An organism's ability to mount an effective immune response after infection or vaccination depends on the type of antibodies and antigen receptors produced by its immune system cells. This set of molecules also plays an important role in the pathogenesis of many kinds of disease, such as autoimmune disease, lymphoma, and leukemia. Thus, analysis of an individual's repertoire of such molecules is applied in a wide variety of basic research, research and development, and clinical contexts. Despite the importance and complexity of repertoire analysis, there is currently no suite of software tools for analysis of repertoire data. Tools exist for only a subset of analysis tasks, and those that do exist were developed for use on a single project by a single research group. These tools do not function together and cannot be utilized in new studies without modification that requires bioinformatics expertise, leaving researchers to perform repetitive and error-prone tasks by hand, to develop internal, idiosyncratic algorithms not easily generalizable or systematically applied, and to expend significant manual labor reformatting primary and derived data for passing between tools. This approach is not only error-prone and time- and labor-intensive, but it comes at the expense of reproducibility, both within and between research groups. We propose to address this critical barrier to progress by developing RepServer, a suite of interoperable repertoire analysis tools and an interface that allows users to upload a set of repertoire sequences and pass them through a seamless workflow that executes all steps in the analysis and generates an analysis report complete with data summary tables, statistical analyses, figures, and workflow logs. The impact of RepServer will be significant. RepServer will improve the efficiency of repertoire analysis by reducing duplication of effort and eliminating the need for significant manual manipulation of data. The latter will in turn improve the accuracy and reliability of analyses by reducing errors. RepServer will make sophisticated computational analyses of repertoires accessible to bench biologists and clinicians. RepServer will provide the infrastructure for reproducibility, a critical step towards translation of repertoire analysis into clinical settings. RepServer will have impact in all areas of research, development, and clinical practice that rely on repertoire analysis.
The proposed work will improve technical capability for analyzing antibody and antigen receptor repertoires leading to improvements in scientific knowledge about their role in disease. The improved analysis capability along with the resulting increased scientific knowledge will facilitate the use of repertoire analysis in the development of novel therapeutics, and enable repertoire analysis in clinical settings for diagnostic and prognostic applications and for patient monitoring.
|Corrie, Brian D; Marthandan, Nishanth; Zimonja, Bojan et al. (2018) iReceptor: A platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories. Immunol Rev 284:24-41|
|Vander Heiden, Jason Anthony; Marquez, Susanna; Marthandan, Nishanth et al. (2018) AIRR Community Standardized Representations for Annotated Immune Repertoires. Front Immunol 9:2206|
|Christley, Scott; Scarborough, Walter; Salinas, Eddie et al. (2018) VDJServer: A Cloud-Based Analysis Portal and Data Commons for Immune Repertoire Sequences and Rearrangements. Front Immunol 9:976|
|Breden, Felix; Luning Prak, Eline T; Peters, Bjoern et al. (2017) Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data. Front Immunol 8:1418|
|Rubelt, Florian; Busse, Christian E; Bukhari, Syed Ahmad Chan et al. (2017) Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data. Nat Immunol 18:1274-1278|
|Christley, Scott; Levin, Mikhail K; Toby, Inimary T et al. (2017) VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data. BMC Bioinformatics 18:448|
|Ostmeyer, Jared; Christley, Scott; Rounds, William H et al. (2017) Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis. BMC Bioinformatics 18:401|
|Rivas, Jacqueline R; Ireland, Sara J; Chkheidze, Rati et al. (2017) Peripheral VH4+ plasmablasts demonstrate autoreactive B cell expansion toward brain antigens in early multiple sclerosis patients. Acta Neuropathol 133:43-60|
|Vincent, Benjamin; Buntzman, Adam; Hopson, Benjamin et al. (2016) iWAS--A novel approach to analyzing Next Generation Sequence data for immunology. Cell Immunol 299:6-13|
|Toby, Inimary T; Levin, Mikhail K; Salinas, Edward A et al. (2016) VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements. BMC Bioinformatics 17:333|
Showing the most recent 10 out of 15 publications