Biology subjects

Liu, M.

Publications and source records attributed to Liu, M..

522 records · Page 29Linked to original sources

bioRxiv2018

Sequencing of Panax notoginseng genome reveals genes involved in disease resistance and ginsenoside biosynthesis

Panax notoginseng is a traditional Chinese herb with high medicinal and economic value. There has been considerable research on the pharmacological activities of ginsenosides contained in Panax spp.; however, very little is known about the ginsenoside biosynthetic pathway. We reported the first de novo genome of 2.36 Gb of sequences from P. notoginseng with 35,451 protein-encoding genes. Compared to other plants, we found notable gene family contraction of disease-resistance genes in P. notoginseng, but notable expansion for several ATP-binding cassette (ABC) transporter subfamilies, such as the Gpdr subfamily, indicating that ABCs might be an additional mechanism for the plant to cope with biotic stress. Combining eight transcriptomes of roots and aerial parts, we identified several key genes, their transcription factor binding sites and all their family members involved in the synthesis pathway of ginsenosides in P. notoginseng, including dammarenediol synthase, CYP716 and UGT71. The complete genome analysis of P. notoginseng, the first in genus Panax, will serve as an important reference sequence for improving breeding and cultivation of this important nutraceutical and medicinal but vulnerable plant species.

genomics↗

bioRxiv2018

How do wind speed, release height, seed morphology interact to determine seed dispersal trajectory of Calligonum (Polygonaceae) species

How seed dispersal trajectory shifts with abiotic and biotic factors and what is the relationship between seed dispersal distance and dispersal trajectory are remain unclear. We used wind tunnel and video camera to track the seed dispersal trajectory of 7 Calligonum species with different appendages under the different wind speeds and the release heights. Dispersal trajectories and distances were determined by video analysis and spatial coordinate transformation. Based on perspective principle, 4 modes of trajectories were determined. Wind speed, seed mass and release height were the key factors determining seed dispersal trajectory modes. Release height and wind speed tended to have the strongest explanatory power on seeds with bristles and wings, respectively. Different trajectory modes lead to different dispersal distance, while the same dispersal distance can be the result of different trajectory modes. The proportion of species trajectory modes formed its trajectory spectrum. Wind speed tends to have strong influence on light and low-wind-loading seeds, release height tends to have that on heavy and high-wind-loading seeds. Species with high proportion of horizontal projectile and projectile have high dispersal capacity, vice versa. Therefore, trajectory spectrum of a species reveals its primary dispersal strategies and evolutionary consequences.

ecology↗

bioRxiv2018

The Mediator CDK8-Cyclin C complex modulates vein patterning in Drosophila by stimulating Mad-dependent transcription

Dysregulations of CDK8 and its regulatory partner CycC, two subunits of the conserved Mediator complex, have been linked to diverse human diseases such as cancer, thus it is essential to understand the regulatory network mobilizing the CDK8-CycC complex in both normal development and tumorigenesis. To identify upstream regulators or downstream effectors of CDK8, we performed a dominant modifier genetic screen in Drosophila based on the defects in vein patterning caused by specific depletion or overexpression of CDK8 or CycC in wing imaginal discs. We identified 26 genomic loci whose haploinsufficiency can modify these CDK8-specific phenotypes. Further analysis of two deficiency lines and mutant alleles led us to identify interactions between CDK8-CycC and the components of the Decapentaplegic (Dpp, the Drosophila homolog of TGF{beta}) signaling pathway. We observed that CDK8-CycC positively regulates transcription activated by Mad (Mothers against dpp), the primary transcription factor downstream of the Dpp/TGF{beta} signaling pathway. CDK8 can directly interact with Mad in vitro through the linker region between the DNA-binding MH1 (Mad homology 1) domain and the carboxy terminal MH2 transactivation domain. Besides CDK8 and CycC, further analyses of other subunits of the Mediator complex have revealed six additional Mediator subunits that are required for Mad-dependent transcription in the wing discs, including Med12, Med13, Med15, Med23, Med24, and Med31. Furthermore, CDK9 and Yorkie also positively regulate Mad-dependent gene expression in vivo. These results suggest that the Mediator complex may coordinate with other transcription cofactors in regulating Mad-dependent transcription during the wing vein patterning in Drosophila.\n\nSignificanceCDK8 is a conserved subunit of the transcription cofactor Mediator complex that bridges transcription factors with RNA Polymerase II in eukaryotes. Here we explore the role of CDK8 in Drosophila by performing a dominant modifier genetic screen based on vein patterning defects caused by alteration of CDK8-specific activities. We show that components of the Dpp/TGF{beta} signaling pathway genetically interact with CDK8; CDK8 positively regulates gene expression activated by Mad, the key transcription factor downstream of Dpp/TGF{beta} signaling, by directly interacting with the linker region of Mad protein. Given the fundamental roles of Dpp/TGF{beta} signaling in regulating development and its misregulation in various diseases, understanding how Mad/Smad interacts the Mediator complex may have broad implications in understanding and treating these diseases.

