Metagenomics - Science method

Prodigal is a frequently used tool to find genes from contigs by a lot of metagenomic pipeline to software. 'Gene comparison', do you mean that you want to know the function of genes? In this step, you need a proper reference database and then annotate your sequences against the database (for example CARD above mentioned), using tools like blastp, diamond, Hmmer, eggNog mapper.

The gel picture of DNA extracts looks smearing. It is not very surprising if the sample was bead processed fiercely and a bit long. The spectral parameter looks good.

With this DNA material, you can go further for PCR to get 16S or ITS fragment. The PCR products will used for amplicon sequences. If you would like to do short-gun metagenomic sequences for the genomic DNA, you can send to sequencing company, they will check the quality and quantity of your DNA extracts with good instruments, and contact you whether the samples are good or not for sequencing.

Hi, our lab members have used MOTHUR to analyze the amplicon data. The team members of mothur are extremely active and give you instant feedbacks on your work problems. You can check these papers for reference:

Hevar Neaz This forum is not ChatGPT. Try to figure out the basic information yourself and then ask any remaining specific questions, which only experts in the field may be able to answer.

Metagenomics: DNA level.

Metatranscriptomics: RNA level.

Hi Zhongquan Dai

* The Mice stool samples can be stored in -80°C for up to 2 years without significant loss of microbial diversity or composition.

References:

1.

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3.

Thanks

Samir

You can use NCBI for that. Use project in SRA explorer.

Hi Mirza, for metagenomics analysis the required edaphic properties may vary based on your specific research question as also mentioned by Pankaj Kumar Singh . Physical parameters such as soil pH, soil moisture or soil water content, bulk density, texture, porosity, water holding capacity, aggregate stability, electric conductivity etc. may be investigated. Further, Soil nutrients in terms of C (organic and inorganic carbon, dissolved organic carbon, total carbon, labile and recalcitrant carbon), N (organic and inorganic, total nitrogen), Phosphorus etc. may be determined. I also suggest to analyze soil microbial biomass carbon to understand the variabilities in the metagenomic data better.

If you want help on how to understand the correlations of these parameters with the metagenomic data please check the following article:

Thanks and all the best in exploring your research.

Microtaxonomy is the study of classifying and identifying microorganisms based on their genetic and phenotypic characteristics, such as morphology, biochemistry, and physiology. On the other hand, metagenomics is a field that involves the study of the genetic material recovered directly from environmental samples, including the DNA of microbial communities.

In terms of identifying microbiota, both metataxonomics and shotgun metagenomics can be successful and reliable, depending on the research question and the samples being analyzed. Metataxonomics typically involves amplifying and sequencing a particular gene, such as the 16S rRNA gene, which is commonly used to identify and classify bacterial species. Shotgun metagenomics, on the other hand, involves sequencing all the DNA present in a sample, allowing for a more comprehensive analysis of the microbial community. The choice between metataxonomics and shotgun metagenomics will depend on the specific goals of the study, the complexity of the microbial community being analyzed, and the available resources. For example, metataxonomics may be preferred if the goal is to identify specific bacterial species in a relatively simple microbial community. However, shotgun metagenomics may be more appropriate if the microbial community is complex, and a more comprehensive analysis is required.

Yes you can find gene families by using HUMAnN (https://github.com/biobakery/humann). And then you can perform differential analyses say LDA using LEfSe (https://huttenhower.sph.harvard.edu/lefse/) to identify markers.

The abundance calculation for genes annotated by a reference database such as MegaRes depends on the specific approach used for gene quantification. In general, the most common method involves mapping the sequencing reads to a reference database and then counting the number of reads that map to each gene.

In your case, it sounds like you are using a de novo gene assembly approach to generate contigs, which are then aligned to the MegaRes database using BLAST. To quantify the abundance of each gene, you could count the number of contigs that align to each gene in the database. However, it is important to keep in mind that the number of contigs is not necessarily directly proportional to the expression level of a gene, as different genes may have different lengths and levels of expression.

