Gene Coexpression Networks for the Analysis of DNA Microarray Data

Weirauch, Matthew T.

doi:10.1002/9783527638079.ch11

Cited by 61 publications

(39 citation statements)

References 211 publications

(230 reference statements)

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“…The inevitable experimental noise within microarray data may give rise to false positive interactions in which pairs of genes have high degree of coexpression in only one dataset but very low coexpression in other datasets16. The traditional approach relies on increasing the number of samples to infer more reliable correlation relationships17. On the other hand, indiscriminately combining multiple samples may not be universally good.…”

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confidence: 99%

Pan- and core- network analysis of co-expression genes in a model plant

Maslov

2016

Sci Rep

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Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ and ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.

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confidence: 99%

Pan- and core- network analysis of co-expression genes in a model plant

Maslov

2016

Sci Rep

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“…However, our aim was to assess regulation of connective tissue metabolism downstream of translation in order to identify differences in gene expression as well as genes that are co-expressed. Gene expression correlational analyses are routinely performed throughout the basic sciences and co-expressed genes are of biological interest because they are likely controlled by the same transcriptionally regulatory pathway and are functionally related 35,36,52 . Given our results regarding increased BMP1 mRNA levels in women with POP compared to those without POP, comprehensive analyses of the various isoforms of this protein should be an area of future research.…”

Section: Discussionmentioning

confidence: 99%

Transcriptional Regulation of Connective Tissue Metabolism Genes in Women With Pelvic Organ Prolapse

Borazjani

Kow

Harris

et al. 2017

Female Pelvic Med Reconstr Surg

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Objective To compare differences in expressions and relationships between key genes involved in extracellular matrix metabolism and tissue cellularity in women with and without pelvic organ prolapse (POP) Methods A total of 80 biopsies (anterior cuff, posterior cuff, and/or leading edge) were obtained from 30 women: n=10 premenopausal without POP (controls), n=10 premenopausal with POP, and n=10 postmenopausal with POP. Quantitative reverse-transcriptase polymerase chain reaction was used to assess gene expression of BMP1, COL1, COL3, RXFP1, MMP2, TIMP2, and TIMP3. H&E staining was used to assess cellularity of the connective tissue layer. Kruskal-Wallis, Mann-Whitney, Pearson’s correlation, or linear regression analyses were used as appropriate. Results BMP1 expression was significantly upregulated in patients with POP compared to controls. BMP1 expression was correlated with COL1 expression in all groups, but only correlated with TIMP3 expression in controls. Similarly, COL3 expression was correlated with RXFP1 expression in women with POP but not in controls. The degree of dependence (slope of the regression line) between COL1 and COL3 expression was significantly elevated in premenopausal women with POP compared to both other groups. The slopes between COL1-COL3, COL3-MMP2, COL1-RXFP1, and COL3-RXFP1 expression were significantly lower in postmenopausal women compared to premenopausal women with POP. No differences were found in overall tissue cellularity. Conclusions BMP1 expression may play a significant role in the pathophysiology of POP. The finding that BMP1 expression was correlated with COL1 expression in all groups suggests a conserved association between BMP1 and collagen synthesis in the vaginal wall. The elevated slope between COL1-COL3 expressions may be associated with early (premenopausal) development of POP. The expression of RXFP1 in postmenopausal women and its altered inter-gene regulation, suggests a role for RXFP1 in connective tissue metabolism outside of pregnancy.

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“…GCNs, on the other hand, capture similarities in activities of functions in a given biological condition. They thus provide valuable information functional similarities of genes, sharing of transcription factors, or membership in biological pathways [39, 40]. The complementary information about genetic interactions from these two networks can help predict biological functions of a given gene set and potential involvement in disease mechanisms.…”

Section: Methodsmentioning

confidence: 99%

Identifying functional cancer-specific miRNA–mRNA interactions in testicular germ cell tumor

Sedaghat

Modarressi

Shojaie

2016

Journal of Theoretical Biology

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Testicular cancer is the most common cancer in men aged between 15 and 35 and more than 90% of testicular neoplasms are originated at germ cells. Recent research has shown the impact of microRNAs (miRNAs) in different types of cancer, including testicular germ cell tumor (TGCT). MicroRNAs are small non-coding RNAs which affect the development and progression of cancer cells by binding to mRNAs and regulating their expressions. The identification of functional miRNA-mRNA interactions in cancers, i.e. those that alter the expression of genes in cancer cells, can help delineate post-regulatory mechanisms and may lead to new treatments to control the progression of cancer. A number of sequence-based methods have been developed to predict miRNA-mRNA interactions based on the complementarity of sequences. While necessary, sequence complementarity is, however, not sufficient for presence of functional interactions. Alternative methods have thus been developed to refine the sequence-based interactions using concurrent expression profiles of miRNAs and mRNAs. This study aims to find functional cancer-specific miRNA-mRNA interactions in TGCT. To this end, the sequence-based predicted interactions are first refined using an ensemble learning method, based on two well-known methods of learning miRNA-mRNA interactions, namely, TaLasso and GenMiR++. Additional functional analyses were then used to identify a subset of interactions to be most likely functional and specific to TGCT. The final list of 13 miRNA-mRNA interactions can be potential targets for identifying TGCT-specific interactions and future laboratory experiments to develop new therapies.

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Gene Coexpression Networks for the Analysis of DNA Microarray Data

Cited by 61 publications

References 211 publications

Pan- and core- network analysis of co-expression genes in a model plant

Pan- and core- network analysis of co-expression genes in a model plant

Transcriptional Regulation of Connective Tissue Metabolism Genes in Women With Pelvic Organ Prolapse

Identifying functional cancer-specific miRNA–mRNA interactions in testicular germ cell tumor

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