Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 63,068 bioRxiv papers from 279,747 authors.
Most downloaded bioRxiv papers, since beginning of last month
61,421 results found. For more information, click each entry to expand.
815 downloads evolutionary biology
Introduction Cancer genomes exhibit surprisingly weak signatures of negative selection,. This may be because tumors evolve either under very weak selective pressures (‘weak selection’) or under conditions that prevent the elimination of many deleterious passenger mutations (‘poor efficacy of selection’)xs. Rationale The weak selection model argues that the majority of genes are only important for multicellular function. The poor efficacy of selection model argues, in contrast, that genome-wide linkage in cancer prevents many deleterious mutations from being removed via Hill-Robertson interference. Since these linkage effects weaken as mutation rates decrease, we predict that cancers with lower mutational burdens should exhibit stronger signals of negative selection. Furthermore, because linkage affects driver mutations as well, low mutational burden cancers should also show stronger evidence of positive selection in driver genes. Neither pattern — in drivers or passengers — is expected under the weak selection model. We leverage the 10,000-fold variation in mutational burden across cancer subtypes to stratify tumors by their genome-wide mutational burden and used a normalized ratio of nonsynonymous to synonymous substitutions (dN/dS) to quantify the extent that selection varies with mutation rate. Results We find that appreciable negative selection (dN/dS ~ 0.4) is present in tumors with a low mutational burden, while the remaining cancers (96%) exhibit dN/dS ratios approaching 1, suggesting that the majority of tumors do not remove deleterious passengers. A parallel pattern is seen in drivers, where positive selection attenuates as the mutational burden of cancers increases. Both trends persist across tumor-types, are not exclusive to essential or housekeeping genes, and are present in clonal and subclonal mutations. Two additional orthogonal lines of evidence support the weak efficacy model: passengers are less damaging in low mutational burden cancers, and patterns of attenuated selection also emerge in Copy Number Alterations. Finally, we find that an evolutionary model incorporating Hill-Robertson interference can reproduce both patterns of attenuated selection in drivers and passengers if the average fitness cost of passengers is 1.0% and the average fitness benefit of drivers is 19%. Conclusion Collectively, our findings suggest that the lack of signals of negative selection in most tumors is not due to relaxed selective pressures, but rather the inability of selection to remove individual deleterious mutations in the presence of genome-wide linkage. As a result, despite the weak individual fitness effects of passengers, most cancers harbor a large mutational load (median ~40% total fitness cost) and succeed due to acquisition of additional strong drivers (~5 with an overall benefit of ~130%). Understanding how this deleterious load is overcome may help identify cancer vulnerabilities that may be targeted by new and existing therapies. : #ref-1 : #ref-2 : #ref-3
813 downloads cancer biology
Ludmil Alexandrov, Jaegil Kim, Nicholas J Haradhvala, Mi Ni Huang, Alvin W T Ng, Yang Wu, Arnoud Boot, Kyle R Covington, Dmitry A. Gordenin, Erik N Bergstrom, S. M. Ashiqul Islam, Nuria Lopez-Bigas, Leszek J. Klimczak, John R McPherson, Sandro Morganella, Radhakrishnan Sabarinathan, David A Wheeler, Ville Mustonen, the PCAWG Mutational Signatures Working Group, Gad Getz, Steven G. Rozen, Michael R. Stratton
Somatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.
805 downloads systems biology
A major biomedical challenge is the interpretation of genetic variation and the ability to design functional novel sequences. Since the space of all possible genetic variation is enormous, there is a concerted effort to develop reliable methods that can capture genotype to phenotype maps. State-of-art computational methods rely on models that leverage evolutionary information and capture complex interactions between residues. However, current methods are not suitable for a large number of important applications because they depend on robust protein or RNA alignments. Such applications include genetic variants with insertions and deletions, disordered proteins, and functional antibodies. Ideally, we need models that do not rely on assumptions made by multiple sequence alignments. Here we borrow from recent advances in natural language processing and speech synthesis to develop a generative deep neural network-powered autoregressive model for biological sequences that captures functional constraints without relying on an explicit alignment structure. Application to unseen experimental measurements of 43 deep mutational scans predicts the effect of insertions and deletions while matching state-of-art missense mutation prediction accuracies. We then test the model on single domain antibodies, or nanobodies, a complex target for alignment-based models due to the highly variable complementarity determining regions. We fit the model to a naïve llama immune repertoire and generate a diverse, optimized library of 105 nanobody sequences for experimental validation. Our results demonstrate the power of the 'alignment-free' autoregressive model in mutation effect prediction and design of traditionally challenging sequence families.
