Application Note. NGS Analysis of B-Cell Receptors & Antibodies by AptaAnalyzer -BCR

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1 Reduce to the Best Application Note NGS Analysis of B-Cell Receptors & Antibodies by AptaAnalyzer -BCR The software AptaAnalyzer harnesses next generation sequencing (NGS) data to monitor the immune response on the level of B-cell receptors (BCRs) or antibodies. The AptaAnalyzer software in the analysis workflow count BCR CDR3 BCR-status of individuals Next Generation Sequencing (NGS) Big data Software analysis Snap-shot of the immune system Digitalization of the immune status Analysis of data and preparation of results

2 Archiving and structuring of the BCR immune status AptaAnalyzer -BCR supports the complete workflow in a very intuitive way. It is one tool for parsing of raw data, archiving of datasets, analysis of experiments, and generation of results in form of table views and publication level graphics. r1 r2 r3 NGS-Data Organization and analysis of NGS-Data Experiments are digitalized and easily organized in a database within projects and subprojects. The graphical Parser enables an almost parameter free reading of heavy and light chains from human, mouse or rabbit. 2

3 Archiving and structuring of the BCR immune status Quick decision making becomes possible by easy NGS data interpretation and easy generation of publication level material for actual experiments. Publication level graphs can be configured and its data values can be exported to Excel. Table views Overview results are given on the level of monoclonal sequences. Heavy and light chains are automatically identified. For flexible analysis, the receptor chains are divided into V-, CDR3 and J-regions and listed in separate columns. Tables can be exported as csv-files for comfortable data transfer into Excel (or other software). 3

4 Table views Results are provided on multiple levels of detail. The table on the top shows leader sequences each representing a CDR3 family. The table in the middle indicates sequences of all family members of the selected sequence and the table on the bottom shows the selected clone on the DNA-level. Information and table views are interlinked and change with the selected region. Custom defined table filters can be applied to view only clones that match defined criteria. 4

5 Graphs for single dataset analysis Frequency distribution of defined sequence regions: A region of a human IGH CDR3 is shown here. CDR3 spectratyping: Shown is the length distribution of a human CDR3 IGH. The Gaussian distribution reflects an unbiased immune system. 5

6 Graphs for single dataset analysis Region connectivities are visualized by a heatmap. It shows frequent combinations of dominant CDR3s with V-genes. Highly frequent combinations (red and yellow) are marked according to the color ramp. Amino acid distributions for CDR regions are shown in stacked bar chart diagrams. They help to identify conserved and variable sequence parts of defined populations. The data values of graphs can be exported to be further processed in Excel or other third party software. 6

7 Graphs for comparative dataset analysis Tracing of sequences: Bar chart series enable for example to track the frequency of CDR3 sequences over multiple experiments (e.g. different individuals and/or time points). Graphs show information on several levels of detail. A click on a defined bar chart series of a leader sequence visualizes the frequency of its respective family members (blue framed). aa ab ac 1 Comparison of two experiments can be done by scatter plot analysis, for example to compare two different time points or duplicate experiments to identify outliers. Graphs give information on several levels of detail. A click on a defined dot of a leader CDR3 sequence visualizes the frequency distribution of its respective family members. CARDLGGDGYSHFDYW 7

8 Graphs for comparative dataset analysis Distribution of BCRs or CDR3 regions in multiple experiments can be accomplished by Venn diagram analysis. They enable to identify BCRs or CDR3s, which occur in defined experiments. A click on a respective segment (here orange) extracts all sequences that are part of positive experiments A and B but not of control experiments C and D. Distribution of Clones A: Exp-1 (+) B: Exp-2 (+) C: Exp-3 (-) D: Exp-4 (-) 8

9 Summary The integration of NGS and AptaAnalyzer into BCR and antibody analysis enables: Parsing of raw NGS data (also paired end data) Archiving and structuring of experiments in a database Individual as well as comparable dataset analysis (derived from different individuals and/or time points) Easy generation of results in form of graphs and tables on several levels of detail You benefit from: Identification of rare antibodies (not accessible by conventional cloning and sequencing) In silico prediction of BCRs/antibodies addressing antigen (at sites) of interest Analysis of NGS data over night quick decision making More comprehensive understanding of the adaptive immune system Opening the black box of the BCR immune response About With its complementary team of scientists in the fields of physics, molecular biology, informatics and medicine, AptaIT bundles knowledge for the development of high performance software solutions to leverage biomedical research and drug discovery. AptaIT developed the software AptaAnalyzer, which efficiently exploits the technology of deep sequencing for the discovery of therapeutic lead structures. In doing so, AptaIT s software AptaAnalyzer optimizes and shortens the laborious and cost intensive drug development process. AptaIT GmbH Am Klopferspitz 19A D Planegg/Martinsried Germany Phone: info@aptait.de Web: 9