A method to identify differential expression profiles of time-course gene data with Fourier transformation

Kim, Jaehee; Ogden, R. Todd; Kim, Haseong

doi:10.1186/1471-2105-14-310

Cited by 14 publications

(11 citation statements)

References 33 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the fact that time series are not explicitly modelled in this work, our framework may still serve as a basic ‘grammar' for future extensions to the dynamic case. Other methodologies may be additionally used to account for the time factor, such as Bayesian methods 33 , Gaussian models 34 or Fourier transformation 35 .…”

Section: Discussionmentioning

confidence: 99%

Combinatorial code governing cellular responses to complex stimuli

et al. 2015

View full text Add to dashboard Cite

Cells adapt to their environment through the integration of complex signals. Multiple signals can induce synergistic or antagonistic interactions, currently considered as homogenous behaviours. Here, we use a systematic theoretical approach to enumerate the possible interaction profiles for outputs measured in the conditions 0 (control), signals X, Y, X+Y. Combinatorial analysis reveals 82 possible interaction profiles, which we biologically and mathematically grouped into five positive and five negative interaction modes. To experimentally validate their use in living cells, we apply an original computational workflow to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each interaction mode was preferentially used in specific biological pathways, suggesting a functional role in the adaptation to multiple signals. Our work defines an exhaustive map of interaction modes for cells integrating pairs of physiopathological and pharmacological stimuli.

show abstract

Section: Discussionmentioning

confidence: 99%

Combinatorial code governing cellular responses to complex stimuli

et al. 2015

View full text Add to dashboard Cite

show abstract

“…This method is suffering with increase in false positive rate. Another method based on Fourier transform is proposed in [21]. In this method genes which are not differentially expressed are filtered out on the basis of Fourier coefficients.…”

Section: Methods For Non-replicated Microarray Datasetmentioning

confidence: 99%

Methods used for Identification of Differentially Expressing Genes (DEGs) from Microarray Gene Dataset: A Review

Panse¹,

Kshirsagar²,

Wajgi³

2017

IJBTT

View full text Add to dashboard Cite

Genes contain blue print of living organism. Malfunctioning occurred in cellular life is indicated by proteins which are responsible for behavior of genes. Fixed set of genes decides behavior and functioning of cells. They guide the cells what to do and when to do. To analyze the insight of biological activities, analysis of gene expressions is necessary. Advanced technology like microarray plays an important role in gene analysis. It captures expressions of thousands of genes under different conditions simultaneously. Out of thousands of genes, very few behave differently which are called as Differentially Expressed Genes (DEGs). Identification of these most significant genes is a crucial task in molecular biology and is a major area of research for bioinformaticians because DEGs are the major source of disease prediction. They help in planning therapeutic strategies for a disease through Gene Regulatory Network (GRN) which is constructed from them. GRN is a graphical representation containing genes as nodes and regulatory interactions between them as edges. GRN helps in knowing how genes regulate each other and in sense maintain underlined state of art working of cells. Deregulation between genes is the cause of major genetic diseases. In this paper we have discussed many methods proposed by researchers for identifying differentially expressing genes based upon changes in their expressions patterns.

show abstract

“…FFT has been used to detect genes relevant to specific biological processes, such as cell cycle and cardian clock [ 20 - 22 ]. FFT converts a signal in the time domain into one in the frequency domain, thereby showing the magnitude of each frequency present in the signal.…”

Section: Methodsmentioning

confidence: 99%

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites

Liu

Huang

et al. 2014

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundThe invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.ResultsIn this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of parasites and APP of humans may function in the invasion of RBCs by parasites.ConclusionsWe predicted a small-scale PPI network that may be involved in parasite invasion of RBCs by integrating DDI information and expression profiles. Experimental studies should be conducted to validate the predicted interactions. The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0393-z) contains supplementary material, which is available to authorized users.

show abstract

A method to identify differential expression profiles of time-course gene data with Fourier transformation

Cited by 14 publications

References 33 publications

Combinatorial code governing cellular responses to complex stimuli

Combinatorial code governing cellular responses to complex stimuli

Methods used for Identification of Differentially Expressing Genes (DEGs) from Microarray Gene Dataset: A Review

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites

Contact Info

Product

Resources

About