796 downloads neuroscience
Johan Winnubst, Erhan Bas, Tiago A. Ferreira, Zhuhao Wu, Michael N Economo, Patrick Edson, Ben J. Arthur, Christopher Bruns, Konrad Rokicki, David Schauder, Donald J. Olbris, Sean D. Murphy, David G. Ackerman, Cameron Arshadi, Perry Baldwin, Regina Blake, Ahmad Elsayed, Mashtura Hasan, Daniel Ramirez, Bruno Dos Santos, Monet Weldon, Amina Zafar, Joshua T. Dudmann, Charles R Gerfen, Adam W Hantman, Wyatt Korff, Scott M. Sternson, Nelson Spruston, Karel Svoboda, Jayaram Chandrashekar
Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.
791 downloads neuroscience
Pattern recognition predictive models have become an important tool for analysis of neuroimaging data and answering important questions from clinical and cognitive neuroscience. Regardless of the application, the most commonly used method to quantify model performance is to calculate prediction accuracy, i.e. the proportion of correctly classified samples. While simple and intuitive, other performance measures are often more appropriate with respect to many common goals of neuroimaging pattern recognition studies. In this paper, we will review alternative performance measures and focus on their interpretation and practical aspects of model evaluation. Specifically, we will focus on 4 families of performance measures: 1) categorical performance measures such as accuracy, 2) rank based performance measures such as the area under the curve, 3) probabilistic performance measures based on quadratic error such as Brier score, and 4) probabilistic performance measures based on information criteria such as logarithmic score. We will examine their statistical properties in various settings using simulated data and real neuroimaging data derived from public datasets. Results showed that accuracy had the worst performance with respect to statistical power, detecting model improvement, selecting informative features and reliability of results. Therefore in most cases, it should not be used to make statistical inference about model performance. Accuracy should also be avoided for evaluating utility of clinical models, because it does not take into account clinically relevant information, such as relative cost of false-positive and false-negative misclassification or calibration of probabilistic predictions. We recommend alternative evaluation criteria with respect to the goals of a specific machine learning model.
790 downloads molecular biology
We previously described a novel alternative to Chromatin Immunoprecipitation, Cleavage Under Targets & Release Using Nuclease (CUT&RUN), in which unfixed permeabilized cells are incubated with antibody, followed by binding of a Protein A-Micrococcal Nuclease (pA/MNase) fusion protein (1). Upon activation of tethered MNase, the bound complex is excised and released into the supernatant for DNA extraction and sequencing. Here we introduce four enhancements to CUT&RUN: 1) a hybrid Protein A-Protein G-MNase construct that expands antibody compatibility and simplifies purification; 2) a modified digestion protocol that inhibits premature release of the nuclease-bound complex; 3) a calibration strategy based on carry-over of E. coli DNA introduced with the fusion protein; and 4) a novel peak-calling strategy customized for the low-background profiles obtained using CUT&RUN. These new features, coupled with the previously described low-cost, high efficiency, high reproducibility and high- throughput capability of CUT&RUN make it the method of choice for routine epigenomic profiling.
