scholarly journals Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Baoman Wang ◽  
Fei Yuan ◽  
Xiangyin Kong ◽  
Lan-Dian Hu ◽  
Yu-Dong Cai
Keyword(s):  
Cell Death ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Weighted Graph ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Multicellular Organisms

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Identification of Age-Related Macular Degeneration Related Genes by Applying Shortest Path Algorithm in Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jian Zhang ◽  
Min Jiang ◽  
Fei Yuan ◽  
Kai-Yan Feng ◽  
Yu-Dong Cai ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Shortest Path ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Age Related Macular Degeneration ◽  
Shortest Path Algorithm ◽  
Protein Protein Interaction ◽  
Age Related ◽  
Protein Protein Interaction Network

This study attempted to find novel age-related macular degeneration (AMD) related genes based on 36 known AMD genes. The well-known shortest path algorithm, Dijkstra’s algorithm, was applied to find the shortest path connecting each pair of known AMD related genes in protein-protein interaction (PPI) network. The genes occurring in any shortest path were considered as candidate AMD related genes. As a result, 125 novel AMD genes were predicted. The further analysis based on betweenness and permutation test indicates that there are 10 genes involved in the formation or development of AMD and may be the actual AMD related genes with high probability. We hope that this contribution would promote the study of age-related macular degeneration and discovery of novel effective treatments.

scholarly journals Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Fei Yuan ◽  
Yu-Hang Zhang ◽  
Sibao Wan ◽  
ShaoPeng Wang ◽  
Xiang-Yin Kong
Keyword(s):  
Pancreatic Cancer ◽  
Candidate Genes ◽  
Shortest Path ◽  
Protein Interactions ◽  
Large Scale ◽  
Permutation Test ◽  
Computational Method ◽  
Large Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction

Pancreatic cancer (PC) is a highly malignant tumor derived from pancreas tissue and is one of the leading causes of death from cancer. Its molecular mechanism has been partially revealed by validating its oncogenes and tumor suppressor genes; however, the available data remain insufficient for medical workers to design effective treatments. Large-scale identification of PC-related genes can promote studies on PC. In this study, we propose a computational method for mining new candidate PC-related genes. A large network was constructed using protein-protein interaction information, and a shortest path approach was applied to mine new candidate genes based on validated PC-related genes. In addition, a permutation test was adopted to further select key candidate genes. Finally, for all discovered candidate genes, the likelihood that the genes are novel PC-related genes is discussed based on their currently known functions.

COMPUTATIONAL METHOD FOR PROTEIN FUNCTION PREDICTION BY CONSTRUCTING PROTEIN INTERACTION NETWORK DICTIONARY

2006 ◽  
Vol 20 (02) ◽  
pp. 285-295 ◽  
Author(s):  
HEE-JEONG JIN ◽  
HWAN-GUE CHO
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Computational Method ◽  
Chi Square ◽  
Protein Protein Interaction ◽  
Protein Functions ◽  
Novel Method

In the post-genomic era, predicting protein function is a challenging problem. It is difficult and burdensome work to unravel the functions of a protein by wet experiments only. In this paper, we propose a novel method to predict protein functions by building a "Protein Interaction Network Dictionary (PIND)". This method deduces the protein functions by searching the most similar "words"(an anagram of functions in neighbor proteins on a protein–protein interaction graph) using global alignments. An evaluation of sensitivity and specificity shows that this PIND approach outperforms previous approaches such as Majority Rule and Chi-Square measure, and that it competes with the recently introduced Random Markov Model approach.

scholarly journals A Gene-Set Enrichment and Protein–Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 316 ◽  
Author(s):  
Krishnamoorthy Srikanth ◽  
Seung-Hwan Lee ◽  
Ki-Yong Chung ◽  
Jong-Eun Park ◽  
Gul-Won Jang ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Carcass Traits ◽  
Interaction Network ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Protein Protein Interaction ◽  
Regulatory Snps ◽  
Protein Protein Interaction Network

