In-field metagenome and 16S rRNA gene amplicon nanopore sequencing robustly characterize glacier microbiota, bioRxiv, 2016-09-08

ABSTRACTIn the field of observation, chance favours only the prepared mind (Pasteur). Impressive developments in genomics have led microbiology to its third “Golden Age”. However, conventional metagenomics strategies necessitate retrograde transfer of samples from extreme or remote environments for later analysis, rendering the powerful insights gained retrospective in nature, striking a contrast with Pasteur’s dictum. Here we implement highly portable USB-based nanopore DNA sequencing platforms coupled with field-adapted environmental DNA extraction, rapid sequence library generation and off-line analyses of shotgun metagenome and 16S ribosomal RNA gene amplicon profiles to characterize microbiota dwelling within cryoconite holes upon Svalbard glaciers, the Greenland Ice Sheet and the Austrian Alps. We show in-field nanopore sequencing of metagenomes captures taxonomic composition of supraglacial microbiota, while 16S rRNA Furthermore, comparison of nanopore data with prior 16S rRNA gene V1-V3 pyrosequencing from the same samples, demonstrates strong correlations between profiles obtained from nanopore sequencing and laboratory based sequencing approaches. gene amplicon sequencing resolves bacterial community responses to habitat changes. Finally, we demonstrate the fidelity and sensitivity of in-field sequencing by analysis of mock communities using field protocols. Ultimately, in-field sequencing potentiated by nanopore devices raises the prospect of enhanced agility in exploring Earth’s most remote microbiomes.

biorxiv microbiology 100-200-users 2016

Population-genomic inference of the strength and timing of selection against gene flow, bioRxiv, 2016-09-02

AbstractThe interplay of divergent selection and gene flow is key to understanding how populations adapt to local environments and how new species form. Here, we use DNA polymorphism data and genome-wide variation in recombination rate to jointly infer the strength and timing of selection, as well as the baseline level of gene flow under various demographic scenarios. We model how divergent selection leads to a genome-wide negative correlation between recombination rate and genetic differentiation among populations. Our theory shows that the selection density, i.e. the selection coefficient per base pair, is a key parameter underlying this relationship. We then develop a procedure for parameter estimation that accounts for the confounding effect of background selection. Applying this method to two datasets from Mimulus guttatus, we infer a strong signal of adaptive divergence in the face of gene flow between populations growing on and off phytotoxic serpentine soils. However, the genome-wide intensity of this selection is not exceptional compared to what M. guttatus populations may typically experience when adapting to local conditions. We also find that selection against genome-wide introgression from the selfing sister species M. nasutus has acted to maintain a barrier between these two species over at least the last 250 ky. Our study provides a theoretical framework for linking genome-wide patterns of divergence and recombination with the underlying evolutionary mechanisms that drive this differentiation.

biorxiv evolutionary-biology 100-200-users 2016

Canu scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation, bioRxiv, 2016-08-25

AbstractLong-read single-molecule sequencing has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. However, given the relatively high error rates of such technologies, efficient and accurate assembly of large repeats and closely related haplotypes remains challenging. We address these issues with Canu, a successor of Celera Assembler that is specifically designed for noisy single-molecule sequences. Canu introduces support for nanopore sequencing, halves depth-of-coverage requirements, and improves assembly continuity while simultaneously reducing runtime by an order of magnitude on large genomes versus Celera Assembler 8.2. These advances result from new overlapping and assembly algorithms, including an adaptive overlapping strategy based on tf-idf weighted MinHash and a sparse assembly graph construction that avoids collapsing diverged repeats and haplotypes. We demonstrate that Canu can reliably assemble complete microbial genomes and near-complete eukaryotic chromosomes using either PacBio or Oxford Nanopore technologies, and achieves a contig NG50 of greater than 21 Mbp on both human and Drosophila melanogaster PacBio datasets. For assembly structures that cannot be linearly represented, Canu provides graph-based assembly outputs in graphical fragment assembly (GFA) format for analysis or integration with complementary phasing and scaffolding techniques. The combination of such highly resolved assembly graphs with long-range scaffolding information promises the complete and automated assembly of complex genomes.

biorxiv bioinformatics 100-200-users 2016

Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine Approach, bioRxiv, 2016-08-20

ABSTRACTA major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs for each individual patient. For complex diseases such as cancer, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the pathological processes at the molecular level. While the “omics” era provides unique opportunities to dissect the molecular features of diseases, the ability to utilize it in targeted therapeutic efforts is hindered by both the massive size and diverse nature of the “omics” data. Recent advances with Deep Learning Neural Networks (DLNNs), suggests that DLNN could be trained on large data sets to efficiently predict therapeutic responses in cancer treatment. We present the application of Association Rule Mining combined with DLNNs for the analysis of high-throughput molecular profiles of 1001 cancer cell lines, in order to extract cancer-specific signatures in the form of easily interpretable rules and use these rules as input to predict pharmacological responses to a large number of anti-cancer drugs. The proposed algorithm outperformed Random Forests (RF) and Bayesian Multitask Multiple Kernel Learning (BMMKL) classification which currently represent the state-of-the-art in drug-response prediction. Moreover, the in silico pipeline presented, introduces a novel strategy for identifying potential therapeutic targets, as well as possible drug combinations with high therapeutic potential. For the first time, we demonstrate that DLNNs trained on a large pharmacogenomics data-set can effectively predict the therapeutic response of specific drugs in different cancer types. These findings serve as a proof of concept for the application of DLNNs to predict therapeutic responsiveness, a milestone in precision medicine.

biorxiv bioinformatics 100-200-users 2016

 

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