782 downloads developmental biology
Jacob M. Musser, Klaske J. Schippers, Michael Nickel, Giulia Mizzon, Andrea B Kohn, Constantin Pape, Jörg U. Hammel, Florian Wolf, Cong Liang, Ana Hernández-Plaza, Kaia Achim, Nicole Schieber, Warren R Francis, Sergio Vargas, Svenja Kling, Maike Renkert, Roberto Feuda, Imre Gaspar, Pawel Burkhardt, Peer Bork, Martin Beck, Anna Kreshuk, Gert Woerheide, Jaime Huerta-Cepas, Yannick Schwab, Leonid L Moroz, Detlev Arendt
The evolutionary origin of metazoan cell types such as neurons, muscles, digestive, and immune cells, remains unsolved. Using whole-body single-cell RNA sequencing in a sponge, an animal without nervous system and musculature, we identify 18 distinct cell types comprising four major families. This includes nitric-oxide sensitive contractile cells, digestive cells active in macropinocytosis, and a family of amoeboid-neuroid cells involved in innate immunity. We uncover presynaptic genes in an amoeboid-neuroid cell type, and postsynaptic genes in digestive choanocytes, suggesting asymmetric and targeted communication. Corroborating this, long neurite-like extensions from neuroid cells directly contact and enwrap choanocyte microvillar collars. Our data indicate a link between neuroid and immune functions in sponges, and suggest that a primordial neuro-immune system cleared intruders and controlled ciliary beating for feeding.
779 downloads genomics
The past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.
771 downloads genomics
Indigenous peoples have occupied the island of Puerto Rico since at least 3000 B.C. Due to the demographic shifts that occurred after European contact, the origin(s) of these ancient populations, and their genetic relationship to present-day islanders, are unclear. We use ancient DNA to characterize the population history and genetic legacies of pre-contact Indigenous communities from Puerto Rico. Bone, tooth and dental calculus samples were collected from 124 individuals from three pre-contact archaeological sites: Tibes, Punta Candelero and Paso del Indio. Despite poor DNA preservation, we used target enrichment and high-throughput sequencing to obtain complete mitochondrial genomes (mtDNA) from 45 individuals and autosomal genotypes from two individuals. We found a high proportion of Native American mtDNA haplogroups A2 and C1 in the pre-contact Puerto Rico sample (40% and 44%, respectively). This distribution, as well as the haplotypes represented, support a primarily Amazonian South American origin for these populations, and mirrors the Native American mtDNA diversity patterns found in present-day islanders. Three mtDNA haplotypes from pre-contact Puerto Rico persist among Puerto Ricans and other Caribbean islanders, indicating that present-day populations are reservoirs of pre-contact mtDNA diversity. Lastly, we find similarity in autosomal ancestry patterns between pre-contact individuals from Puerto Rico and the Bahamas, suggesting a shared component of Indigenous Caribbean ancestry with close affinity to South American populations. Our findings contribute to a more complete reconstruction of pre-contact Caribbean population history and explore the role of Indigenous peoples in shaping the biocultural diversity of present-day Puerto Ricans and other Caribbean islanders.
766 downloads plant biology
The ability to generate long reads on the Oxford Nanopore Technologies sequencing platform is dependent on the isolation of high molecular weight DNA free of impurities. For some taxa, this is relatively straightforward; however, for plants, the presence of cell walls and a diverse set of specialized metabolites such as lignin, phenolics, alkaloids, terpenes, and flavonoids present significant challenges in the generation of DNA suitable for production of long reads. Success in generating long read lengths and genome assemblies of plants has been reported using diverse DNA isolation methods, some of which were tailored to the target species and/or required extensive labor. To avoid the need to optimize DNA isolation for each species, we developed a taxa-independent DNA isolation method that is relatively simple and efficient. This method expands on the Oxford Nanopore Technologies high molecular weight genomic DNA protocol from plant leaves and utilizes a conventional cetyl trimethylammonium bromide extraction followed by removal of impurities and short DNA fragments using commercially available kits that yielded robust N50 read lengths and yield on Oxford Nanopore Technologies flow cells. * CTAB : cetyl trimethylammonium bromide ONT : Oxford Nanopore Technologies
759 downloads synthetic biology
In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In biology, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Learning the natural distribution of evolutionary protein sequence variation is a logical step toward predictive and generative modeling for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million sequences spanning evolutionary diversity. The resulting model maps raw sequences to representations of biological properties without labels or prior domain knowledge. The learned representation space organizes sequences at multiple levels of biological granularity from the biochemical to proteomic levels. Learning recovers information about protein structure: secondary structure and residue-residue contacts can be extracted by linear projections from learned representations. With small amounts of labeled data, the ability to identify tertiary contacts is further improved. Learning on full sequence diversity rather than individual protein families increases recoverable information about secondary structure. We show the networks generalize by adapting them to variant activity prediction from sequences only, with results that are comparable to a state-of-the-art variant predictor that uses evolutionary and structurally derived features.
