bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting Selection in Low-Coverage High-Throughput Sequencing Data using Principal Component Analysis
Authors: Meisner Jonas , Albrechtsen Anders , Hanghøj Kristian 
Published: March 2021

1AbstractIdentification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. Here, we present two selections statistics which we have implemented in thePCAngsdframework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. Moreover, we show thatPCAngsdoutperform selection statistics obtained from called genotypes from low-coverage sequencing data.
Using empirical datasets to quantify uncertainty in inferences of landscape genetic resistance due to variation of individual-based genetic distance metrics
Authors: Beninde Joscha , Frantz Alain C. 
Published: March 2021

AbstractEstimates of gene flow are commonly based on inferences of landscape resistance in ecological and evolutionary research and they frequently inform decision-making processes in conservation management. It is therefore imperative that inferences of a landscape factors relevance and its resistance are robust across approaches and reflect real-world gene flow instead of methodological artefacts. Here, we tested the impact of 160 different individual-based pairwise genetic metrics on consistency of landscape genetic inferences.We used three empirical datasets that adopted individual-based sampling schemes and varied in scale (35-25,000 km2) and total number of samples (184-790) and comprise the wild boar,Sus scrofa,the red fox,Vulpes vulpesand the common wall lizard,Podarcis muralis. We made use of a machine-learning algorithm implemented in ResistanceGA to optimally fit resistances of landscape factors to genetic distance metrics and ranked their importance. Employed for nine landscape factors this resulted in 4,320 unique combinations of dataset, landscape factor and genetic distance metric, which provides the basis for quantifying uncertainty in inferences of landscape resistance.Our results demonstrate that there are clear differences in Akaike information criteria (AICc)-based model support and marginal R2-based model fit between different genetic distance metrics. Metrics based on between 1-10 axes of eigenvector-based multivariate analyses (Factorial correspondence analysis, FCA; Principal component analysis, PCA) outperformed more widely used metrics, including the proportion of shared alleles (DPS), with AICc and marginal R2values often an order of magnitude greater in the former. Across datasets, inferences of the directionality of a landscape factors influence on gene flow, e.g. facilitating or impeding it, changed across different genetic distance metrics. The directionality of the inferred resistance was largely consistent when considering metrics based on between 1-10 FCA/PCA axes.Inferences of landscape genetic resistance need to be corroborated using calculations of multiple individual-based pairwise genetic distance metrics. Our results call for the adoption of eigenvector-based quantifications of pairwise genetic distances. Specifically, a preliminary step of analysis should be incorporated, which explores model ranks across genetic distance metrics derived from FCA and PCA, and, contrary to findings of a simulation study, we demonstrate that it suffices to quantify these distances spanning the first ten axes only.
Characterization of induction methods forDrosophilaseizure mutations
Authors: Mituzaite Jurga , Petersen Rasmus , Claridge-Chang Adam , Baines Richard A. 
Published: March 2021

AbstractEpilepsy is one of the most common neurological disorders. Around one third of patients do not respond to current medications. This lack of treatment indicates a need for better understanding of the underlying mechanisms and, importantly, the identification of novel targets for drug manipulation. The fruitflyDrosophila melanogasterhas a fast reproduction time, powerful genetics, and facilitates large sample sizes, making it a strong model of seizure mechanisms. However, there has not yet been a systematic analysis of the wide range of behavioral and physiological phenotypes observed across major fly seizure genotypes. To understand this, we systematically measured seizure severity and secondary behavioral phenotypes at both the larval and adult stage. Comparison of several seizure-induction methods; specifically electrical, mechanical and heat-induction, show that larval electroshock is the most effective at inducing seizures across a wide range of seizure-prone mutants. Locomotion in adults and larvae was found to be non-predictive of seizure susceptibility. Recording activity in identified larval motor neurons revealed variations in action potential patterns, across different genotypes, but these patterns did not correlate with seizure susceptibility. To conclude, while there is wide variation in mechanical induction, heat induction, and secondary phenotypes, electroshock is the most consistent method of seizure induction across known major seizure genotypes inDrosophila.Significance StatementEpilepsy is a neurological disorder affecting 1 in 130 people globally, with a significant impact on patients, families, and society. Approximately one third of epileptics do not respond to currently available medication. Thus, better insights into underlying disease mechanisms and identification of new drugs are needed. Fruit flies (Drosophila melanogaster) are a powerful genetic model: a number of single gene mutant flies exhibit seizures, phenotypes that have been shown to respond to established antiepileptic drugs. We compare methods of seizure induction and their utility, to establish which induction method is the most consistent across a range of different seizure-inducing genetic backgrounds. Adopting a common method for seizure analysis in this model will, we predict, speed identification of novel anti-convulsive treatments.
Interpretable Drug Target Predictions using Self-Expressiveness
Authors: Galeano Diego , Noto Santiago , Jimenez Ruben , Paccanaro Alberto 
Published: March 2021

