scholarly journals Metagenomic and near full-length 16S rRNA sequence data in support of the phylogenetic analysis of the rumen bacterial community in steers

Data in Brief ◽  
2016 ◽  
Vol 8 ◽  
pp. 1048-1053 ◽  
Author(s):  
Phillip R. Myer ◽  
MinSeok Kim ◽  
Harvey C. Freetly ◽  
Timothy P.L. Smith
2021 ◽  
Author(s):  
Jie Zhou ◽  
Jiang Gui ◽  
Weston D Viles ◽  
Anne G Hoen

Though being vital for human health, microbial interactions with their host and with each other are still largely obscure for researchers. To deepen the understanding, the analyses based on longitudinal data are a better choice than the cross-sectional data since the information provided by the former is usually more stable. To this end, in this paper, we first propose an EM-type algorithm to identify microbial interaction network for the irregularly spaced longitudinal measurements. Correlation functions are employed to account for the correlation across the temporal measurements for a given subject. The algorithms take advantage of the efficiency of the popular graphical lasso algorithm and can be implemented straightforwardly. Simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms such as graphical lasso or neighborhood method when the correlation between measurements grows larger. In second part of the paper, based on a 16S rRNA sequence data set of gut microbiome, module-preserving permutation test is proposed to test the independence of the estimated network and the phylogeny of the microbe species. The results demonstrate evidences of strong association between the interaction network and the phylogenetic tree which indicates that the taxa closer in their genomes tend to have more/stronger interactions in their functions. The proposed algorithms can be implemented through R package lglasso at \url{https://github.com/jiezhou-2/lglasso


2010 ◽  
Vol 89 (2) ◽  
pp. 262-265 ◽  
Author(s):  
Zuoyong Zhou ◽  
Kui Nie ◽  
Cheng Tang ◽  
Zhiying Wang ◽  
Rongqiong Zhou ◽  
...  

GCB Bioenergy ◽  
2015 ◽  
Vol 8 (5) ◽  
pp. 867-879 ◽  
Author(s):  
Leonardo M. Pitombo ◽  
Janaína B. Carmo ◽  
Mattias Hollander ◽  
Raffaella Rossetto ◽  
Maryeimy V. López ◽  
...  

2005 ◽  
Vol 71 (12) ◽  
pp. 7724-7736 ◽  
Author(s):  
Kevin E. Ashelford ◽  
Nadia A. Chuzhanova ◽  
John C. Fry ◽  
Antonia J. Jones ◽  
Andrew J. Weightman

ABSTRACT A new method for detecting chimeras and other anomalies within 16S rRNA sequence records is presented. Using this method, we screened 1,399 sequences from 19 phyla, as defined by the Ribosomal Database Project, release 9, update 22, and found 5.0% to harbor substantial errors. Of these, 64.3% were obvious chimeras, 14.3% were unidentified sequencing errors, and 21.4% were highly degenerate. In all, 11 phyla contained obvious chimeras, accounting for 0.8 to 11% of the records for these phyla. Many chimeras (43.1%) were formed from parental sequences belonging to different phyla. While most comprised two fragments, 13.7% were composed of at least three fragments, often from three different sources. A separate analysis of the Bacteroidetes phylum (2,739 sequences) also revealed 5.8% records to be anomalous, of which 65.4% were apparently chimeric. Overall, we conclude that, as a conservative estimate, 1 in every 20 public database records is likely to be corrupt. Our results support concerns recently expressed over the quality of the public repositories. With 16S rRNA sequence data increasingly playing a dominant role in bacterial systematics and environmental biodiversity studies, it is vital that steps be taken to improve screening of sequences prior to submission. To this end, we have implemented our method as a program with a simple-to-use graphic user interface that is capable of running on a range of computer platforms. The program is called Pintail, is released under the terms of the GNU General Public License open source license, and is freely available from our website at http://www.cardiff.ac.uk/biosi/research/biosoft/ .


2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Resma Rajan ◽  
Alekhya Rani Chunduri ◽  
Anugata Lima ◽  
Anitha Mamillapalli

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