Non-synonymous SNPs and protein coding SNPs within the promoter region of genes (regulatory SNPs) might have a significant effect on carcass traits. Imputed sequence level data of 10,215 Hanwoo bulls, annotated and filtered to include only regulatory SNPs (450,062 SNPs), were used in a genome-wide association study (GWAS) to identify loci associated with backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). A total of 15, 176, and 1 SNPs were found to be significantly associated (p < 1.11 × 10−7) with BFT, CWT, and EMA, respectively. The significant loci were BTA4 (CWT), BTA6 (CWT), BTA14 (CWT and EMA), and BTA19 (BFT). BayesR estimated that 1.1%~1.9% of the SNPs contributed to more than 0.01% of the phenotypic variance. So, the GWAS was complemented by a gene-set enrichment (GSEA) and protein–protein interaction network (PPIN) analysis in identifying the pathways affecting carcass traits. At p < 0.005 (~2,261 SNPs), 25 GO and 18 KEGG categories, including calcium signaling, cell proliferation, and folate biosynthesis, were found to be enriched through GSEA. The PPIN analysis showed enrichment for 81 candidate genes involved in various pathways, including the PI3K-AKT, calcium, and FoxO signaling pathways. Our finding provides insight into the effects of regulatory SNPs on carcass traits.

scholarly journals Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ao Li ◽  
Mengqu Ge ◽  
Yao Zhang ◽  
Chen Peng ◽  
Minghui Wang
Keyword(s):  
Network Model ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Heterogeneous Network ◽  
Noncoding Rna ◽  
Interaction Network ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Leave One Out

Recent study shows that long noncoding RNAs (lncRNAs) are participating in diverse biological processes and complex diseases. However, at present the functions of lncRNAs are still rarely known. In this study, we propose a network-based computational method, which is called lncRNA-protein interaction prediction based on Heterogeneous Network Model (LPIHN), to predict the potential lncRNA-protein interactions. First, we construct a heterogeneous network by integrating the lncRNA-lncRNA similarity network, lncRNA-protein interaction network, and protein-protein interaction (PPI) network. Then, a random walk with restart is implemented on the heterogeneous network to infer novel lncRNA-protein interactions. The leave-one-out cross validation test shows that our approach can achieve an AUC value of 96.0%. Some lncRNA-protein interactions predicted by our method have been confirmed in recent research or database, indicating the efficiency of LPIHN to predict novel lncRNA-protein interactions.

Identification of potential molecular mechanisms of radiation pneumonitis development in non-small-cell lung cancer treatment by data mining

2020 ◽  
Vol 55 (3) ◽  
pp. 173-178
Author(s):  
L. Zhu ◽  
J. Zhang ◽  
B. Xia ◽  
S. Chen ◽  
Y. Xu
Keyword(s):  
Text Mining ◽  
Protein Interaction ◽  
Molecular Mechanisms ◽  
Radiation Pneumonitis ◽  
Interaction Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Gene Sets ◽  
Underlying Mechanisms ◽  
Unique Genes

Introduction: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity in patients receiving thoracic radiotherapy. The underlying mechanisms of RP are still inconclusive. Our objective was to determine the genes and molecular pathways associated with RP using computational tools and publicly available data. Methods: RP-associated genes were determined by text mining, and the intersection of the two gene sets was selected for Gene Ontology analysis using the GeneCodis program. Protein-protein interaction network analysis was performed using STRINGdb to identify the final genes. Results: Our analysis identified 256 genes related to RP with text mining. The enriched biological process annotations resulted in 47 sets of annotations containing a total of 156 unique genes. KEGG analysis of the enriched pathways identified 24 pathways containing a total of 41 unique genes. The protein-protein interaction analysis yielded 23 genes (mostly the PI3K family). Conclusion: Gene discovery using in silico text mining and pathway analysis tools can facilitate the identification of the underlying mechanisms of RP.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein-protein interaction network

2021 ◽  
Author(s):  
Backiyarani Suthanthiram ◽  
Sasikala Rajendran ◽  
Sharmiladevi Simeon ◽  
Uma Subbaraya
Keyword(s):  
Candidate Genes ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Banana, one of the most important staple, delicious fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein-protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By exploring the PPI of candidate genes from the putative network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLV-WUSHEL signaling pathway in addition to gibberellin mediated auxin signaling pathway in parthenocarpy. Further validation of candidate genes in seeded and seedless accession of Musa spp using qRT-PCR put forward AGL8, MADS16, IAA (GH3.8), RGA1, EXPA1, GID1C, HK2 and BAM1 as possible target genes in natural parthenocarpy. In contrary, expression profile of ACLB-2 and ZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through protein-protein interaction network.

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