Number of the retroelement insertions detected within genes and promoters (for each library, predictions are derived from 1000 simulations of coordinates sample sets).

<p>Error bars show 1 SD. (A)—L1 in genes; (B)—L1 in promoters; (C)—Alu in genes; (D)—Alu in promoters.</p

The oligonucleotides used for the preparation of the DNA libraries.

<p>The oligonucleotides used for the preparation of the DNA libraries.</p

Validation of the potentially somatic retroelement insertions.

<p>Black arrows show the primers. GP primers are complementary to the flanking sequences, RE primers are complementary to the retroelement sequence (RE).</p

TCR nucleotide sequences shared between twins are statistically different from sequences shared between unrelated individuals.

<p>Distribution of log₁₀ <i>P</i>_gen, with <i>P</i>_gen the probability that a sequence is generated by the VJ recombination process, for shared out-of-frame TCR alpha clonotypes between one individual and the other five. While the distribution of shared sequences between unrelated individuals (red curves) is well explained by coincidental convergent recombination as predicted by our stochastic model (blue), sequences shared between two twins (green) have an excess of low probability sequences: 31 sequences with log₁₀ <i>P</i>_gen < −10. For comparison the distribution of <i>P</i>_gen in regular (not necessarily shared) sequences is shown in black.</p

Sharing of alpha out-of-frame TCR clonotypes as a function of clonal abundance.

<p>The normalized number of shared out-of-frame alpha CDR3 nucleotide sequences between two individuals is showed as a function of clonotype abundance (e.g. normalized sharing for 2000 most abundant clones from both repertoires, 4000 most abundant, etc.), and compared to the amount of sharing that would be expected by chance (blue curve), taking into account the variable fraction of zero-insertion clonotypes as a function of their abundance. Data and predictions show excellent quantitative agreement (inset), with one fitting parameter. Error bars show one standard deviation.</p

Estimation of repertoire diversity using multinomial model.

<p><b>A.</b> Rarefaction analysis of repertoire samples from healthy donors and multiple sclerosis patients. The number of unique clonotypes in a sub-sample plotted against its size (number of T-cell receptor cDNA molecules, TRBM). Solid and dashed lines are diversity estimates computed by interpolating and extrapolating using a multinomial model respectively [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004503#pcbi.1004503.ref029" target="_blank">29</a>]. Note that generally rarefaction curves for MS samples go below those of control donors. Post-HSCT sample (MS8-HSCT) displays the lowest diversity. <b>B.</b> Comparison of repertoire diversity using normalized Chao1 estimate. Normalization is performed by down-sampling datasets to the size of smallest dataset and computing the estimate for resulting datasets (mean estimate value from n = 3 re-samples is used). MS8-HSCT sample is discarded from calculations. *—P = 0.022, two-tailed T-test; effect size estimated by Cohen’s d is 0.98.</p

Overview of VDJtools software package.

<p>VDJtools analysis routines can be grouped into 6 modules and are applicable to results produced by commonly used immune repertoire sequencing processing software. Basic statistics and segment usage module include general statistics (clonotype and read count, number and frequency of non-coding clonotypes, convergent recombination of CDR3 amino acid sequences, insert size statistics, etc), spectratyping (distribution of clonotype frequency by CDR3 length), Variable and Joining segment usage profiles and their pairing frequency in re-arranged receptor junction sequences. Repertoire overlap module includes routines for computing sets of overlapping clonotypes and their characteristics, and scatter plots of clonotype frequencies. Diversity analysis includes routines for visualizing clonotype frequency distribution, computing repertoire diversity estimates and rarefaction plots. The fourth set of routines can be used to create clonotype abundance profiles and track clonotypes in time course of vaccination, myeloablation and blood cell transplant. Sample clustering is implemented based on computed repertoire similarity measures and could be used to distinguish various biological conditions, cell subsets and tissues. Auxiliary routines provide means for clonotype table filtering (e.g. by segment usage or non-coding CDR3 sequence) as well as annotation with custom or pre-built pathogen-specific clonotype database. VDJtools can be incorporated in Java programming language-based pipelines as demonstrated by VDJviz clonotype browser.</p