Algorithms for modeling global and context-specific functional relationship networks

Zhu, Fan; Panwar, Bharat; Guan, Yuanfang

doi:10.1093/bib/bbv065

Cited by 3 publications

(3 citation statements)

References 113 publications

(124 reference statements)

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“…The GTEx portal is useful to explore relationships between genetic variation and gene/isoform expression in various human tissues . The present IsoFunc Web server is also linked with our recently developed in-house tools Hisonet − and MI-PVT . The MI-PVT is a tool for visualizing the chromosome-centric human proteome, while Hisonet displays predicted isoform-level functional networks.…”

Section: Resultsmentioning

confidence: 99%

Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning

et al. 2016

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The vast majority of human multiexon genes undergo alternative splicing and produce a variety of splice variant transcripts and proteins, which can perform different functions. These protein-coding splice variants (PCSVs) greatly increase the functional diversity of proteins. Most functional annotation algorithms have been developed at the gene level; the lack of isoform-level gold standards is an important intellectual limitation for currently available machine learning algorithms. The accumulation of a large amount of RNA-seq data in the public domain greatly increases our ability to examine the functional annotation of genes at isoform level. In the present study, we used a multiple instance learning (MIL)-based approach for predicting the function of PCSVs. We used transcript-level expression values and gene-level functional associations from the Gene Ontology database. A support vector machine (SVM)-based 5-fold cross-validation technique was applied. Comparatively, genes with multiple PCSVs performed better than single PCSV genes, and performance also improved when more examples were available to train the models. We demonstrated our predictions using literature evidence of ADAM15, LMNA/C, and DMXL2 genes. All predictions have been implemented in a web resource called "IsoFunc", which is freely available for the global scientific community through http://guanlab.ccmb.med.umich.edu/isofunc .

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Section: Resultsmentioning

confidence: 99%

Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning

et al. 2016

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“…Transcriptome datasets integrated in a global manner capture broad, constitutive functional relationships that might not vary much with different tissues or organs, developmental phases, or environmental cues like biotic or abiotic stress 67, 76 . On the other hand, specifying an overarching biological theme in selection of datasets offers intuitive concepts that can be objectively tested.…”

Section: Discussionmentioning

confidence: 99%

“…There are more than a dozen other published algorithms for inference of GRNs from large-scale expression data. Several concepts in GRN inference, available algorithms, and their limitations and applications in plant studies are well summarized by others as a primer to interested researchers 61, 63, 67, 68 . An earlier meta-analysis of some of the popular approaches suggests integrating predictions from different algorithms to boost the accuracy of the consensus GRN 59 .…”

Section: The Curious Case Of Transcription Factorsmentioning

confidence: 99%

Recent advances in gene function prediction using context-specific coexpression networks in plants

Gupta

Pereira

2019

F1000Res

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Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

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Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks

et al. 2016

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BackgroundSeveral recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading.ResultsIn this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions.ConclusionsOur study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1233-0) contains supplementary material, which is available to authorized users.

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Algorithms for modeling global and context-specific functional relationship networks

Cited by 3 publications

References 113 publications

Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning

Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning

Recent advances in gene function prediction using context-specific coexpression networks in plants

Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks

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