Biology subjects

He, Y.

Publications and source records attributed to He, Y..

At least 631 records · Page 35Linked to original sources

bioRxiv2018

Dynamic Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model

MotivationNowadays brain connectivity analysis has attracted tremendous attention and has been at the foreground of neuroscience research. Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activity. Growing evidence now suggests that the brain connectivity network experiences alternations with the presence of numerous neurological disorders, thus differential brain network analysis may provides new insights into disease pathologies. For the matrix-valued data in brain connectivity analysis, existing graphical model estimation methods assume a vector normal distribution that in essence requires the columns of the matrix data to be independent. It is obviously not true, they have limited applications. Among the few solutions on graphical model estimation under a matrix normal distribution, none of them tackle the estimation of differential graphs across different populations. This motivates us to consider the differential network for matrix-variate data to detect the brain connectivity alternation.\n\nResultsThe primary interest is to detect spatial locations where the connectivity, in terms of the spatial partial correlation, differ across the two groups. To detect the brain connectivity alternation, we innovatively propose a Matrix-Variate Differential Network (MVDN) model. MVDN assumes that the matrix-variate data follows a matrix-normal distribution. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix where the temporal information is fully excavated. We propose an ADMM algorithm for the Lasso penalized D-trace loss optimization problem. We investigate theoretical properties of the estimator. We show that under mild and regular conditions, the proposed method can identify all differential edges accurately with probability tending to 1 in high-dimensional setting where dimensions of matrix-valued data p, q and sample size n are all allowed to go to infinity. Simulation studies demonstrate that MVDN provides more accurate differential network estimation than that achieved by other state-of-the-art methods. We apply MVDN to Electroencephalography (EEG) dataset, which consists of 77 alcoholic individuals and 45 controls. The hub genes and differential interaction patterns identified are consistent with existing experimental studies.\n\nContactheyong@sdufe.edu.cn\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2018

TCR-pMHC bond length controls TCR ligand discrimination

T-cell receptors (TCRs) detect specifically and sensitively a small number of agonist peptide-major histocompatibility complexes (pMHCs) from an ocean of structurally similar self-pMHCs to trigger antigen-specific adaptive immune responses1-4. Despite intense efforts, the mechanism underlying TCR ligand discrimination remains a major unanswered question in immunology. Here we show that a TCR discriminates between closely related peptides by forming TCR-pMHC bonds with different lengths, which precisely control the accessibility of CD3{zeta} immunoreceptor tyrosine-based activation motifs (ITAMs) for phosphorylation. Using in situ fluorescence resonance energy transfer (FRET)3,5, we measured the intermolecular length of single TCR-pMHC bonds and the intramolecular distance of individual TCR-CD3{zeta} complexes at the membrane of live primary T cells. We found that an agonist forms a short TCR-pMHC bond to pull the otherwise sequestered CD3{zeta} off the inner leaflet of the plasma membrane, leading to full exposure of its ITAMs for strong phosphorylation. By contrast, a structurally similar weaker peptide forms a longer bond with the TCR, resulting in partial dissociation of CD3{zeta} from the membrane and weak phosphorylation. Furthermore, we found that TCR-pMHC bond length determines 2D TCR binding kinetics and affinity, T-cell calcium signaling and T-cell proliferation, governing the entire process of signal reception, transduction and regulation. Thus, our data reveal the fundamental mechanism by which a TCR deciphers the structural differences between foreign antigens and self-peptides via TCR-pMHC bond length to initiate different TCR signaling for ligand discrimination.

immunology↗

bioRxiv2018

A new insight into underlying disease mechanism through semi-parametric latent differential network model

BackgroundIn genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.\n\nResultsWe propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness.\n\nConclusionsThe proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended.

