Biology subjects

Shur, A.

Publications and source records attributed to Shur, A..

7 recordsLinked to original sources

bioRxiv2026

10-minimizers: a promising class of constant-space minimizers

Minimizers are sampling schemes which are ubiquitous in almost any high-throughput sequencing analysis. Assuming a fixed alphabet of size{sigma} , a minimizer is defined by two positive integers k, w and a linear order{rho} on k-mers. A sequence is processed by a sliding window algorithm that chooses in each window of length w + k- 1 its minimal k-mer with respect to{rho} . A key characteristic of a minimizer is its density, which is the expected frequency of chosen k-mers among all k-mers in a random infinite{sigma} -ary sequence. Minimizers of smaller density are preferred as they produce smaller samples, which lead to reduced runtime and memory usage in downstream applications. Recent studies developed methods to generate minimizers with optimal and near-optimal densities, but they require to explicitly store k-mer ranks in{Omega} (2k) space. While constant-space minimizers exist, and some of them are proven to be asymptotically optimal, no constant-space minimizers was proven to guarantee lower density compared to a random minimizer in the non-asymptotic regime, and many minimizer schemes suffer from long k-mer key-retrieval times due to complex computation. In this paper, we introduce 10-minimizers, which constitute a class of minimizers with promising properties. First, we prove that for every k > 1 and every w[≥] k- 2, a random 10-minimizer has, on expectation, lower density than a random minimizer. This is the first provable guarantee for a class of minimizers in the non-asymptotic regime. Second, we present spacers, which are particular 10-minimizers combining three desirable properties: they are constant-space, low-density, and have small k-mer key-retrieval time. In terms of density, spacers are competitive to the best known constant-space minimizers; in certain (k, w) regimes they achieve the lowest density among all known (not necessarily constant-space) minimizers. Notably, we are the first to benchmark constant-space minimizers in the time spent for k-mer key retrieval, which is the most fundamental operation in many minimizers-based methods. Our empirical results show that spacers can retrieve k-mer keys in competitive time (a few seconds per genome-size sequence, which is less than required by random minimizers), for all practical values of k and w. We expect 10-minimizers to improve minimizers-based methods, especially those using large window sizes. We also propose the k-mer key-retrieval benchmark as a standard objective for any new minimizer scheme.

bioinformatics↗

bioRxiv2026

Generating minimum-density minimizers

Minimizers are sampling schemes which are ubiquitous in almost any high-throughput sequencing analysis. Assuming a fixed alphabet of size{sigma} , a minimizer is defined by two positive integers k, w and a linear order{rho} on k-mers. A sequence is processed by a sliding window algorithm that chooses in each window of length w + k - 1 its minimal k-mer with respect to{rho} . A key characteristic of a minimizer is its density, which is the expected frequency of chosen k-mers among all k-mers in a random infinite{sigma} -ary sequence. Minimizers of smaller density are preferred as they produce smaller samples, which lead to reduced runtime and memory usage in downstream applications. While the hardness of finding a minimizer of minimum density for given input parameters ({sigma}, k, w) is unknown, it has a huge search space of ({sigma}k)! and there is no known algorithm apart from a trivial brute-force search. In this paper, we tackle the minimum density problem for minimizers. We first formulate this problem as an ILP of size{Theta} (w{sigma}w+k), which has worst-case solution time that is doubly-exponential in (k + w) under standard complexity assumptions. Our experiments show that an ILP solver terminates with an optimal solution only for very small k and w. We then present our main method, called OptMini, which computes an optimal minimizer in [Formula] time and thus is capable of processing large w values. In experiments, OptMini works much faster than the runtime predicts due to several additional tricks shrinking the search space without harming optimality. We use OptMini to compute minimum-density minimizers for ({sigma}, k) [isin] {(2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (4, 2)} and w [isin] [2, 3{sigma}k], with the exception of certain w-ranges for k = 6 and the single case of k = 5, w = 2. Finally, we derive conclusions and insights regarding the density values as a function of w, patterns in optimal minimizer orders, and the relation between minimum-size universal hitting sets and minimum-density minimizers.

bioinformatics↗

bioRxiv2024

Generating low-density minimizers

Minimizers is the most popular k-mer selection scheme in algorithms and data structures analyzing high-throughput sequencing (HTS) data. In a minimizers scheme, the smallest k-mer by some predefined order is selected as the representative of a sequence window containing w consecutive k-mers, which results in overlapping windows often selecting the same k-mer. Minimizers that achieve the lowest frequency of selected k-mers over a random DNA sequence, termed the expected density, are desired for improved performance of HTS analyses. Yet, no method to date exists to generate minimizers that achieve minimum expected density. Moreover, for k and w values used by common HTS algorithms and data structures there is a gap between the densities achieved by existing selection schemes and a recent theoretical lower bound. Here, we present GreedyMini, a toolkit of methods to generate minimizers with low expected or particular density, to improve minimizers, to extend minimizers to larger alphabets, k, and w, and to measure the expected density of a given minimizer efficiently. We demonstrate over various combinations of k and w values, including those of popular HTS methods, that GreedyMini can generate DNA minimizers that achieve expected densities very close to the lower bound, and both expected and particular densities much lower compared to existing selection schemes. Additionally, we show that the k-mer rank-retrieval time by GreedyMini is comparable to that of common k-mer hash functions. We expect GreedyMini to improve the performance of many HTS algorithms and data structures and advance the research of k-mer selection schemes.