AbstractThe identification of missing drug targets is critical for the development of treatments and for the molecular elucidation of drug side effects. Drug targets have been predicted by exploiting molecular, biological or pharmacological features of drugs and protein targets. Yet, developing integrative and interpretable machine learning models for predicting drug targets remains a challenging task. We present Inception, an integrative and interpretable matrix completion model for predicting drug targets. Inception is a self-expressive model that learns two similarity matrices: one for drugs and another for protein targets. These learned similarity matrices are key for our models’ interpretability: they can explain how a predicted drug-target interaction can be explain in terms of a linear combination of chemical, biological and pharmacological similarities. We develop a novel objective function with efficient closed-form solution. To demonstrate the ability of Inception at recovering missing drug-target interactions (DTIs), we perform cross-validation experiments with stringent controls of data imbalance, chemical similarities between drugs and sequence similarities between targets. We also assess the performance of our model using a simulated prospective approach. Having trained our model with DTIs from a snapshot 2011 of the DrugBank database, we test whether we could predict DTIs from a 2020 snapshot of DrugBank. Inception outperforms two state-of-the-art drug target prediction models in all the scenarios. This suggests that Inception could be useful for predicting missing drug target interactions while providing interpretable predictions.
Comparison of cerebrospinal fluid biomarkers relevant to neurodegenerative diseases in healthy cynomolgus and rhesus macaque monkeys
Authors: Robertson Emma L. , Boehnke Susan E. , Lyra e Silva Natalia M. , Armitage-Brown Brittney , Winterborn Andrew , Cook D.J. , De Felice Fernanda G. , Munoz Douglas P. 
Published: March 2021

Structured AbstractINTRODUCTIONNon-human primates are important translational models of neurodegenerative disease. We characterized how species, sex, age, and site of sampling affected concentrations of key biomarkers of neurodegeneration.METHODSAmyloid-beta (Aβ40, Aβ42), tau (tTau, pTau), and neurofilament light (NFL) in CSF were measured in 82 laboratory-housed naïve cynomolgus and rhesus macaques of both sexes.RESULTSAβ40, Aβ42, and NFL were significantly higher in rhesus compared with cynomolgus macaques. tTau and NFL were higher in males. pTau was not affected by species or sex. Site of acquisition only affected NFL, with NFL being higher in CSF acquired from lumbar compared with cisterna magna puncture.DISCUSSIONNormative values for key neurodegeneration biomarkers were established for laboratory housed cynomolgus and rhesus macaque monkeys. Differences were observed as a function of species, sex and site of CSF acquisition that should be considered when employing primate models.Research In ContextSystematic review: We reviewed reports characterizing CSF biomarkers of neurodegenerative diseases in non-human primates – an increasingly important model of disease - revealing that studies with laboratory housed macaque monkeys were of small sample size, with a paucity of data about how biomarkers varied as a function of species, sex, age, and site of acquisition.Interpretation: To address this gap, we collected CSF from 82 naïve laboratory housed male and female macaques of two species and measured Aβ40, Aβ42, tTau, pTau, and NFL. In addition to providing normative statistics for concentrations of these biomarkers, we revealed various species and sex differences.Future directions: Establishing normative values of biomarkers is an important step to the efficient development of cynomolgus and rhesus macaques as models of neurodegenerative disorders such as Alzheimer’s disease. Reference values reduce the need for large control groups by which to compare with disease model animals.
Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images
Authors: Pécot Thierry , Cuitiño Maria C. , Johnson Roger H. , Timmers Cynthia , Leone Gustavo 
Published: March 2021