bioinformatics↗

bioRxiv2018

Unbiased Age-Appropriate Structural Brain Atlases for Chinese Pediatrics

In magnetic resonance imaging (MRI) studies of children brain development, structural brain atlases usually serve as important references of pediatric population in which individual images are spatially normalized into a common or standard stereotactic space. However, the existing popular children brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD atlases) are made mostly based on MR images from Western populations, and are thus insufficient to characterize the brains of Chinese children due to the neuroanatomical differences that are relevant to genetic and environmental factors. By collecting high-quality T1- and T2- weighted MR images from 328 typically developing Chinese children aged from 6 to 12 years old, we created a set of age-appropriate Chinese pediatric (CHN-PD) atlases using an unbiased template construction algorithm. The CHN-PD atlases included the head/brain templates, the symmetric brain template, the gender-specific brain templates and the corresponding tissue probability atlases. Moreover, the atlases contained multiple age-specific templates with a one-year interval. A direct comparison of the CHN-PD and the NIH-PD atlases revealed remarkable anatomical differences bilaterally in the lateral frontal and parietal regions and somatosensory cortex. While applying the CHN-PD atlases to two independent Chinese pediatric datasets (N = 114 and N = 71, respectively), machine-learning regression approaches revealed higher prediction accuracy on brain ages than the usage of NIH-PD atlases. These results suggest that the CHN-PD brain atlases are necessary and important for future typical and atypical developmental studies in Chinese pediatric population. Currently, the CHN-PD atlases have been released on the NITRC website (https://www.nitrc.org/projects/chn-pd).

neuroscience↗

bioRxiv2018

Tree Shape-based approaches for the Comparative study of Cophylogeny

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long-term interaction, such as between host and pathogen species. Discordance between host and pathogen phylogenies may occur due to pathogen host-switch events, pathogen speciation within a host species, and extinction. Here, we investigated the use of tree shape distance measures to quantify the degree of cophylogeny for the comparative analysis of host-pathogen interactions across taxonomic groups.\n\nWe firstly implemented a coalescent model to simulate pathogen phylogenies within a fixed host tree, given the cospeciation probability, migration rate between hosts, and pathogen speciation rate within hosts.\n\nNext, we used simulations from this model to evaluate 13 distance metrics between these trees and the host tree, including Robinson-Foulds distance and two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and can accommodate trees of different sizes. Finally, we used these distance metrics to revisit actual datasets from published cophylogenetic studies across all taxonomic groups, where authors described the observed associations as representing a high or low degree of cophylogeny.\n\nOur simulation analyses demonstrated that some metrics are more informative than others with respect to specific coevolution parameters. For example, the Sim metric was the most responsive to variation in coalescence rates, whereas the unlabeled kernel metric was the most responsive to cospeciation probabilities. We also determined that distance metrics were more informative about the model parameters when the underlying parameter values did not assume extreme values, e.g., rapid host switching. When applied to real datasets, projection of these trees associations into a parameter space defined by the 13 distance metrics revealed some clustering of studies reporting low concordance. This suggested that different investigators are describing concordance in a consistent way across biological systems, and that these expert subjective assessments can be at least partly quantified using distance metrics.\n\nOur results support the hypothesis that tree distance measures can be useful for quantifying host and pathogen cophylogeny. This motivates the usage of distance metrics in the field of coevolution and supports the development of simulation-based methods, i.e., approximate Bayesian computation, to estimate coevolutionary parameters from the discordant shapes of host and pathogen trees. [tree shape; cophylogeny; codivergence; coevolution; host switching; tree metrics; kernel]

evolutionary biology↗

bioRxiv2018

Inducible formation of leading cells driven by CD44 switching gives rise to collective invasion

