Metagenomics - Science method

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!

Hi Franciele Camargo, did you find sommething? iam trying to do the same thing but i don't find any thing actually

Yes,metagenomics extraction of DNA is the best. But when you are experiencing low level of DNA,then check humus content,it may be a hindrance! Also,try changing the extraction kit. Use bioline product, it will help in getting more concentration of DNA.

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)

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.

David Rosado-Porto yeah, but at the moment i'm struggling with the qza_to_phyloseq command, which somehow doesen't like my metadata file at all. Tried tsv, csv, excel formats, but nothing worked. I will update you!

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.

Shi Li

Sorry!

Here is the link to dada2(https://benjjneb.github.io/dada2/tutorial_1_8.html)

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

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

Thank you for the answers! It sounds like I will just use the prescribed amount then.

Yes I am also checking the ingredients of this Qiagen Allprotect reagent too. Qiagen should not just cover it this information as a business secret because we need to know for risk assessment. Some researchers bring this reagent to operation theratre for collection of human tissue samples and this maybe accidently contaminate with patient especially in operartion site etc.

If any one knows about the ingredients of allprotect (Qiagen), pls share too. Thanks.

How much elution buffer/TE buffer/nuclease free water you used to elute the extracted DNA? If you used more than 25 micro litre of elution buffer/TE buffer/nuclease free water to elute the DNA, then you may need to reduce the volume by drying the sample at 56 degree celcius temperature to make the final volume of 20-30 micro litre. Then, use 0.5 microliter of 6X loading dye and 2.5 microliter of extracted DNA for gel electrophoresis and run the DNA for 45 minutes at 50V in 1% agarose gel. I think you can see the DNA band (Photo attached).

Dear Farhan,

Brian gave to you some good ideas. For more details, I can recommend using DNA barcoding DB, BOLD where you can find a lot of certain info on animals and other organisms COI and other molecular markers. Also, there you may get info on primers from the special library.

YK

Use random sampling, correct sample size, proper controls, consistent methodology, and true replication to avoid post-sampling bias :)

How to Avoid Sampling Bias in Research | Alchemer

https://www.alchemer.com › Blog

Hi Leila!

In my research, "I used AccuPrep® Stool DNA Extraction Kit". I hope this can help you.

Best,

Romina.

Dear Jessica, thank you for posting this interesting technical question. uUseful information about this and related databases can be found right here on RG. For example, please have a look the the following article:

This paper has been posted by the authors as public full text on RG. Thus you can freely download it as pdf file.

Moreover, please see the following relevant reference:

Unfortunately this very interesting paper is not yet available as public full text on RG. However, most of the authors have RG profiles. Thus there is a good chance that you can request the full text directly from on of the authors via RG.

I hope this helps. Good luck with your research and best wishes, Frank Edelmann

I think you will find some guidelines here in this article.

Thank you.

Thanks. I had a look at the QIITA workflow. seems like it is similar to what I did with my data except for DGE, which I am skeptical to use with metagenomic data. Sounds like TPM will be the best suit for my analysis. Thanks.

You can as well contact Inqaba Technology Limited in South Africa. They provide quality services. info@inqababiotec.co.za

I think it's worth trying. Generally, PEG 8000 is considered effective for DNA precipitation in case of humic acid contamination, but PEG 6000 and 4000 can also be used. They all can show DNA precipitating properties, but have differences in their molecular weights and efficiency. You may increase the concentration of PEG 4000 to compensate for its molecular weight.

examples:

Protein structure prediction

single cell RNA/DNA unsupervised learning/clustering

correlation of gene expression & variation with clinical outcomes

many more open problems tbh

e.g. see DeepVariant by google research

Sourmash or GTDB-tk for instance.

You can get the taxonomic classification of a bin, but if you are thinking you will get the same taxonomy to each and every scaffold, then that is practically not possible.

First, use CheckM for quality of metagenome assemble genome

and refining used https://github.com/cmks/DAS_Tool

Yes. It is possible. All you need is the appropriate database and software for analysis depending on the type of 16S (Illumina or PacBio)

Younes Boundir thank, but I can't read French

thank you

check out this chapter

Hello, easy simple and reliable method for DNA extraction is benzyl chloride method.

Metagenomics is compositional. You can compare compositions but you will not have absolute values of concentration of bacteria. What you can do is to use a normalization factor. For example, you can quantify the number of reads mapped to a universal gene (better if a single copy, for example, recA). But still, always keep in mind that you are dealing with a composition.

Hello Stephanie Celine, I am also trying to come out with a better protocol for DNA extraction in the trout gut for 16s. did you find a better one. Please help me if you did.

You may see the image attached herewith. I hope this resolves your query.

Hi Sanchita! Thank you very much for the important question. For species level identification, you have limited database options, and includes only PathoScope (https://doi.org/10.1186/2049-2618-2-33) and IDSeq (https://idseq.net/).

Hi Abhijeet!Thank you very much for the important question. Very difficult question to answer. You know metagenomic data can be of two different types i.e. Targeted/amplicon (16S rRNA) metagenomic data and shotgun whole metagenome sequencing (WMS) data. For the first one, you can analyse your data using QIIME, MOTHUR, MetaMLST, PanPhlAn, IDSeq, Pathoscope and MG-RAST (http://www.metagenomics.wiki/tools/pathogen-screening).

For WMS, you have limited database options, and includes only PathoScope (https://doi.org/10.1186/2049-2618-2-33) and IDSeq (https://idseq.net/) and MG-RAST (https://www.mg-rast.org/) (see our latest articles: https://doi.org/10.1038/s41598-019-49468-4; https://doi.org/10.1016/j.ygeno.2020.09.039).@

For details of the possible methods to use, the article suggested hereunder refers.

Hi and good day! I have been finding the answer to this too, and found it myself in the end. I thought it would be good to share the information. The sequence files will be in red font and unselectable if the total bp is less than 1,000,000 bp, which is the minimum threshold.

Your observation that you cannot visualize DNA on an agarose gel is a hint that you might have very low "true" DNA concentrations in your sample. UV-Vis measurements - and this includes Nanodrop - are sensitive to contaminations in your DNA extract. Therefore, my recommendation is to check the DNA concentration with a more sensitive and more specific assay such as SyBrGreen. Also, you could spike your DNA extract with a known amount of a pure DNA (e.g., DNA ladder for sizing on gels), to see whether your instrument works well.

Q1 The axes model the variation of the dependent matrix and the eigenvalues are a measure of how much of the variance is explained by a certain axis*. One axis is not a certain independent variable, but a linear combination of all of them (RDA1 means x*pH + y*N + z*T; so usually, none of the axis directly represents a variable).

*since the calculated R² is biased, it has to be corrected and so have the eigenvalues

I can recommend the Numerical Ecology with R book (ISBN 978-3-319-71404-2) for explanations and examples (the binding of the paperback version is not as high quality as the content, though).

I am at the second year of teaching the metagenomic course to the master degree in molecular biology and bioinformatics. At the end, I decided to collect many presentations reported in youtube and in other online resources and I used them to design the course. Still, I did not find a very good book for metagenomics but some presentations (for example those from Dan Knight) are very useful!

Functional diversity metrics are calculated by associating species-by-site matrices, such as presence-absence or abundance of species, to the species' functional traits; i.e. morphological or behavioural traits that are related to the role the species may perform in the ecosystem. Functional diversity is a type of alpha or Simpson diversity and the link below will show you how to calculate it.

Thanks for sharing!