ABSTRACTAutomatic characterization of fluorescent labeling in intact mammalian tissues remains a challenge due to the lack of quantifying techniques capable of segregating densely packed nuclei and intricate tissue patterns. Here, we describe a powerful deep learning-based approach that couples remarkably precise nuclear segmentation with quantitation of fluorescent labeling intensity within segmented nuclei, and then apply it to the analysis of cell cycle dependent protein concentration in mouse tissues using 2D fluorescent still images. First, several existing deep learning-based methods were evaluated to accurately segment nuclei using different imaging modalities with a small training dataset. Next, we developed a deep learning-based approach to identify and measure fluorescent labels within segmented nuclei, and created an ImageJ plugin to allow for efficient manual correction of nuclear segmentation and label identification. Lastly, using fluorescence intensity as a readout for protein concentration, a three-step global estimation method was applied to the characterization of the cell cycle dependent expression of E2F proteins in the developing mouse intestine.
Mako: a graph-based pattern growth approach to detect complex structural variants
Authors: Lin Jiadong , Yang Xiaofei , Kosters Walter , Xu Tun , Jia Yanyan , Wang Songbo , Zhu Qihui , Ryan Mallory , Guo Li , Zhang Chengsheng ,   The Human Genome Structural Variation Consortium, Lee Charles , Devine Scott E. , Eichler Evan E. , Ye Kai 
Published: March 2021

AbstractComplex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. We systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections and pattern growth enables CSV detection without predefined models. Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms. Notably, validation rates of CSV on real data based on experimental and computational validations as well as manual inspections are around 70%, where the medians of experimental and computational breakpoint shift are 13bp and 26bp, respectively. Moreover, Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types, including two novel types of adjacent segments swap and tandem dispersed duplication. Further analysis of these CSVs also revealed impact of sequence homology in the formation of CSVs. Mako is publicly available at https://github.com/jiadong324/Mako.
Imaging-based evaluation of pathogenicity by novelDNM2 variantsassociated with centronuclear myopathy
Authors: Fujise Kenshiro , Okubo Mariko , Abe Tadashi , Yamada Hiroshi , Takei Kohji , Nishino Ichizo , Takeda Tetsuya , Noguchi Satoru 
Published: March 2021

ABSTRACTCentronuclear myopathy (CNM) is characterized clinically by muscle weakness and pathologically by the presence of centralized nuclei and disarrangement of T-tubules in muscle fibers.DNM2which encodes a large GTPase dynamin 2 have been identified as a causative gene for CNM. Nevertheless, the identification ofDNM2variants may not always lead to the definitive diagnosis as their pathogenicity is often unknown.In this study, by imaging T-tubule-like structures reconstitutedin cellulo, we demonstrated that aberrant membrane remodeling by mutant dynamin 2 is tightly associated with gain-of-function features ofDNM2variants. This simplein celluloassay provided quantitative data required for accurately evaluating pathogenicity of reported and novelDNM2variants identified from CNM patients in our cohort. Our approaches combining thein celluloassay with clinical information of the patients enabled to explain the course of a disease progression by pathogenesis of each variant inDNM2-associated CNM.
Copy number evolution in simple and complex tandem repeats across the C57BL/6 and C57BL/10 inbred mouse lines
Authors: Flynn Jullien M. , Brown Emily J. , Clark Andrew G. 
Published: March 2021

AbstractSimple sequence tandem repeats are among the most rapidly evolving compartments of the genome. Some repeat expansions are associated with mammalian disease or meiotic segregation distortion, yet the rates of copy number change across generations are not well known. Here, we use 14 distinct sub-lineages of the C57BL/6 and C57BL/10 inbred mouse strains, which have been evolving independently over about 300 generations, to estimate the rates of copy number changes in genome-wide tandem repeats. Rates of change varied across simple repeats and across lines. Notably, CAG, whose expansions in coding regions are associated with many neurological and other genetic disorders, was highly stable in copy number, likely indicating purifying selection. Rates of change were generally positively correlated with copy number, but the direction and magnitude of changes varied across lines. Some mouse lines experienced consistent losses or gains across most genome-wide simple repeats, but this did not correlate with copy number changes in complex repeats. Rates of copy number change were similar between simple repeats and the much more abundant complex repeats once they were normalized by copy number. Finally, the Y-specific centromeric repeat had a 4-fold higher rate of change than the homologous centromeric repeat on other chromosomes. Structural differences in satellite complexity, or restriction to the Y chromosome and the elevated mutation rate of the male germline, may explain the higher rate of change. Overall, our work underscores the mutational fluidity of long tandem arrays of repeats, and the correlations and constraints between genome-wide tandem repeats which suggest that turnover is not a completely neutral process.
The dynamic interactive network of long non-coding RNAs and chromatin accessibility facilitates erythroid differentiation
Authors: Ren Yunxiao , Zhu Junwei , Han Yuanyuan , Li Pin , Qu Hongzhu , Zhang Zhaojun , Fang Xiangdong 
Published: March 2021

