In this case, the sequence will be assigned to the closest known allele and any polymorphisms will be incorrectly identified as somatic mutations. However, this process is inaccurate for sequences that utilize previously undetected alleles. Germline segment assignment tools, such as IMGT/HighV-QUEST, work by aligning each sequence against a database of known alleles. 2.1 Inference of novel alleles and individual genotype As detailed later, several repertoire analyses may be carried out, depending on the nature of the study. Example workflow scripts are provided on the website, which can easily be modified by adding, removing or reordering analysis steps to meet different analysis goals. Each utility includes detailed help documentation and optional logging to track errors. The more computationally expensive components have built-in multiprocessing support. Change-O provides tools to import data from the frequently used IMGT/HighV-QUEST ( Alamyar et al., 2012) tool as well as a set of utilities to perform basic database operations, such as sorting, filtering and modifying annotations. Each utility identifies the relevant input data based on standardized column names and adds new columns to the file with the output information to be carried through to the next analysis step. Data are passed to Change-O utilities in the form of a tab-delimited text file.
B cell repertoire software#
The Change-O suite is composed of four software packages: a collection of Python commandline tools (changeo-ctl) and three separate R ( R Core Team, 2015) packages (alakazam, shm, and tigger) ( Table 1). Here, we introduce Change-O, a suite of utilities that cover a range of complex analysis tasks for Ig repertoire sequencing data. However, extracting measures of biological and clinical interest from the resulting germline-annotated repertoire remains a time-consuming and error-prone process that is often dependent upon custom analysis scripts. We previously developed the repertoire sequencing toolkit (pRESTO) for producing assembled and error-corrected reads from high-throughput lymphocyte receptor sequencing experiments ( Vander Heiden et al., 2014), which may then be fed into existing methods for alignment against V(D)J germline databases.
Repertoire sequencing is a rapidly growing area, with applications including detection of minimum residual disease, prognosis following transplant, monitoring vaccination responses, identification of neutralizing antibodies and inferring B cell trafficking patterns ( Robins, 2013 Stern et al., 2014). Large-scale characterization of immunoglobulin (Ig) repertoires is now feasible due to dramatic improvements in high-throughput sequencing technology. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow.Īvailability and implementation: Change-O is freely available for non-commercial use and may be downloaded from. Change-O includes tools for determining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pressure, and calculating sequence chemical properties. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized computational methods. Summary: Advances in high-throughput sequencing technologies now allow for large-scale characterization of B cell immunoglobulin (Ig) repertoires.
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