Relative diversity and community structure analysis of rumen protozoa according to T-RFLP and microscopic methods

2012 ◽  
Vol 88 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Lisa Tymensen ◽  
Cindy Barkley ◽  
Tim A. McAllister
2012 ◽  
Vol 78 (11) ◽  
pp. 4051-4056 ◽  
Author(s):  
Lisa D. Tymensen ◽  
Tim A. McAllister

ABSTRACTThe diversity of protozoan-associated methanogens in cattle was investigated using five universal archaeal small-subunit (SSU) rRNA gene primer sets.Methanobrevibacterspp. and rumen cluster C (distantly related toThermoplasmaspp.) were predominant. Significant differences in species composition among libraries indicate that some primers used previously to characterize rumen methanogens exhibit biased amplification.


2017 ◽  
Author(s):  
Takumi Imajo ◽  
◽  
Takeshi Kobayashi ◽  
Takeshi Terahara ◽  
Chiaki Imada ◽  
...  

2012 ◽  
Vol 7 (3) ◽  
pp. 034007 ◽  
Author(s):  
Paolo D’Odorico ◽  
Joel Carr ◽  
Francesco Laio ◽  
Luca Ridolfi

2018 ◽  
Vol 75 (7) ◽  
pp. 918-924 ◽  
Author(s):  
Luanfeng Hou ◽  
Qingping Wu ◽  
Qihui Gu ◽  
Qin Zhou ◽  
Jumei Zhang

2012 ◽  
Vol 20 (01) ◽  
pp. 57-66
Author(s):  
DE-WU DING ◽  
LONG YING

Community structure analysis methods are important tools in modeling and analyzing large-scale metabolic networks. However, traditional community structure methods are mainly solved by clustering nodes, which results in each metabolite belonging to only a single community, which limits their usefulness in the study of metabolic networks. In the present paper, we analyze the community structure and functional modules in the Staphylococcus aureus (S. aureus) metabolic network, using a link clustering algorithm, and we obtain 10 functional modules with better biological insights, which give better results than our previous study. We also evaluate the essentiality of nodes in S. aureus metabolic networks. We suggest that link clustering could identify functional modules and key metabolites in metabolic networks.


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