developmental biology↗

bioRxiv2018

Microbial coexistence through chemical-mediated interactions

Many microbial functions happen within communities of interacting species. Explaining how species with intrinsically disparate fitness can coexist is important for applications such as manipulating host-associated microbiota or engineering industrial communities. Previous coexistence studies have often neglected interaction mechanisms. Here, we formulate and experimentally constrain a model in which chemical mediators of microbial interactions (e.g. metabolites or waste-products) are explicitly incorporated. We construct many instances of coexistence by simulating community assembly through enrichment and ask how species interactions can explain coexistence. We show that growth-facilitating influences between members are favored in assembled communities. Among negative influences, self-restraint, such as production of self-inhibiting waste, contributes to coexistence, whereas inhibition of other species disrupts coexistence. Coexistence is also favored when interactions are mediated by depletable chemicals that get consumed or degraded, rather than by reusable chemicals that are unaffected by recipients. Our model creates null predictions for coexistence driven by chemical-mediated interactions.

ecology↗

bioRxiv2018

Neural Mechanisms Associated with Semantic and Basic Self-Oriented Memory Processes Interact to Modulate Self-Esteem.

Individuals constantly encounter feedback from others and process this feedback in various ways to maintain positive situational state self-esteem (SSE) in relation to semantic-based or trait self-esteem (TSE). Individuals may utilize a data-driven, or episodic-based process that encodes positive, but not negative, self-relevant information automatically, or employ a semantic-driven process that manipulates encoded negative feedback post-hoc. Its unclear, however, how these processes work either alone or in concert while individuals receive positive and negative feedback to modulate feedback encoding and subsequent SSE. Utilizing neural regions associated with semantic self-oriented and basic encoding processes (mPFC and PCC respectively), and time-frequency and Granger causality analyses to assess mPFC and PCC interactions, this study examined how encoding of positive and negative self-relevant feedback modulated individuals post-task SSE in relation to their TSE while continuous EEG was recorded. Among those with higher levels of TSE, the encoding of positive or negative feedback was not associated with SSE. Rather, higher SSE was associated with mPFC activity to all feedback and higher TSE. The relationship between TSE and SSE was moderated by mPFC-PCC communication such that increases in mPFC-PCC communication led to SSE levels that were consistent with TSE levels. Furthermore, Granger causality analyses indicated that individuals exhibited higher SSE to the extent mPFC influenced PCC in response to positive and negative feedback. Findings highlight the dynamic interplay between semantic self-oriented and basic encoding processes that modulate SSE in relation to TSE, to maintain more positive global selfperceptions in the moment and over time.

neuroscience↗

bioRxiv2018

Coupled Mixed Model for joint genetic analysis of complex disorders from independently collected data sets: application to Alzheimer’s disease and substance use disorder

In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratication, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimers disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases.\n\nThe software is available at https://github.com/HaohanWang/CMM

genetics↗

bioRxiv2018

The lysine demethylase dKDM2 is non-essential for viability, but regulates circadian rhythms in Drosophila