757 downloads bioinformatics
Vladimir Gligorijević, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Kyunghyun Cho, Tommi Vatanen, Daniel Berenberg, Bryn C Taylor, Ian M Fisk, Ramnik J. Xavier, Rob Knight, Richard Bonneau
Recent massive increases in the number of sequences available in public databases challenges current experimental approaches to determining protein function. These methods are limited by both the large scale of these sequences databases and the diversity of protein functions. We present a deep learning Graph Convolutional Network (GCN) trained on sequence and structural data and evaluate it on ~40k proteins with known structures and functions from the Protein Data Bank (PDB). Our GCN predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and competing methods. Feature extraction via a language model removes the need for constructing multiple sequence alignments or feature engineering. Our model learns general structure-function relationships by robustly predicting functions of proteins with ≤ 30% sequence identity to the training set. Using class activation mapping, we can automatically identify structural regions at the residue-level that lead to each function prediction for every protein confidently predicted, advancing site-specific function prediction. De-noising inherent in the trained model allows an only minor drop in performance when structure predictions are used, including multiple de novo protocols. We use our method to annotate all proteins in the PDB, making several new confident function predictions spanning both fold and function trees.
754 downloads neuroscience
Cognitive capacities afford contingent associations between sensory information and behavioral responses. We studied this problem using an olfactory delayed match to sample task whereby a sample odor specifies the association between a subsequent test odor and rewarding action. Multi-neuron recordings revealed representations of the sample and test odors in olfactory sensory and association cortex, which were sufficient to identify the test odor as match/non-match. Yet, inactivation of a downstream premotor area (ALM), but not orbitofrontal cortex, confined to the epoch preceding the test odor, led to gross impairment. Olfactory decisions that were not context dependent were unimpaired. Therefore, ALM may not receive the outcome of a match/non-match decision from upstream areas but contextual information--the identity of the sample--to establish the mapping between test odor and action. A novel population of pyramidal neurons in ALM layer 2 may mediate this process.
740 downloads bioinformatics
Karen H Miga, Sergey Koren, Arang Rhie, Mitchell R. Vollger, Ariel Gershman, Andrey Bzikadze, Shelise Brooks, Edmund Howe, David Porubsky, Glennis A. Logsdon, Valerie A Schneider, Tamara Potapova, Jonathan Wood, William Chow, Joel Armstrong, Jeanne Fredrickson, Evgenia Pak, Kristof Tigyi, Milinn Kremitzki, Christopher Markovic, Valerie Maduro, Amalia Dutra, Gerard G Bouffard, Alexander M Chang, Nancy F Hansen, Françoisen Thibaud-Nissen, Anthony D Schmitt, Jon-Matthew Belton, Siddarth Selvaraj, Megan Y. Dennis, Daniela C Soto, Ruta Sahasrabudhe, Gulhan Kaya, Josh Quick, Nicholas J Loman, Nadine Holmes, Matthew Loose, Urvashi Surti, Rosa ana Risques, Tina A. Graves Lindsay, Robert Fulton, Ira Hall, Benedict Paten, Kerstin Howe, Winston Timp, Alice Young, James C. Mullikin, Pavel A Pevzner, Jennifer E. Gerton, Beth A Sullivan, Evan E Eichler, Adam M Phillippy
After nearly two decades of improvements, the current human reference genome (GRCh38) is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has been finished end to end, and hundreds of unresolved gaps persist ,. The remaining gaps include ribosomal rDNA arrays, large near-identical segmental duplications, and satellite DNA arrays. These regions harbor largely unexplored variation of unknown consequence, and their absence from the current reference genome can lead to experimental artifacts and hide true variants when re-sequencing additional human genomes. Here we present a de novo human genome assembly that surpasses the continuity of GRCh38 , along with the first gapless, telomere-to-telomere assembly of a human chromosome. This was enabled