Collective invasion into adjacent tissue is a hallmark of luminal breast cancer, with about 20% of cases that eventually undergo metastasis. It remained unclear how less aggressive luminal-like breast cancer transit to invasive cancer. Our study revealed that CD44hi cancer cells are the leading subpopulation in collective invading cancer cells, which could efficiently lead the collective invasion of CD44lo/follower cells. CD44hi/leading subpopulation showed specific gene signature of a cohort of hybrid epithelial/mesenchymal state genes and key functional co-regulators of collective invasion, which was distinct from CD44lo/follower cells. However, the CD44hi/leading cells, in partial-EMT state, were readily switching to CD44lo phenotype along with collective movements and vice versa, which is spontaneous and sensitive to tumor microenvironment. The CD44lo-to-CD44hi conversion is accompanied with a shift of CD44s-to-CD44v, but not corresponding to the conversion of non-CSC-to-CSC. Therefore, the CD44hi leader cells are not a stable subpopulation in breast tumors. This plasticity and ability to generate CD44hi carcinoma cells with enhanced invasion-initiating powers might be responsible for the transition from in situ to invasive behavior of luminal-type breast cancer.\n\nSignificanceNow, the mechanisms involved in local invasion and distant metastasis are still unclear. We identified a switch of CD44 that drives leader cell formation during collective invasion in luminal breast cancer. We provided evidence that interconversions between low and high CD44 states occur frequently during collective invasion. Furthermore, these findings demonstrated that the CD44hi/leader cells featuring partial EMT are inducible and attainable in response to tumor microenvironment. The CD44lo cancer cells are plastic that readily shift to CD44hi state, accompanied with shifts of CD44s-to-CD44v, thereby increasing tumorigenic and malignant potential. There are many \"non-invasiveness\" epithelial/follower cells with reversible invasive potential within an individual tumor, that casting some challenges on molecular targeting therapy.

cancer biology↗

bioRxiv2018

Trio deep-sequencing does not reveal unexpected mutations in Cas9-edited monkeys

CRISPR-Cas9 is a widely-used genome editing tool, but its off-target effect remains a concern, especially in view of future clinical applications. Non-human primates (NHPs) share close genetic and physiological similarities with humans, making them an ideal preclinical model for developing Cas9-based therapies. However, no comprehensive in vivo off-target assessment has been conducted in NHPs. Here we performed whole genome trio sequencing of Cas9-treated monkeys. We found they only carried a small number of de novo mutations that can be explained by expected spontaneous mutations, and no unexpected mutations were detected.

bioengineering↗

bioRxiv2018

What can computational modeling tell us about the diversity of odor-capture structures in the Pancrustacea?

A major transition in the history of the Pancrustacea was the invasion of several lineages of these animals onto land. We investigated the functional performance of odor-capture organs, antennae with olfactory sensilla arrays, through the use of a computational model of advection and diffusion of odorants to olfactory sensilla while varying three parameters thought to be important to odor capture (Reynolds number, gap-width-to-sensillum-dameter ratio, and angle of the sensilla array with respect to oncoming flow). We also performed a sensitivity analysis on these parameters using uncertainty quantification to analyze their relative contributions to odor-capture performance. The results of this analysis indicate that odor capture in water and in air are fundamentally different. Odor capture in water and leakiness of the array are highly sensitive to Reynolds number and moderately sensitive to angle, whereas odor capture in air is highly sensitive to gap widths between sensilla and moderately sensitive to angle. Leakiness is not a good predictor of odor capture in air, likely due to the relative importance of diffusion to odor transport in air compared to water. We also used the sensitivity analysis to make predictions about morphological and kinematic diversity in extant groups of aquatic and terrestrial crustaceans. Aquatic crustaceans will likely exhibit denser arrays and induce flow within the arrays, whereas terrestrial crustaceans will rely on more sparse arrays with wider gaps and little-to-no animal-induced currents.

physiology↗

bioRxiv2018

An expanded toolkit for gene tagging based on MiMIC and scarless CRISPR tagging in Drosophila

We generated new genetic tools to efficiently tag genes in Drosophila. Double Header (DH) utilizes intronic MiMIC/CRIMIC insertions to generate artificial exons for GFP mediated protein trapping or T2A-GAL4 gene trapping in vivo based on CRE recombinase to avoid embryo injections. DH significantly increases integration efficiency compared to previous strategies and faithfully reports the expression pattern of genes and proteins. The second technique targets genes lacking coding introns using a two-step cassette exchange. First, we replace the endogenous gene with an excisable compact dominant marker using CRISPR making a null allele. Second, the insertion is replaced with a protein::tag cassette.\n\nThis sequential manipulation allows the generation of numerous tagged alleles or insertion of other DNA fragments that facilitates multiple downstream applications. Both techniques allow precise gene manipulation and facilitate detection of gene expression, protein localization and assessment of protein function, as well as numerous other applications.

