ABSTRACT
Adaptive immunity is driven by the expansion, somatic hypermutation, and selection of B cell clones. Each clone is the progeny of a single B cell responding to Ag, with diversified Ig receptors. These receptors can now be profiled on a large scale by next-generation sequencing. Such data provide a window into the microevolutionary dynamics that drive successful immune responses and the dysregulation that occurs with aging or disease. Clonal relationships are not directly measured, but they must be computationally inferred from these sequencing data. Although several hierarchical clustering-based methods have been proposed, they vary in distance and linkage methods and have not yet been rigorously compared. In this study, we use a combination of human experimental and simulated data to characterize the performance of hierarchical clustering-based methods for partitioning sequences into clones. We find that single linkage clustering has high performance, with specificity, sensitivity, and positive predictive value all >99%, whereas other linkages result in a significant loss of sensitivity. Surprisingly, distance metrics that incorporate the biases of somatic hypermutation do not outperform simple Hamming distance. Although errors were more likely in sequences with short junctions, using the entire dataset to choose a single distance threshold for clustering is near optimal. Our results suggest that hierarchical clustering using single linkage with Hamming distance identifies clones with high confidence and provides a fully automated method for clonal grouping. The performance estimates we develop provide important context to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other clonal grouping algorithms.
Subject(s)
Antibody Diversity , B-Lymphocytes/physiology , Electronic Data Processing/methods , Adaptive Immunity/genetics , Biological Evolution , Clone Cells , Cluster Analysis , Computational Biology , Computer Simulation , Datasets as Topic , Genetic Linkage , High-Throughput Nucleotide Sequencing , Humans , Immunoglobulins/genetics , Somatic Hypermutation, ImmunoglobulinABSTRACT
Genetic exchange is common among bacteria, but its effect on population diversity during ecological differentiation remains controversial. A fundamental question is whether advantageous mutations lead to selection of clonal genomes or, as in sexual eukaryotes, sweep through populations on their own. Here, we show that in two recently diverged populations of ocean bacteria, ecological differentiation has occurred akin to a sexual mechanism: A few genome regions have swept through subpopulations in a habitat-specific manner, accompanied by gradual separation of gene pools as evidenced by increased habitat specificity of the most recent recombinations. These findings reconcile previous, seemingly contradictory empirical observations of the genetic structure of bacterial populations and point to a more unified process of differentiation in bacteria and sexual eukaryotes than previously thought.
Subject(s)
Ecosystem , Evolution, Molecular , Genome, Bacterial , Recombination, Genetic , Seawater/microbiology , Selection, Genetic , Vibrio/genetics , Chromosomes, Bacterial/genetics , Gene Flow , Gene Transfer, Horizontal , Genes, Bacterial , Genetic Variation , Models, Genetic , Molecular Sequence Data , Mutation , Oceans and Seas , Phylogeny , Polymorphism, Single Nucleotide , Vibrio/classificationABSTRACT
BACKGROUND: Different high-throughput nucleic acid sequencing platforms are currently available but a trade-off currently exists between the cost and number of reads that can be generated versus the read length that can be achieved. METHODOLOGY/PRINCIPAL FINDINGS: We describe an experimental and computational pipeline yielding millions of reads that can exceed 200 bp with quality scores approaching that of traditional Sanger sequencing. The method combines an automatable gel-less library construction step with paired-end sequencing on a short-read instrument. With appropriately sized library inserts, mate-pair sequences can overlap, and we describe the SHERA software package that joins them to form a longer composite read. CONCLUSIONS/SIGNIFICANCE: This strategy is broadly applicable to sequencing applications that benefit from low-cost high-throughput sequencing, but require longer read lengths. We demonstrate that our approach enables metagenomic analyses using the Illumina Genome Analyzer, with low error rates, and at a fraction of the cost of pyrosequencing.