by high-coverage, ultra-long-read nanopore sequencing of the complete hydatidiform mole CHM13 genome, combined with complementary technologies for quality improvement and validation. Focusing our efforts on the human X chromosome , we reconstructed the ∼2.8 megabase centromeric satellite DNA array and closed all 29 remaining gaps in the current reference, including new sequence from the human pseudoautosomal regions and cancer-testis ampliconic gene families (CT-X and GAGE). This complete chromosome X, combined with the ultra-long nanopore data, also allowed us to map methylation patterns across complex tandem repeats and satellite arrays for the first time. These results demonstrate that finishing the human genome is now within reach and will enable ongoing efforts to complete the remaining human chromosomes. : #ref-1 : #ref-2 : #ref-3
729 downloads neuroscience
Deep convolutional neural networks have emerged as the state of the art for predicting single-unit responses in a number of visual areas. While such models outperform classical linear-nonlinear and wavelet-based feature representations, we currently do not know what additional nonlinear computations they approximate. Divisive normalization (DN) has been suggested as one such nonlinear, canonical cortical computation, which has been found to be crucial for explaining nonlinear responses to combinations of simple stimuli such as gratings. However, it has neither been tested rigorously for its ability to account for spiking responses to natural images nor do we know to what extent it can close the gap to high-performing black-box models. Here, we developed an end-to-end trainable model of DN that learns the pool of normalizing neurons and the magnitude of their contribution directly from the data. We used this model to investigate DN in monkey primary visual cortex (V1) under stimulation with natural images. We found that this model outperformed linear-nonlinear and wavelet-based feature representations and came close to the performance of deep neural networks. Surprisingly, within the classical receptive field, oriented features were normalized preferentially by features with similar orientation preference rather than non-specifically as assumed by current models of DN. Thus, our work provides a new, quantitative and interpretable predictive model of V1 applicable to arbitrary images and refines our view on the mechanisms of gain control within the classical receptive field.
728 downloads neuroscience
One of the main ways we interact with the world is using our hands. In macaques, the circuit formed by the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and trained to transform visual features into the muscle fiber velocity required to grasp objects would recapitulate neural data in the macaque grasping circuit. While a number of network architectures produced the required kinematics, modular networks with visual input and activity that was encouraged to be biologically realistic best matched neural data and the inter-area differences present in the biological circuit. Network dynamics could be explained by simple rules that also allowed the correct prediction of kinematics and neural responses to novel objects, providing a potential mechanism for flexibly generating grasping movements.
727 downloads biophysics
The ultimate goal of biological superresolution fluorescence microscopy is to provide three-dimensional resolution at the size scale of a fluorescent marker. Here, we show that, by localizing individual switchable fluorophores with a probing doughnut-shaped excitation beam, MINFLUX nanoscopy provides 1 to 3 nanometer resolution in fixed and living cells. This progress has been facilitated by approaching each fluorophore iteratively with the probing doughnut minimum, making the resolution essentially uniform and isotropic over scalable fields of view. MINFLUX imaging of nuclear pore complexes of a mammalian cell shows that this true nanometer scale resolution is obtained in three dimensions and in two color channels. Relying on fewer detected photons than popular camera-based localization, MINFLUX nanoscopy is poised to open a new chapter in the imaging of protein complexes and distributions in fixed and living cells.
