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Limited By:	Author = Kellis, Manolis;

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Displaying Results 1 - 4 of 4 on page 1 of 1

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Evidence of efficient stop codon readthrough in four mammalian genes (2014)

Loughran, Gary; Chou, Ming-Yuan; Ivanov, Ivaylo P.; Jungreis, Irwin; Kellis, Manolis; K...

Evidence of efficient stop codon readthrough in four mammalian genes (2014)

Loughran, Gary; Chou, Ming-Yuan; Ivanov, Ivaylo P.; Jungreis, Irwin; Kellis, Manolis; Kiran, Anmol M.; Baranov, Pavel V.; Atkins, John F.

Abstract:

Stop codon readthrough is used extensively by viruses to expand their gene expression. Until recent discoveries in Drosophila, only a very limited number of readthrough cases in chromosomal genes had been reported. Analysis of conserved protein coding signatures that extend beyond annotated stop codons identified potential stop codon readthrough of four mammalian genes. Here we use a modified targeted bioinformatic approach to identify a further three mammalian readthrough candidates. All seven genes were tested experimentally using reporter constructs transfected into HEK-293T cells. Four displayed efficient stop codon readthrough, and these have UGA immediately followed by CUAG. Comparative genomic analysis revealed that in the four readthrough candidates containing UGA-CUAG, this motif is conserved not only in mammals but throughout vertebrates with the first six of the seven nucleotides being universally conserved. The importance of the CUAG motif was confirmed using a systemati...

http://hdl.handle.net/10468/5016

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Network Infusion to Infer Information Sources in Networks (2019)

Feizi, Soheil; Duffy, Ken R.; Kellis, Manolis; Medard, Muriel

Network Infusion to Infer Information Sources in Networks (2019)

Feizi, Soheil; Duffy, Ken R.; Kellis, Manolis; Medard, Muriel

Abstract:

Several models exist for diffusion of signals across biological, social, or engineered networks. However, the inverse problem of identifying the source of such propagated information appears more difficult even in the presence of multiple network snapshots, and especially for the singlesnapshot case, given the many alternative, often similar, progression of diffusion that may lead to the same observed snapshots. Mathematically, this problem can be undertaken using a diffusion kernel that represents diffusion processes in a given network, but computing this kernel is computationally challenging in general. Here, we propose a path-based network diffusion kernel which considers edge-disjoint shortest paths among pairs of nodes in the network and can be computed efficiently for both homogeneous and heterogeneous continuous-time diffusion models. We use this network diffusion kernel to solve the inverse diffusion problem, which we term Network Infusion (NI), using both likelihood maximiz...

http://mural.maynoothuniversity.ie/13077/

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Network Maximal Correlation (2017)

Feizi, Soheil; Makhdoumi, Ali; Duffy, Ken R.; Kellis, Manolis; Medard, Muriel

Network Maximal Correlation (2017)

Feizi, Soheil; Makhdoumi, Ali; Duffy, Ken R.; Kellis, Manolis; Medard, Muriel

Abstract:

We introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association among random variables. NMC is defined via an optimization that infers transformations of variables by maximizing aggregate inner products between transformed variables. For finite discrete and jointly Gaussian random variables, we characterize a solution of the NMC optimization using basis expansion of functions over appropriate basis functions. For finite discrete variables, we propose an algorithm based on alternating conditional expectation to determine NMC. Moreover we propose a distributed algorithm to compute an approximation of NMC for large and dense graphs using graph partitioning. For finite discrete variables, we show that the probability of discrepancy greater than any given level between NMC and NMC computed using empirical distributions decays exponentially fast as the sample size grows. For jointly Gaussian variables, we show that under some conditions the NMC optimizati...

http://mural.maynoothuniversity.ie/10167/

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Stop codon readthrough generates a C-terminally extended variant of the human vitamin D receptor with reduced calcitriol response (2018)

Loughran, Gary; Jungreis, Irwin; Tzani, Ioanna; Power, Michael; Dmitriev, Ruslan I.; Iv...

Stop codon readthrough generates a C-terminally extended variant of the human vitamin D receptor with reduced calcitriol response (2018)

Loughran, Gary; Jungreis, Irwin; Tzani, Ioanna; Power, Michael; Dmitriev, Ruslan I.; Ivanov, Ivaylo P.; Kellis, Manolis; Atkins, John F.

Abstract:

Although stop codon readthrough is used extensively by viruses to expand their gene expression, verified instances of mammalian readthrough have only recently been uncovered by systems biology and comparative genomics approaches. Previously our analysis of conserved protein coding signatures that extend beyond annotated stop codons predicted stop codon readthrough of several mammalian genes, all of which have been validated experimentally. Four mRNAs display highly efficient stop codon readthrough, and these mRNAs have a UGA stop codon immediately followed by CUAG (UGA_CUAG) that is conserved throughout vertebrates. Extending on the identification of this readthrough motif, we here investigated stop codon readthrough, using tissue culture reporter assays, for all previously untested human genes containing UGA_CUAG. The readthrough efficiency of the annotated stop codon for the sequence encoding vitamin D receptor (VDR) was 6.7%. It was the highest of those tested but all showed nota...

http://hdl.handle.net/10468/5716

Displaying Results 1 - 4 of 4 on page 1 of 1