scholarly journals Genome-wide identification of directed gene networks using large-scale population genomics data

2017 ◽  
Author(s):  
René Luijk ◽  
Koen F. Dekkers ◽  
Maarten van Iterson ◽  
Wibowo Arindrarto ◽  
Annique Claringbould ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Target Genes ◽  
Population Genomics ◽  
Regulatory Gene ◽  
Sequencing Data ◽  
Scale Population ◽  
Transcriptional Changes ◽  
New Gene ◽  
Novel Target

ABSTRACTIdentification of causal drivers behind regulatory gene networks is crucial in understanding gene function. We developed a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). The analysis of genotype and whole-blood RNA-sequencing data from 3,072 individuals identified 49 genes as drivers of downstream transcriptional changes (P < 7 × 10−10), among which transcription factors were overrepresented (P = 3.3 × 10−7). Our analysis suggests new gene functions and targets including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (novel target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6,600 genes with a genetic instrument can be explored individually using a web-based browser.

scholarly journals Genome-wide identification of directed gene networks using large-scale population genomics data

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
René Luijk ◽  
◽  
Koen F. Dekkers ◽  
Maarten van Iterson ◽  
Wibowo Arindrarto ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Population Genomics ◽  
Genome Wide ◽  
Scale Population

scholarly journals Large scale RNAi screen in Tribolium reveals novel target genes for pest control and the proteasome as prime target

BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Julia Ulrich ◽  
Van Anh Dao ◽  
Upalparna Majumdar ◽  
Christian Schmitt-Engel ◽  
Jonas Schwirz ◽  
...  
Keyword(s):  
Pest Control ◽  
Large Scale ◽  
Target Genes ◽  
Rnai Screen ◽  
Novel Target ◽  
Prime Target

scholarly journals Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Peanut

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 536 ◽  
Author(s):  
Xiaobo Zhao ◽  
Liming Gan ◽  
Caixia Yan ◽  
Chunjuan Li ◽  
Quanxi Sun ◽  
...  
Keyword(s):  
Large Scale ◽  
Target Genes ◽  
Sequencing Data ◽  
Regulatory Processes ◽  
Genome Wide ◽  
Non Coding Rnas ◽  
Identification And Characterization ◽  
Lower Expression ◽  
Weighted Correlation

Long non-coding RNAs (lncRNAs) are involved in various regulatory processes although they do not encode protein. Presently, there is little information regarding the identification of lncRNAs in peanut (Arachis hypogaea Linn.). In this study, 50,873 lncRNAs of peanut were identified from large-scale published RNA sequencing data that belonged to 124 samples involving 15 different tissues. The average lengths of lncRNA and mRNA were 4335 bp and 954 bp, respectively. Compared to the mRNAs, the lncRNAs were shorter, with fewer exons and lower expression levels. The 4713 co-expression lncRNAs (expressed in all samples) were used to construct co-expression networks by using the weighted correlation network analysis (WGCNA). LncRNAs correlating with the growth and development of different peanut tissues were obtained, and target genes for 386 hub lncRNAs of all lncRNAs co-expressions were predicted. Taken together, these findings can provide a comprehensive identification of lncRNAs in peanut.

scholarly journals sRNATargetDigger: A bioinformatics software for bidirectional identification of sRNA-target pairs with co-regulatory sRNAs information

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244480
Author(s):  
Xinghuo Ye ◽  
Zhihong Yang ◽  
Yeqin Jiang ◽  
Lan Yu ◽  
Rongkai Guo ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Target Genes ◽  
Small Interfering Rnas ◽  
Sequencing Data ◽  
Software Packages ◽  
Bioinformatics Software ◽  
Function Modules ◽  
Target Regulation

Identification of the target genes of microRNAs (miRNAs), trans-acting small interfering RNAs (ta-siRNAs), and small interfering RNAs (siRNAs) is an important step for understanding their regulatory roles in plants. In recent years, many bioinformatics software packages based on small RNA (sRNA) high-throughput sequencing (HTS) and degradome sequencing data analysis have provided strong technical support for large-scale mining of sRNA-target pairs. However, sRNA-target regulation is achieved using a complex network of interactions since one transcript might be co-regulated by multiple sRNAs and one sRNA may also affect multiple targets. Currently used mining software can realize the mining of multiple unknown targets using known sRNA, but it cannot rule out the possibility of co-regulation of the same target by other unknown sRNAs. Hence, the obtained regulatory network may be incomplete. We have developed a new mining software, sRNATargetDigger, that includes two function modules, “Forward Digger” and “Reverse Digger”, which can identify regulatory sRNA-target pairs bidirectionally. Moreover, it has the ability to identify unknown sRNAs co-regulating the same target, in order to obtain a more authentic and reliable sRNA-target regulatory network. Upon re-examination of the published sRNA-target pairs in Arabidopsis thaliana, sRNATargetDigger found 170 novel co-regulatory sRNA-target pairs. This software can be downloaded from http://www.bioinfolab.cn/sRNATD.html.

scholarly journals The single-cell eQTLGen consortium

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
MGP van der Wijst ◽  
DH de Vries ◽  
HE Groot ◽  
G Trynka ◽  
CC Hon ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Sequencing Data ◽  
Scale Population ◽  
Trait Locus ◽  
Different Cell Types ◽  
Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

scholarly journals Detecting cancer vulnerabilities through gene networks under purifying selection in 4,700 cancer genomes

