ResistoXplorer
is developed based on three open source programming languages - Java, R and JavaScript.
In particular, the web framework was developed based on the Java Server Faces technology using the
PrimeFaces (version: 6.1) and BootsFaces (version: 1.2.0) component library. The interactive
networks and other 3D visualization is implemented based on the sigma.js (version: 1.2.0) ,
CanvasXpress.js (version: 7.8) and D3.js (version: 3.5.17)
JavaScript libraries.
The backend statistical computation is implemented using the
R (version: 4.1.0) scripts, and the packages from the
Bioconductor project. Here is the list of R packages that been mainly used for analysis and visualization purposes:
-
reshape (0.8.8), dplyr (0.8.5) & tidyr (1.0.2): for data manipulation and processing;
-
ggplot2 (3.3.0) & gplots (3.0.3): data visualization;
-
igraph (1.2.5): for network creation and layout;
-
pheatmap (1.0.12): for heatmap visualization;
-
viridis (0.5.1) & RColorBrewer (1.1-2): color palettes for generating nice graphics;
-
Cairo (1.5-11):create a new Cairo-based graphics device;
-
RJSONIO (1.3-1.4) & jsonlite (1.6.1): to convert the result data to JSON object for visualization;
-
phyloseq (1.30.0): storing data as S4 class object and general data manipulation;
-
compositions (1.40-4): for log-ratio transformations;
-
vegan (2.5-6): for rarefaction curve, diversity and procrustes analysis;
-
metagenomeSeq (1.28.2),edgeR (3.28.1),DESeq2 (1.26.0) & ALDEx2 (1.18.0): differential abundance testing of metagenomic data;
-
randomForest (4.6-14): for random Forest classification;
-
mixOmics (6.10.8): for vertical data integrative analysis (sPLS & rCCA);
-
made4 (1.64.0): for co-inertia analysis;
-
minerva (1.5.8): for Maximal Information Coeffecient (MIC) correlation analysis;
-
And several other open source R scripts from their respective GitHub repositories.
The R code repository for the ResistoXplorer web server to perform downstream analysis is accessible from: https://github.com/FCPLab007/ResistoXplorerR
ResistoXplorer is hosted on a remote dedicated server running Ubuntu OS 18.04 LTS with 32GB RAM and 4 CPU cores (3.1 GHz each).
The application server is Glassfish 5.0. Please note, the client-side data visualization requires a modern browser
that supports HTML5 canvas and JavaScript. ResistoXplorer has been tested under Google Chrome (5.0+), Firefox (3.0+), and
Internet Explorer (9.0+).
Database statistics
Detailed statistics on data collection and organization of information across databases (present in ARG Table module for functional annotation mapping):
Database |
Version |
Date |
No. of Features |
Annotation Header |
No. of functional levels |
Functional levels |
Note |
Link |
ResFinder |
4.1 |
2021-04-20 |
3152 |
ARG name_Accession |
3 |
Class, Mechanism & Gene |
- |
Link
|
CARD |
3.1.3 |
2021-07-05 |
2979 |
ARG accession |
3 |
Mechanism, Family & Gene |
only ARGs present in “nucleotide fasta protein homolog” model |
Link
|
ARDB |
1.1 |
2009-07-03 |
377 |
ARG type |
2 |
Class & Mechanism |
- |
Link
|
ARG-ANNOT |
4.0 |
2018-05 |
2025 |
ARG name |
1 |
Class |
- |
Link
|
MegaRes (Full) |
2.0 |
2019-10-14 |
7868 |
MegaRes ID (Accession) |
4 |
Type, Class, Mechanism & Group |
drugs, biocide and metal resistance genes |
Link
|
MegaRes (Drugs) |
2.0 |
2019-10-14 |
7126 |
MegaRes ID (Accession) |
3 |
Class, Mechanism & Group |
only drugs related resistance genes |
Link
|
MegaRes |
1.0.1 |
2016-12-01 |
3824 |
MegaRes ID (Accession) |
3 |
Class, Mechanism & Group |
- |
Link
|
AMRFinder |
1.0 |
2019-11-04 |
4156 |
ARG name |
2 |
Class & Mechanism |
- |
Link
|
SARG |
2.0 |
2019-12-07 |
12085 |
ARG name |
2 |
Type & SubType |
- |
Link
|
DeepARG-DB |
1.0 |
2020-02-07 |
4511 |
ARG name |
2 |
Subtypes & Types |
- |
Link
|
ARGminer |
1.1.1 |
2019-04-24 |
14872 |
Accession |
3 |
Class, Mechanism & ARG_NAME |
only contain known ARGs |
Link
|
BacMet |
2.0 |
2018-03-11 |
607 |
Biocide & Metal resistance gene name |
2 |
Mechanism & Class |
only experimentally confirmed genes |
Link
|
Antimicrobial peptide (AMP) dataset |
- |
- |
131 |
AMP gene name |
1 |
Mechanism |
- |
Link
|
Acknowledgements
The work is financed by the INDNOR and INTPART programs funded by The Research Council of Norway, grant numbers 273833 and 274867,
and the Olav Thon foundation, and supported by the University of Oslo.
|