Data Storage Based on Combinatorial Synthesis of DNA Shortmers

Storage needs represent a significant burden on the economy and the environment. Some of this can potentially be offset by improved density molecular storage. The potential of using DNA for storing data is immense. DNA can be harnessed as a high density, durable archiving medium for compressing and storing the exponentially growing quantities of digital data that mankind generates. Several studies have demonstrated the potential of DNA-based data storage systems. These include exploration of different encoding and error correction schemes and the use of different technologies for DNA synthesis and sequencing. Recently, the use of composite DNA letters has been demonstrated to leverage the inherent redundancy in DNA based storage systems to achieve higher logical density, offering a more cost-effective approach. However, the suggested composite DNA approach is still limited due to its sensitivity to the stochastic nature of the process. Combinatorial assembly methods were also suggested to encode information on DNA in high density, while avoding the challenges of the stochastic system. These are based on enzynatic assembly processes for producing the synthetic DNA. In this paper, we propose a novel method to encode information into DNA molecules using combinatorial encoding and shortmer DNA synthesis, in compatibility with current chemical DNA synthesis technologies. Our approach is based on a set of easily distinguishable DNA shortmers serving as building blocks and allowing for near-zero error rates. We construct an extended combinatorial alphabet in which every letter is a subset of the set of building blocks. We suggest different combinatorial encoding schemes and explore their theoretical properties and practical implications in terms of error probabilities and required sequencing depth. To demonstrate the feasibility of our approach, we implemented an end-to-end computer simulation of a DNA-based storage system, using our suggested combinatorial encodings. We use simulations to assess the performance of the system and the effect of different parameters. Our simulations suggest that our combinatorial approach can potentially achieve up to 6.5-fold increase in the logical density over standard DNA based storage systems, with near zero reconstruction error. Implementing our approach at scale to perform actual synthesis, requires minimal alterations to current technologies. Our work thus suggests that the combination of combinatorial encoding with standard DNA chemical synthesis technologies can potentially improve current solutions, achieving scalable, efficient and cost- effective DNA-based storage.

Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens

Genetic interactions, defined as the non-additive phenotypic impact of combinations of genes, are a hallmark of the mapping from genotype to phenotype. However, genetic interactions remain challenging to systematically test given the massive number of possible combinations. In particular, while large-scale screening efforts in yeast have quantified pairwise interactions that affect cell viability, or synthetic lethality, between all pairs of genes as well as for a limited number of three-way interactions, it has previously been intractable to perform the large screens needed to comprehensively assess interactions in a mammalian genome. Here, we develop Shuffle-Seq, a scalable method to assay genetic interactions. Shuffle-Seq leverages the co-inheritance of genetically encoded barcodes in dividing cells and can scale in proportion to sequencing throughput. We demonstrate the technical validity of Shuffle-Seq and apply it to screening for mechanisms underlying drug resistance in a melanoma model. Shuffle-Seq should allow screens of hundreds of millions of combinatorial perturbations and facilitate the understanding of genetic dependencies and drug sensitivities.

GenEpi: Gene-based Epistasis Discovery Using Machine Learning

Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer’s disease (AD). In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. Availability: GenEpi is an open-source python package and available free of charge only for non-commercial users. The package can be downloaded from https://github.com/Chester75321/GenEpi, and has also been published on The Python Package Index.

A Molecular Odorant Transduction Model and the Complexity of Combinatorial Encoding in the Drosophila Antenna

In the past two decades, a substantial amount of work characterized the odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized and, the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined.Identity and concentration, two key aspects of olfactory coding, originate in the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model identity and concentration in OTP using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the ligand binding/dissociation process. We model the BSG as a Connor-Stevens point neuron.The resulting combinatorial encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the combinatorial complexity of the transformation taking place in the antennae. The concentration-dependent combinatorial code determines the complexity of the input space driving olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiology recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Olfactory receptors are sensitive to molecular volume of odorants

To study olfaction, first we should know which physical or chemical properties of odorant molecules determine the response of olfactory receptor neurons, and then we should study the effect of those properties on the combinatorial encoding in olfactory system. In this work we show that the response of an olfactory receptor neuron in Drosophila depends on molecular volume of an odorant; The molecular volume determines the upper limits of the neural response, while the actual neural response may depend on other properties of the molecules. Each olfactory receptor prefers a particular volume, with some degree of flexibility. These two parameters predict the volume and flexibility of the binding-pocket of the olfactory receptors, which are the targets of structural biology studies. At the end we argue that the molecular volume can affects the quality of perceived smell of an odorant via the combinatorial encoding, molecular volume may mask other underlying relations between properties of molecules and neural responses and we suggest a way to improve the selection of odorants in further experimental studies.