Biology subjects

Vannberg, F.

Publications and source records attributed to Vannberg, F..

4 recordsLinked to original sources

bioRxiv2019

Using Earth Mover’s Distance for Viral Outbreak Investigations

RNA viruses mutate at extremely high rates forming an intra-host viral population of closely related variants (or quasi-species) [4]. High variability of Human Immunodeficiency Virus (HIV) and Hepatitis C virus (HCV) making them particularly dangerous by allowing them to evade the hosts immune system. HIV and HCV outbreaks pose a significant problem for public health for solving which it is critical to infer transmission clusters, i.e., to decide whether two viral samples belong to the same outbreak. Initial approach [10] was based on estimating relatedness between two samples as the distance between consensuses of the corresponding viral populations. The distance between closest pair of representatives from two populations, MinDist, has been shown to be significantly more accurate [2]. Unfortunately, MinDist computation requires a cumbersome RNA-seq data assembly and identification of all viral sequences from a given project. We present a novel approach that allows to bypass read assembly and estimate the distance between viral samples based on k-mer (i.e. a substring of length k) distribution in RNA-seq reads. The experimental validation using sequencing data from HCV outbreaks shows that the proposed algorithms can successfully identify genetic relatedness between viral populations, infer transmission clusters and outbreak sources, as well decide whether the primary spreader is present in the sequenced outbreak sample.

bioinformatics↗

bioRxiv2019

A beneficial genomic rearrangement creates multiple versions of calcipressin in C. elegans

Gene duplication is a major source of genetic novelty and evolutionary adaptation, providing a molecular substrate that can generate biological complexity and diversity (Ohno 1967, Taylor and Raes 2004). Despite an abundance of genomic evidence from extant organisms suggesting the importance of gene duplication, consensus about how they arise and functionally diversify is lacking (Innan and Kondrashov 2010). In the process of studying the adaptation of laboratory strains of C. elegans to new food sources, we identified a recombinant inbred line (RIL) with higher relative fitness and hyperactive exploration behavior compared to either parental strain. Using bulk-segregant analysis and short-read resequencing, we identified a de novo beneficial, complex rearrangement of the rcan-1 gene, which we resolved into five new unique tandem inversion/duplications using Oxford Nanopore long-read sequencing. rcan-1 encodes an ortholog to human RCAN1/DSCR1, which has been implicated as a causal gene for Down syndrome (Fuentes, Genesca et al. 2000). The genomic rearrangement in rcan-1 causes two complete and two truncated versions of the rcan-1 coding region, with a variety of modified promoter and 3 regions, which ultimately reduce whole-body gene expression. This rearrangement does not phenocopy a loss-of-function allele, which indicates that the rearrangement was necessary for the observed fitness gains. Our results demonstrate that adaptation can occur through unexpectedly complex genetic changes that can simultaneously duplicate and diversify a gene, providing the molecular substrate for future evolutionary change.

evolutionary biology↗

bioRxiv2018

Next-generation Sequence-analysis Toolkit (NeST): A standardized bioinformatics framework for analyzing Single Nucleotide Polymorphisms in next-generation sequencing data

Rapid advancements in next-generation sequencing (NGS) technologies have led to the development of numerous bioinformatics tools and pipelines. As these tools vary in their output function and complexity and some are not well-standardized, it is harder to choose a suitable pipeline to identify variants in NGS data. Here, we present NeST (NGS-analysis Toolkit), a modular consensus-based variant calling framework. NeST uses a combination of variant callers to overcome potential biases of an individual method used alone. NeST consists of four modules, that integrate open-source bioinformatics tools, a custom Variant Calling Format (VCF) parser and a summarization utility, that generate high-quality consensus variant calls. NeST was validated using targeted-amplicon deep sequencing data from 245 Plasmodium falciparum isolates to identify single-nucleotide polymorphisms conferring drug resistance. The results were verified using Sanger sequencing data for the same dataset in a supporting publication [28]. NeST offers a user-friendly pipeline for variant calling with standardized outputs and minimal computational demands for easy deployment for use with various organisms and applications.

bioinformatics↗

bioRxiv2017

Mapping-free variant calling using haplotype reconstruction from k-mer frequencies

1MotivationThe standard protocol for detecting variation in DNA is to map millions of short sequence reads to a known reference and find loci that differ. While this approach works well, it cannot be applied where the sample contains dense variants or is too distant from known references. De novo assembly or hybrid methods can recover genomic variation, but the cost of computation is often much higher. We developed a novel k-mer algorithm and software implementation, Kestrel, capable of characterizing densely-packed SNPs and large indels without mapping, assembly, or de Bruijn graphs.\n\nResultsWhen applied to mosaic penicillin binding protein (PBP) genes in Streptococcus pneumoniae, we found near perfect concordance with assembled contigs at a fraction of the CPU time. Multilocus sequence typing (MLST) with this approach was able to bypass de novo assemblies. Kestrel has a very low false-positive rate when calling variants over the whole genome, but limitations of a purely k-mer based approach affect sensitivity.\n\nAvailabilitySource code and documentation for a Java implementation of Kestrel can be found at https://github.com/paudano/kestrel. All test code for this publication is located at https://github.com/paudano/kescases.\n\nContactpaudano@gatech.edu, fredrik.vannberg@biology.gatech.edu

bioinformatics↗