Machine learning based DNA melt curve profiling enables automated novel genotype detection.

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power.

MOTIVATION: The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers. RESULTS: This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution. AVAILABILITY AND IMPLEMENTATION: Data and code available on https://github.com/lenlan/dHRM-noise-modeling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A microfluidic design for desalination and selective removal and addition of components in biosamples.

We present the development of a microfluidic device that is able to selectively and nondisturbingly remove or add components to liquid samples, which allows control and conditioning of the samples for biomedical tests. The device consists of a series of chambers for sample retention and a through channel. Because smaller particles diffuse faster, small particles in the sample such as salt ions rapidly escape the chamber by diffusion and are subsequently removed by a carrier flow in the channel, leaving macromolecules of interest in the "desalted" solution. Conversely, components lacking in the sample can be diffused in by reversing the concentration gradient between the flow and the sample chamber. The ability to control the ionic strength of a sample offers many advantages in biological sample preparation as most biofluids contain high salt contents, making them unsuitable for downstream molecular analyses without additional sample treatments which could cause sample loss, contamination, and cost increase. Making use of the nature of laminar flow in a microfluidic device and mass transport by diffusion, we have developed an analytical model to calculate concentration profiles for different particles. Excellent agreements were found between the theory and the experiment, making the results highly reliable and predictable. Since the device and the principle is applicable to a wide range of biological samples, it can be incorporated into the workflow of various applications for research and in vitro diagnosis such as ion exchange, DNA sequencing, immuno assay, vesicle, cell secretion analysis, etc.

High-performance molybdenum disulfide field-effect transistors with spin tunnel contacts.

Molybdenum disulfide has recently emerged as a promising two-dimensional semiconducting material for nanoelectronic, optoelectronic, and spintronic applications. Here, we investigate the field-effect transistor behavior of MoS2 with ferromagnetic contacts to explore its potential for spintronics. In such devices, we elucidate that the presence of a large Schottky barrier resistance at the MoS2/ferromagnet interface is a major obstacle for the electrical spin injection and detection. We circumvent this problem by a reduction in the Schottky barrier height with the introduction of a thin TiO2 tunnel barrier between the ferromagnet and MoS2. This results in an enhancement of the transistor on-state current by 2 orders of magnitude and an increment in the field-effect mobility by a factor of 6. Our magnetoresistance calculation reveals that such integration of ferromagnetic tunnel contacts opens up the possibilities for MoS2-based spintronic devices.