Publications
154 results found
Sechkar K, Harrison S, Perrino G, et al., 2024, A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits, Nature Communications, Vol: 15, ISSN: 2041-1723
Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model’s usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.
Zhao X, Li Z, Shen M, et al., 2024, Enhancing Real-World Complex Network Representations with Hyperedge Augmentation., CoRR, Vol: abs/2402.13033
Li Z, Ni Y, Beardall WAV, et al., 2024, DiscDiff: Latent Diffusion Model for DNA Sequence Generation., CoRR, Vol: abs/2402.06079
Csibra E, Stan G-B, 2023, Parsley: a web app for parsing data from plate readers., Bioinformatics, Vol: 39
SUMMARY: As demand for the automation of biological assays has increased over recent years, the range of measurement types implemented by multiwell plate readers has broadened and the list of published software packages that caters to their analysis has grown. However, most plate readers export data in esoteric formats with little or no metadata, while most analytical software packages are built to work with tidy data accompanied by associated metadata. 'Parser' functions are therefore required to prepare raw data for analysis. Such functions are instrument- and data type-specific, and to date, no generic tool exists that can parse data from multiple data types or multiple plate readers, despite the potential for such a tool to speed up access to analysed data and remove an important barrier for less confident coders. We have developed the interactive web application, Parsley, to bridge this gap. Unlike conventional programmatic parser functions, Parsley makes few assumptions about exported data, instead employing user inputs to identify and extract data from data files. In doing so, it is designed to enable any user to parse plate reader data and can handle a wide variety of instruments (10+) and data types (53+). Parsley is freely available via a web interface, enabling access to its unique plate reader data parsing functionality, without the need to install software or write code. AVAILABILITY AND IMPLEMENTATION: The Parsley web application can be accessed at: https://gbstan.shinyapps.io/parsleyapp/. The source code is available at: https://github.com/ec363/parsleyapp and is archived on Zenodo: https://zenodo.org/records/10011752.
Ingram D, Stan G-B, 2023, Modelling genetic stability in engineered cell populations (vol 14, 3471, 2023), NATURE COMMUNICATIONS, Vol: 14
Stan G-B, Ingram D, 2023, Modelling genetic stability in engineered cell populations, Nature Communications, Vol: 14, Pages: 1-12, ISSN: 2041-1723
Predicting the evolution of engineered cell populations is a highly sought-after goal in biotechnology. While models of evolutionary dynamics are far from new, their application to synthetic systems is scarce where the vast combination of genetic parts and regulatory elements creates a unique challenge. To address this gap, we here-in present a framework that allows one to connect the DNA design of varied genetic devices with mutation spread in a growing cell population. Users can specify the functional parts of their system and the degree of mutation heterogeneity to explore, after which our model generates host-aware transition dynamics between different mutation phenotypes over time. We show how our framework can be used to generate insightful hypotheses across broad applications, from how a device’s components can be tweaked to optimise long-term protein yield and genetic shelf life, to generating new design paradigms for gene regulatory networks that improve their functionality.
Cella F, Perrino G, Tedeschi F, et al., 2023, MIRELLA: a mathematical model explains the effect of microRNA-mediated synthetic genes regulation on intracellular resource allocation, Nucleic Acids Research, Vol: 51, Pages: 3452-3464, ISSN: 0305-1048
Competition for intracellular resources, also known as gene expression burden, induces coupling between independently co-expressed genes, a detrimental effect on predictability and reliability of gene circuits in mammalian cells. We recently showed that microRNA (miRNA)-mediated target downregulation correlates with the upregulation of a co-expressed gene, and by exploiting miRNAs-based incoherent-feed-forward loops (iFFLs) we stabilise a gene of interest against burden. Considering these findings, we speculate that miRNA-mediated gene downregulation causes cellular resource redistribution. Despite the extensive use of miRNA in synthetic circuits regulation, this indirect effect was never reported before. Here we developed a synthetic genetic system that embeds miRNA regulation, and a mathematical model, MIRELLA, to unravel the miRNA (MI) RolE on intracellular resource aLLocAtion. We report that the link between miRNA-gene downregulation and independent genes upregulation is a result of the concerted action of ribosome redistribution and ‘queueing-effect’ on the RNA degradation pathway. Taken together, our results provide for the first time insights into the hidden regulatory interaction of miRNA-based synthetic networks, potentially relevant also in endogenous gene regulation. Our observations allow to define rules for complexity- and context-aware design of genetic circuits, in which transgenes co-expression can be modulated by tuning resource availability via number and location of miRNA target sites.
