You can check with this link it may helps you

http://www.nature.com/nbt/journal/v31/n9/full/nbt.2676.html

Good luck

To the best of my knowledge there isn't. When people have looked at functional roles of microbes they've often performed shotgun metagenomics or metatranscriptomics. However, it is possible to search the literature for the organisms you are finding and see their putative functions. From their you can make assessments of how changes in composition may effect the overall functioning. I have done this with some 18S data for fungi that I have in review and would be happy to email it to you as an example of what I've tried to do. Of course, there will be lots of different functions occurring at once, so it's not as simple as finding an overall effect and unfortunately, it may also take a while to scan through the literature for all of your organisms, but I hope that helps.

Regards,

Chris

The online tool you see is the galaxy server hosting picrust.

http://huttenhower.sph.harvard.edu/galaxy/root

for the picrust input you would need to make biome format files using OTU referenced to greengenes using mothur/qiime or other similar software.

Thanks  for all the suggestion. I am using V4 region of 16srRNA generated from illumina Mi sec. As you mentioned, I  tried to run picrust in galaxy server but it always showing wrong biome format. Is their any tools to check the biome  file before using in galaxy.

how did you generate you biome formatted file ?

what is the software you are using and how are you going about it ?

We have used QIIME pipeline for generating the biome formatted file.

ok,

what greengenes version and biome format was used ?

http://biom-format.org/

biome format details.

The file is started with

{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "QIIME 1.8.0","date": "2014-07-01T09:28:32.065698","matrix_type": "sparse","matrix_element_type": "int","shape": [1279, 1],"data": [[0,0,2],[1,0,41],[2,0,6],[3,0,5636],[4,0,57],[5,0,33],[6,0,59],[7,0,2],[8,0,3],[9,0,2],[10,0,45],[11,0,13],[12,0,25],

and ends with

"metadata": {"taxonomy": ["p__Synergistetes", "c__Synergistia", "o__Synergistales", "f__Dethiosulfovibrionaceae", "g__HA73", "s__"]}}],"columns": [{"id": "CPN", "metadata": null}]}

HI Surajit,

Did you by any chance use the August 2013 release of Greengenes (13_8)? Picrust did not implement this latest update and still runs using the May 2013 Greengenes database (13_5) so you need to generate your BIOM file using that version of Greengenes as well.

I hope this helps,

Chris

We love to make a well-educated guess about certain microbial function based on the  phylogeny. Yet in many cases this is not right. Distant related bacteria can share same function traits while distinct functional communities can share almost identical 16S rRNA sequences. 16S rRNA can serve as an indicator for monitoring the dynamics of  microbiota but it doesn't has any direct relationship with microbial ecological roles. 

For the better understanding of microbial functions, GeoChip is a cost-friendly and efficient platform

Mr Surajit:

I have a BIOM file as you,when I upload my BIOM file to galaxy server , it always showing wrong biome format.

My file is started with

{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "QIIME 1.8.0","date": "2015-01-04T16:06:22.313977","matrix_type": "sparse","matrix_element_type": "float","shape": [1596, 10],"data": [[0,0,1.0],[0,1,5.0],[0,2,3.0],[0,3,1.0],[1,0,1.0],[1,4,1.0],[2,5,6.0],[2,6,22.0],[3,1,8.0],[3,2,4.0],[3,5,6.0],[3,6,3.0],[3,7,1.0],[4,8,1.0],[5,5,2.0],[6,9,1.0],[7,2,1.0],[7,8,1.0],[7,9,1.0],[8,0,1.0],[8,1,4.0],..................................

and ended with "N40", "metadata": null},{"id": "DN0", "metadata": null},{"id": "DN40", "metadata": null},{"id": "N10", "metadata": null},{"id": "N20", "metadata": null},{"id": "NO", "metadata": null},{"id": "DN10", "metadata": null},{"id": "DN80", "metadata": null},{"id": "DN20", "metadata": null},{"id": "N80", "metadata": null}]}.

I have been confused for a long time here,today I am very lucky to see your problem.Maybe you have solve the problem,So could you give me some advices about this ?Thank you very much !

You may want to have a look at

PICRUSt and

http://www.ncbi.nlm.nih.gov/pubmed/25957349

Tax4fun is the answer, you can predict and compare functions deduced from 16S-based community composition

this prediction will be extremely uncertain

Dear R Daniel and guys. 

I have no experience on these methods, but wish to try for my own samples. Have you try Tax4fun and can you shave with us why you prefers this one? Thanks

Hi Daniel, 

May I know how to interpret the output of Tax4Fun? or is there any example manual that we can try?