Post-translational modification of histones, such as histone methylation controlled by specific methyltransferases and demethylases, play critical roles in modulating chromatin dynamics and transcription in eukaryotes. Misregulation of histone methylation can lead to aberrant gene expression, thereby contributing to abnormal development and diseases such as cancer. As such, the mammalian lysine-specific demethylase 2 (KDM2) homologs, KDM2A and KDM2B, are either oncogenic or tumor suppressive, depending on specific pathological contexts. However, the role of KDM2 proteins during development in the whole organisms remains poorly understood. Unlike vertebrates, Drosophila has only one KDM2 homolog (dKDM2), but its functions in vivo remain elusive due to the complexities of the existing mutant alleles. To address this problem, we have generated two dKdm2 null alleles using the CRISPR/Cas9 technique. These dKdm2 homozygous mutants are fully viable and fertile, with no developmental defects observed under laboratory conditions. However, the dKdm2 null mutant adults display defects in circadian rhythms. Most of the dKdm2 mutants become arrhythmic under constant darkness, while the circadian period of the rhythmic mutant flies is approximately one hour shorter than the control. Interestingly, opposite defects are observed when dKDM2 is overexpressed in circadian pacemaker neurons. Taken together, these results demonstrate that dKdm2 is not essential for viability; instead, dKDM2 protein plays important roles in regulating circadian rhythms in Drosophila. Further analyses of the molecular mechanisms of how dKDM2 and its orthologs in vertebrates regulate circadian rhythms will advance our understanding of the epigenetic regulations of circadian clocks.

developmental biology↗

bioRxiv2018

Structure of the receptor-activated human TRPC6 ion channel

TRPC6 is a receptor-activated nonselective cation channel that belongs to the family of canonical transient receptor potential (TRPC) channels. It is activated by diacylglycerol, a lipid second messenger. TRPC6 is involved in many physiological processes and implicated in human genetic diseases. Here we present the structure of human TRPC6 homotetramer in complex with a newly identified high affinity inhibitor BTDM solved by single-particle cryo-electron microscopy to 3.8 [A] resolution. The structure shows a two-layer architecture, in which the bell-shaped cytosolic layer holds the transmembrane layer. Extensive inter-subunit interactions of cytosolic domain, including N terminal ankyrin repeats and C terminal coiled-coil, contribute to the tetramer assembly. The high affinity inhibitor BTDM wedges between S5-S6 pore domain and voltage sensor-like domain to inhibit channel opening. Our structure uncovers the molecular architecture of TRPC channels and provides a structural basis for understanding the mechanism of these channels.

biophysics↗

bioRxiv2018

WDR45 contributes to neurodegeneration through regulation of ER homeostasis and neuronal death

Mutations in the autophagy gene WDR45 cause {beta}-propeller protein-associated neurodegeneration (BPAN); however the molecular and cellular mechanism of the disease process is largely unknown. Here we generated constitutive Wdr45 knockout (KO) mice that displayed cognitive impairments, abnormal synaptic transmission and lesions in hippocampus and basal ganglia. Immunohistochemistry analysis shows loss of neurons in prefrontal cortex and basal ganglion in aged mice, and increased apoptosis in these regions, recapitulating a hallmark of neurodegeneration. Quantitative proteomic analysis shows accumulation of endoplasmic reticulum (ER) proteins in KO mouse. Furthermore, we show that a defect in autophagy results in impaired ER turnover and ER stress. The unfolded protein response (UPR) is elevated through IRE1 and possibly other kinase signaling pathways, and eventually leads to neuronal apoptosis. Suppression of ER stress, or activation of autophagy through inhibition of mTOR pathway rescues neuronal death. Thus, our study not only provides mechanistic insights for BPAN, but also suggests that a defect in macroautophagy machinery leads to impairment in selective organelle autophagy.

cell biology↗

bioRxiv2018

Distinct value encoding in striatal direct and indirect pathways during adaptive learning

The striatum is thought to play a central role in action selection and reinforcement, and optogenetic experiments suggest differential roles for direct- and indirect-pathway medium spiny neurons (dMSNs and iMSNs). However, the encoding of value-related information in dMSNs and iMSNs during adaptive decision-making is not well understood. We trained mice on a dynamic foraging task where they had to learn the value of different options based on their recent history of choices and outcomes. Single-cell calcium imaging in dorsomedial striatum revealed that dMSNs and iMSNs were oppositely modulated by the updated value of the different options. Additionally, we found that iMSNs were more active as animals slowed between trials, likely reflecting ongoing changes in motivational state. Together, our results demonstrate that co-activation of dMSNs and iMSNs during action initiation does not simply encode action identity, but instead reflects pathway-specific encoding of movement, motivation, and value information necessary for adaptive decision-making.

