Dataset Information

Network-Based and Binless Frequency Analyses.

ABSTRACT: We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±? around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (±?). The value with the highest degree (i.e., most connections) is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method.

SUBMITTER: Derrible S

PROVIDER: S-EPMC4631440 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Network-Based and Binless Frequency Analyses.

Derrible Sybil S Ahmad Nasir N

PloS one 20151103 11

We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two va ...[more]

PMID: 26529207

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Project description:BACKGROUND: Understanding gene expression and metabolic re-programming that occur in response to limiting nitrogen (N) conditions in crop plants is crucial for the ongoing progress towards the development of varieties with improved nitrogen use efficiency (NUE). To unravel new details on the molecular and metabolic responses to N availability in a major food crop, we conducted analyses on a weighted gene co-expression network and metabolic profile data obtained from leaves and roots of rice plants adapted to sufficient and limiting N as well as after shifting them to limiting (reduction) and sufficient (induction) N conditions. RESULTS: A gene co-expression network representing clusters of rice genes with similar expression patterns across four nitrogen conditions and two tissue types was generated. The resulting 18 clusters were analyzed for enrichment of significant gene ontology (GO) terms. Four clusters exhibited significant correlation with limiting and reducing nitrate treatments. Among the identified enriched GO terms, those related to nucleoside/nucleotide, purine and ATP binding, defense response, sugar/carbohydrate binding, protein kinase activities, cell-death and cell wall enzymatic activity are enriched. Although a subset of functional categories are more broadly associated with the response of rice organs to limiting N and N reduction, our analyses suggest that N reduction elicits a response distinguishable from that to adaptation to limiting N, particularly in leaves. This observation is further supported by metabolic profiling which shows that several compounds in leaves change proportionally to the nitrate level (i.e. higher in sufficient N vs. limiting N) and respond with even higher levels when the nitrate level is reduced. Notably, these compounds are directly involved in N assimilation, transport, and storage (glutamine, asparagine, glutamate and allantoin) and extend to most amino acids. Based on these data, we hypothesize that plants respond by rapidly mobilizing stored vacuolar nitrate when N deficit is perceived, and that the response likely involves phosphorylation signal cascades and transcriptional regulation. CONCLUSIONS: The co-expression network analysis and metabolic profiling performed in rice pinpoint the relevance of signal transduction components and regulation of N mobilization in response to limiting N conditions and deepen our understanding of N responses and N use in crops.

Project description:BACKGROUND: Phylogenetic approaches are commonly used to predict which amino acid residues are critical to the function of a given protein. However, such approaches display inherent limitations, such as the requirement for identification of multiple homologues of the protein under consideration. Therefore, complementary or alternative approaches for the prediction of critical residues would be desirable. Network analyses have been used in the modelling of many complex biological systems, but only very recently have they been used to predict critical residues from a protein's three-dimensional structure. Here we compare a couple of phylogenetic approaches to several different network-based methods for the prediction of critical residues, and show that a combination of one phylogenetic method and one network-based method is superior to other methods previously employed. RESULTS: We associate a network with each member of a set of proteins for which the three-dimensional structure is known and the critical residues have been previously determined experimentally. We show that several network-based centrality measurements (connectivity, 2-connectivity, closeness centrality, betweenness and cluster coefficient) accurately detect residues critical for the protein's function. Phylogenetic approaches render predictions as reliable as the network-based measurements, although, interestingly, the two general approaches tend to predict different sets of critical residues. Hence we propose a hybrid method that is composed of one network-based calculation--the closeness centrality--and one phylogenetic approach--the Conseq server. This hybrid approach predicts critical residues more accurately than the other methods tested here. CONCLUSION: We show that network analysis can be used to improve the prediction of amino acids critical for protein function, when utilized in combination with phylogenetic approaches. It is proposed that such improvement is due to the complementary nature of these approaches: network-based methods tend to predict as critical those residues that are highly connected and internal (i.e., non-surface), although some surface residues are indeed identified as critical by network analyses; whereas residues chosen by phylogenetic approaches display a lower overall probability of being surface inaccessible.

Dataset Information

Network-Based and Binless Frequency Analyses.

Publications

Network-Based and Binless Frequency Analyses.

Similar Datasets

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