A more accurate approach to quantifying gene expression would be to use tools such as RSEM, Kallisto, or Salmon, which use a statistical model to estimate the expression level of each gene based on the mapping of the sequencing reads. These tools take into account the differences in gene length and the potential for reads to map to multiple genes, which can improve the accuracy of the abundance estimation.

Thank you so much for your reply.

Hello, soil microbial communities are complex and susceptible to change this emphasizes the importance of establishing a baseline for the study. Controls are needed to provide such a baseline for determining whether a change in the soil microbial community is due to the treatment and use. The tests at least need to be performed before and after the metagenomic study.

Ciara Maria Ines Palma Del Rosario Look at work of Gyraite G, Katarzyte M, Schernewski G. First findings of potentially human pathogenic bacteria Vibrio in the south-eastern Baltic Sea coastal and transitional bathing waters. Marine pollution bulletin. 2019 Dec 1;149:110546.

It has most of important methods regarding identification pathogenic bacteria in water

Thanks Mr. Milan, satisfied with your reply.

Is there a minimal sequence depth or length of the sequence read (>xxx bp) required to use Tax4Fun2 and PICRUSt2?

Hello Vladimir,

I agree with Dr. Abhijeet. Working with 10000 genomes requires a lot of planning and very powerful servers.

However for smaller and more modest projects you could try working with cyverse servers, or with usegalaxy servers.

Its better to ask help from someone experienced in your lab, otherwise it will be just waste of time and resources.

How does your institute financially support the applicants?

There are a number of methods that can be used to concentrate viruses from soil samples for DNA extraction and shotgun metagenomics analysis. Some of the most commonly used methods include:

Overall, the most efficient method for virus concentration in DNA extraction from soil will depend on the specific goals of your study and the resources available to you.

Input all the OTU. I used excel to calculated the Relative Read Abundance.

I think you need a file with all the OTU (columns) and samples (rows). You also need another file: first column with all the samples' name and a second column with the characterization of the geochemical parameters for each sample. The NMDS plot will show points (every sample) and a centroid (mean value), and there will be as many centroids as geochemical parameters were characterized.

I used TXT files for the process.

I hope I helped you!

Oksana Kisil You should look at dinamic changes of different taxons through the season (OK, permafrost is not a case) and on taxons differentiating locations or soil levels. Even low frequency taxons (0.1-0.2%) can be important for certain biological processes.

Péter Gyarmati I will try again with Qubit, but regardless of contaminations, most samples have purification level that does not meet the requirement. I may have to alter the sequencing method. Thank you for the recommendations.

I would highly recommend to discuss the topic with your sequencing facility. They will understand the aim and project and can suggest the best way. Or if what you are trying to do is the best way or not.

There are many things/information missing in your question missing without which no one can suggest any logical solution.

This can help you

awk '/@/{ n=NR+1 } NR==n{ gsub(/x/, "N") }1' input.fq > output.fq

Hi Dr. Connor!

I used RGI on assembled reads (min length of contigs was >200bp).

Awaiting your response.

Thanks and regards

Yasir

Hello use membrane filtration method for your ground water.

Use 0.45 um filters to filtrate water if your water highly dirty first use paper filter or mechanical filter than perform membrane filtration than use pure sterile water add to filter to wash and remove all inhibitors. the amount of filtrated water depends from content of microorganisms in initial sample if it fro 10-to100 CFU/ml the 100ml is quite enough. if more you can reduce the volume. By this way you can have pure microorganisms without any inhibitors. Than you can use any method for extraction of DNA from microorganisms. For example the benzyl chloride method for DNA extraction highly recommended.

Good luck.

Hello all,

Thank you so much for your answer. Lately I have tried DNeasy PowerClean Pro Cleanup Kit for an additional clean up process of DNA samples and ran PCR. I think I saw some bands in the region of 200-300bp.

The link posted above is useless and it does not relate what was asked in the question.

Thanks!

It seems that HUMAnN3.0 / MetaPhlAn uses the Chocophlan database. I'll try that out :)

If it is open access I see no reason not to use it. It is however essential that you should properly cite them.

Man Kit Cheung is probably right. In any case, I think this article might answer your second question.