Hello.

Some people have asked about Tax4Fun and how to use its output. I recommend Tax4Fun since its is easy and accessible. The output is a list of things one of which is a matrix that contains the sample names as the rows and many enzyme names as the columns. Each entry in this matrix is an abundance score of the enzyme in the corresponding column as estimated in the sample from the corresponding row.  It basically tells you how much of a certain enzyme is present in a sample. Do not interpret these scores as real abundances!!

Although easy in theory, from my experience one may run into walls if not carefully. Two common mistakes are:

 -  the use of the biom file name as an argument for Tax4Fun() function will raise an error. The correct way is to create a variable by calling the function importQIIMEBiomData() using the biom file name and then pass this variable as argument for Tax4Fun();

- if you are using QIIME use the SILVA reference base for taxonomy assigments. Using other dbs (GG or RDP) will raises errors because Tax4Fun is based on SILVA db;

Hope this helps,

Dorin

Hi. Thanks for the explanations. We will try it. 

An idea came to my mind, how should we validate the Tax4fun or PICRUSt    prediction and compare with shotgun data? I have PICRUSt and shot run data, how should I compare them? A collaboration is open here. 

Hi all,

I have one question for you regarding predicted metagenome output from PICRUSt. I would like to do downstream analysis using R Phyloseq package. Does anyone have a clue how to import PICRUSt biom file into phyloseq?

Thanks a lot

Hi

You can try this to see your prdicted data:

Tax4FunOutput

Hi Carolina Hoyos-Hernandez,

Thanks a lot. Your answer is very helpful.

My pleasure!

Carolina, thank you so much. No-where in the Tax4Fun documentation is it clear how to access/export the T4F output - you have saved my Friday. 

A quick article on 'Predictive Metabolic Profiling' / PMP

And a relevant paper: Predictive functional profiling of microbial

communities using 16S rRNA marker gene sequences - Langille et al., 2013

http://bioinformaticsreview.com/20160320/predictive-metagenomics-profiling-why-what-and-how/

Have you tried Vikodak for predicting functional profiles based on 16S data ?

We did, but this is what we keep getting:

Service Temporarily Unavailable

The server is temporarily unable to service your request due to maintenance downtime or capacity problems. Please try again later.

Best,

Blaz

Hi,

Someone could recommende me a R package for the analysis of Tax4Fun results? This results could be analize using multivariate routines, like those used for OTUs analysis? Which type of statistical analysis would you suggested me?

Thanks in advance,

Carolina

Hi

Does anyone know how to use Taxy-Pro?  After the prediction of Pfam protein domain families in UproC, I follow the usage step of Taxy-Pro in MATLAB/Octave:

>TaxyProMainO('the path of file.txt','S')

error: called from:

　　TaxyProMainO at line 5 column 5

Can anyone help this problem?

Try these two :

MicrobiomeAnalyst

https://academic.oup.com/nar/article/45/W1/W180/3760191

link : http://www.microbiomeanalyst.ca

BURRITO

https://www.frontiersin.org/articles/10.3389/fmicb.2018.00365/full

link : https://elbo-spice.gs.washington.edu/shiny/burrito/

Hello! Can anyone explain Tax4Fun output numbers? What do they mean? And if higher numbers are related to their functional abundance?

Article Comparison of two bioinformatics tools used to characterize ...

This paper provides an R script that may help.

http://picrust.github.io/picrust/

PICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States

PICRUSt (pronounced “pie crust”) is a bioinformatics software package designed to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes.

PICRUSt is freely available under the GPL.

Click here to go to the PICRUSt GitHub repository.

Please note that PICRUSt2 is now available. PICRUSt2 is a re-written version of PICRUSt and is available here. We are no longer developing PICRUSt1 and we recommend users shift to PICRUSt2.

Using PICRUSt

If you’re new to PICRUSt, you’ll want to work through these documents in order:

Installing PICRUSt OR Use online Galaxy version on either the Langille Lab (v1.1.1) or Huttenhower Lab (v1.0.0) servers. Quickstart Guide Metagenome Prediction Tutorial Analyzing PICRUSt predicted metagenomes Quality Control of PICRUSt Predictions

More advanced users may be interested in the following (in no particular order):

• How PICRUSt Works
• Genome Prediction Tutorial
• Installing PICRUSt in Galaxy
• PICRUSt Script Index

Contact

Anyway to find downstream analysis using R script after picrust2?