Microbiome - Science topic

Tell us what you think (atlasbiomed.com)

Hi Alejandro Mejia Muñoz,

Thank you so much for your brief explanation. Sorry, I might not describe my questions accordingly. I was just curious if we could identify or estimate bacterial composition in my samples without relying on the NGS.

I read that MALDI-TOF is quite reliable for bacterial identification, but it needs a single or pure bacteria colony. So, it would be hard to identify bacteria that are still unculturable. I understand that the majority of bacteria from environmental samples are still uncultured or unculturable.

Anyway, thank you for your insight.

Regards!

Hi Himani,

If you know which organisms are included in your samples you can work with a joined database fused from downloaded fasta format databases (i.e. from Uniprot) of the respective organisms.

With Maxquant you can than look at the number of unique/razor peptides to check from which organisms the proteins are more likely.

Best,

Murat

Timur Yergaliyev Jeferson Lourenco Thank you very much for answering! I really appreciated it.

You two have a great weekend!

Using SPIEC-EASI for microbiome study, an input dataset in the form of an abundance table or a count matrix is needed. This matrix represents the abundance levels of different microbial taxa across multiple samples. Each row corresponds to a specific microbial taxon, and each column represents a sample.

Here's an example of how the input data format may look like:

```

Taxon Sample1 Sample2 Sample3 Sample4

Taxon1 10 5 0 3

Taxon2 2 8 7 1

Taxon3 0 4 6 2

```

In this example, there are three microbial taxa (Taxon1, Taxon2, and Taxon3) and four samples (Sample1, Sample2, Sample3, and Sample4). The abundance values represent the number of reads or the relative abundance of each taxon in the respective samples.

=========

Certainly! Here's an example R script that demonstrates network analysis using SPIEC-EASI for a microbiome study:

```R

# Install necessary packages

install.packages("SPIEC-EASI")

install.packages("igraph")

# Load libraries

library(SPIEC-EASI)

library(igraph)

# Read the abundance table or count matrix

abundance_table <- read.csv("path/to/abundance_table.csv", header = TRUE, row.names = 1)

# Preprocess the abundance table

# You may need to perform data normalization, filtering, or transformation based on your specific requirements

# Run SPIEC-EASI to infer the network

network <- spiec.easi(abundance_table)

# Get the adjacency matrix from the network

adjacency_matrix <- network$network

# Convert the adjacency matrix to an igraph object

graph <- graph.adjacency(adjacency_matrix, mode = "undirected", weighted = TRUE)

# Visualize the network

plot(graph, edge.width = E(graph)$weight, edge.color = "gray", vertex.size = 10, vertex.label = NA)

# Perform additional network analysis or visualization as needed

# You can calculate network properties, identify clusters, or customize the network visualization

# Save the network as a graphml file

write.graph(graph, file = "path/to/network.graphml", format = "graphml")

```

Make sure to replace `"path/to/abundance_table.csv"` with the actual path to your abundance table or count matrix file. Additionally, you may need to customize the script based on your specific preprocessing steps and network analysis requirements.

Good luck

credit AI tools

plants hot a mesmerizing diversity of microbes inside and around their roots, known as the microbiome is composed mostly of fungi, bacteria, oomycetes and archea that can be either pathogenic or beneficial for plants health and fitness. to grow healthy, plants need to surviel soil niches around the roots for the detection of pathogenic microbes and growth promotion we review the current understanding on the composition and activity of the root microbione communities. example are given on interaction that occur in the rhizosphere between plants and soilborne fungi, we also present some well established example of microbiome harnessing to highlight how plants manipulate their microbiome can aid in the design of next-generation microbial inoculants for targeted disease suppresion and enhanced plants growth.

@ Cedrick, the attached file may be useful to you.

Hi David, we are doing quite a bit of work in this field and are just down the road from you:) Very happy to have a chat. We're also collaborating on a new ARC Biofilm training centre. Just get in touch. Scott

Thank you very much!

@ Kehinde, the attached files may be useful to you.