genetics↗

bioRxiv2018

Domain Insertion Permissibility is a Measure of Engineerable Allostery in Ion Channels

Allostery is a fundamental principle of protein regulation that remains poorly understood and hard to engineer, in particular in ion channels. Here we use human Inward Rectifier K+ Channel Kir2.1 to establish domain insertion permissibility as a new experimental paradigm to identify engineerable allosteric sites. We find that permissibility is best explained by dynamic protein properties, such as conformational flexibility. Many allosterically regulated sites in Kir2.1 or sites equivalent to those regulated in homologs, such as G-protein-gated inward rectifier K+ channels (GIRK), have differential permissibility; that is, for these sites permissibility depends on the structural properties of the inserted domain. Our data and the well-established link between protein dynamics and allostery led us to propose that differential permissibility is a metric of both existing and latent allostery in Kir2.1. In support of this notion, inserting light-switchable domains into either existing or latent allosteric sites, but not elsewhere, renders Kir2.1 activity sensitive to light.

biochemistry↗

bioRxiv2018

MTAP loss correlates with an immunosuppressive profile in GBM and its substrate MTA stimulates alternative macrophage polarization

Glioblastoma (GBM) is a lethal brain cancer known for its potent immunosuppressive effects. Loss of Methylthioadenosine Phosphorylase (MTAP) expression, via gene deletion or epigenetic silencing, is one of the most common alterations in GBM. Here, we show that MTAP loss in GBM cells is correlated with differential expression of immune regulatory genes. In silico analysis of gene expression profiles in GBM samples revealed that low MTAP expression is correlated with reduced proportions of {gamma}{delta}T cells, fewer activated CD4 cells, and an increased proportion of M2 macrophages. Using in vitro macrophage models, we found that methylthioadenosine (MTA), the metabolite that accumulates as a result of MTAP loss in GBM cells, promotes the immunosuppressive alternative activation (M2) of macrophages. We show that this effect of MTA on macrophages is independent of IL4/IL3 signaling, is mediated by the adenosine A2B receptor, and can be pharmacologically reversed. This study suggests that MTAP loss in GBM cells contributes to the immunosuppressive microenvironment, and that MTAP status should be a factor for consideration in understanding GBM immune states and devising immunotherapy-based approaches for treating MTAP-null GBM.

cancer biology↗

bioRxiv2018

Autophagy determines osimertinib resistance through regulation of stem cell-like properties in EGFR-mutant lung cancer

Drug resistance to Osimertinib, a 3rd-generation EGFR-TKI is inevitable. Autophagy plays a contradictory role in resistance of 1st and 2nd generation EGFR-TKI, and its significance in osimertinib resistance is much less clear. We therefore investigated whether autophagy determines osimertinib resistance. First, osimertinib induced autophagy to a much greater extent than that of gefitinib, and autophagy inhibition further increased osimertinib efficacy. Next, enhanced autophagy was found in osimertinib resistant cells and autophagy inhibition partially reversed osimertinib resistance. Enhanced stem-cell like properties were found in resistant cells, and siRNA-knock down of SOX2 or ALDH1A1reversed osimertinib resistance. Of note, autophagy inhibition or siRNA-knock down of Beclin-1 decreased expression of SOX2 and ALDH1A1 and stem-cell like properties. Next, autophagy inhibition and osimertinib in combination effectively blocked tumor growth in xenografts, which was associated with decreased autophagy and stem cell-like properties in vivo. Finally, enhanced autophagy was found in lung cancer patients with resistance to osimertinib. In conclusion, the current study delineates a previously unknown function of autophagy in determining osimertinib resistance through promoting stem-cell like properties.