AbstractErythroid differentiation is a dynamic process regulated by multiple factors, while the interaction between long non-coding RNAs and chromatin accessibility and its influence on erythroid differentiation remains unclear. To elucidate this interaction, we employed hematopoietic stem cells, multipotent progenitor cells, common myeloid progenitor cells, megakaryocyte-erythroid progenitor cells, and erythroblasts from human cord blood as an erythroid differentiation model to explore the coordinated regulatory functions of lncRNAs and chromatin accessibility in erythropoiesis by integrating RNA-Seq and ATAC-Seq data. We revealed that the integrated network of chromatin accessibility and LncRNAs exhibits stage-specific changes throughout the erythroid differentiation process, and that the changes at the EB stage of maturation are dramatic. We identified a subset of stage-specific lncRNAs and transcription factors (TFs) that coordinate with chromatin accessibility during erythroid differentiation, in which lncRNAs are key regulators of terminal erythroid differentiation via a lncRNA-TF-gene network. LncRNAPCED1B-AS1was revealed to regulate terminal erythroid differentiation by coordinating GATA1 dynamically binding to the chromatin during erythroid differentiation.DANCR, another lncRNA that is highly expressed at the MEP stage, was verified to promote erythroid differentiation by compromising megakaryocyte differentiation and coordinating with chromatin accessibility and TFs, such as RUNX1. Overall, our results identified the interactive network of lncRNAs and chromatin accessibility in erythropoiesis and provide novel insights into erythroid differentiation and abundant resources for further study.Key PointsLncRNAs regulate erythroid differentiation through coordinating with chromatin accessibility.The integrative multi-omics analysis reveals stage-specific interaction network of LncRNAs and chromatin accessibility in erythropoiesis.
Selecting an appropriate learning management system in open and distance learning: a strategic approach
Authors: Kant Nikhil , Prasad K.D. , Anjali Kumari 
Published: March 2021

PurposeThis paper aims to derive criteria for a strategic selection of learning management system (LMS) after making an analysis of the feedback data collected from learners and academic counselors in open and distance learning (ODL) to evaluate their perceptions. This analysis hints at the need to implement a learning management system (LMS) in ODL. Selecting an appropriate LMS can prove to be a strategic approach for ODL in achieving self-reliance and competitiveness.Design/methodology/approachResearch design includes qualitative design intended to discuss the features, advantages and attributes of different popular LMSs and compare them. In addition, the quantitative design (a questionnaire-based online feedback) to analyze the perceptions of the learners and academic counselors in order to know their e-learning needs has also been used. Results have been exhibited in tabular/graphical formats for easy comprehension and enhanced understanding.FindingsFindings of the study suggest that availability of plethora of LMSs in the market, which also include open source (OS) LMSs, makes the decision- making as regards selection of an appropriate LMS strategically crucial requiring adequate consideration of every aspect such as cost, quality, usage, capacity, budget and most importantly priorities and objectives.Research limitations/implicationsThis study will help educational administrators and decision-makers in ODL for building a quality civilized life and empowered society by removing the constraints related to financial problems, disabilities, time, geographical conditions and many others in bringing education to the doorstep of every willing learner. The technical details of LMS, however, were intentionally kept simplified to achieve the objective and provide easy comprehensibility for the reader with little technical background, which might be a limitation of this study.Originality/valueThis study highlights that ODL has tasted success with optimum exploitation of different technological advancements in its transformational expedition from conventional learning to innovative e-learning and to the newest adaptive e-learning system. The huge potential of LMS, in providing learners and educators in ODL with an effective web-based learning system incorporating almost all the academic activities, has attracted organizations for using it not only for imparting education to learners but also for providing appropriate trainings to their human resources.