Assessing diversity metrics in 16S data is an important step in analyzing microbial communities. Handling count data in this context can be challenging due to the compositional nature of the data, as you mentioned. While there is no one-size-fits-all approach, there are several techniques and considerations you can explore. Here are some suggestions:

Remember that there is ongoing research in the field, and best practices continue to evolve. It's important to critically evaluate the methods, consider the specific characteristics of your data, and consult with your advisor or peers with expertise in microbiome analysis to make informed decisions about data transformations and diversity metric assessment.

ultrafilterize the raw milk from fats and proteins.

acid precipitation and centrifugation for fats. and special starin for casein proteins. most bacteria should be with the watery part that leavesthe rest of the fats and proteins.

What we used to do is it suspend around 100uL into 900 of buffer or water and then homogenize it.

Afterwards, centrifuge at 10,000G and collect 400uL with the pellet inside.

Biomes are regions on the earth that are influenced by similar climate patterns and have similar vegetation and wildlife communities. Climate is the longer trend in the weather that defines the weather pattern in a year in general. Climate change can alter where species live, how they interact, and the timing of biological events, which could fundamentally transform current ecosystems and food webs. Climate change can overwhelm the capacity of ecosystems to mitigate extreme events and disturbance, such as wildfires, floods, and drought. Because climate determines plant growth, it also influences the number and variety of other organisms in a terrestrial biome. Biodiversity generally increases from the poles to the equator. It is also usually greater in more humid climates. Climate is a major factor in determining types of life that reside in a particular biome, and there are several factors that influence climate, such as latitude, geographic features, and atmospheric processes disseminating heat and moisture. As seen in other biomes, each landscape attracts different plant and animal species, and therefore harbors a specific ecosystem. Different ecosystems within a biome often interact with one another, resulting in a much larger 'biome-wide' ecosystem.Biomes don't really affect each other they usually just gradually grade into one another based on temperature and moisture conditions. Climate change drastically influences different environmental cycles. The microbiome responds and adapts to changes in ecological conditions, which results in a change in the balance of the microbiome in nature. It will adversely affect the microbiome imbalance in the human body, which affects the health of human. Warmer temperatures affect the densities of airborne microbes and can accelerate their long-distance transport. Higher temperatures and environmental stresses can also alter human and animal physiologies and defenses against pathogens. Skin and gut microbiomes may become less protective.

1. Select an appropriate homogenization/lysis method: There are several methods available for homogenizing/lysing skin biopsies, including mechanical disruption (e.g., bead-beating, sonication), chemical lysis (e.g., detergents), and enzymatic lysis. The choice of method depends on the sample type, amount, and quality, as well as the downstream application.

2. Cut the biopsy into small pieces: Use a clean, sharp scalpel to cut the biopsy into small pieces (approximately 1-2 mm). This will help to increase the surface area and facilitate the homogenization/lysis step.

3. Determine the optimal volume of lysis buffer: The amount of lysis buffer required for efficient homogenization/lysis depends on the size and number of the biopsies, as well as the lysis method. It is recommended to use at least 500 µl of lysis buffer per biopsy.

4. Add lysis buffer: Add the appropriate volume of lysis buffer to the biopsy sample in a sterile tube. If using a mechanical method (such as bead-beating or sonication), add a suitable amount of beads to the tube as well.

5. Homogenize/Lyse the sample: Homogenize/Lyse the sample using your chosen method. The duration and intensity of the homogenization/lysis step depends on the method used, and should be optimized for each specific sample.

6. Centrifuge and collect the supernatant: Centrifuge the homogenized/lysed sample at a suitable speed and duration to pellet any debris. Collect the supernatant, which should contain the microbial DNA.

7. Proceed with the manufacturer's instructions: Follow the manufacturer's instructions for DNA extraction, using the collected supernatant as the starting material.

It is recommended to use sterile tools and work in a clean, sterile environment to avoid contamination during the homogenization/lysis step.

Hi Umra, do you mean the conservation of animal microbiome samples?

If so, the gold standard is the cryoconservation by immediate freezing at -20°C.

Here are some references:

Hope it helps,

Cheers

Hi Cameron Joseph Mann

In my opinion...