Patel A, Stan GB, 2023, Exploiting Resource Constraints for Controlling Biomolecular Circuits, Pages: 2699-2705, ISSN: 0743-1546
Advances in synthetic biology depend on our ability to predictably engineer robust biomolecular systems in living cells. The functioning of these synthetic biomolecular systems requires the consumption of shared cellular resources, which imposes a gene expression burden that may impact the performance of the cell and the synthetic system. In this paper, we show the effect of resource constraints on quantitative and qualitative aspects of gene expression in multiple circuits. We utilise a resource-aware modelling framework to show that stabilization can be achieved in a class of integral controllers. The results open possibilities for the design of lean biomolecular controllers.
Li Z, Das A, Beardall WAV, et al., 2023, Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer., CoRR, Vol: abs/2306.05143
Li Z, Ni Y, Huygelen TAB, et al., 2023, Latent Diffusion Model for DNA Sequence Generation., CoRR, Vol: abs/2310.06150
Li Z, Zhao X, Shen M, et al., 2023, Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs., CoRR, Vol: abs/2306.05108
Csibra E, Stan G-B, 2022, Absolute protein quantification using fluorescence measurements with FPCountR, Nature Communications, Vol: 40, ISSN: 2041-1723
This paper presents a generalisable method for the calibration of fluorescence readings on microplate readers, in order to convert arbitrary fluorescence units into absolute units. FPCountR relies on the generation of bespoke fluorescent protein (FP) calibrants, assays to determine protein concentration and activity, and a corresponding analytical workflow. We systematically characterise the assay protocols for accuracy, sensitivity and simplicity, and describe an ‘ECmax’ assay that outperforms the others and even enables accurate calibration without requiring the purification of FPs. To obtain cellular protein concentrations, we consider methods for the conversion of optical density to either cell counts or alternatively to cell volumes, as well as examining how cells can interfere with protein counting via fluorescence quenching, which we quantify and correct for the first time. Calibration across different instruments, disparate filter sets and mismatched gains is demonstrated to yield equivalent results. It also reveals that mCherry absorption at 600 nm does not confound cell density measurements unless expressed to over 100,000 proteins per cell. FPCountR is presented as pair of open access tools (protocol and R package) to enable the community to use this method, and ultimately to facilitate the quantitative characterisation of synthetic microbial circuits.
Perrino G, Stan G-B, 2022, Robust set-point regulation of gene expression using resource competition couplings in mammalian cells, American Control Conference (ACC), Publisher: IEEE, Pages: 1373-1378
Gene expression depends on the cellular con-text. One major contributor to gene expression variability is competition for limited transcriptional and translational re-sources, which may induce indirect couplings among otherwise independently-regulated genes. Here, we apply control theoretical concepts and tools to design an incoherent feedforward loop (iFFL) biomolecular controller operating in mammalian cells using translational-resource competition couplings. Harnessing a resource-aware mathematical model, we demonstrate analytically and computationally that our resource-aware design can achieve near-constant set-point regulation of gene expression whilst ensuring robustness to plasmid uptake variation. We also provide an analytical condition on the model parameters to guide the design of the resource-aware iFFL controller ensuring robustness and performance in set-point regulation. Our theoretical design based on translational-resource competition couplings represents a promising approach to build more sophisticated resource-aware control circuits operating at the host-cell level.
