bluuebunny/crossref_metadata_2025_split huggingface dataset:
MUMdex: MUM-based structural variation detection
Authors: Andrews Peter A. , Iossifov Ivan , Kendall Jude , Marks Steven , Muthuswamy Lakshmi , Wang Zihua , Levy Dan , Wigler Michael 
Published: October 2016

AbstractMotivationStandard genome sequence alignment tools primarily designed to find one alignment per read have difficulty detecting inversion, translocation and large insertion and deletion (indel) events. Moreover, dedicated split read alignment methods that depend only upon the reference genome may misidentify or find too many potential split read alignments because of reference genome anomalies.MethodsWe introduce MUMdex, a Maximal Unique Match (MUM)-based genomic analysis software package consisting of a sequence aligner to the reference genome, a storage-indexing format and analysis software. Discordant reference alignments of MUMs are especially suitable for identifying inversion, translocation and large indel differences in unique regions. Extracted population databases are used as filters for flaws in the reference genome. We describe the concepts underlying MUM-based analysis, the software implementation and its usage.ResultsWe demonstrate via simulation that the MUMdex aligner and alignment format are able to correctly detect and record genomic events. We characterize alignment performance and output file sizes for human whole genome data and compare to Bowtie 2 and the BAM format. Preliminary results demonstrate the practicality of the analysis approach by detectingde novomutation candidates in human whole genome DNA sequence data from 510 families. We provide a population database of events from these families for use by others.Availabilityhttp://mumdex.com/Contactandrewsp@cshl.edu(orpaa@drpa.us)Supplementary informationSupplementary data are available online.
Self-oligomerization regulates stability of Survival Motor Neuron (SMN) protein isoforms by sequestering an SCFSlmbdegron
Authors: Gray Kelsey M. , Kaifer Kevin A. , Baillat David , Wen Ying , Bonacci Thomas R. , Ebert Allison D. , Raimer Amanda C. , Spring Ashlyn M. , Have Sara ten , Glascock Jacqueline J. , Gupta Kushol , Van Duyne Gregory D. , Emanuele Michael J. , Lamond Angus I. , Wagner Eric J. , Lorson Christian L. , Matera A. Gregory 
Published: October 2016

AbstractSpinal muscular atrophy (SMA) is caused by homozygous mutations in humanSMN1. Expression of a duplicate gene (SMN2) primarily results in skipping of exon 7 and production of an unstable protein isoform, SMNΔ7. AlthoughSMN2exon skipping is the principal contributor to SMA severity, mechanisms governing stability of SMN isoforms are poorly understood. We used aDrosophilamodel system and label-free proteomics to identify the SCFSlmbubiquitin E3 ligase complex as a novel SMN binding partner. SCFSlmbinteracts with a phospho-degron embedded within the human and fruitfly SMN YG-box oligomerization domains. Substitution of a conserved serine (S270A) interferes with SCFSlmbbinding and stabilizes SMNΔ7. SMA-causing missense mutations that block multimerization of full-length SMN are also stabilized in the degron mutant background. Overexpression of SMNΔ7S270A, but not wild-type SMNΔ7, provides a protective effect in SMA model mice and human motor neuron cell culture systems. Our findings support a model wherein the degron is exposed when SMN is monomeric, and sequestered when SMN forms higher-order multimers.
Familial migration of the Neolithic contrasts massive male migration during Bronze Age in Europe inferred from ancient X chromosomes
Authors: Goldberg Amy , Günther Torsten , Rosenberg Noah A. , Jakobsson Mattias 
Published: October 2016

AbstractDramatic events in human prehistory, such as the spread of agriculture to Europe from Anatolia and the Late Neolithic/Bronze Age (LNBA) migration from the Pontic-Caspian steppe, can be investigated using patterns of genetic variation among the people that lived in those times. In particular, studies of differing female and male demographic histories on the basis of ancient genomes can provide information about complexities of social structures and cultural interactions in prehistoric populations. We use a mechanistic admixture model to compare the sex-specifically-inherited X chromosome to the autosomes in 20 early Neolithic and 16 LNBA human remains. Contrary to previous hypotheses suggested by the patrilocality of many agricultural populations, we find no evidence of sex-biased admixture during the migration that spread farming across Europe during the early Neolithic. For later migrations from the Pontic steppe during the LNBA, however, we estimate a dramatic male bias, with ~5-14 migrating males for every migrating female. We find evidence of ongoing, primarily male, migration from the steppe to central Europe over a period of multiple generations, with a level of sex bias that excludes a pulse migration during a single generation. The contrasting patterns of sex-specific migration during these two migrations suggest a view of differing cultural histories in which the Neolithic transition was driven by mass migration of both males and females in roughly equal numbers, perhaps whole families, whereas the later Bronze Age migration and cultural shift were instead driven by male migration, potentially connected to new technology and conquest.
Estimating the timing of multiple admixture events using 3-locus Linkage Disequilibrium
Authors: Liang Mason , Shishkin Mikhail , Mikhailova Anastasia , Shchur Vladimir , Nielsen Rasmus 
Published: October 2016

