Tobias Rogosch - Immunoglobulin analysis tool: a novel tool for the analysis of human and mouse heavy and light chain transcripts.

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  Publication Details (including relevant citation   information):

  Rogosch T, Kerzel S, Hoi KH, Zhang Z, Maier RF, Ippolito GC,   Zemlin M.

  Front   Immunol. 2012 Jun 28;3:176.

  doi: 10.3389/fimmu.2012.00176

  Abstract:

  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.

  Address (URL): http://www.ncbi.nlm.nih.gov/pubmed/22754554

 

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