Bubnov DM, Yuzbashev TV, Khozov AA, et al., 2022, Robust counterselection and advanced λRed recombineering enable markerless chromosomal integration of large heterologous constructs., Nucleic Acids Research, Vol: 50, ISSN: 0305-1048
Despite advances in bacterial genome engineering, delivery of large synthetic constructs remains challenging in practice. In this study, we propose a straightforward and robust approach for the markerless integration of DNA fragments encoding whole metabolic pathways into the genome. This approach relies on the replacement of a counterselection marker with cargo DNA cassettes via λRed recombineering. We employed a counterselection strategy involving a genetic circuit based on the CI repressor of λ phage. Our design ensures elimination of most spontaneous mutants, and thus provides a counterselection stringency close to the maximum possible. We improved the efficiency of integrating long PCR-generated cassettes by exploiting the Ocr antirestriction function of T7 phage, which completely prevents degradation of unmethylated DNA by restriction endonucleases in wild-type bacteria. The employment of highly restrictive counterselection and ocr-assisted λRed recombineering allowed markerless integration of operon-sized cassettes into arbitrary genomic loci of four enterobacterial species with an efficiency of 50-100%. In the case of Escherichia coli, our strategy ensures simple combination of markerless mutations in a single strain via P1 transduction. Overall, the proposed approach can serve as a general tool for synthetic biology and metabolic engineering in a range of bacterial hosts.
Beardall WAV, Stan G-B, Dunlop MJ, 2022, Deep Learning Concepts and Applications for Synthetic Biology., GEN Biotechnology, Vol: 1, Pages: 360-371, ISSN: 2768-1556
Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.
Ledesma Amaro R, Stan G-B, Atkinson E, et al., 2022, Resource-aware whole-cell model of division of labour in a two-strain consortium for complex substrate degradation, Microbial Cell Factories, Vol: 21, Pages: 1-12, ISSN: 1475-2859
BackgroundLow-cost sustainable feedstocks are essential for commercially viable biotechnologies. These feedstocks, often derived from plant or food waste, contain a multitude of different complex biomolecules which require multiple enzymes to hydrolyse and metabolise. Current standard biotechnology uses monocultures in which a single host expresses all the proteins required for the consolidated bioprocess. However, these hosts have limited capacity for expressing proteins before growth is impacted. This limitation may be overcome by utilising division of labour (DOL) in a consortium, where each member expresses a single protein of a longer degradation pathway.ResultsHere, we model a two-strain consortium, with one strain expressing an endohydrolase and a second strain expressing an exohydrolase, for cooperative degradation of a complex substrate. Our results suggest that there is a balance between increasing expression to enhance degradation versus the burden that higher expression causes. Once a threshold of burden is reached, the consortium will consistently perform better than an equivalent single-cell monoculture.ConclusionsWe demonstrate that resource-aware whole-cell models can be used to predict the benefits and limitations of using consortia systems to overcome burden. Our model predicts the region of expression where DOL would be beneficial for growth on starch, which will assist in making informed design choices for this, and other, complex-substrate degradation pathways.
Bernier L, Stan G, Junier P, et al., 2022, Spores-on-a-chip: new frontiers for spore research, Trends in Microbiology, Vol: 30, Pages: 515-518, ISSN: 0966-842X
In recent years, microfluidic technologies have become widespread in biological science. However, the suitability of this technique for understanding different aspects of spore research has hardly been considered. Herein, we review recent developments in 'spores-on-a-chip' technologies, highlighting how they could be exploited to drive new frontiers in spore research.
