Targeted Nanopore Sequencing with Cas9 for studies of methylation, structural variants, and mutations, bioRxiv, 2019-04-12

AbstractNanopore sequencing technology can rapidly and directly interrogate native DNA molecules. Often we are interested only in interrogating specific areas at high depth, but conventional enrichment methods have thus far proved unsuitable for long reads1. Existing strategies are currently limited by high input DNA requirements, low yield, short (<5kb) reads, time-intensive protocols, andor amplification or cloning (losing base modification information). In this paper, we describe a technique utilizing the ability of Cas9 to introduce cuts at specific locations and ligating nanopore sequencing adaptors directly to those sites, a method we term ‘nanopore Cas9 Targeted-Sequencing’ (nCATS).We have demonstrated this using an Oxford Nanopore MinION flow cell (Capacity >10Gb+) to generate a median 165X coverage at 10 genomic loci with a median length of 18kb, representing a several hundred-fold improvement over the 2-3X coverage achieved without enrichment. We performed a pilot run on the smaller Flongle flow cell (Capacity ~1Gb), generating a median coverage of 30X at 11 genomic loci with a median length of 18kb. Using panels of guide RNAs, we show that the high coverage data from this method enables us to (1) profile DNA methylation patterns at cancer driver genes, (2) detect structural variations at known hot spots, and (3) survey for the presence of single nucleotide mutations. Together, this provides a low-cost method that can be applied even in low resource settings to directly examine cellular DNA. This technique has extensive clinical applications for assessing medically relevant genes and has the versatility to be a rapid and comprehensive diagnostic tool. We demonstrate applications of this technique by examining the well-characterized GM12878 cell line as well as three breast cell lines (MCF-10A, MCF-7, MDA-MB-231) with varying tumorigenic potential as a model for cancer.ContributionsTG and WT constructed the study. TG performed the experiments. TG, IL, and FS analyzed the data. TG, JG, ER, RB and AH and developed the method. TG and WT wrote the paper

biorxiv genomics 200-500-users 2019

Antibiotics select for novel pathways of resistance in biofilms, bioRxiv, 2019-04-11

AbstractMost bacteria in nature exist in aggregated communities known as biofilms. Bacteria within biofilms are inherently highly resistant to antibiotics. Current understanding of the evolution and mechanisms of antibiotic resistance is largely derived from work from cells in liquid culture and it is unclear whether biofilms adapt and evolve in response to sub-inhibitory concentrations of drugs. Here we used a biofilm evolution model to show that biofilms of a model food borne pathogen, Salmonella Typhimurium rapidly evolve in response to exposure to three clinically important antibiotics. Whilst the model strongly selected for improved biofilm formation in the absence of any drug, once antibiotics were introduced the need to adapt to the drug was more important than the selection for improved biofilm formation. Adaptation to antibiotic stress imposed a marked cost in biofilm formation, particularly evident for populations exposed to cefotaxime and azithromycin. We identified distinct resistance phenotypes in biofilms compared to corresponding planktonic control cultures and characterised new mechanisms of resistance to cefotaxime and azithromycin. Novel substitutions within the multidrug efflux transporter, AcrB were identified and validated as impacting drug export as well as changes in regulators of this efflux system. There were clear fitness costs identified and associated with different evolutionary trajectories. Our results demonstrate that biofilms adapt rapidly to low concentrations of antibiotics and the mechanisms of adaptation are novel. This work will be a starting point for studies to further examine biofilm specific pathways of adaptation which inform future antibiotic use.

biorxiv microbiology 0-100-users 2019

chromoMap An R package for Interactive Visualization and Annotation of Chromosomes, bioRxiv, 2019-04-11

AbstractSummarychromoMap is an R package for constructing interactive visualizations of chromosomeschromosomal regions, and mapping of chromosomal elements (like genes) onto them, of any living organism. The package takes separate tab-delimited files (BED like) to specify the genomic co-ordinates of the chromosomes and the elements to annotate. Each rendered chromosome is composed of continuous loci of specific ranges where each locus, on hover, displays detailed information about the elements annotated within that locus range. By just tweaking parameters of a single function, users can generate a variety of plots that can either be saved as static image or shared as HTML documents. Users can utilize the various prominent features of chromoMap including, but not limited to, visualizing polyploidy, creating chromosome heatmaps, mapping groups of elements, adding hyperlinks to elements, multi-species chromosome visualization.Availability and implementationThe R package chromoMap is available under the GPL-3 Open Source license. It is included with a vignette for comprehensive understanding of its various features, and is freely available from <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsCRAN.R-project.orgpackage=chromoMap>httpsCRAN.R-project.orgpackage=chromoMap<jatsext-link>.Contactlakshayanand15@gmail.com<jatssec sec-type=supplementary-material>Supplementary informationSupplementary data are available online.

biorxiv bioinformatics 100-200-users 2019

Comparing within- and between-family polygenic score prediction, bioRxiv, 2019-04-11

AbstractPolygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight life outcomes (anthropometric, cognitive, personality and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modelling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a source of between-family prediction through rGE mechanisms. These results provide novel insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

biorxiv genomics 100-200-users 2019

 

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