Signing up is easy. Provide an email and password - your account is used to store your data and associated experiment notebooks.
You can permanently delete you data at any time.
Alternatively, try the demo account (press "Try Demo" from the homepage) which includes example datasets from the following studies:
- Sze et al. Host Response to the Lung Microbiome in Chronic Obstructive Pulmonary Disease (can also be added in a newly registered account)
- Moitinho-Silva et al. Investigating the high and low microbial abundance status in marine sponges
- Ravenscraft et al. No evidence that gut microbiota impose a net cost on their butterfly host
If you are following the dada2 tutorial (https://benjjneb.github.io/dada2/tutorial.html) to create an ASV table, note the following instructions when creating the input to Mian.
Because, Mian will require a CSV or TSV file input, so we need to save some of the objects that dada2 produces. These steps assume you have completed the tutorial up to the "Assign Taxonomy" step:
- Save the filtered ASV table:
write.csv(seqtab.nochim, "<some output directory>/seqtab.csv") - Save the taxonomy file:
write.csv(taxa, "<some output directory>/seqtab.csv")
Note that you will still need to create a sample metadata file following the format below.
Mian accepts two commonly-used input formats: BIOM and TSV/CSV files - switch between them using the toggle at the top.
Certain metadata files are common to both formats.
BIOM is a commonly-used biological matrix file format that contains both the OTU/ASV data file and the taxonomy information.
You can either include the sample metadata in the BIOM file or upload it in a separate file.
{% hint style="info" %} Sample IDs should be consistent with the metadata files {% endhint %}
This file must be one of the following formats. The former is a .shared file produced by mothur, the latter is a manually created OTU/ASV file.
Counts should be integers. Headers are required (OTU/ASV names taken from headers).
Subsampling is optional (further subsampling will be an option in the next step).
label Group numOtus Otu01 Otu02 Otu03 Otu04 Otu05 ...
0.03 1a 35 0 5 2 3 3 ...
0.03 1b 45 7 2 5 1 1 ...
Sample Otu01 Otu02 Otu03 Otu04 Otu05 ...
1a 0 5 2 3 3 ...
1b 7 2 5 1 1 ...
{% hint style="info" %} Ensure that this file contains metadata for each of the samples in the OTU table AND the sample IDs are consistently used, otherwise the pre-processing may fail. {% endhint %}
This file should contain any categorical or quantitative metadata specific to each sample. TSV or CSV.
The first row is the header. The headers will be converted to filters.
Sample body_site disease CD8 ...
1a gut Disease 9.352 ...
1b skin Control 11.242 ...
...
{% hint style="info" %} OTU names should be consistent with the uploaded OTU file {% endhint %}
TSV or CSV taxonomy file with each row representing an OTU and its corresponding taxonomy information. Not applicable for BIOM-type uploads. Several taxonomy file contents are accepted:
Example mothur constaxonomy file:
OTU Size Taxonomy
Otu01 35 k__Bacteria;p__Firmicutes;c__Clostridia;o__Halbiales;f__Halbiaceae;g__Halbium;s__conlense
Otu02 45 k__Bacteria;p__Cyanobacteria;c__Nosphycideae;o__Nostocales;f__Nostoceae;g__Dolichospermum
...
Example Taxonomy File (GreenGenes or Silva Taxonomy String):
OTU taxonomy
Otu01 k__Bacteria;p__Firmicutes;c__Clostridia;o__Halbiales;f__Halbiaceae;g__Halbium;s__conlense
Otu02 k__Bacteria;p__Cyanobacteria;c__Nosphycideae;o__Nostocales;f__Nostoceae;g__Dolichospermum
...
Example Taxonomy File (decomposed taxonomy):
OTU kingdom phylum class order family genus species
Otu01 Bacteria Firmicutes Clostridia Halbiales Halbiaceae Halbium conlense
Otu02 Bacteria Cyanobacteria Nosphycideae Nostocales Nostoceae Dolichospermum
...
{% hint style="info" %} Sample IDs should be consistent with the uploaded OTU file {% endhint %}
If you have access to gene expression data (eg. your study involves a human subject and you have access to microarray data), optionally include a TSV or CSV gene expression matrix file where the rows are the genes and each column is a sample.
Every sample in the OTU file must be present in this file.
Gene 1a 1b 1c ...
TLR1 1.2891 2.3992 1.2293 ...
TLR2 6.2894 9.5196 9.0199 ...
IL8R 3.7991 4.1968 2.3449 ...
...
Newick-formatted phylogenetic tree, where the leaves are the OTUs from the OTU table. This is only needed if you want to use Unifrac distances in your analysis. Note that not all OTU-picking pipelines will produce a phylogenetic tree.
(((((('sepal_width':1)Streptococcus:1)Streptococcaceae:1)Lactobacillales:1,((('petal_width':1)AAAAA:1)Blah:1,(('petal_length':1)Staphylococcus:1)Staphylococcaceae:1)Bacillales:1)Bacilli:1)Firmicutes:1,((((('sepal_length':1)Prevotella:1)Prevotellaceae:1)Bacteroidales:1)Bacteroidia:1)Bacteroidetes:1)Bacteria:1;
After uploading the files, you'll be directed to page that looks like the following.
The left panel previews the OTU count for each sample after applying the selected normalization - ensure this is what you expect. Here, 67 samples are subsampled to 676.
If auto-subsampling produces a low number, your data might have outliers. Optionally choose to remove these outliers or define your own subsampling threshold.
Subsampling OTU tables is a point of contention in the research community (1, 2), but is recommended here for comparative analysis. You may choose to proceed without normalization.
