Brief Description
JoinSolver matches immunoglobulin sequences to a database of germlines to help elucidate the underlying recombination mechanism that yields a highly diverse repertoire of immunoglobulin antigen binding sites. It is the first application of its kind to use a consecutive scoring algorithm, which we believe simulates the underlying biology closer than other scoring systems.
This figure shows part of a typical JOINSOLVER® result (sequence accession number Z80479). First, JOINSOLVER® returns a summary of the rearrangement results. After the summary, a base-by-base alignment of the input sequence to the highest scoring V, D, and J germline sequences are returned. Optional analyses, such as Junction and Mutation Analyses, are available.
This figure shows part of a typical JOINSOLVER® result (sequence accession number Z80479). First, JOINSOLVER® returns a summary of the rearrangement results. After the summary, a base-by-base alignment of the input sequence to the highest scoring V, D, and J germline sequences are returned. Optional analyses, such as Junction and Mutation Analyses, are available.
Recent Accomplishments
JoinSolver is impacting a wide range of fields. For example, it has been used to study the repertoire of normal circulating B cells during the transition from naïve fetal to adult B cells, providing a basis for understanding the development of autoimmune and immune deficiency diseases. It has also been used to study immune deficiency diseases such as chronic granulomatous disease, systemic lupus erythematosus, and autoimmune lymphoproliferative disease syndrome. Recently, with the help of JoinSolver, researchers analyzed the genesis and outgrowth of an extranodal marginal-zone B cell lymphoma associated with Sjögren’s Syndrome. Finally, the JoinSolver mutation analysis has helped reveal some of the basic mechanisms of somatic hypermutation in human B cells, which is critical for the development of antigen-specific memory B cells and antigen-secreting cells.
JoinSolver analyzes immunoglobulin sequences one at a time. However, users can have thousands of sequences to analyze. We added a new user interface that can handle running many sequences automatically. The new interface allows users to upload a file containing sequences in FASTA format or accession numbers. Since the output is no longer just one sequence, we changed the output format to XML, an HTML table, or a Microsoft® Excel® spreadsheet.
While analyzing sequences, JoinSolver looks for evidence of germlines that have undergone a recombination process. However, after recombination a directed mutation process can occur. These mutations can cause JoinSolver to ignore a germline simply because it does not match enough consecutive nucleotides. To prevent missing good matches, we developed a new consecutive matching algorithm that identifies mutations. In addition to find better matches, the new algorithm improves JoinSolver’s ability to detect junction regions. For example, a mutation in the J region of the immunoglobulin gene can cause the DJ junction to appear too large because nucleotides upstream from the mutation are considered in the junction not in the J region. The new algorithm improves the results for mutated sequences.
Current and Future Work
HPCIO is planning to add additional analyses such as a hydrophobic analysis and to improve the matching accuracy of immunoglobulin lambda and kappa chains. The immunoglobulin lambda and kappa chain analyses currently work with a set of germlines from the International Immunogenetics Information System. However, because of differences in key definitions, the germlines in the KABAT nomenclature system do not currently work with JoinSolver. Upon completion of the above work, and barring further requests, the development of JoinSolver will largely be complete.
Collaborators
- Peter Lipsky, M.D. Chief, NIAMS, Autoimmunity Branch
- Longo, Ph.D. Postdoctoral Fellow, NIAMS. Autoimmunity Branch, Repertoire Analysis Group.
- Gary Sims, Ph.D., Postdoctoral Fellow, NIAMS. Autoimmunity Branch, Repertoire Analysis Group
Recent Publications
M. Margarida Souto-Carneiro, Nancy S. Longo, Daniel E. Russ, Hong-wei Sun and Peter E. Lipsky, “Characterization of the Human Ig Heavy Chain Antigen Binding Complementarity Determining Region 3 Using a Newly Developed Software Algorithm, JOINSOLVER,” J. Immuol., 172, pp. 6790-6802, 2004
Citations
J. M. Volpe, L. G. Cowell, and T. B. Kepler, “SoDA: implementation of a 3D alignment algorithm for inference of antigen receptor recombinations”, Bioinformatics, 22(4), pp. 438 – 444, 2006.
M. M. Souto-Carneiro, G. P. Sims, H. Girschik, J. Lee, and P. E. Lipsky, “Developmental Changes in the Human Heavy Chain CDR3”, J. Immunol., 175(11), pp. 7425 – 7436, 2005.
M. Viau, N. S. Longo, P. E. Lipsky, and M. Zouali, “Staphylococcal Protein A Deletes B-1a and Marginal Zone B Lymphocytes Expressing Human Immunoglobulins: An Immune Evasion Mechanism,” J. Immunol., 175(11): 7719 – 7727, 2005.
G. P. Sims, R. Ettinger, Y. Shirota, C. H. Yarboro, G. G. Illei, and P. E. Lipsky, “Identification and characterization of circulating human transitional B cells”, Blood, 105(11): 4390 – 4398,2005.
Katherine JL Jackson, Bruno Gaeta, William Sewel, and Andrew M Collins, “Exonuclease activity and P nucleotide addition in the generation of the expressed immunoglobulin repertoire”, BMC Immunology, 5:19, http://www.biomedcentral.com/1471-2172/5/19.
Metrics
- Sequences analyzed: 46,139
- Different hosts: 346
URL
joinsolver.niams.nih.gov