cancer biology↗

bioRxiv2018

TBtools, a Toolkit for Biologists integrating various HTS-data handling tools with a user-friendly interface

The rapid development of high-throughput sequencing (HTS) techniques has led biology into the big-data era. Data analyses using various bioinformatics tools rely on programming and command-line environments, which are challenging and time-consuming for most wet-lab biologists. Here, we present TBtools (a Toolkit for Biologists integrating various biological data handling tools), a stand-alone software with a user-friendly interface. The toolkit incorporates over 100 functions, which are designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing to interactive data visualization. A wide variety of graphs can be prepared in TBtools, with a new plotting engine ("JIGplot") developed to maximum their interactive ability, which allows quick point-and-click modification to almost every graphic feature. TBtools is a platform-independent software that can be run under all operating systems with Java Runtime Environment 1.6 or newer. It is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases.

bioinformatics↗

bioRxiv2018

PAGE4 and Conformational Switching: Insights from Molecular Dynamics Simulations and Implications for Prostate Cancer

Prostate-Associated Gene 4 (PAGE4) is a disordered protein implicated in the progression of prostate cancer. PAGE4 can be phosphorylated at two residue sites by Homeodomain-Interacting Protein Kinase 1 (HIPK1) to facilitate its binding to the Activator Protein-1 (AP-1) transcription factor. In contrast, a further hyperphosphorylation of PAGE4 by CDC-Like Kinase 2 (CLK2) reduces its binding affinity to AP-1, thus affecting the androgen receptor (AR) activity. Both SAXS and smFRET experiments have shown a structural expansion of PAGE4 upon hyperphosphorylation and a significant increase in size at its N-terminal half than that at its C-terminus. To understand the molecular mechanism underlying this structural transition, we performed a series of constant temperature molecular dynamics simulations using Atomistic AWSEM -- a multi-scale molecular model combining detailed atomistic and coarse-grained simulation approaches. Our simulations show that electrostatic interaction drives a transient formation of an N-terminal loop, which causes the change in size for different phosphorylated forms of PAGE4. Phosphorylation also changes the preference of secondary structure formation of PAGE4, which signifies a transition between states that display different degree of disorder. Finally, we construct a mechanism-based mathematical model that allows us to capture the interactions of different forms of PAGE4 with AP-1 and AR, a key therapeutic target in prostate cancer. Our model predicts intracellular oscillatory dynamics of HIPK1-PAGE4, CLK2-PAGE4 and AR activity, indicating phenotypic heterogeneity in an isogenic cell population. Thus, conformational switching among different forms of PAGE4 may potentially affect the efficiency of therapeutic targeting of AR.

biophysics↗

bioRxiv2018

Exploring morphological motifs for a single neuron based on multiple 3D reconstructions

The morphology of individual neurons is useful to study structures and functions of nervous system. Researchers have invented many semi or fully automatic tracing methods to efficiently generate a reconstruction from a single neuron. Different tracing methods have different design principles, and could produce different reconstructions. However, the \"common substructures\" of various reconstructions, called morphological motifs, should be highly reliable. In this work, we propose a Vaa3D based framework to explore morphological motifs of 3D reconstructions from a single neuron. The framework contains four steps: (1) resampling and sorting each reconstruction according to a standard reconstruction, such as a gold standard reconstruction, consensus reconstruction or a certain reliable reconstruction; (2) applying local alignment algorithm for each pair of the standard reconstruction and a reconstruction, or each pair of two reconstructions; (3) constructing overlaps based on selected points in local alignment pairs; (4) obtaining morphological motifs by post-processing these overlaps. Under the proposed framework, three methods were implemented and tested on a dataset of 73 fruitfly neurons released by the BigNeuron project (http://bigneuron.org), which contains a gold standard reconstruction, a consensus reconstruction and about 40 automatic reconstructions for each neuron. We quantitatively evaluated these three methods to choose reliable morphological motifs.