727 downloads ecology
Greg Boyce, Emile Gluck-Thaler, Jason C. Slot, Jason E Stajich, William J. Davis, Tim Y. James, John R. Cooley, Daniel G. Panaccione, Jørgen Eilenberg, Henrik H. De Fine Licht, Angie M. Macias, Matthew C. Berger, Kristen L. Wickert, Cameron M. Stauder, Ellie J. Spahr, Matthew D. Maust, Amy M. Metheny, Chris Simon, Gene Kritsky, Kathie T. Hodge, Richard A. Humber, Terry Gullion, Dylan P. G. Short, Teiya Kijimoto, Dan Mozgai, Nidia Arguedas, Matt T. Kasson
Entomopathogenic fungi routinely kill their hosts before releasing infectious spores, but select species keep insects alive while sporulating, which enhances dispersal. Transcriptomics and metabolomics studies of entomopathogens with post-mortem dissemination from their parasitized hosts have unraveled infection processes and host responses, yet mechanisms underlying active spore transmission by Entomophthoralean fungi in living insects remain elusive. Here we report the discovery, through metabolomics, of the plant-associated amphetamine, cathinone, in four Massospora cicadina-infected periodical cicada populations, and the mushroom-associated tryptamine, psilocybin, in annual cicadas infected with Massospora platypediae or Massospora levispora, which appear to represent a single fungal species. The absence of some fungal enzymes necessary for cathinone and psilocybin biosynthesis along with the inability to detect intermediate metabolites or gene orthologs are consistent with possibly novel biosynthesis pathways in Massospora. The neurogenic activities of these compounds suggest the extended phenotype of Massospora that modifies cicada behavior to maximize dissemination is chemically-induced.
725 downloads bioinformatics
Nicola De Maio, Liam P. Shaw, Alasdair Hubbard, Sophie George, Nick Sanderson, Jeremy Swann, Ryan Wick, Manal AbuOun, Emma Stubberfield, Sarah J Hoosdally, Derrick W Crook, Timothy E. A. Peto, Anna E Sheppard, Mark J. Bailey, Daniel S Read, Muna F. Anjum, A Sarah Walker, Nicole Stoesser, The REHAB consortium
Illumina sequencing allows rapid, cheap and accurate whole genome bacterial analyses, but short reads (<300 bp) do not usually enable complete genome assembly. Long read sequencing greatly assists with resolving complex bacterial genomes, particularly when combined with short-read Illumina data (hybrid assembly); however, it is not clear how different long-read sequencing methods impact on assembly accuracy. Relative automation of the assembly process is also crucial to facilitating high-throughput complete bacterial genome reconstruction, avoiding multiple bespoke filtering and data manipulation steps. In this study, we compared hybrid assemblies for 20 bacterial isolates, including two reference strains, using Illumina sequencing and long reads from either Oxford Nanopore Technologies (ONT) or from SMRT Pacific Biosciences (PacBio) sequencing platforms. We chose isolates from the Enterobacteriaceae family, as these frequently have highly plastic, repetitive genetic structures and complete genome reconstruction for these species is relevant for a precise understanding of the epidemiology of antimicrobial resistance. We de novo assembled genomes using the hybrid assembler Unicycler and compared different read processing strategies. Both strategies facilitate high-quality genome reconstruction. Combining ONT and Illumina reads fully resolved most genomes without additional manual steps, and at a lower cost per isolate in our setting. Automated hybrid assembly is a powerful tool for complete and accurate bacterial genome assembly.
714 downloads synthetic biology
Biology offers compelling proof that macroscopic "living materials" can emerge from reactions between diffusing biomolecules. Here, we show that molecular self-organization could be a similarly powerful approach for engineering functional synthetic materials. We introduce a programmable DNA-hydrogel that produces tunable patterns at the centimeter length scale. We generate these patterns by implementing chemical reaction networks through synthetic DNA complexes, embedding the complexes in hydrogel, and triggering with locally applied input DNA strands. We first demonstrate ring pattern formation around a circular input cavity and show that the ring width and intensity can be predictably tuned. Then, we create patterns of increasing complexity, including concentric rings and non-isotropic patterns. Finally, we show "destructive" and "constructive" interference patterns, by combining several ring-forming modules in the gel and triggering them from multiple sources. We further show that computer simulations based on the reaction-diffusion model can predict and inform the programming of target patterns.
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