scholarly journals Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence data

2021 ◽  
Author(s):  
Jie Zhou ◽  
Jiang Gui ◽  
Weston D Viles ◽  
Anne G Hoen
Keyword(s):  
16S Rrna ◽  
Permutation Test ◽  
Sequence Data ◽  
Interaction Network ◽  
Microbial Interactions ◽  
Microbial Interaction ◽  
Rrna Sequence ◽  
16S Rrna Sequence ◽  
Data Set ◽  
Graphical Lasso

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

scholarly journals Exploring soil microbial 16S rRNA sequence data to increase carbon yield and nitrogen efficiency of a bioenergy crop

GCB Bioenergy ◽  
2015 ◽  
Vol 8 (5) ◽  
pp. 867-879 ◽  
Author(s):  
Leonardo M. Pitombo ◽  
Janaína B. Carmo ◽  
Mattias Hollander ◽  
Raffaella Rossetto ◽  
Maryeimy V. López ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sequence Data ◽  
Nitrogen Efficiency ◽  
Bioenergy Crop ◽  
Carbon Yield ◽  
Rrna Sequence ◽  
16S Rrna Sequence ◽  
Soil Microbial

scholarly journals At Least 1 in 20 16S rRNA Sequence Records Currently Held in Public Repositories Is Estimated To Contain Substantial Anomalies

2005 ◽  
Vol 71 (12) ◽  
pp. 7724-7736 ◽  
Author(s):  
Kevin E. Ashelford ◽  
Nadia A. Chuzhanova ◽  
John C. Fry ◽  
Antonia J. Jones ◽  
Andrew J. Weightman
Keyword(s):  
16S Rrna ◽  
Sequence Data ◽  
Dominant Role ◽  
Separate Analysis ◽  
Graphic User Interface ◽  
Ribosomal Database Project ◽  
Rrna Sequence ◽  
16S Rrna Sequence ◽  
Sequencing Errors ◽  
Public Repositories

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/ .

scholarly journals 16S rRNA sequence data of Bombyx mori gut bacteriome after spermidine supplementation

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Resma Rajan ◽  
Alekhya Rani Chunduri ◽  
Anugata Lima ◽  
Anitha Mamillapalli
Keyword(s):  
16S Rrna ◽  
Bombyx Mori ◽  
Sequence Data ◽  
Rrna Sequence ◽  
16S Rrna Sequence

scholarly journals Evaluation of recA Sequences for Identification of Mycobacterium Species

2000 ◽  
Vol 38 (8) ◽  
pp. 2846-2852 ◽  
Author(s):  
Kym S. Blackwood ◽  
Cheng He ◽  
James Gunton ◽  
Christine Y. Turenne ◽  
Joyce Wolfe ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Gene Sequencing ◽  
Genetic Identification ◽  
Rrna Gene ◽  
Rrna Sequence ◽  
16S Rrna Sequence ◽  
Accurate Identification

16S rRNA sequence data have been used to provide a molecular basis for an accurate system for identification of members of the genusMycobacterium. Previous studies have shown thatMycobacterium species demonstrate high levels (>94%) of 16S rRNA sequence similarity and that this method cannot differentiate between all species, i.e., M. gastri and M. kansasii. In the present study, we have used the recAgene as an alternative sequencing target in order to complement 16S rRNA sequence-based genetic identification. The recA genes of 30 Mycobacterium species were amplified by PCR, sequenced, and compared with the published recA sequences of M. tuberculosis, M. smegmatis, and M. leprae available from GenBank. By recA sequencing the species showed a lower degree of interspecies similarity than they did by 16S rRNA gene sequence analysis, ranging from 96.2% betweenM. gastri and M. kansasii to 75.7% betweenM. aurum and M. leprae. Exceptions to this were members of the M. tuberculosis complex, which were identical. Two strains of each of 27 species were tested, and the intraspecies similarity ranged from 98.7 to 100%. In addition, we identified new Mycobacterium species that contain a protein intron in their recA genes, similar to M. tuberculosis and M. leprae. We propose thatrecA gene sequencing offers a complementary method to 16S rRNA gene sequencing for the accurate identification of theMycobacterium species.

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