All experiments produced fluorescence signal values for each peptide in the array and for each assay performed. These data can be searched, explored, and filtered in Chagastope in tabular format. Raw signals can be explored at the Peptide data - All Peptide data section, while processed (smoothed) signals can be explored at Peptide data - Grouped by protein or Peptide data - Grouped by antigenic region sections.
If you need to download large amounts of data, it’s simpler to do so from one of the following links:
We produced signal plots to visualize the antibody-binding signal (antigenicity) for peptides along proteins. This plots show the location of antigenic regions and epitopes in antigens. Static plots are PDFs with the same plots found in the supplementary files of our paper. Dynamic plots are generated in real time from the data, and allow users to choose the serum samples (assays) to plot, change the scale of the plots, cutoff and tweak a few additional visualization options. Dynamic plots are also interactive, they are rendered by plotly and can be zoomed, rescaled, panned, and additional information can be displayed by moving the mouse cursor over data points. After tweaking the plots these can be exported directly in SVG format.
If you are interested in downloading a large number of plots, it is maybe simpler to do so from one of the following links:
There are two main types of Static Plots in Chagastope: 1) Proteins - Sample Pools: antibody-binding profiles for complete proteins, obtained using pooled samples with CHAGASTEOPE-v1 arrays; and 2) Antigenic regions - Individual Samples: individual antibody-binding profiles for 9,547 antigenic regions, obtained with CHAGASTOPE-v2 arrays. In both scenarios, we only listed the plots proteins where at least 1 of its peptides had a processed antigenicity signal that surpassed the corresponding antigenicity threshold. Antibody-binding profiles for proteins or regions with ubiquitous low signals can be seen using the Dynamic Plots.
These plots are generated on the fly from the data, and allow you to visually explore the antigenicity of all proteins, even those not included in the static plots due to their low signal. When plotting data for more than one serum sample, you can choose from two ways to combine the plots (Individual and Combined) and you can change many other plot options, such as showing the standard deviation or focusing on a specific area of the plot. Also, thanks to plotly, you can see the exact peptide for each point in the plot and compare its signals across serums, all within the same plot.
The Trypanosomatics Laboratory is interested in the study of human pathogens, particularly trypanosomes and other pathogens that cause Neglected Tropical Diseases. We develop computational tools and produce (and re-use) large data sets to formulate and guide our research hypotheses in the quest for new drugs and diagnostics. Smart and intensive data integration, data mining, and high-throughput assays and experiments are at the core of our research activities. In particular, we have a special interest in the study of trypanosomes such as Trypanosoma cruzi (causative of Chagas disease).
More details on our past and present projects, as well as the people that are part of our lab, can be seen in the official Trypanosomatics lab webpage.
This paper is based on the findings from the Chagas Antigen and Epitope Atlas, data which can be explored in Chagastope Web.
Data processing and analysis were carried out by Alejandro D. Ricci, Leonel Bracco and Juan Mucci under the supervision of Fernán Agüero. Cardio Chagastope Web v1.0 was created by Alejandro D. Ricci.
Data was generated by Justo Carbajales, Mario Principato, Analía Paolucci, Natalia Ciampi and Alejandra von Wulffen. All these investigators correspond to the Hospital General de Agudos “José María Ramos Mejía”, Ciudad Autónoma de Buenos Aires, Argentina.
Development of The Chagas Antigen and Epitope Atlas and the Chagastope website were supported by the following grants:
R01 AI123070 (2016-2022), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH).
PICT-2017-0175 (2018-2022), Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (Agencia I+D+i)