Yes, the relationship between systemic lactate and Veillonella is an interesting aspect of the exercise-induced microbiome changes. Veillonella is a genus of bacteria that can metabolize lactate, which is produced in muscles during high-intensity exercise. It has been proposed that the abundance of Veillonella increases in the gut microbiota of endurance athletes due to their high lactate production.

The idea that increased aerobic capacity is conferred via fecal/microbiome replacement stems from studies where fecal microbiota transplantation (FMT) from athletes to sedentary mice resulted in improved exercise performance in the recipient mice. This suggests that the microbiome may play a role in exercise performance, although the exact mechanisms are not yet completely understood.

The turnover rate of Veillonella may indeed be favored by the presence of lactate, as it serves as an energy source for these microbes. However, it is important to note that the abundance of Veillonella is only one factor among many that can influence exercise capacity, and further research is needed to determine the precise mechanisms and extent of this relationship.

As for the mechanism of intestinal entry for systemic lactate, lactate produced in muscle tissue during exercise can be transported into the bloodstream, where it can be taken up by other tissues such as the liver for gluconeogenesis, or by the heart and skeletal muscles as an energy source. Lactate can also be transported into the gastrointestinal lumen, although the exact mechanisms are not entirely clear. It is thought that lactate may passively diffuse across the gut epithelial cells, facilitated by monocarboxylate transporters (MCTs), or be actively transported by other transport mechanisms. Once in the gastrointestinal lumen, lactate can be utilized by Veillonella as a substrate for metabolism.

These articles are worth reading:

Please note that these references may not directly address all of the statements I made in the answer, but they may provide relevant and contextual information about the topic at hand. For a more complete understanding, I would recommend reading these and other related studies.

Magnesium, too, has been extensively reported in decline. Agricultural practices are discussed in Cazzola R, Della Porta M, Manoni M, Iotti S, Pinotti L, Maier JA. Going to the roots of reduced magnesium dietary intake: A tradeoff between climate changes and sources. Heliyon. 2020 Nov 3;6(11):e05390. doi: 10.1016/j.heliyon.2020.e05390. PMID: 33204877; PMCID: PMC7649274.

Your input is much appreciated, Dr. Yergaliyev! We will take note of your suggestions for our research.

Microbes play an important role in climate because they release carbon dioxide into the atmosphere when they eat. Bacteria and their main predators, protists, account for more than 40 times the biomass of all animals on Earth. As a result, they have a huge effect on carbon dioxide emissions. Oceans, soils and rivers all host microbiome communities that impact entire ecosystems. A healthy soil microbiome is crucial for the growth of crops and wildlifeand water microbiomes in oceans and rivers help feed and support a vast range of species. This combination of microbiomes upholds the fabric of life. As part of these biogeochemical cycles, microbes both produce and consume heat-trapping gases such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Over Earth's history, the climate has changed in response to changes in the abundance of greenhouse gases in the atmosphere.

Microbes are involved in many processes, including the carbon and nitrogen cycles, and are responsible for both using and producing greenhouse gases such as carbon dioxide and methane. Microbes can have positive and negative responses to temperature, making them an important component of climate change models. Microbes play an important role in climate because they release carbon dioxide into the atmosphere when they eat. Bacteria and their main predators, protists, account for more than 40 times the biomass of all animals on Earth. As a result, they have a huge effect on carbon dioxide emissions. Climate change increases the activities of pathogens and heterotrophic microorganisms, shifting of beneficial microbes from one ecological niche to another. Precipitation is an important regulator of activities of plant and microbiomes.

The microbiome already exists on the surface of our body, including the intestinal area, and is closely related to our health and disease. The administration of live microorganisms, such as probiotics or live biotherapeutics, may improve the health status or be used to treat diseases of the host, such as Clostridium difficile infection, irritable bowel disease, and colon cancer. The paper about CDI therapeutics that passed FDA 3rd trial is attached below.

The potential mechanisms of probiotics and LBPs on the host include immune regulation, as well as the ability to affect and interact with our body in various ways, such as pathogen inhibition and SCFA production.

After acknowledging receipt of necessary documents to refund my expenses by end of January Curtis&Wyss have fallen silent. This company is not serious. and should be avoided.