Sechkar K, Tuza ZA, Stan G-B, 2022, A linear programming-based strategy to save pipette tips in automated DNA assembly, Synthetic Biology, Vol: 7, Pages: 1-8, ISSN: 2397-7000
Laboratory automation and mathematical optimization are key to improving the efficiency of synthetic biology research.While there are algorithms optimizing the construct designs and synthesis strategies for DNA assembly, the optimizationof how DNA assembly reaction mixes are prepared remains largely unexplored. Here, we focus on reducing the pipettetip consumption of a liquid-handling robot as it delivers DNA parts across a multi-well plate where several constructsare being assembled in parallel. We propose a linear programming formulation of this problem based on the capacitatedvehicle routing problem, as well as an algorithm which applies a linear programming solver to our formulation, henceproviding a strategy to prepare a given set of DNA assembly mixes using fewer pipette tips. The algorithm performedwell in randomly generated and real-life scenarios concerning several modular DNA assembly standards, proving capableof reducing the pipette tip consumption by up to 59% in large-scale cases. Combining automatic process optimizationand robotic liquid-handling, our strategy promises to greatly improve the efficiency of DNA assembly, either used aloneor combined with other algorithmic DNA assembly optimization methods.
Climent-Catala A, Ouldridge TE, Stan G-BV, et al., 2022, Building an RNA-based toggle switch using inhibitory RNA aptamers, ACS Synthetic Biology, Vol: 11, Pages: 562-569, ISSN: 2161-5063
Synthetic RNA systems offer unique advantages such as faster response, increased specificity, and programmability compared to conventional protein-based networks. Here, we demonstrate an in vitro RNA-based toggle switch using RNA aptamers capable of inhibiting the transcriptional activity of T7 or SP6 RNA polymerases. The activities of both polymerases are monitored simultaneously by using Broccoli and malachite green light-up aptamer systems. In our toggle switch, a T7 promoter drives the expression of SP6 inhibitory aptamers, and an SP6 promoter expresses T7 inhibitory aptamers. We show that the two distinct states originating from the mutual inhibition of aptamers can be toggled by adding DNA sequences to sequester the RNA inhibitory aptamers. Finally, we assessed our RNA-based toggle switch in degrading conditions by introducing controlled degradation of RNAs using a mix of RNases. Our results demonstrate that the RNA-based toggle switch could be used as a control element for nucleic acid networks in synthetic biology applications.
Dwijayanti A, Storch M, Stan G-B, et al., 2022, A modular RNA interference system for multiplexed gene regulation, Nucleic Acids Research, Vol: 50, ISSN: 0305-1048
The rational design and realisation of simple-to-use genetic control elements that are modular, orthogonal and robust is essential to the construction of predictable and reliable biological systems of increasing complexity. To this effect, we introduce modular Artificial RNA interference (mARi), a rational, modular and extensible design framework that enables robust, portable and multiplexed post-transcriptional regulation of gene expression in Escherichia coli. The regulatory function of mARi was characterised in a range of relevant genetic contexts, demonstrating its independence from other genetic control elements and the gene of interest, and providing new insight into the design rules of RNA based regulation in E. coli, while a range of cellular contexts also demonstrated it to be independent of growth-phase and strain type. Importantly, the extensibility and orthogonality of mARi enables the simultaneous post-transcriptional regulation of multi-gene systems as both single-gene cassettes and poly-cistronic operons. To facilitate adoption, mARi was designed to be directly integrated into the modular BASIC DNA assembly framework. We anticipate that mARi-based genetic control within an extensible DNA assembly framework will facilitate metabolic engineering, layered genetic control, and advanced genetic circuit applications.
Slutsky I, Schratt G, Stan G-B, et al., 2021, Homeostasis, CELL SYSTEMS, Vol: 12, Pages: 1124-1126, ISSN: 2405-4712
Boo AR, Ledesma Amaro R, Stan G-B, 2021, Quorum sensing in synthetic biology: a review, Current Opinion in Systems Biology, Vol: 28, Pages: 1-14, ISSN: 2452-3100
In nature, quorum sensing is one of the mechanism bacterial populations use to communicate withtheir own species or across species to coordinate behaviours. For the last 20 years, synthetic biologistshave recognised the remarkable properties of quorum sensing to build genetic circuits responsive topopulation density. This has led to progress in designing dynamic, coordinated and sometimes multicellular systems for bio-production in metabolic engineering and for increased spatial and temporalcomplexity in synthetic biology. In this review, we highlight recent works focused on using quorumsensing to engineer cell-cell behaviour.
