10,806 research outputs found
Pathway and network analysis in proteomics
Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics
A Computational Framework for Host-Pathogen Protein-Protein Interactions
Infectious diseases cause millions of illnesses and deaths every year, and raise great health concerns world widely. How to monitor and cure the infectious diseases has become a prevalent and intractable problem. Since the host-pathogen interactions are considered as the key infection processes at the molecular level for infectious diseases, there have been a large amount of researches focusing on the host-pathogen interactions towards the understanding of infection mechanisms and the development of novel therapeutic solutions. For years, the continuously development of technologies in biology has benefitted the wet lab-based experiments, such as small-scale biochemical, biophysical and genetic experiments and large-scale methods (for example yeast-two-hybrid analysis and cryogenic electron microscopy approach). As a result of past decades of efforts, there has been an exploded accumulation of biological data, which includes multi omics data, for example, the genomics data and proteomics data.
Thus, an initiative review of omics data has been conducted in Chapter 2, which has exclusively demonstrated the recent update of âomicsâ study, particularly focusing on proteomics and genomics. With the high-throughput technologies, the increasing amount of âomicsâ data, including genomics and proteomics, has even further boosted. An upsurge of interest for data analytics in bioinformatics comes as no surprise to the researchers from a variety of disciplines. Specifically, the astonishing rate at which genomics and proteomics data are generated leads the researchers into the realm of âBig Dataâ research. Chapter 2 is thus developed to providing an update of the omics background and the state-of-the-art developments in the omics area, with a focus on genomics data, from the perspective of big data analytics..
Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92060/1/pmic7070.pd
Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.
MOTIVATION: Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies. RESULTS: PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve ofââ€0.97, outperforming go2ppi-a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations. AVAILABILITY AND IMPLEMENTATION: https://github.com/ima23/maxent-ppi. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
Complex biological systems have been successfully modeled by biochemical and
genetic interaction networks, typically gathered from high-throughput (HTP)
data. These networks can be used to infer functional relationships between
genes or proteins. Using the intuition that the topological role of a gene in a
network relates to its biological function, local or diffusion based
"guilt-by-association" and graph-theoretic methods have had success in
inferring gene functions. Here we seek to improve function prediction by
integrating diffusion-based methods with a novel dimensionality reduction
technique to overcome the incomplete and noisy nature of network data. In this
paper, we introduce diffusion component analysis (DCA), a framework that plugs
in a diffusion model and learns a low-dimensional vector representation of each
node to encode the topological properties of a network. As a proof of concept,
we demonstrate DCA's substantial improvement over state-of-the-art
diffusion-based approaches in predicting protein function from molecular
interaction networks. Moreover, our DCA framework can integrate multiple
networks from heterogeneous sources, consisting of genomic information,
biochemical experiments and other resources, to even further improve function
prediction. Yet another layer of performance gain is achieved by integrating
the DCA framework with support vector machines that take our node vector
representations as features. Overall, our DCA framework provides a novel
representation of nodes in a network that can be used as a plug-in architecture
to other machine learning algorithms to decipher topological properties of and
obtain novel insights into interactomes.Comment: RECOMB 201
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