Here you can find the comparison of commonly used DA tests.

https://www.nature.com/articles/s41467-022-28034-z

Sorry Yann - my error. There were three publication, all from Google scholar search.

If the classify on any level as bacteria I would include them into the analysis.

I would say that there is not best in research in general. Everything is relative. And the results can also be dependent on what is your experiment about and what and how you design the experiment in terms of microbiology. As of your question, point 1 and 2 in not separate and can be done in parallel.

There are so many studies done on this topic which surely can help you in designing the study and to understand what is needed.

However, in the end everything would come to you what you are doing to do and how present the study.

I do this type of conversion/matches using the E-Direct tools, it's a command line tool but if you have tons of accessions it can be very useful:

There is a GitHub repo with a large number of "recipes" that can help you start: https://github.com/NCBI-Hackathons/EDirectCookbook/

1. Why can't you try with the QIAamp Fast DNA Stool Mini Kit from Qiagen.

2. 5 ng/ul is not good enough but it can be worked so first try with cPCR with gene specific primer rather than adapter ligated primers.

3. amples might be contaminated with chemical components from the kit , poor washing step leads to ethanol residues etc.

4. Poor ratio some tims happens while using kits also low yield of nucleic acid etc.

If you are able to amplify the 16S rRNA then don't worry about yield and purity unless you have a plan to outsource library preparation and sequencing.

Hi Anna,

Unfortunately, there is no definite answer--microbiome differential analysis methods are still in their infancy and the answer depends on your question. Some statisticians favor compositional data analysis (CoDa) methods because microbiota data, like other NGS 'omics data, is compositional in nature. Popular CoDa methods are ALDEx2 and ANCOM-II. Recently, these two methods were demonstrated to produce the most consistent results across many datasets, compared to other methods. See doi.org/10.1038/s41467-022-28401-w.

Best regards,

Ali

A high p value occurs with a high R2 value due to low sample size. However, if your R2 is very high, say > 0.85, you can probably disregard the p value and say your model explains a lot of the response variability :)

Hi! It was suddenly closed and the authors do not respond requests. We must find other options and yes, microbiomeanalyst is an excellent alternative. Each has some pros/cons. Calypso had already implemented more advanced/recent ways for transformations and statistical analyses, but all this is now nothing if the service is gone, while microbiomeanalyst strengths are outstanding speed, complete pipeline for the most common analyses, graphical display is really nice and of course: reliability of the service! Unfortunately it is still very common to have articles about bioinformatic programs and platforms online that after few years and good impact, then are closed and web pages deleted. This is due to the lack of funds to maintain the people, servers or upgrading. Or because the system is then used as a paid service in derived business /startups to make some money out of it (involving some of the authors) to sell the services/uses as it seems to be the case for Calypso https://microba.com/. This is very common even if development is part of the main result of a published manuscript and the research/development was paid with public funds. But the truth is that public funds are very unreliable/temporary and the researchers, databases or services suffer such cuts. For example in Australia, people working in research had a harsh time last years!... https://www.theguardian.com/australia-news/2021/oct/11/australian-scientists-fear-job-insecurity-as-morale-plummets-amid-covid-survey-finds However this also creates a huge problem about reproducibility in science. There are some changes and requirements now asking the authors/developers to deploy the data and software in public open data repositories https://en.wikipedia.org/wiki/Open-access_repository github, zenodo, etc. when reported in a manuscript, but usually is not the same version upgraded or published. I contacted the first and corresponding authors of Calypso when the web page was suddenly shut down last year. We were in the middle of a manuscript analyses were we were using it. There was no response from any of them and we must stop using some results from it, as we could not prepare the final figures with the same system/formatting. Annoying! For Calypso there was a compressed file uploaded in 2016 https://zenodo.org/record/50931#.YxhhvuxBz0p including the programs and web page content in java and it is possible to run this locally, it may need to include several missing libraries and preparing all with Docker but it is possibly an old version. Of course there are many other developed web platforms to analyze 16S amplicon data or standalone programs appearing very often, and we must keep watching so, there are colleagues working hard and publishing nice proposals , https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac494/6649618 but so far, in my opinion, the best, reliable, complete, highly cited, and easy to use web platform to analyse outputs of dada2, qiime2, mothur, of amplicon sequencing frequencies is in my opinion microbiomeanalyst

Talking about network comparison, you can opt for different topological comparisons network the groups.

