Global assessment of network inference algorithms based on available literature of gene/protein interactions

Altay, Gökmen; N, Altay; Neal, David E.

doi:10.3906/biy-1210-8

Cited by 15 publications

(12 citation statements)

References 29 publications

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“…Recently, bioinformatics and genomic tools have revolutionized the studies of organisms' metabolism by showing new results about proteins and their structure (Altay et al 2013;Darabi & Farhadi-Nejad 2013;Kesici et al 2013;Seddigh & Darabi 2014). The aim of the present study was to deliver structural, functional and phylogenetic analyses on a key gene in insects to determine the biochemical characterization in order to gain a better understanding of the digestive physiology of insects.…”

Section: Introductionmentioning

confidence: 99%

Structural and phylogenetic analysis of α-glucosidase protein in insects

Seddigh

Darabi

2015

Biologia

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α-Glucosidases (αglus) (EC 3.2.1.20) have been detected in a wide range of plants, animals and microorganisms and are described by their ability to catalyze the hydrolysis of 1,4-α-glucosidic linkages, releasing α-glucose. In this study, 96 αglu protein sequences from 33 organisms species including different insects species, plants (including thale cress and rice), mammals (including human and mouse) and Mycobacterium tuberculosis as a bacterium were aligned. Sequences were analyzed by computational tools to predict the protein properties, such as molecular mass, isoelectric point, signal peptide, conserved motifs, transmembrane domain and secondary structure. Drosophila melanogaster (GenBank Accession No.: NP 610382) was chosen as one of the representatives of insects for further analyses. The tertiary structure of representative samples were acquired using the tertiary structure of oligo-1,6-glucosidase from Bacillus cereus (Protein Data Bank code: 1UOK) as a template by Phyre2 server. Protein structure analysis revealed there is a high identity among insects and other organisms. There were some similar functional domains between D. melanogaster and M. tuberculosis. The modeled αglu has a typical spatial structure in insects and exhibits a high similarity with other organisms, especially Arabidopsis thaliana. Phylogenetic analysis indicated that D. melanogaster αglu has a close relationship with other αglus from different insect families. According to these results, αglu in insects should be evolved from a common ancestor.

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Section: Introductionmentioning

confidence: 99%

Structural and phylogenetic analysis of α-glucosidase protein in insects

Seddigh

Darabi

2015

Biologia

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“…Most published network inference methods attempt to validate their models through comparison with biological databases, calculating the proportion of interactions found both in the inferred networks and those databases [2]. However, this assumes that the network topologies are static and do not change between phenotypes or in response to perturbations.…”

Section: Introduction/methodsmentioning

confidence: 99%

Inference and validation of predictive gene networks from biomedical literature and gene expression data

et al. 2014

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Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.

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“…The results of DC3NET can be validated through literature using R package, ganet . (Altay et al, 2013).…”

Section: Lncap Examplementioning

confidence: 99%

Disease mechanism, drug-target and biomarker prediction software: Application on prostate cancer and validation

Altay

Nurmemmedov

Kesari

et al. 2017

Preprint

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We introduce an R software package for condition-specific gene regulatory network analysis based on DC3NET algorithm. We also present an application of it on a real prostate dataset and demonstrate the benefit of the software. We performed genome-wide differential gene network analysis with the software on the LnCap androgen stimulated and deprived prostate cancer gene expression datasets (GSE18684) and inferred the androgen stimulated prostate cancer specific differential network. As an outstanding result, CXCR7 along with CXCR4 appeared to have the most important role in the androgen stimulated prostate specific genome-wide differential network. This blind estimation is strongly supported from the literature. The critical roles for CXCR4, a receptor over-expressed in many cancers, and CXCR7 on mediating tumor metastasis, along with their contributions as biomarkers of tumor behavior as well as potential therapeutic target were studied in several other types of cancers. In fact, a pharmaceutical company had already developed a therapy by inhibiting CXCR4 to block non-cancerous immuno-suppressive and pro-angiogenic cells from populating the tumor for disrupting the cancer environment and restoring normal immune surveillance functions. Considering this strong confirmation, our inferred regulatory network might reveal the driving mechanism of LnCap androgen stimulated prostate cancer. Because, CXCR4 appeared to be in the center of the largest subnetwork of our inferred differential network. Moreover, enrichment analyses for the largest subnetwork of it appeared to be significantly enriched in terms of axon guidance, fc gamma R-mediated phagocytosis and endocytosis. This also conforms with the recent literature in the field of prostate cancer.We demonstrate how to derive condition-specific gene targets from expression datasets on genome-wide level using differential gene network analysis. Our results showed that differential gene network analysis worked well in a prostate cancer dataset, which suggest the use of this approach as essential part of current expression data processing.Availability: The introduced R software package available in CRAN at https://cran.r-project.org/web/packages/dc3net and also at https://github.com/altayg/dc3net . CC-BY-NC 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/129742 doi: bioRxiv preprint first posted online Apr. 24, 2017; 2 BackgroundProstate cancer is the second most common cancer in the male population, with an estimated 417,000 new cases diagnosed each year in Europe (Ferlay, 2013). The activation of androgen receptor (AR) through androgens plays a crucial role in the development and progression of prostate cancer (Kaur, 2016; Anantharaman, 2015; Choudhary, 2011;Massie, 2011). For early detection of prostate cancer, prostate specific antigen (PSA) screening method has been used widely as a diagnostic tool (Karatas, 2015). However, PSA fails...

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Global assessment of network inference algorithms based on available literature of gene/protein interactions

Cited by 15 publications

References 29 publications

Structural and phylogenetic analysis of α-glucosidase protein in insects

Structural and phylogenetic analysis of α-glucosidase protein in insects

Inference and validation of predictive gene networks from biomedical literature and gene expression data

Disease mechanism, drug-target and biomarker prediction software: Application on prostate cancer and validation

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