Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways

Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase.

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Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽

10.3390/math9070772 ◽

2021 ◽

Vol 9 (7) ◽

pp. 772

Author(s):

Seonghun Kim ◽

Seockhun Bae ◽

Yinhua Piao ◽

Kyuri Jo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Large Scale ◽

Drug Response ◽

Response Prediction ◽

Biological Data ◽

Expression Data ◽

Convolutional Network ◽

Essential Information ◽

Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

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Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

Molecular BioSystems ◽

10.1039/c5mb00353a ◽

2015 ◽

Vol 11 (11) ◽

pp. 3137-3148

Author(s):

Nazanin Hosseinkhan ◽

Peyman Zarrineh ◽

Hassan Rokni-Zadeh ◽

Mohammad Reza Ashouri ◽

Ali Masoudi-Nejad

Keyword(s):

Gene Expression ◽

Escherichia Coli ◽

Pseudomonas Aeruginosa ◽

Systems Biology ◽

Gene Expression Data ◽

High Throughput ◽

Expression Data ◽

P Gene ◽

Stress Related Genes ◽

High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

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GENE DISCOVERY METHODS FROM LARGE-SCALE GENE EXPRESSION DATA

Quantum Bio-Informatics III ◽

10.1142/9789814304061_0040 ◽

2010 ◽

Author(s):

AKIFUMI SHIMIZU ◽

KENTARO YANO

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Large Scale ◽

Gene Discovery ◽

Expression Data

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High-Throughput In Situ Hybridization: Systematical Production of Gene Expression Data and Beyond

In Situ Hybridization Methods - Neuromethods ◽

10.1007/978-1-4939-2303-8_11 ◽

2015 ◽

pp. 221-245

Author(s):

Lars Geffers ◽

Gregor Eichele

Keyword(s):

Gene Expression ◽

In Situ Hybridization ◽

Gene Expression Data ◽

High Throughput ◽

Expression Data

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LSTrAP-Crowd: Prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data

10.1101/2020.04.20.005249 ◽

2020 ◽

Author(s):

Benedict Hew ◽

Qiao Wen Tan ◽

William Goh ◽

Jonathan Wei Xiong Ng ◽

Kenny Koh ◽

...

Keyword(s):

Gene Expression ◽

Protein Synthesis ◽

Rna Sequencing ◽

Gene Expression Data ◽

Large Scale ◽

Bacterial Resistance ◽

Expression Data ◽

Sequencing Data ◽

Novel Proteins ◽

Novel Antibiotics

AbstractBacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

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