Cloanalyst is the software implementation of our most recent suite of statistical methods for the inference of antigen receptor rearrangements and other analyses useful in the study of antibody somatic genetics. Cloanalyst performs a Bayesian analysis of antibody genes to compute posterior probabilities over rearrangement parameters and unmutated ancestral rearrangements, using either single immunoglobulin polynucleotide sequences or sets of clonally related immunoglobulin sequences. It also performs basic clonal analysis on sequence sets. The installer is available here<\/a> (Windows only). Gain-Scan is an R software package implemented to integrate the protein microarray data acquired under different photomultiplier (PMT) gain settings. The integration of the multi-gain array data significantly reduces the technical variations in the protein microarray data acquisition. It avoid the trouble of selecting one single optimal PMT gain setting for imaging the arrays. It aims to achieve this goal of avoiding saturation of the strong signals while maximizing the detection of the low singles at the same time. Besides the gainscan modeling and integration, the package currently also include other functions to preprocess\/nomalize the protein array data. Download the software at: Download Gain-Scan package<\/a><\/p>\n ELISA Data Analysis with Batch Correction (SoDA<\/a> and SoDA2<\/a>) A Bayesian approach to estimate calibration curves and unknown concentrations in immunoassays. Click here<\/a> to download the Linux&Mac version and\u00a0here<\/a> for Win UI(64-bit) version. To view and cite this publication, please refer to this\u00a0link<\/a>.<\/p>\n A C++ program used to generate input files suitable for Baesc from Luminex system output. Click here<\/a> for Linux OS and here<\/a> for Windows version.<\/p>\n <\/strong> Cloanalyst Cloanalyst is the software implementation of our most recent suite of statistical methods for the inference of antigen receptor rearrangements and other analyses useful in the study of antibody somatic genetics. Cloanalyst performs a Bayesian analysis of antibody genes to compute posterior probabilities over rearrangement parameters and unmutated ancestral rearrangements, using either single immunoglobulin […]<\/p>\n","protected":false},"author":7993,"featured_media":0,"parent":153,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/pages\/34"}],"collection":[{"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/users\/7993"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/comments?post=34"}],"version-history":[{"count":52,"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/pages\/34\/revisions"}],"predecessor-version":[{"id":1723,"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/pages\/34\/revisions\/1723"}],"up":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/pages\/153"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/computationalimmunology\/wp-json\/wp\/v2\/media?parent=34"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n <\/p>\nGain-Scan<\/strong><\/h3>\n
ELISAtools<\/strong><\/h3>\n
\nData analysis for enzyme-link immunosorbent assays (ELISAs). Either the five- or four-parameter logistic model will be fitted for data from a single ELISA. Moreover, the batch effect correction\/normalization will be carried out for batch data from more than one ELISA. Feng (2018) <doi:10.1101\/483800>.<\/a> The software is available at ELISAtools R CRAN project<\/a> and ELISAtools at GitHub<\/a>
\n<\/p>\nSomatic Diversification Analysis <\/strong><\/h3>\n
<\/strong><\/strong>SoDA is an earlier system we developed for parsing antigen receptor rearrangements. SoDA is based on 3D sequence alignment and SoDA2 is based on a Hidden Markov Model. Neither program is currently supported, having been succeeded by Cloanalyst.<\/p>\nBaescs<\/strong><\/h3>\n
Giflu<\/strong><\/h3>\n
\n<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"