bioinformatics↗

bioRxiv2018

Dissecting historical changes of selective pressures in the evolution of human pigmentation

Human pigmentation is a highly diverse and complex trait among populations, and has drawn particular attention from both academic and non-academic investigators for thousands of years. Previous studies detected selection signals in several human pigmentation genes, but few studies have integrated contribution from multiple genes to the evolution of human pigmentation. Moreover, none has quantified selective pressures on human pigmentation over epochs and between populations. Here, we dissect dynamics and differences of selective pressures during different periods and between distinct populations with new approaches. We propose a new model with multiple populations to estimate historical selective pressures by summarizing selective pressures on multiple genes. We use genotype data of 19 genes associated with human pigmentation from 17 datasets, and obtain data for 2346 individuals of six representative population groups from worldwide. Our results quantify selective pressures on light pigmentation not only in modern Europeans (0.0249/generation) but also in proto-Eurasians (0.00665/generation). Our results also support several derived alleles associated with human dark pigmentation may under directional selection by quantifying differences of selective pressures between populations. Our study provides a first attempt to quantitatively investigate the dynamics of selective pressures during different time periods in the evolution of human pigmentation, and may facilitate studies of the evolution of other complex traits.\n\nAuthor SummaryThe color variation of human skin, hair, and eye is affected by multiple genes with different roles. This diversity may be shaped by natural selection and adapted for ultraviolet radiation in different environments around the world. As human populations migrated out from Africa, the ultraviolet radiation in the environment they encountered also changed. It is possible that the selective pressures on human pigmentation varied throughout human evolutionary history. In this study, we develop a new approach and estimate historical selective pressures on light pigmentation not only in modern Europeans but also in proto-Eurasians. To our best knowledge, this is the first study that quantifies selective pressures during different time periods in the evolution of human pigmentation. Besides, we provide statistical evidence to support several genes associated with human dark pigmentation may be favored by natural selection. Thus, natural selection may not only affect light pigmentation in Eurasians, but also influence dark pigmentation in Africans.

evolutionary biology↗

bioRxiv2018

Network-based asymmetry of the human auditory system

Converging evidence from activation, connectivity and stimulation studies suggests that auditory brain networks are lateralized. Here we show that these findings can be at least partly explained by the asymmetric network embedding of the primary auditory cortices. Using diffusion-weighted imaging in three independent datasets, we investigate the propensity for left and right auditory cortex to communicate with other brain areas by quantifying the centrality of the auditory network across a spectrum of communication mechanisms, from shortest path communication to diffusive spreading. Across all datasets, we find that the right auditory cortex is better integrated in the connectome, facilitating more efficient communication with other areas, with much of the asymmetry driven by differences in communication pathways to the opposite hemisphere. Critically, the primacy of the right auditory cortex emerges only when communication is conceptualized as a diffusive process, taking advantage of more than just the topologically shortest paths in the network. Altogether, these results highlight how the network configuration and embedding of a particular region may contribute to its functional lateralization.

neuroscience↗

bioRxiv2017

Characterization and Validation of a Novel Group of Type V, Class 2 Nucleases for in vivo Genome Editing

CRISPR-based genome editing is an enabling technology with potential to dramatically transform multiple industries. Identification of additional editing tools will be imperative for broad adoption and application of this technology. A novel Type V, Class 2 CRISPR nuclease system was identified from Microgenomates and Smithella bacterial species (CRISPR from Microgenomates and Smithella, Cms1). This system was shown to efficiently generate indel mutations in the major crop plant rice (Oryza sativa). Cms1 are distinct from other Type V nucleases, are smaller than most other CRISPR nucleases, do not require a tracrRNA, and have an AT-rich protospacer-adjacent motif site requirement. A total of four novel Cms1 nucleases across multiple bacterial species were shown to be functional in a eukaryotic system. This is a major expansion of the Type V CRISPR effector protein toolbox and increases the diversity of options available to researchers.

molecular biology↗