scholarly journals In Silico Gene-Level Evolution Explains Microbial Population Diversity through Differential Gene Mobility

2015 ◽  
Vol 8 (1) ◽  
pp. 176-188 ◽  
Author(s):  
Bram van Dijk ◽  
Paulien Hogeweg
Keyword(s):  
In Silico ◽  
Microbial Population ◽  
Population Diversity ◽  
Gene Level ◽  
Differential Gene

scholarly journals Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 952 ◽  
Author(s):  
Michael I. Love ◽  
Charlotte Soneson ◽  
Rob Patro
Keyword(s):  
Software Package ◽  
Gene Expression Analysis ◽  
Real Data ◽  
Transcript Level ◽  
Bioinformatic Analysis ◽  
Rna Seq ◽  
Statistical Framework ◽  
Gene Level ◽  
Show Evidence ◽  
Differential Gene

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

scholarly journals The Physiological and Microbiological Characteristics of Cement Dust Polluted Soil Around Cement Industry

2021 ◽  
Author(s):  
Kehinde Oluwadamilare Sowunmi
Keyword(s):  
Heavy Metals ◽  
Microbial Population ◽  
Nutrient Status ◽  
Polluted Soil ◽  
Population Diversity ◽  
Cement Industry ◽  
Dust Deposition ◽  
Cement Dust ◽  
Bacteria And Fungi ◽  
The Impact

Abstract A study investigated impact of cement dust pollution from Ewekoro cement industry on soil microbes. pH of the soil ranged from 6.27±0.03- 6.47 and soil moisture content ranged from 15.78±2.52- 9.65±1.16. The levels of heavy metals except Mg, Zn and Na were higher within the factory than in the control. Microbial population diversity increased steadily away from the factory and this variation could be attributed to the impact of pH and heavy metals on microbial population. The lower counts of bacteria compared to fungi may be as a result of the nutrient status of the soil and the bacteria counts in polluted soil were lower than the fungal counts in control soil. The bacteria and fungi was influenced by the cement dust deposition. The study was published in the journal ‘Phenomenon: Microbes and the Cement Industry’.

scholarly journals Comparison of Nasal Epithelial Smoking-Induced Gene Expression on Affymetrix Exon 1.0 and Gene 1.0 ST Arrays

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoling Zhang ◽  
Marc E. Lenburg ◽  
Avrum Spira
Keyword(s):  
Gene Expression ◽  
Gene Array ◽  
Cost Effective ◽  
Exon Array ◽  
Nasal Epithelium ◽  
Gene Level ◽  
Differential Gene ◽  
Never Smokers ◽  
High Degree ◽  
The Impact

We have previously defined the impact of tobacco smoking on nasal epithelium gene expression using Affymetrix Exon 1.0 ST arrays. In this paper, we compared the performance of the Affymetrix GeneChip Human Gene 1.0 ST array with the Human Exon 1.0 ST array for detecting nasal smoking-related gene expression changes. RNA collected from the nasal epithelium of five current smokers and five never smokers was hybridized to both arrays. While the intersample correlation within each array platform was relatively higher in the Gene array than that in the Exon array, the majority of the genes most changed by smoking were tightly correlated between platforms. Although neither array dataset was powered to detect differentially expressed genes (DEGs) at a false discovery rate (FDR)<0.05, we identified more DEGs than expected by chance using the Gene ST array. These findings suggest that while both platforms show a high degree of correlation for detecting smoking-induced differential gene expression changes, the Gene ST array may be a more cost-effective platform in a clinical setting for gene-level genomewide expression profiling and an effective tool for exploring the host response to cigarette smoking and other inhaled toxins.

scholarly journals Microbial Population Diversity in the Urethras of Healthy Males and Males Suffering from Nonchlamydial, Nongonococcal Urethritis

2003 ◽  
Vol 41 (5) ◽  
pp. 1977-1986 ◽  
Author(s):  
W. A. Riemersma ◽  
C. J. C. van der Schee ◽  
W. I. van der Meijden ◽  
H. A. Verbrugh ◽  
A. van Belkum
Keyword(s):  
Microbial Population ◽  
Population Diversity ◽  
Nongonococcal Urethritis ◽  
Healthy Males

scholarly journals Microbiology and Heavy Metal Content of Wetlands Impacted by Crude Oil Pollution in Rivers State, Southern Nigeria

2021 ◽  
pp. 53-63
Author(s):  
Mebom Princess Chibuike ◽  
N. David Ogbonna ◽  
Williams Janet Olufunmilayo
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Concentration ◽  
Microbial Population ◽  
Soil Depth ◽  
Heavy Metal Concentration ◽  
Soil Samples ◽  
Population Diversity ◽  
Wetland Soil ◽  
Subsurface Soil

Wetland soils constitute vast, under-exploited and sometimes undiscovered ecologies in many countries of the World, including Nigeria. A total of 54 wetland soil samples including surface and subsurface soil at depths of 0-15 cm and 15-30 cm were collected using a sterile hand auger for a period of three months between August and October and subjected to standard and analytical microbiological procedures. The wetland soil samples were further subjected to atomic absorption spectroscopy (AAS) to check for presence and concentration of heavy metals. Results obtained showed that apart from heterotrophic bacterial and fungal counts, hydrocarbon utilizing bacteria (HUB) counts were higher in the surface soil ranging from 12.06±3.43bX107 cfu/g at Iwofe to 6.19±2.67aX107 cfu/g at Chokocho while subsurface soil had HUB ranging from 8.91±6.67aX103 cfu/g at Eagle Island to 4.93±3.95aX103cfu/g at Chokocho. Heavy metals such as Fe, Pb, Cd and Ni were recorded in concentrations above FEPA permissible limit in the surface and subsurface soil across the three wetlands. The heavy metal concentration in each wetland however, decreased with an increase in soil depth. According to literatures, elevated levels of heavy metals in soils decrease microbial population, diversity and activities. However, the microbial population in this study increased with increasing heavy metal concentration which indicates that the microbes can tolerate or utilize heavy metals in their systems; as such can be used for bioremediation of heavy metal polluted soils. 

scholarly journals Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 952 ◽  
Author(s):  
Michael I. Love ◽  
Charlotte Soneson ◽  
Rob Patro
Keyword(s):  
Software Package ◽  
Gene Expression Analysis ◽  
Real Data ◽  
Transcript Level ◽  
Bioinformatic Analysis ◽  
Rna Seq ◽  
Statistical Framework ◽  
Gene Level ◽  
Show Evidence ◽  
Differential Gene

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

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