neuroscience↗

bioRxiv2018

Electron transfer dictates metabolic reprogramming in proliferating cells under hypoxia

Metabolic reprogramming extensively occurs in proliferating cancer cells. This phenomenon occurs highly heterogeneously, but its origin has remained unclear. Here we use a physicochemical concept of free electron potential (FEP) and its equation of state to profile metabolites. We demonstrate that FEP change between substrates and products exactly reflects electrons dissipated in a metabolic transformation. Based on the law of conservation of electron in chemical reactions, a function of FEP change for central metabolism in proliferating cells are further derived, and it can accurately predict metabolic behaviors under hypoxia by maximizing the cellular FEP change to consume electrons. Therefore, enabling electron transfer dictates metabolic reprogramming in hypoxic cells, which underlies the major findings in cancer metabolism and is supported by our experiments. Importantly, our model established on FEP helps to reveal a combination of promising targets to inhibit tumor growth under hypoxia by blocking electron consumption, and could also guide future studies on cancer metabolism under hypoxia.

biochemistry↗

bioRxiv2017

Brain-Computer Interfaces for Post-Stroke Motor Rehabilitation: A Meta-Analysis

ObjectiveBrain-computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here we provide a first meta-analysis evaluating the clinical effectiveness of BCI-based post-stroke motor rehabilitation.\n\nMethodsTrials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post-stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random-effects inverse variance method was used to calculate the summary effect size.\n\nResultsWe initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post-stroke survivors fulfilling the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta-analysis. Motor improvements, mostly quantified by the upper limb Fugl-Meyer Assessment (FMA-UE), exceeded the minimal clinical important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference (SMD) of 0.79 (95% CI: 0.37 to 1.20) in FMA-UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI-induced functional and structural neuroplasticity at a sub-clinical level.\n\nInterpretationWe found a medium to large effect size of BCI therapy compared to controls. This suggests that BCI technology might be an effective intervention for post-stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

neuroscience↗

bioRxiv2017

Proper Conditional Analysis in the Presence of Missing Data Identified Novel Independently Associated Low Frequency Variants in Nicotine Dependence Genes

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values. In practice, genotype imputation is not always effective, e.g. when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Naive extensions of existing methods either replace missing summary statistics with 0 or discard studies with missing data. These approaches can bias genetic effect estimates and lead to seriously inflated type-I or II errors in conditional analysis, which is a critical tool for identifying independently associated variants.\n\nTo address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amount of missing values. Based on this estimator, we propose a score statistic we call PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to analyze the CHRNA5-CHRNB4-CHRNA3 locus in a large-scale meta-analysis for cigarettes-per-day. Using the new method, we identified three novel variants, independent of known association signals, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants is .46%, improving that of previously reported associations by 17%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.\n\nAUTHOR SUMMARYIt is of great interest to estimate the joint and conditional effects of multiple correlated variants from large scale meta-analysis, in order to fine map causal variants and understand the genetic architecture for complex traits. The contributed summary statistics from participating studies in a meta-analysis often contain missing values, as the imputation methods are not often effective, especially when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing meta-analysis methods do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly biased estimates of joint effects and highly inflated type-I errors for conditional analysis, which will in turn result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics in the meta-analysis contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

genetics↗

bioRxiv2017

Association Analysis and Meta-Analysis of Multi-allelic Variants for Large Scale Sequence Data

MotivationThere is great interest to understand the impact of rare variants in human diseases using large sequence datasets. In deep sequences datasets of >10,000 samples, [~]10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results.\n\nResultsWe propose novel methods to encode multi-allelic sites, conduct single variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of [~]18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single variant association tests, and enhanced gene-level tests over existing approaches.\n\nAvailabilitySoftware packages implementing these methods are available at (https://github.com/zhanxw/rvtests http://genome.sph.umich.edu/wiki/RareMETAL).\n\nContactxiaowei.zhan@utsouthwestem.edu; dajiang.liu@psu.edu

bioinformatics↗

bioRxiv2017

Porcine commensal Escherichia coli: A reservoir for class 1 integrons associated with IS26