bioinformatics↗

bioRxiv2020

Engineering Logical Inflammation Sensing Circuit for Modulating Gut Conditions

Bacteria genetically engineered to execute defined therapeutic and diagnostic functions in physiological settings can be applied to colonize the human microbiome, providing in situ surveillance and conditional disease modulation. However, many engineered microbes can only respond to single-input environmental factors, limiting their tunability, precision, and effectiveness as living diagnostic and therapeutic systems. For engineering microbes to improve complex chronic disorders such as inflammatory bowel disease, the bacteria must respond to combinations of stimuli in the proper context and time. This work implements a previously characterized split activator AND logic gate in the probiotic Escherichia coli strain Nissle 1917. Our system can respond to two input signals: the inflammatory biomarker tetrathionate and a second input signal, anhydrotetracycline (aTc), for manual control. We report 4-6 fold induction with a minimal leak when the two chemical signals are present. We model the AND gate dynamics using chemical reaction networks and tune parameters in silico to identify critical perturbations that affect our circuits selectivity. Finally, we engineer the optimized AND gate to secrete a therapeutic anti-inflammatory cytokine IL-22 using the hemolysin secretion pathway in the probiotic E. coli strain. We used a germ-free transwell model of the human gut epithelium to show that our engineering bacteria produce similar host cytokine responses compared to pure cytokine. Our study presents a scalable workflow to engineer cytokine-secreting microbes. It demonstrates the feasibility of IL-22 derived from probiotic E. coli Nissle with minimal off-target effects in a gut epithelial context.

synthetic biology↗

bioRxiv2020

BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts

Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions. Author summaryThis paper describes a new software package BioCRNpyler (pronounced "Biocompiler") designed to support rapid development and exploration of mathematical models of biochemical networks and circuits by computational biologists, systems biologists, and synthetic biologists. BioCRNpyler allows its users to generate large complex models using very few lines of code in a way that is modular. To do this, BioCRNpyler uses a powerful new representation of biochemical circuits which defines their parts, underlying biochemical mechanisms, and chemical context independently. BioCRNpyler was developed as a Python scripting language designed to be accessible to beginning users as well as easily extendable and customizable for advanced users. Ultimately, we see Biocrnpyler being used to accelerate computer automated design of biochemical circuits and model driven hypothesis generation in biology.

synthetic biology↗

bioRxiv2017

Proof of concept continuous event logging in living cells

Cells must detect and respond to molecular events such as the presence or absence of specific small molecules. To accomplish this, cells have evolved methods to measure the presence and concentration of these small molecules in their environment and enact changes in gene expression or behavior. However, cells dont usually change their DNA in response to such outside stimuli. In this work, we have engineered a genetic circuit that can enact specific and controlled genetic changes in response to changing small molecule concentrations. Known DNA sequences can be repeatedly integrated into a genomic array such that their identity and order encodes information about past small molecule concentrations that the cell has experienced. To accomplish this, we use catalytically inactive CRISPR-Cas9 (dCas9) to bind to and block attachment sites for the integrase Bxb1. Therefore, through the co-expression of dCas9 and guide RNA, Bxb1 can be directed to integrate one of two engineered plasmids, which correspond to two orthogonal small molecule inducers that can be recorded with this system. We identified the optimal location of guide RNA binding to the Bxb1 attP integrase attachment site, and characterized the detection limits of the system by measuring the minimal small molecule concentration and shortest induction time necessary to produce measurable differences in array composition as read out by Oxford Nanopore long read sequencing technology.

synthetic biology↗

bioRxiv2017

Repressing Integrase attachment site operation with CRISPR-Cas9 in E. coli

Serine integrases are bacteriophage proteins responsible for integrating the phage genome into that of the host. Synthetic biologists have co-opted these proteins into useful tools for permanent DNA logic, utilizing their specific DNA recombination abilities to build synthetic cell differentiation and genetic memory systems. Each integrase has a specific pair of DNA sequences (attP/attB sites) that it recombines, but multiple identical sites can result in unpredictable recombination. We have developed a way to control integrase activity on identical attP/attB sites by using catalytically dead Cas9 (dCas9) as a programmable binding protein that can compete with integrase for binding to specific attachment sites. Utilizing a plasmid that contains two identical Bxb1 attP sites, integration can be repressed up to 8 fold at either one of the two attP sites when guide RNA and dCas9 are present. Guide RNA sequences that bind specifically to attB, or either of two attP sites, have been developed. Future goals are to utilize this technology to construct larger and more complex integrase logic circuits.

synthetic biology↗