Samir Ranjan Panda , Man Kit Cheung , Phil Geis thank you very muche for your answers :)

Unless you have overlapping contigs at either end of the plasmid it is impossible to tell in silico and would require experimental verification. It is possible to design primers near the ends of your plasmid assembly that would amplify across the remainder of the plasmid. This amplicon can then be sequenced to identify the remainder.

Rocio Parra Laca A heatmap is a graphical approach to visualizing visitor activity data as hot and cold patches using a warm-to-cool color scheme. Warm colors represent areas with the greatest visitor engagement, red areas with the most interaction, and cold colors show areas with the least contact.

Also, have a look at this:

1. https://github.com/topics/heatmap?l=r

2. https://github.com/topics/heat-map

Biological replicates of environmental samples will have some variations. Additionally, the quality/quantity of the DNA, as well as the library construction method will also interfere with the sequencing outcome.

I'm assuming you extracted DNA from the biological sample using the same method and at the same time. In addition, both were sequenced using the same library preparation techniques.

The final choice to merge or not will depend on the aim of the experiment. For comparative analysis, you can merge the samples to get a single assembly and then get the gene/contig counts from non-merged biological replicate reads.

If the comparative analysis is not your aim, you can merge the biological replicates and then use the merged reads to get the gene/contig counts.

Thank you very much, Daniel Carrera Lopez!

"Comparative studies on the extraction of metagenomic DNA from various soil and sediment samples of Jammu and Kashmir region in prospect for novel biocatalysts" by R singh.

This is perhaps the best protocol that have yielded me excellent results.

The methods of Zhou et al. (1996), Volossiouk et al. (1995), Tsai and Olson (1991), Siddhapura et al. (2010), Verma and Satyanarayana (2011) are very helpful too.

Hi,

EZbiocloud offers simple processing for 16S amplicon sequencing analysis, although, if I recall correctly, not much of control/information over the analysis pipeline.

dada2 + phyloseq or Qiiime2 are surely the better options if 16S sequencing analysis is a reoccuring task for you.

Several platform you can prefer to submit your metagenome sequence data to NCBI-SRA(Sequence Read Archive), MG-RAST and ENA(European Nucleotide Archive)

Interested in gut resistome study

Hello Ishtiaque,

I have found that Kaiju (taxonomy based on sequence homology) and Metagenomic Intra-species Diversity Analysis System (MIDAS; taxonomy based on curated phylogenetic trees) work terrifically for analyzing relative abundances of taxa with WGS data. These two tools complement each other as well.

Ryan

The species level discrimination not depends of the number or reads, depends of the technical approach, and V3-V4 is risky to go to species level, but good to go to genus level.

As an option you can do it manually by BLASTing your reads against 18s sets like SILVA ones. Next counting the relative presence of the species by assessing number of reads (and/or proportion of reads) aligned to a specific organism.

If your outputs are from qiime2, you can easily extract the feature table in .biom format and extract the taxonomic information and merge the two objects. From that point you can just create a phyloseq object and use it for the network.

Excelente information Jeferson thanks!

ANCOM-BC (ANCOM with Bias Control). You can run it easily in R using your relative abundance data without having to transform it. Here is some documentation you may find helpful:

Many statistical analyses can be done on metagenomic data. Adding some details to your question will help narrow down the type of analysis you can do based on your research question.

You can send a private message if it's easier to chat.

Best,

Hamza

If you are using trimming program, use parameter which discard the sequence which will be untrimmed because it doesn't have a primer sequence.

Hi Islam M. Khattab

It's hard to cluster when the diversity is very high. Here's another reference that might help you:

Deconvolute individual genomes from metagenome sequences through short read clustering

doi: 10.7717/peerj.8966

I wouldn't expect that the difference is because of rarity of isolates, if the samples are not novel, because people have done a lot of isolation work for various environements. One possibiliy is that Enterobacteriaceae could be super low in abudance compared to others e.g. Clostridium, and thus it can be rarely detected by amplicon sequencing. This happened to samples e.g. sewage sludge or animal manure. In contrast, under a certain culturing conditions you provided for the bacterial community, Enterobacteriacease may happen to gain competence to grow. In that case, you could obtain the Enterobacteriacease, which you don't see in amplicon sequencing.

