On July 10th 2019 we updated our database, thereby introducing substantial changes to the underlying data. Any analysis performed before this date is invalid and has to be repeated.
The MS Atlas is a database providing transcriptome information of multiple sclerosis (MS) white matter lesion. We include NAWM, active, chronic active, inactive and remyelinating lesions.
The study is based on post mortem brain tissue from 10 MS and 5 control (non-neurological disease) patients. Altogether, 100 samples were classified, RNA was extracted and next-generation sequencing was applied. The resulting sequencing data were analyzed with edgeR.
Based on the normalized read count, a generalized linear model (accounting for age, sex and lesion distribution) was trained for every lesion type. Finally, the calculated p-values were normalized with FDR-correction (Benjamini-Hochberg).
The MS Atlas is able to visualize differentially expressed genes and extract mechanistic markers based on de novo network enrichment (KeyPathwayMiner).
For more information how to use the MS Atlas we recommend to watch the screencast you can find on the rigth side. Under “Guide” you can find a similar tutorial in text form.
Until publication of the MS Atlas database paper, please kindly cite the following paper when using MS Atlas (data) for your research:
Tobias Frisch, Maria L. Elkjaer, Richard Reynolds, Tanja Maria Michel, Tim Kacprowski, Mark Burton, Torben A. Kruse, Mads Thomassen, Jan Baumbach, Zsolt Illes
“MS Atlas - A molecular map of brain lesion stages in progressive multiple sclerosis”
bioRxiv 584920; doi: https://doi.org/10.1101/584920
The database contains all genes as well as the corresponding Log2FC and p-Values (FDR corrected) from the statistical analysis. Depending on the lesion type of interest, and the thresholds you consider to be significant, you can select a subset of all genes. In the following section we describe the parameter you can select and elaborate on the consequences. In the first step of the standard workflow you will start by selecting the set of genes you are interested in. Afterwards, you can select the parameters for de novo network enrichment and visualize the resulting IID subnetwork.
The resulting network is shown in the figure below. Every node represents one gene and the corresponding name will be shown when you hover your mouse above the node. The color coding shows betweenness centrality where red indicates nodes that are more central and hence more important for the network.