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What consists of microbial communities analysis? Microbiome and microbiota explain the collective genomes of the microorganisms that inhabit an environmental niche or the microorganisms themselves. Microbiota are the microorganisms present within a particular environment. The approach to describe microbial diversity relies on analyzing the gene diversity 16S ribosomal RNA (16S rRNA) through next-generation sequencing. The “S” in 16S rRNA genes sequencing represents a Svedberg unit. Microbiome refers to the entire habitat; including the microorganisms, their genes, and the surrounding environmental setting. Metagenome is the collection of genomes and genes from the members of a microbiota.


MR DNA Lab offers microbial sequencing. Microbial sequencing is the focused sequencing of a single microbe or relatively small group of microbes, in contrast with metagenomics. It can assist in the discovery of genetic variations that support the designing of antimicrobial compounds, vaccines, and even engineered microbes for industrial applications. (1)


21. Environ Sci Technol. 2015 Jul 21;49(14):8531-40. doi: 10.1021/acs.est.5b01879.

Epub 2015 Jul 10.


Differential Decay of Wastewater Bacteria and Change of Microbial Communities in

Beach Sand and Seawater Microcosms.


Zhang Q(1), He X(1), Yan T(1).


Author information:

(1)Department of Civil and Environmental Engineering, University of Hawaii at

Manoa, Honolulu, Hawaii 96822, United States.


Laboratory microcosm experiments were conducted to determine the decay kinetics

of wastewater bacteria and the change of microbial communities in beach sand and

seawater. Cultivation-based methods showed that common fecal indicator bacteria

(FIBs; Escherichia coli, enterococci, and Clostridium perfringens) exhibited

biphasic decay patterns in all microcosms. Enterococci and C. perfringens, but

not E. coli, showed significantly smaller decay rates in beach sand than in

seawater. Cultivation-independent qPCR quantification of 16S rRNA gene also

showed significantly slower decrease of total bacterial densities in beach sand

than in seawater. Microbial community analysis by next-generation sequencing

(NGS) further illustrated that the decreasing relative abundance of wastewater

bacteria was contrasted by the increase in indigenous beach sand and seawater

microbiota, and the overall microbial community dynamics corresponded well with

the decay of individual FIB populations. In summary, the differential decay of

wastewater bacteria in beach sand and in seawater provides a kinetic explanation

to the often-observed higher abundance of FIBs in beach sand, and the NGS-based

microbial community analysis can provide valuable insights to understanding the

fate of wastewater bacteria in the context of indigenous microbial communities in

natural environments.


DOI: 10.1021/acs.est.5b01879

PMID: 26125493  [PubMed - indexed for MEDLINE]



22. Int J Food Microbiol. 2016 Oct 3;234:53-9. doi:

10.1016/j.ijfoodmicro.2016.06.031. Epub 2016 Jun 24.


The use of propidium monoazide in conjunction with qPCR and Illumina sequencing

to identify and quantify live yeasts and bacteria.


Tantikachornkiat M(1), Sakakibara S(2), Neuner M(3), Durall DM(3).


Author information:

(1)The University of British Columbia, Okanagan, Biology and Physical Geography

Unit, 3333 University Way, Kelowna, BC VIV 1V7, Canada. Electronic address:

mansak.ca@hotmail.com. (2)Okanagan College, Department of Biology, 1000 KLO Rd,

Kelowna, BC V1Y 4X8, Canada. (3)The University of British Columbia, Okanagan,

Biology and Physical Geography Unit, 3333 University Way, Kelowna, BC VIV 1V7,



Culture-independent methods of microbial identification have been developed,

which allow for DNA extraction directly from environmental samples without

subjecting microbes to growth on nutrient media. These methods often involve next

generation DNA sequencing (NGS) for identifying microbes and qPCR for quantifying

them. Despite the benefits of extracting all DNA from the sample, results may be

compromised by amplifying DNA from dead cells. To address this short-coming, the

use of propidium monoazide (PMA) has been used to deactivate DNA in non-viable

cells. Nevertheless, its optimization has not been fully explored under a variety

of conditions. In this study, we optimized the PMA method for both yeasts and

bacteria. Specifically, we explored the effect different PMA concentrations and

different cell densities had on DNA amplification (as part of next generation DNA

sequencing) from both dead and viable bacterial and yeast cells. We found PMA was

effective in eliminating DNA that was associated with dead yeast and bacterial

cells for all cell concentrations. Nevertheless, DNA (extracted from viable yeast

and bacterial cells) amplified most abundantly when PMA concentration was at 6μM

and when yeast densities ranged between 10(6) to 10(7)CFU/mL and bacterial

densities were approximately 10(8)CFU/mL.


Copyright © 2016 Elsevier B.V. All rights reserved.


DOI: 10.1016/j.ijfoodmicro.2016.06.031

PMID: 27371903  [PubMed - in process]



23. BMC Bioinformatics. 2013;14 Suppl 7:S6. doi: 10.1186/1471-2105-14-S7-S6. Epub

2013 Apr 22.


GAM-NGS: genomic assemblies merger for next generation sequencing.


Vicedomini R(1), Vezzi F, Scalabrin S, Arvestad L, Policriti A.


Author information:

(1)Department of Mathematics and Computer Science, University of Udine, 33100

Udine, Italy. rvicedomini@appliedgenomics.org


BACKGROUND: In recent years more than 20 assemblers have been proposed to tackle

the hard task of assembling NGS data. A common heuristic when assembling a genome

is to use several assemblers and then select the best assembly according to some

criteria. However, recent results clearly show that some assemblers lead to

better statistics than others on specific regions but are outperformed on other

regions or on different evaluation measures. To limit these problems we developed

GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary

goal is to merge two or more assemblies in order to enhance contiguity and

correctness of both. GAM-NGS does not rely on global alignment: regions of the

two assemblies representing the same genomic locus (called blocks) are identified

through reads' alignments and stored in a weighted graph. The merging phase is

carried out with the help of this weighted graph that allows an optimal

resolution of local problematic regions.

RESULTS: GAM-NGS has been tested on six different datasets and compared to other

assembly reconciliation tools. The availability of a reference sequence for three

of them allowed us to show how GAM-NGS is a tool able to output an improved

reliable set of sequences. GAM-NGS is also a very efficient tool able to merge

assemblies using substantially less computational resources than comparable

tools. In order to achieve such goals, GAM-NGS avoids global alignment between

contigs, making its strategy unique among other assembly reconciliation tools.

CONCLUSIONS: The difficulty to obtain correct and reliable assemblies using a

single assembler is forcing the introduction of new algorithms able to enhance de

novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order

to improve contiguity and correctness. It can be used on all NGS-based assembly

projects and it shows its full potential with multi-library Illumina-based

projects. With more than 20 available assemblers it is hard to select the best

tool. In this context we propose a tool that improves assemblies (and, as a

by-product, perhaps even assemblers) by merging them and selecting the generating

that is most likely to be correct.


DOI: 10.1186/1471-2105-14-S7-S6

PMCID: PMC3633056

PMID: 23815503  [PubMed - indexed for MEDLINE]



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