Genomics explores the complete genetic information of a single organism be it a microbe, plant or animal, whereas metagenomics explores a mixture of DNA or RNA from multiple organisms and organs, and environmental samples

In my experience, yes, it takes many days to do base-calling with the guppy base-caller. However, it depends on what guppy version you used; if you use the guppy CPU version it will be much slower compared with the GPU version. The number of threads you used also affects the base-calling process.

I think no time limit for this. You just need to wait until 100% to get all your data converted to FASTQ by the program, or, you may need to adjust the base-calling parameters or change the program version and re-run the guppy base-caller to make it faster.

Hi Alba.

I had experience with Novogene, both for whole-genome and metagenomics.

The sequencing is very good, and I had no problems at all in terms of quality.

I don´t recommend to subscribe for their bioinformatic analysis, since the analyses performed are pretty basic and can be done easily by the researcher, and many times you need more complex analysis that are not included in the price.

Let me know if you need more information.

Ricardo

DADA2 is widely used due to its accuracy and ease of use for marker gene-based community profiling https://benjjneb.github.io/dada2/index.html

Hi Ishtiaque Ahammad, MG-Rast (https://www.mg-rast.org/) is a good tool for analyzing metagenomics data. After submitting your data it is possible to obtain a dissimilarity matrix for beta diversity analysis. With the generated abundance table you can also calculate diversity by other programs, such as R (as mentioned in the previous answer), or PAST (if you have difficulties in using software that demands greater programming skills).

Genetic and functional analysis of the bovine uterine ...

https://www.sciencedirect.com › science › article › p

Hi thanks, do you know if Centrifuge is better than Kaiju?

16s amplicon, ITS, shotgun which one?

DEAR Amsale Melkamu

Genovo: De Novo Assembly For Metagenomes, Journal of Computational Biology: a Journal of Computational Molecular Cell Biology 18(3):429DOI:10.1089/CMB.2010.0244.

Meta-IDBA: A de Novo assembler for metagenomic data,DOI:10.1093/bioinformatics/btr216

GOOD LUCK

Abid Hussain Bhat

1. You are using 16S and that is not a metagenomics analysis

2. Not clear what do you want to do by replacing the degenerate bases in your primers, and I am not sure if you know why they are used in primers

3. No analysis require the degeneracy of bases removed from primers, and that is why the question "why do you want to do that".

4. Interpreting the question and your reply above, it appears that there is a lack of understanding the basics of molecular microbiology and I would recommend to clear the basics before moving forward.

There is no set rules in choosing binning tool unless you are doing supervised binning or binning from long reads. For shotgun metagenomics, there are many binning tools, of which MAXBIN, METABAT2, CONCOCT and BINSANITY are widely used. Whats importent is rather than relying on one tool (unless your study goal needs) it is better to use multiple tools and then use any BIN refiner ( DASTool, Binning-refiner and MetaWRAP refinement module) to select quality bins.

Workflows/pipelines such as ANVIO, MetaWRAP,SqueezeMeta, ATLAS can do the automatic binning using multiple binners and extrecting quality bins via bin refiners. Personally, i assemble my contig via metaspades then use metawrap binning ( MAXBIN, METABAT2, CONCOCT) refinement module and later import these bins in ANVIO for manual refining and other analysiss.

The answer is found in the results of this study:

Nat Commun. 2017; 8: 16054.

Published online 2017 Jun 28. doi: 10.1038/ncomms16054

PMCID: PMC5493757

PMID: 28656958

Virus-host relationships of marine single-celled eukaryotes resolved from metatranscriptomics

