Direct observation of DNA knots using a solid-state nanopore.

Long DNA molecules can self-entangle into knots. Experimental techniques for observing such DNA knots (primarily gel electrophoresis) are limited to bulk methods and circular molecules below 10 kilobase pairs in length. Here, we show that solid-state nanopores can be used to directly observe individual knots in both linear and circular single DNA molecules of arbitrary length. The DNA knots are observed as short spikes in the nanopore current traces of the traversing DNA molecules and their detection is dependent on a sufficiently high measurement resolution, which can be achieved using high-concentration LiCl buffers. We study the percentage of molecules with knots for DNA molecules of up to 166 kilobase pairs in length and find that the knotting occurrence rises with the length of the DNA molecule, consistent with a constant knotting probability per unit length. Our experimental data compare favourably with previous simulation-based predictions for long polymers. From the translocation time of the knot through the nanopore, we estimate that the majority of the DNA knots are tight, with remarkably small sizes below 100 nm. In the case of linear molecules, we also observe that knots are able to slide out on application of high driving forces (voltage).

In silico phenotyping via co-training for improved phenotype prediction from genotype.

MOTIVATION: Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction. RESULTS: Here we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico phenotyping can lead to significant improvements in prediction accuracy. We demonstrate this on a dataset of patients with two diagnostic types of migraine, termed migraine with aura and migraine without aura, from the International Headache Genetics Consortium. CONCLUSIONS: Imputing missing disease phenotypes for patients via co-training leads to larger training datasets and improved prediction accuracy in phenotype prediction. AVAILABILITY AND IMPLEMENTATION: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/co-training.html

Detection of Individual Proteins Bound along DNA Using Solid-State Nanopores.

DNA in cells is heavily covered with all types of proteins that regulate its genetic activity. Detection of DNA-bound proteins is a challenge that is well suited to solid-state nanopores as they provide a linear readout of the DNA and DNA-protein volume in the pore constriction along the entire length of a molecule. Here, we demonstrate that we can realize the detection of even individual DNA-bound proteins at the single-DNA-molecule level using solid-state nanopores. We introduce and use a new model system of anti-DNA antibodies bound to lambda phage DNA. This system provides several advantages since the antibodies bind individually, tolerate high salt concentrations, and will, because of their positive charge, not translocate through the pore unless bound to the DNA. Translocation of DNA-antibody samples reveals the presence of short 12 µs current spikes within the DNA traces, with amplitudes that are about 4.5 times larger than that of dsDNA, which are associated with individual antibodies. We conclude that transient interactions between the pore and the antibodies are the primary mechanism by which bound antibodies are observed. This work provides a proof-of-concept for how nanopores could be used for future sensing applications.