Porcine faecal waste is a serious environmental pollutant. Carriage of antimicrobial resistance and virulence-associated genes (VAGs) and the zoonotic potential of commensal Escherichia coli from swine is largely unknown. Furthermore, little is known about the role of commensal E. coli as contributors to the mobilisation of antimicrobial resistance genes between food animals and the environment. Here, we report whole genome sequence analysis of 141 E. coli from the faeces of healthy pigs. Most strains belonged to phylogroups A and B1 and carried i) a class 1 integron; ii) VAGs linked with extraintestinal infection in humans; iii) antimicrobial resistance genes blaTEM, aphAl, cmlA, strAB, tet(A)A, dfrA12, dfrA5, sul1, sul2, sul3; iv) IS26; and v) heavy metal resistance genes (merA, cusA, terA). Carriage of the sulphonamide resistance gene sul3 was notable in this study. The 141 strains belonged to 42 multilocus sequence types, but clonal complex 10 featured prominently. Structurally diverse class 1 integrons that were frequently associated with IS26 carried unique genetic features that were also identified in extraintestinal pathogenic E. coli (ExPEC) from humans. This study provides the first detailed genomic analysis and point of reference for commensal E. coli of porcine origin, facilitating tracking of specific lineages and the mobile resistance genes they carry.\n\nConflict of Interest StatementNone to declare.

genomics↗

bioRxiv2017

The pomegranate (Punica granatum L.) genome provides insights into fruit quality and ovule developmental biology

Pomegranate (Punica granatum L.) with an uncertain taxonomic status has an ancient cultivation history, and has become an emerging fruit due to its attractive features such as the bright red appearance and the high abundance of medicinally valuable ellagitannin-based compounds in its peel and aril. However, the absence of genomic resources has restricted further elucidating genetics and evolution of these interesting traits. Here we report a 274-Mb high-quality draft pomegranate genome sequence, which covers approximately 81.5% of the estimated 336 Mb genome, consists of 2,177 scaffolds with an N50 size of 1.7 Mb, and contains 30,903 genes. Phylogenomic analysis supported that pomegranate belongs to the Lythraceae family rather than the monogeneric Punicaceae family, and comparative analyses showed that pomegranate and Eucalyptus grandis shares the paleotetraploidy event. Integrated genomic and transcriptomic analyses provided insights into the molecular mechanisms underlying the biosynthesis of ellagitannin-based compounds, the color formation in both peels and arils during pomegranate fruit development, and the unique ovule development processes that are characteristic of pomegranate. This genome sequence represents the first reference in Lythraceae, providing an important resource to expand our understanding of some unique biological processes and to facilitate both comparative biology studies and crop breeding.

genomics↗

bioRxiv2017

A Deep Learning Approach to Estimate Collagenous Tissue Nonlinear Anisotropic Stress-Strain Responses from Microscopy Images

Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen as a representative collagenous tissue. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images.

bioengineering↗

bioRxiv2017

Evaluation of ddRADseq for reduced representation metagenome sequencing

Background Who is doing what is the ultimate open question in microbiome study. Shotgun metagenomics is often applied to gain knowledge of functional roles for bacteria in microbial communities, where the data can be used to predict protein encoding genes and enzymatic pathways present in the community, sometimes leading to testable hypotheses for microbial function. We describe a method and basic analysis for a metagenomic adaptation of the double digest restriction site associated DNA sequencing (ddRADseq) protocol for reduced representation metagenome profiling. This technique takes advantage of the sequence specificity of restriction endonucleases to construct an Illumina-compatible sequencing library containing DNA fragments that are between a pair of restriction sites located within close proximity. This results in a reduced sequencing library with coverage breadth that can be tuned by size selection.\n\nResultsWe assessed the performance of the metagenomic ddRADseq approach by applying the method to human stool samples and generating sequence data. We evaluate the extent to which ddRADseq data provides an unbiased reduced representation for microbiome profiling.\n\nConclusionAlthough ddRADseq does introduce some bias in taxonomic representation, the bias is likely to be small relative to DNA extraction bias. ddRADseq appears feasible and could have value as a tool for metagenome-wide association studies.

genomics↗