Climent-Catala A, Ouldridge T, Stan G-B, et al., 2021, Building an RNA-based togglesSwitch using inhibitory RNA aptamers, Publisher: Cold Spring Harbor Laboratory
Synthetic RNA systems offer unique advantages such as faster response, increased specificity, and programmability compared to conventional protein-based networks. Here, we demonstrate an in-vitro RNA-based toggle switch using RNA aptamers capable of inhibiting the transcriptional activity of T7 or SP6 RNA polymerases. The activities of both polymerases are monitored simultaneously by using Broccoli and Malachite green light-up aptamer systems. In our toggle switch, a T7 promoter drives the expression of SP6 inhibitory aptamers, and an SP6 promoter expresses T7 inhibitory aptamers. We show that the two distinct states originating from the mutual inhibition of aptamers can be toggled by adding DNA sequences to sequester the RNA inhibitory aptamers. Finally, we assessed our RNA-based toggle switch in cell-like conditions by introducing controlled degradation of RNAs using a mix of RNases. Our results demonstrate that the RNA-based toggle switch could be used as a control element for nucleic acid networks in synthetic biology applications. <h4>Graphical TOC Entry</h4>
Perrino G, Hadjimitsis A, Ledesma Amaro R, et al., 2021, Control engineering and synthetic biology: Working in synergy for the analysis and control of microbial systems, Current Opinion in Microbiology, Vol: 62, Pages: 68-75, ISSN: 1369-5274
The implementation of novel functionalities in living cells is a key aspect of synthetic biology. In the last decade, the field of synthetic biology has made progress working in synergy with control engineering, whose solid framework has provided concepts and tools to analyse biological systems and guide their design. In this review, we briefly highlight recent work focused on the application of control theoretical concepts and tools for the analysis and design of synthetic biology systems in microbial cells.
Baig H, Fontanarossa P, Kulkarni V, et al., 2021, Synthetic biology open language visual (SBOL Visual) version 2.3, Journal of Integrative Bioinformatics, Vol: 18, ISSN: 1613-4516
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.3 of SBOL Visual, which builds on the prior SBOL Visual 2.2 in several ways. First, the specification now includes higher-level "interactions with interactions," such as an inducer molecule stimulating a repression interaction. Second, binding with a nucleic acid backbone can be shown by overlapping glyphs, as with other molecular complexes. Finally, a new "unspecified interaction" glyph is added for visualizing interactions whose nature is unknown, the "insulator" glyph is deprecated in favor of a new "inert DNA spacer" glyph, and the polypeptide region glyph is recommended for showing 2A sequences.
Plesa T, Stan G-B, Ouldridge TE, et al., 2021, Quasi-robust control of biochemical reaction networks via stochastic morphing., Journal of the Royal Society Interface, Vol: 18, Pages: 1-14, ISSN: 1742-5662
One of the main objectives of synthetic biology is the development of molecular controllers that can manipulate the dynamics of a given biochemical network that is at most partially known. When integrated into smaller compartments, such as living or synthetic cells, controllers have to be calibrated to factor in the intrinsic noise. In this context, biochemical controllers put forward in the literature have focused on manipulating the mean (first moment) and reducing the variance (second moment) of the target molecular species. However, many critical biochemical processes are realized via higher-order moments, particularly the number and configuration of the probability distribution modes (maxima). To bridge the gap, we put forward the stochastic morpher controller that can, under suitable timescale separations, morph the probability distribution of the target molecular species into a predefined form. The morphing can be performed at a lower-resolution, allowing one to achieve desired multi-modality/multi-stability, and at a higher-resolution, allowing one to achieve arbitrary probability distributions. Properties of the controller, such as robustness and convergence, are rigorously established, and demonstrated on various examples. Also proposed is a blueprint for an experimental implementation of stochastic morpher.