R has lots of packages for that.

Cytoscape is also an option.

metabolite

You need the selective media for Gram negative rods, Gram anaerobic rods, Gram positive rods, Gram positive cocci, Yeast and molds, Lactobacillus.

Yes, then you should either choose a looser (i.e. lower) Q value threshold or use only the forward reads for analysis.

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

How we can maintain the probiotic microbiome again quickly after water exchanges, in order to avoid pathogenic bacteria's bloom ? 1. Hatchery technician commonly use Bacillus sp. & lactobacillus probiotic powder directly apply it to the water or at the first we activated them by culture, 2. We should notice that water supply had been desinfected before use it, 3. We should maintain rasio C:N at raised more than 15, 4. We should control alkalinity as one of limiting factor for maintain probiotic as biofloc.

What are the error messages shown? Theoretically, as long as the same primers are used, there shouldn't be any problem merging the two.

However, in fact you shouldn't use dada2 for denoising the combined dataset since each sequencing run has its own error model. A proper way to do this is to denoise each dataset and then combine the results. Or, you can simply use deblur to denoise the combined dataset.

Interested in gut resistome study

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.

Very dear friend:

Your server is a cynical investigator, but I am under his command.

Dr. Alvaro Zamudio Tiburcio

thank for upraising the question, you are right in such use of chemicals, but my opinion than regular cleaning (more frequently) with moderate effect chemicals is good enough to keep indoor clean.

with regards

Kindly check the following RG link in which a representative strain collection of dominant aerobic bacteria from black soldier fly larvae (Hermetia illucens, BSFL) has been established:

It sounds like you want to sequence bacteria and viruses from the same sample, right? You can of course do a metagenome in one go, but you will miss some of the relevant taxa for viruses (for example RNA viruses). Also some taxa will be higher represented in the data than others due to their abundance, so you will miss the lower abundant viruses that will get less read coverage.

It depends a bit on the sample and question that you are asking. Generally I would say, that if you can separate your sample into a bacteria fraction and virus fraction, it will give you best data.

For example, some use ultracentrifugation with CsCl gradients. You would take the fraction with your intact virus capsids and do DNA and RNA extractions from it. Sequence these along with your DNA extraction of the bacteria fraction. Depending on the extraction protocol, you might introduce biases towards ssDNA, dsDNA, ssRNA and dsRNA viruses. So you probably want to have a good look into it.

Check out this paper here as a start:

Commenting as per my observations to the gel images provides

1. Doesnt look degraded DNA, but yes yield is preety low of the lanes 1 to 3, but as you r looking for full length gene, cant afford degradation

2. You can ask to repeat DNA extraction from those samples, else u can do it by urself n send the samples.

Hi Stephan,

Thank you! We are testing saponins as well, however we have solid tissue samples, not liquid, so I am not convinced about its effectivity for our purposes. Maybe some pre-treatment steps for tissue lysis would help. In addition, we also have total DNA extracted already which I wanted to use.

Please intimate we both will give call for

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.

Hi Micha

As it happens. Am grateful for any good reference and have now already collected quite a bit. Have a good time in England. We will have a Zoom meeting in early April. Until then all the best.

Best Kurt

Which type of kit did you use in dna extraction from fecal sample QIAamp DNA Microbiome Kit or another kit?Lucas Santos@

Hi! Sometimes there are molecules that "survive" to the cleaning steps of the DNA extraction that can inhibit the PCR reaction. Try to dilute your DNAs to have ratios of 1:25, 1:50 or 1:100. Then, repeat the PCRs and good luck :)

Note: I assume that you measured your DNAs and that you know that you have enough DNA.

How do you see the microbial sequencing through shortgun and amplicon , any major difference you see, lets debate thank you

Absolutely correct , not only next generation green revolution , but total sustainable development of agro-ecosystem is likely to be steered by web world of microbes , including the current issue of climate change , solving worlds biggest problem through worlds tiniest particle. ..keep talking ..The introduction of OMICS has further decoded the mystical world of microbes to no limit ...