Mohammad Moniruzzaman,1 Louie L. Wurch,2 Harriet Alexander,3 Sonya T. Dyhrman,3 Christopher J. Gobler,4 and Steven W. Wilhelm

we examined metatranscriptomes from two highly productive sites on the east coast of USA—Quantuck Bay, New York, and Narragansett Bay, Rhode Island. Quantuck Bay experiences recurring ecosystem disruptive brown tide blooms caused by the pelagophyte A. anophagefferens16, which are shaped by a giant virus (AaV)7,17. Narragansett Bay is a highly productive system with seasonal diatom blooms, but a poorly described eukaryotic virus community. By employing selection for polyadenylation before sequencing, we were able to focus on active virus infections within eukaryotes. Using time-series data, we captured emergent relationships of putative virus–host pairings and their ecological dynamics. This approach also allowed us to characterize viruses with diverse nucleic acid genomes actively infecting eukaryotes. The results show that this approach could both confirm known virus–host infections (including the infection of Aureococcus by AaV) as well as identify novel virus–host interactions. These observations demonstrate that the depth of virus–host interaction in the global oceans is likely much deeper than previously anticipated, with viruses containing all forms of genetic material potentially infecting single-celled eukaryotes.

As you said in your question "16S rRNA sequencing uses PCR to amplify a specific region of DNA", that is the main difference. 16S rRNA amplicon sequencing will sequence only the 16s gene in the provided DNA sample. Whereas, metagenomic will sequence all or most of the genes within a provided DNA sample. So, with 16s sequencing, one can get only the taxonomic information (from which bacteria does the 16s sequence came) while from metagenomics, both taxonomy and functional potential of the sample can be determined.

Note: There are some algorithms like PICRUSt which can PREDICT functional genes from 16s sequencing. But this can be used only for prediction and not conformation of a particular functional gene.

First you have plants sample is Wash carefully and after that extract prepared and then filter the method for the concentration of, removal and enumeration of bacteria in liquid and air to avoid contamination of microorganisms for example fungi and then used other filtration method Sach as centrifugation.

It will not break but please do not leave it long time otherwise you will lost some.

The best and most correct way to proceed would be reprocessing all the raw-data from different sequencing run with same parameters and together. The OTUs will surely change. With merging two separate runs, you would be forcing the two datasets to appear similar. Reanalysis would take only a few hours anyway.

Hi,

To get a productive amount of DNA from your environmental samples, you can use Qiagen DNeasy Powersoil Kit to extract the metagenomes. I did use this kit and got a brilliant result after sending it to my external NGS facility.

If your concerned samples are soil or sediment or any other naturally-governed origin, then you must use specific kit to remove pollutants or strong organic matters hindering the DNA isolation process. Using any simple DNA isolation kit might not gives you all the extractable DNA confined to those natural media.

For transportation, you can certainly use dry-ice in a tightly sealed container and that will roughly give you a 3-4 days of time (as in my case, it was 4 days and samples were fine in it).

Best,

I think you are asking about library preparation. You should use TruSeq DNA PCR-Free sample preparation kit (Illumina Inc., San Diego, USA) for library preparation.

I have been using them many times when working with grapevine-associated microbiome. As a rule of thumb I would check before-hand if they match the specific target you want to prevent and then they should work quite well. Only recently I had problems with wheat mitochondrial DNA and so far I have not had any luck using mPNA

For your purpose i.e. to know whether a given organism or closely related representatives are present in your metagenome or not , BBMap (https://sourceforge.net/projects/bbmap/) would be very convenient. This tool requires no installation and the command-line would be something like- bbmap.sh ref=fecal_indicator_genome.fasta in1=paired_end_read_path_1.fastq in2=paired_end_read_path_2.fastq outm1=fecal_genome_mapped_1.fastq outm2=fecal_genome_mapped_2.fastq minidentity=0.95

The above command-line will output only mapped reads that are somewhat closely related to your genome of interest (you may lower the minidentity values but the number of spurious mappings may increase and the overall mapping stringency with respect to your reference may go down; use minidentity=0.98 or 1 if you want very stringent mapping). You may further re-assemble these mapped reads on Spades and then analyze if this new metagenome-assembled-genome is actually represents your ref genome or if it is closely related

Also, this reference is useful

"The Influence of Bt Maize Cultivation on Communities of Arbuscular Mycorrhizal Fungi Revealed by MiSeq Sequencing"

You may visit this site http://aclame.ulb.ac.be/. I hope that it will help your endeavor.