Cabello-Garcia J, Bae W, Stan G-BV, et al., 2021, Handhold-mediated strand displacement: a nucleic acid based mechanism for generating far-from-equilibrium assemblies through templated reactions., ACS Nano, Vol: 15, Pages: 3272-3283, ISSN: 1936-0851
The use of templates is a well-established method for the production of sequence-controlled assemblies, particularly long polymers. Templating is canonically envisioned as akin to a self-assembly process, wherein sequence-specific recognition interactions between a template and a pool of monomers favor the assembly of a particular polymer sequence at equilibrium. However, during the biogenesis of sequence-controlled polymers, template recognition interactions are transient; RNA and proteins detach spontaneously from their templates to perform their biological functions and allow template reuse. Breaking template recognition interactions puts the product sequence distribution far from equilibrium, since specific product formation can no longer rely on an equilibrium dominated by selective copy-template bonds. The rewards of engineering artificial polymer systems capable of spontaneously exhibiting nonequilibrium templating are large, but fields like DNA nanotechnology lack the requisite tools; the specificity and drive of conventional DNA reactions rely on product stability at equilibrium, sequestering any recognition interaction in products. The proposed alternative is handhold-mediated strand displacement (HMSD), a DNA-based reaction mechanism suited to producing out-of-equilibrium products. HMSD decouples the drive and specificity of the reaction by introducing a transient recognition interaction, the handhold. We measure the kinetics of 98 different HMSD systems to prove that handholds can accelerate displacement by 4 orders of magnitude without being sequestered in the final product. We then use HMSD to template the selective assembly of any one product DNA duplex from an ensemble of equally stable alternatives, generating a far-from-equilibrium output. HMSD thus brings DNA nanotechnology closer to the complexity of out-of-equilibrium biological systems.
Kuntz Nussio J, Thomas P, Stan G, et al., 2021, Approximations of countably-infinite linear programs over bounded measure spaces, SIAM Journal on Optimization, Vol: 31, Pages: 604-625, ISSN: 1052-6234
We study a class of countably-infinite-dimensional linear programs (CILPs)whose feasible sets are bounded subsets of appropriately defined spaces ofmeasures. The optimal value, optimal points, and minimal points of these CILPscan be approximated by solving finite-dimensional linear programs. We show howto construct finite-dimensional programs that lead to approximations witheasy-to-evaluate error bounds, and we prove that the errors converge to zero asthe size of the finite-dimensional programs approaches that of the originalproblem. We discuss the use of our methods in the computation of the stationarydistributions, occupation measures, and exit distributions of Markov~chains.
Kuntz J, Thomas P, Stan G-B, et al., 2021, Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations, SIAM Review, ISSN: 0036-1445
Computing the stationary distributions of a continuous-time Markov chaininvolves solving a set of linear equations. In most cases of interest, thenumber of equations is infinite or too large, and cannot be solved analyticallyor numerically. Several approximation schemes overcome this issue by truncatingthe state space to a manageable size. In this review, we first give acomprehensive theoretical account of the stationary distributions and theirrelation to the long-term behaviour of the Markov chain, which is readilyaccessible to non-experts and free of irreducibility assumptions made instandard texts. We then review truncation-based approximation schemes payingparticular attention to their convergence and to the errors they introduce, andwe illustrate their performance with an example of a stochastic reactionnetwork of relevance in biology and chemistry. We conclude by elaborating oncomputational trade-offs associated with error control and some open questions.
Selles Vidal L, Ayala R, Stan G-B, et al., 2021, rfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families, PLoS One, Vol: 16, ISSN: 1932-6203
rfaRm is an R package providing a client-side interface for the Rfam database of non-coding RNA and other structured RNA elements. The package facilitates the search of the Rfam database by keywords or sequences, as well as the retrieval of all available information about specific Rfam families, such as member sequences, multiple sequence alignments, secondary structures and covariance models. By providing such programmatic access to the Rfam database, rfaRm enables genomic workflows to incorporate information about non-coding RNA, whose potential cannot be fully exploited just through interactive access to the database. The features of rfaRm are demonstrated by using it to analyze the SARS-CoV-2 genome as an example case.
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