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 and normalized 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 in the Chagas Antigen and Epitope Atlas 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) Proteins - Individual Samples: individual antibody-binding profiles for 71 Chagas disease positive subjects over 7,707 selected antigenic regions, obtained with CHAGASTOPE-v2 arrays.
In Proteins - Sample Pools you will find antibody-binding plots (antigenicity) for any protein where at least 1 of its peptides had a processed antigenicity signal that surpassed or was slightly below the antigenicity threshold (this happened in 12,459 out of the 30,500 proteins analyzed in CHAGASTOPE-v1, see Summary for details). This also includes the analysis of a pool of Leishmaniasis-positive and Leishmaniasis-negative samples to test for cross-reacting epitopes, which can also be seen in these plots.
Proteins - Individual Samples display the antigenicity plots for each of the 7,707 proteins analyzed in CHAGASTOPE-v2 when assayed with 71 individual serum samples. To make it easier to compare this experiment to those using pooled samples, here data from assays using individual samples are grouped by country of origin, and the first plot in each group corresponds to the profile of that protein in the CHAGASTOPE-v1 arrays. In these plots, the line changes colors when the peptide signal surpasses the antigenicity threshold, which is a different number for CHAGASTOPE-v1 and CHAGASTOPE-v2 (see Summary). Empty areas that may appear in these plots correspond to the regions of the protein that were not analyzed in CHAGASTOPE-v2 arrays (meaning they were not reactive in CHAGASTOPE-v1 arrays). The gray vertical dotted lines every 50 amino acids makes it easy to locate specific regions of the protein.
If you want to look at one specific antigenic region shown in the plot above, you can see it in Antigenic regions - Individual Samples.
Finally, we also performed an Alanine Scan of some specific peptides, which can be seen in Alanine Scans - Individual Samples. These are described in the original paper.
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.
Data processing and analysis were carried out by Alejandro D. Ricci and Leonel Bracco under the supervision of Fernán Agüero. Chagastope Web v1.0 was created by Alejandro D. Ricci.
Data was generated by an extensive collaboration of investigators:
Janine M Ramsey, Centro Regional de Investigación en Salud Pública, Instituto Nacional de Salud Pública, Tapachula, México
Melissa S Nolan and Mary K Lynn, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
Jaime Altcheh, Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas (IMIPP) – GCBA-CONICET, Buenos Aires, Argentina
Faustino Torrico,Fundación CEADES, Cochabamba, Bolivia
Norival Kesper, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brasil
Juan Carlos Villar, Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga y Fundación Cardioinfantil - Instituto de Cardiología, Colombia
Jorge Diego Marco, Instituto de Patología Experimental, Universidad Nacional de Salta – Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Salta, 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)