This question is about carrying out methods such as Permanova on an asymmetric distance matrix. This implies that the distance from sample (or variable) A to B is not the same as the distance from B to A. Most distance-based methods consider symmetric measures (distance A to B = B to A), so the question is not about the code you need to do a Permanova (or other) test, but whether the test is possible given your chosen distance measure. I don't think it is. So, you need to think about why you have chosen to use an asymmetric measure, what that asymmetry tells you, and whether it is important to what you want to know. If it isn't, you could average the distances A to B and B to A to get a single distance for each pair of objects (samples presumably, but could be variables), or choose another symmetric measure, and take it from there. If it is, then some other analytical approach might take you to where you want to be. I'd need to know more to say more.

thank you all for your suggestions! I did explore the Human Microbiome Project and their data portal but it was too much for me. I don't have the skills yet to utilise the raw data. However, I discovered many other projects along the way.

GutFeelingKB—A reference list for healthy human gut organisms. GutFeelingKB is a compilation of highly curated data and metadata associated with organisms identified as present in the samples they analyzed. GutFeelingKB consists of 157 organisms which fall into sixty distinct genera, as seen in S2 Table which is arranged by species. Published article:

The full table can be down-loaded at https://hive.biochemistry.gwu.edu/gfkb.

I had been using the online app Calypso, but that is down at the moment. I found another free online tool: Microbiome Analyst from Xia Lab https://www.microbiomeanalyst.ca/

Here you can do stats and charts but also compare with published data in various populations. You can upload your OTU table and compare with published data.

I am still struggling with my OTU table because it has been formatted in the Calypso format which is not compliant with the standard way eg Greengenes taxonomy. My table has taxonomy headers but each row does not included the full taxons so I can't compare it to anything else until I manage to reformat it.

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.

Saveer Biotech company

hope above mention papers will help you out.

Dymphan Gonsalves Thank you very much, your answer is very helpful.

Dear Prof.

Brazilian Journal of microbiology is a good opportunity

Dear Dr Hira

Please kindly see the following attached files and sites too,

good Luck

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.

TRIPHALA - An excellent ayurvedic colon cleaner manufactured in India .

Sure you can do SDM. SDMs can give you how much the distribution of microbiome is restricted by environmental variables (R square, ROC, TSS, etc.), ranks of environmental variables, and spatial pattern of suitable areas.

Larvae of Lepidoptera they have well developed head capsule, 3 pairs of thoracic leg and 3-5 abdominal leg; Duster headless and legless; Coleopyeta well develped.head capsule and only three pairs of thoracic leg; Hymenopteta well developed head capsule. Three pairs of thoracic legs and greater than five pairs of abdominal leg.

Hello Alessandra Polo

If you are gonna work only with the files in .cvs format you don't need to convert them into .biom. The .biom format is in case you want to have the frequency table and the taxonomy together in the same file. But in your case you can import the three files (Frequency table, Taxonomy table and metadata table) separately and merge them with phyloseq using the commands from my previous comment. One thing to keep in mind, if you're going to use that way, then the Frequency table only should have the ASV ID and their frequency per sample, and the taxonomy table should have only the ASV ID and the taxonomy only (attached examples).

Regarding the taxa labels, you can remove them, as long as each taxa rank is separated from one another phyloseq won't have a problem with it.

Best regards

It seems that you didn't design a clear plan for your study. First, you should choose a number of potential biomarkers based on previous studies and findings. You can also use the same sample size that the other similar articles have used for their study. Remember that you just have a number of potential biomarkers that in your first step you need to confirm their biomarker features for your case. Therefore, at this stage, it is not possible to test the combination of them as a biomarker. After gathering your raw results, by performing Pearson and Spearman statistical analysis you are able to find the type and amount of relationship between your potential biomarkers. Now, you can design a new study that can evaluate the effect of specific combinations as an effective biomarker.

thank you guys

In python, You may try this matplotlib. Recently i used it in my metagenomics analysis.