shazam {shazam}

The shazam package

Description

Dramatic improvements in high-throughput sequencing technologies now enable large-scale characterization of Ig repertoires, defined as the collection of transmembrane antigen-receptor proteins located on the surface of T and B lymphocytes. The shazam package provides tools for advanced analysis of somatic hypermutation (SHM) in immunoglobulin (Ig) sequences. The key functions in shazam, broken down topic, are described below.

Mutational profiling

shazam provides tools to quantify the extent and nature of SHM within full length V(D)J sequences as well as sub-regions (eg, FWR and CDR). Quantification of expected mutational loaded, under specific SHM targeting models, can also be performed along with model driven simulations of SHM.

• collapseClones: Build clonal consensus sequences.

• consensusSequence: Build a single consensus sequence.

• observedMutations: Compute observed mutation counts and frequencies.

• expectedMutations: Compute expected mutation frequencies.

• shmulateSeq: Simulate mutations in a single sequence.

• shmulateTree: Simulate mutations over a lineage tree.

• setRegionBoundaries: Extends a region definition to include CDR3 and FWR4.

SHM targeting models

Computational models and analyses of SHM have separated the process into two independent components:

1. A mutability model that defines where mutations occur.

2. A nucleotide substitution model that defines the resulting mutation.

Collectively these are what form the targeting model of SHM. shazam provides empirically derived targeting models for both humans and mice, along with tools to build these mutability and substitution models from data.

• createTargetingModel: Build a 5-mer targeting model.

• plotMutability: Plot 5-mer mutability rates.

• HH_S5F: Human 5-mer SHM targeting model.

• MK_RS5NF: Mouse 5-mer SHM targeting model.

Quantification of selection pressure

Bayesian Estimation of Antigen-driven Selection in Ig Sequences is a novel method for quantifying antigen-driven selection in high-throughput Ig sequence data. Targeting models created using shazam can be used to estimate the null distribution of expected mutation frequencies used by BASELINe, providing measures of selection pressure informed by known AID targeting biases.

• calcBaseline: Calculate the BASELINe probability density functions (PDFs).

• groupBaseline: Combine PDFs from sequences grouped by biological or experimental relevance.

• summarizeBaseline: Compute summary statistics from BASELINe PDFs.

• testBaseline: Perform significance testing for the difference between BASELINe PDFs.

• plotBaselineDensity: Plot the probability density functions resulting from selection analysis.

• plotBaselineSummary: Plot summary stastistics resulting from selection analysis.

Mutational distance calculation

shazam provides tools to compute evolutionary distances between sequences or groups of sequences, which can leverage SHM targeting models. This information is particularly useful in understanding and defining clonal relationships.

• findThreshold: Identify clonal assignment threshold based on distances to nearest neighbors.

• distToNearest: Tune clonal assignment thresholds by calculating distances to nearest neighbors.

• calcTargetingDistance: Construct a nucleotide distance matrix from a 5-mer targeting model.

References

1. Hershberg U, et al. Improved methods for detecting selection by mutation analysis of Ig V region sequences. Int Immunol. 2008 20(5):683-94.

2. Uduman M, et al. Detecting selection in immunoglobulin sequences. Nucleic Acids Res. 2011 39(Web Server issue):W499-504. (Corrections at http://selection.med.yale.edu/baseline/correction/)

3. Yaari G, et al. Quantifying selection in high-throughput immunoglobulin sequencing data sets. Nucleic Acids Res. 2012 40(17):e134.

4. Yaari G, et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data. Front Immunol. 2013 4:358.

5. Cui A, Di Niro R, Vander Heiden J, Briggs A, Adams K, Gilbert T, O'Connor K, Vigneault F, Shlomchik M and Kleinstein S (2016). A Model of Somatic Hypermutation Targeting in Mice Based on High-Throughput Ig Sequencing Data. The Journal of Immunology, 197(9), 3566-3574.