Biology subjects

Gerton Lunter

Publications and source records attributed to Gerton Lunter.

4 recordsLinked to original sources

bioRxiv2016

A phylogenetic codon substitution model for antibody lineages

Phylogenetic methods have shown promise in understanding the development of broadly neutralizing antibody lineages (bNAbs). However, the mutational process that generates these lineages - somatic hypermutation (SHM) - is biased by hotspot motifs, which violates important assumptions in most phylogenetic substitution models. Here, we develop a modified GY94-type substitution model that partially accounts for this context-dependency while preserving independence of sites during calculation. This model shows a substantially better fit to three well-characterized bNAb lineages than the standard GY94 model. We show through simulations that accounting for hotspot motifs can lead to reduced bias of other substitution parameters, and more accurate ancestral state reconstructions. We also demonstrate how our model can be used to test hypotheses concerning the roles of different hotspot and coldspot motifs in the evolution of B-cell lineages. Further, we explore the consequences of the idea that the number of hotspot motifs - and perhaps the mutation rate in general - is expected to decay over time in individual bNAb lineages.

Evolutionary Biology↗

bioRxiv2016

Modelling the decay of hotspot motifs in broadly neutralizing antibody lineages

Phylogenetic methods have shown great promise in understanding the development of broadly neutralizing antibody lineages (bNAbs). However, mutational process for generating these lineages - somatic hypermutation (SHM) - is biased by hotspot motifs, which violates important assumptions in most phylogenetic substitution models. Here, we develop a modified GY94-type substitution model which partially accounts for this context-dependency while preserving independence of sites in calculation. This model shows a substantially better fit to three well-characterized bNAb lineages than the standard GY94 model. We show through simulations that accounting for this can lead to reduced bias of other substitution parameters, and more accurate ancestral state reconstructions. We further explore other implications of this model; namely, that the number of hotspot motifs - and therefore likely the mutation rate in general - is expected to decay over time in individual bNAb lineages.

Evolutionary Biology↗

bioRxiv2016

Fast haplotype matching in very large cohorts using the Li and Stephens model

The Li and Stephens model, which approximates the coalescent describing the pattern of variation in a population, underpins a range of key tools and results in genetics. Although highly efficient compared to the coalescent, standard implemen- tations of this model still cannot deal with the very large reference cohorts that are starting to become available, and practical implementations use heuristics to achieve reasonable runtimes. Here I describe a new, exact algorithm (fastLS) that implements the Li and Stephens model and achieves runtimes independent of the size of the reference cohort. Key to achieving this runtime is the use of the Burrows-Wheeler transform, allowing the algorithm to efficiently identify partial haplotype matches across a cohort. I show that the proposed data structure is very similar to, and generalizes, Durbin's positional Burrows-Wheeler transform.

Bioinformatics↗

bioRxiv2015

CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinic

BackgroundNext-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics. It also presents challenges with respect to integration with data from other sequencing methods and historical data. Provision of consistent, clinically applicable variant annotation of NGS data has proved difficult, particularly of indels, an important variant class in clinical genomics. Annotation in relation to a reference genome sequence, the DNA strand of coding transcripts and potential alternative variant representations has not been well addressed. Here we present tools that address these challenges to provide rapid, standardized, clinically appropriate annotation of NGS data in line with existing clinical standards.\n\nMethodsWe developed a clinical sequencing nomenclature (CSN), a fixed variant annotation consistent with the principles of the Human Genome Variation Society (HGVS) guidelines, optimized for automated variant annotation of NGS data. To deliver high-throughput CSN annotation we created CAVA (Clinical Annotation of VAriants), a fast, lightweight tool designed for easy incorporation into NGS pipelines. CAVA allows transcript specification, appropriately accommodates the strand of a gene transcript and flags variants with alternative annotations to facilitate clinical interpretation and comparison with other datasets. We evaluated CAVA in exome data and a clinical BRCA1/BRCA2 gene testing pipeline.\n\nResultsCAVA generated CSN calls for 10,313,034 variants in the ExAC database in 13.44 hours, and annotated the ICR1000 exome series in 6.5 hours. Evaluation of 731 different indels from a single individual revealed 92% had alternative representations in left aligned and right aligned data. Annotation of left aligned data, as performed by many annotation tools, would thus give clinically discrepant annotation for the 339 (46%) indels in genes transcribed from the forward DNA strand. By contrast, CAVA provides the correct clinical annotation for all indels. CAVA also flagged the 370 indels with alternative representations of a different functional class, which may profoundly influence clinical interpretation. CAVA annotation of 50 BRCA1/BRCA2 gene mutations from a clinical pipeline gave 100% concordance with Sanger data; only 8/25 BRCA2 mutations were correctly clinically annotated by other tools.\n\nConclusionsCAVA is a freely available tool that provides rapid, robust, high-throughput clinical annotation of NGS data, using a standardized Clinical Sequencing Nomenclature.

Genomics↗