Biology subjects

Nekrutenko, A.

Publications and source records attributed to Nekrutenko, A..

5 recordsLinked to original sources

bioRxiv2018

A high resolution view of adaptive events

Coadaptation between bacterial hosts and plasmids often involves a small number of highly reproducible mutations. Yet little is known about the underlying complex dynamics that leads to such a single \"correct\" solution. Observing mutations in fine detail along the adaptation trajectory is necessary for understanding this phenomenon. We studied coadaptation between E. coli and a common artificial plasmid, pBR322, in a continuous turbidostat culture. To obtain a high resolution picture of early adaptive events, we used a highly sensitive duplex sequencing strategy to directly observe and track mutations with frequencies undetectable with conventional methods. The observed highly reproducible trajectories are governed by clonal interference and show rapid increases in the frequencies of beneficial mutations controlling plasmid replication followed by a profound diversity crash corresponding to the emergence of chromosomal variants. To the best of our knowledge our study represents the first comprehensive assessment of adaptive processes at a very fine level of resolution. Our work highlights the hidden complexity of coadaptation, and provides an experimental and theoretical foundation for future studies.

evolutionary biology↗

bioRxiv2018

Understanding trivial challenges of microbial genomics: An assembly example

The perceived \"simplicity\" of bacterial genomics (these genomes are small and easy to assemble) feeds the decentralized state of the field where computational analysis standards have been slow to evolve. This situation has a historical explanation. In cases of human, mouse, fly, worm and other model organisms there have been large sustained multinational genome sequencing efforts and analysis consortia such as the 1,000 genomes, ENCODE, modENCODE, GTEx and others. These resulted in development and proliferation of common tools, workflows, and data standards. This is not the case in microbiology. After the development of highly parallel sequencing methodologies in mid-2000s bacterial genomes no longer required initiatives of such scale. The flipside of this is the extreme heterogeneity of approaches to many well established microbial genomic analysis problems such as genome assembly. While competition amongst different methods is good, we argue that the quality of data analyses will improve if cutting edge tools are more accessible and microbiologists become more computationally savvy. Here we use genome assembly as an example to highlight current challenges and to provide a possible solution.

microbiology↗

bioRxiv2017

Community-driven data analysis training for biology

The primary problem with the explosion of biomedical datasets is not the data itself, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web-browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences.

bioinformatics↗

bioRxiv2017

Infant Weight Gain Trajectories Linked To Oral Microbiome Composition

Gut and oral microbiome perturbations have been observed in obese adults and adolescents. Less is known about how weight gain in early childhood is influenced by gut, and particularly oral, microbiomes. Here we analyze the relationships among weight gain and gut and oral microbiomes in 226 two-year-olds who were followed during the first two years of life, as part of a larger study, with weight and length measured at seven time points. We used these data to identify children with rapid weight gain (a strong risk factor for childhood obesity), and to derive growth curves with novel Functional Data Analysis (FDA) techniques. The childrens oral and gut microbiomes were sampled at the end of the two-year period, and surveyed with 16S sequencing. First, we show that growth curves are associated negatively with diversity and positively with Firmicutes-to-Bacteroidetes ratio of the oral microbiome - a relationship that is also observed in children with rapid (vs. non-rapid) weight gain. We also demonstrate an association between the gut microbiome and child growth, but only when considering the effect of diet on the microbiome. Lastly, we identify several bacterial genera that are associated with child growth patterns. These results suggest that by the age of two, the oral microbiome may have already begun to establish patterns often seen in older obese individuals. They also suggest that the gut microbiome, while strongly influenced by diet, at age two does not harbor obesity signatures many researchers identified in later life stages.

genomics↗

bioRxiv2017

Practical computational reproducibility in the life sciences

Many areas of research suffer from poor reproducibility. This problem is particularly acute in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but practical implementation of these practices remains difficult. This is because reproducing published computational analyses requires installing many software tools plus associated libraries, connecting tools together into the complete pipeline, and specifying parameters. Here we present a suite of recently emerged technologies which make computational reproducibility not just possible, but, finally, practical in both time and effort. By combining a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and an integrated workflow system that automatically orchestrates the composition of these packages for entire pipelines, an unprecedented level of computational reproducibility can be achieved.

bioinformatics↗