Immunoglobulin Analysis Tool: A Novel Tool for the Analysis of Human and Mouse Heavy and Light Chain Transcripts

Sequence analysis of immunoglobulin (Ig) heavy and light chain transcripts can refine categorization of B cell subpopulations and can shed light on the selective forces that act during immune responses or immune dysregulation, such as autoimmunity, allergy, and B cell malignancy. High-throughput sequencing yields Ig transcript collections of unprecedented size. The authoritative web-based IMGT/HighV-QUEST program is capable of analyzing large collections of transcripts and provides annotated output files to describe many key properties of Ig transcripts. However, additional processing of these flat files is required to create figures, or to facilitate analysis of additional features and comparisons between sequence sets. We present an easy-to-use Microsoft(®) Excel(®) based software, named Immunoglobulin Analysis Tool (IgAT), for the summary, interrogation, and further processing of IMGT/HighV-QUEST output files. IgAT generates descriptive statistics and high-quality figures for collections of murine or human Ig heavy or light chain transcripts ranging from 1 to 150,000 sequences. In addition to traditionally studied properties of Ig transcripts – such as the usage of germline gene segments, or the length and composition of the CDR-3 region – IgAT also uses published algorithms to calculate the probability of antigen selection based on somatic mutational patterns, the average hydrophobicity of the antigen-binding sites, and predictable structural properties of the CDR-H3 loop according to Shirai’s H3-rules. These refined analyses provide in-depth information about the selective forces acting upon Ig repertoires and allow the statistical and graphical comparison of two or more sequence sets. IgAT is easy to use on any computer running Excel(®) 2003 or higher. Thus, IgAT is a useful tool to gain insights into the selective forces and functional properties of small to extremely large collections of Ig transcripts, thereby assisting a researcher to mine a data set to its fullest.