2017 ◽  
Author(s):  
Anika Gupta ◽  
Heiko Horn ◽  
Parisa Razaz ◽  
April Kim ◽  
Michael Lawrence ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Significant Proportion ◽  
Low Frequency ◽  
Interaction Network ◽  
Purifying Selection ◽  
Sequencing Data ◽  
Gene Sets ◽  
Sequencing Studies ◽  
Significant Enrichment

ABSTRACTLarge-scale cancer sequencing studies have uncovered dozens of mutations critical to cancer initiation and progression. However, a significant proportion of genes linked to tumor propagation remain hidden, often due to noise in sequencing data confounding low frequency alterations. Further, genes in networks under purifying selection (NPS), or those that are mutated in cancers less frequently than would be expected by chance, may play crucial roles in sustaining cancers but have largely been overlooked. We describe here a statistical framework that identifies genes that have a first order protein interaction network significantly depleted for mutations, to elucidate key genetic contributors to cancers. Not reliant on and thus, unbiased by, the gene of interest’s mutation rate, our approach has identified 685 putative genes linked to cancer development. Comparative analysis indicates statistically significant enrichment of NPS genes in previously validated cancer vulnerability gene sets, while further identifying novel cancer-specific candidate gene targets. As more tumor genomes are sequenced, integrating systems level mutation data through this network approach should become increasingly useful in pinpointing gene targets for cancer diagnosis and treatment.

scholarly journals Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

2019 ◽  
Author(s):  
Paul J. Hop ◽  
René Luijk ◽  
Lucia Daxinger ◽  
Maarten van Iterson ◽  
Koen F. Dekkers ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Large Scale ◽  
Population Genomics ◽  
Epigenetic Modification ◽  
Binding Activity ◽  
Dna Binding Activity ◽  
Genome Wide ◽  
Scale Population ◽  
Genes Encoding ◽  
Methylation Patterns

SUMMARYDNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identified 818 genes that influence DNA methylation patterns in blood using large-scale population genomics data. By employing genetic instruments as causal anchors, we identified directed associations between gene expression and distant DNA methylation levels, whilst ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. We found that DNA methylation patterns are commonly shaped by transcription factors that consistently increase or decrease DNA methylation levels. However, we also observed genes encoding proteins without DNA binding activity with widespread effects on DNA methylation (e.g. NFKBIE, CDCA7(L) and NLRC5) and we suggest plausible mechanisms underlying these findings. Many of the reported genes were unknown to influence DNA methylation, resulting in a comprehensive resource providing insights in the principles underlying epigenetic regulation.

Reconstruction of gene network associated with Parkinson disease for gene targets search

2021 ◽  
Vol 67 (3) ◽  
pp. 222-230
Author(s):  
Y.L. Orlov ◽  
A.G. Galieva ◽  
N.G. Orlova ◽  
E.N. Ivanova ◽  
Y.A. Mozyleva ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gene Networks ◽  
Gene Network ◽  
Target Genes ◽  
Network Connectivity ◽  
Disease Occurrence ◽  
New Gene ◽  
On Line ◽  
Gene Ontologies

Accumulation of genetic data in the field of Parkinson's disease research culminated in identifying risk factors and confident prediction of the disease occurrence. To find new gene-targets for diagnostics and therapy we have to reconstruct gene network of the disease, to cluster genes in the network, to reveal key (hub) genes with largest number of interactions in the network. Using the on-line bioinformatics tools OMIM, PANTHER, g:Profiler, GeneMANIA, and STRING-DB, we have analyzed the current array of data related to Parkinson's disease, calculated the categories of gene ontologies for a large list of genes, visualized them, and built gene networks containing the identified key objects and their relationships. However, translating the results into biological understanding is still a promising major challenge. The analysis of the genes associated with the disease, the assessment of their place in the gene network (connectivity) allows us to evaluate them as target genes for medicinal effects.

Return of Results: Towards a Lexicon?

2011 ◽  
Vol 39 (4) ◽  
pp. 577-582 ◽  
Author(s):  
Bartha Maria Knoppers ◽  
Amy Dam
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Population Genomics ◽  
Return Of Results ◽  
The Public ◽  
International Policies ◽  
Gene Environment ◽  
Scale Population ◽  
National Norms ◽  
Potential Use

The last few years have witnessed the growth of large-scale, population genomics biobanks, which serve as longitudinal, gene-environment databases for future yet unspecified research. An international consortium, the Public Population Project in Genomics (P3G), builds harmonization tools for such biobanks and has catalogued numerous studies — at least 139 with over 10,000 banked participants and 34 with over 100,000. As their potential use for translational, clinical research draws near, it is opportune to clarify the duties of such biobanks to communicate results to participants. To identify the potential obligations, some demystification of the terminology surrounding the return of results as found in international and national norms on biobanking generally is essential. On the whole, our proposed lexicon is based on a study of norms as found in national and international policies but excludes debates found in the literature.

scholarly journals Reveel: large-scale population genotyping using low-coverage sequencing data

2015 ◽  
Vol 32 (11) ◽  
pp. 1686-1696 ◽  
Author(s):  
Lin Huang ◽  
Bo Wang ◽  
Ruitang Chen ◽  
Sivan Bercovici ◽  
Serafim Batzoglou
Keyword(s):  
Large Scale ◽  
Sequencing Data ◽  
Scale Population ◽  
Low Coverage
Sign in / Sign up

Export Citation Format

Share Document