Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier.

We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own.

Efficient molecular encoding in multifunctional self-immolative urethanes.

Molecular encoding in sequence-defined polymers shows promise as a new paradigm for data storage. Here, we report what is, to our knowledge, the first use of self-immolative oligourethanes for storing and reading encoded information. As a proof of principle, we describe how a text passage from Jane Austen's was encoded in sequence-defined oligourethanes and reconstructed via self-immolative sequencing.

Long-term neural and physiological phenotyping of a single human.

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics.

Modeling Scalable Pattern Generation in DNA Reaction Networks.

We have developed a theoretical framework for developing patterns in multiple dimensions using controllable diffusion and designed reactions implemented in DNA. This includes so-called strand displacement reactions in which one single-stranded DNA hybridizes to a hemi-duplex DNA and displaces another single-stranded DNA, reversibly or irreversibly. These reactions can be designed to proceed with designed rate and molecular specificity.

Pattern transformation with DNA circuits.

Readily programmable chemical networks are important tools as the scope of chemistry expands from individual molecules to larger molecular systems. Although many complex systems are constructed using conventional organic and inorganic chemistry, the programmability of biological molecules such as nucleic acids allows for precise, high-throughput and automated design, as well as simple, rapid and robust implementation. Here we show that systematic and quantitative control over the diffusivity and reactivity of DNA molecules yields highly programmable chemical reaction networks (CRNs) that execute at the macroscale.

Modelling amorphous computations with transcription networks.

The power of electronic computation is due in part to the development of modular gate structures that can be coupled to carry out sophisticated logical operations and whose performance can be readily modelled. However, the equivalences between electronic and biochemical operations are far from obvious. In order to help cross between these disciplines, we develop an analogy between complementary metal oxide semiconductor and transcriptional logic gates.