scholarly journals A simple test to infer mode of selection in metagenomics time series of evolving asexual populations

Author(s):  
Rohan Maddamsetti ◽  
Nkrumah A. Grant

ABSTRACTWe introduce a simple test to infer mode of selection (STIMS) in metagenomic time series of evolving asexual populations. STIMS compares the tempo of molecular evolution for a gene set of interest against a null distribution that is bootstrapped on random gene sets. We test STIMS on metagenomic data spanning 62,750 generations of Lenski’s long-term evolution experiment with E. coli (LTEE). Our method successfully recovers signals of purifying selection and positive selection on gold standard sets of genes. We then use STIMS to study the evolution of genetic modules in the LTEE. We find strong evidence of ongoing positive selection on key regulators of the E. coli gene regulatory network. Key regulatory genes show evidence of positive selection over the entire time series, even in some hypermutator populations. By contrast, we found no signal of selection on the genetic modules that show the strongest transcriptional responses to changes in growth conditions. In addition, the cis-regulatory regions of key regulators are evolving faster than the cis-regulatory regions of their downstream regulatory targets. These results indicate that one mechanistic cause for ongoing fitness gains in the LTEE is ongoing fine-tuning of the gene regulatory network.

2018 ◽  
Vol 19 (10) ◽  
pp. 3178 ◽  
Author(s):  
Bin Yang ◽  
Yuehui Chen ◽  
Wei Zhang ◽  
Jiaguo Lv ◽  
Wenzheng Bao ◽  
...  

Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in E. coli and Saccharomyces cerevisiae are utilized to evaluate the performance of the HSCVFNT algorithm. As a result, HSCVFNT obtains outstanding F-scores of 0.923, 0.8 and 0.625 for SOS network and (In vivo Reverse-Engineering and Modeling Assessment) IRMA network inference, respectively, which are 5.5%, 14.3% and 72.2% higher than the best performance of other state-of-the-art GRN inference methods and time-delayed methods.


Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  

2013 ◽  
Vol 7 (1-2) ◽  
pp. 23-32 ◽  
Author(s):  
Vijai Singh ◽  
Indra Mani ◽  
Dharmendra Kumar Chaudhary

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