating average crystal temperature under lasing conditions. It is shown that the thermal lens induced by transverse temperature gradients is the main limiting factor and strategies for future improvements are discussed. To the best of our knowledge, the achieved results in this study (375 W in cw, and 90 W in regenerative amplification) are the highest average powers ever obtained from this system via employing the broadband E//a axis.

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