AbstractEstimating admixture histories is crucial for understanding the genetic diversity we see in present-day populations. Allele frequency or phylogeny-based methods are excellent for inferring the existence of admixture or its proportions. However, to estimate admixture times, spatial information from admixed chromosomes of local ancestry or the decay of admixture linkage disequilibrium (ALD) is used. One popular method, implemented in the programs ALDER and ROLLOFF, uses two-locus ALD to infer the time of a single admixture event, but is only able to estimate the time of the most recent admixture event based on this summary statistic. To address this limitation, we derive analytical expressions for the expected ALD in a three-locus system and provide a new statistical method based on these results that is able to resolve more complicated admixture histories. Using simulations, we evaluate the performance of this method on a range of different admixture histories. As an example, we apply the method to the Colombian and Mexican samples from the 1000 Genomes project. The implementation of our method is available athttps://github.com/Genomics-HSE/LaNeta.Author summaryWe establish a theoretical framework to model 3-locus admixture linkage disequilibrium of an admixed population taking into account the effects of genetic drift, migration and recombination. The theory is used to develop a method for estimating the times of multiple admixtures events. We demonstrate the accuracy of the method on simulated data and we apply it to previously published data from Mexican and Columbian populations to explore the complex history of American populations in the post-Columbian period.
PyVar: An Extensible Framework for Variant Annotator Comparison
Authors: Wertz Julie , Liao Qianli , Bair Thomas B , Chimenti Michael S 
Published: October 2016

AbstractModern genomics projects are generating millions of variant calls that must be annotated for predicted functional consequences at the level of gene expression and protein function. Many of these variants are of interest owing to their potential clinical significance. Unfortunately, state-of-the-art methods do not always agree on downstream effects for any given variant. Here we present a readily extensible python framework (PyVar) for comparing the output of variant annotator methods in order to aid the research community in quickly assessing differences between methods and benchmarking new methods as they are developed. We also apply our framework to assess the annotation performance of ANNOVAR, VEP, and SnpEff when annotating 81 million variants from the ‘1000 Genomes Project’ against both RefSeq and Ensembl human transcript sets.
AGO4 is specifically required for heterochromatic siRNA accumulation at Pol V-dependent loci inArabidopsis thaliana
Authors: Wang Feng , Axtell Michael J. 
Published: October 2016

Significance statementGenome-wide characterization ofAGO4-dependent siRNAs revealed thatAGO4is required for the accumulation of a small subset of heterochromatic siRNAs inArabidopsis thaliana. TheseAGO4-depdenent siRNAs are likely secondary het-siRNAs produced by a self-reinforcing loop of RdDM. Slicing-defectiveAGO4is unable to fully complement het-siRNA accumulation from anago4mutant, demonstrating the critical role of AGO4 catalytic ability in het-siRNA accumulation.Summary: 152; Introduction: 618; Results: 1291; Discussion: 1013; Experimental procedures: 881; Acknowledgements: 24; Figure legends: 568; Author contribution: 25; Conflict of interest: 13; Funding: 34; References: 1289SummaryIn plants, 24 nucleotide long heterochromatic siRNAs (het-siRNAs) transcriptionally regulate gene expression by RNA-directed DNA methylation (RdDM). The biogenesis of most het-siRNAs depends on the plant-specific RNA polymerase IV (Pol IV), and ARGONAUTE4 (AGO4) is a major het-siRNA effector protein. Through genome-wide analysis of sRNA-seq data sets, we found thatAGO4is required for the accumulation of a small subset of het-siRNAs. The accumulation ofAGO4-dependent het-siRNAs also requires several factors known to participate in the effector portion of the RdDM pathway, including RNA POLYMERASE V (POL V), DOMAINS REARRANGED METHYLTRANSFERASE 2 (DRM2) and SAWADEE HOMEODOMAIN HOMOLOG 1 (SHH1). Like many AGO proteins, AGO4 is an endonuclease that can ‘slice’ RNAs. We found that a slicing-defective AGO4 was unable to fully recoverAGO4-dependent het-siRNA accumulation fromago4mutant plants. Collectively, our data suggest thatAGO4-dependent siRNAs are secondary siRNAs dependent on the prior activity of the RdDM pathway at certain loci.
A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control
Authors: Herreros-Alonso Ivan , Arsiwalla Xerxes D. , Verschure Paul F.M.J. 
Published: October 2016

AbstractHow does our motor system solve the problem of anticipatory control in spite of a wide spectrum of response dynamics from different musculo-skeletal systems, transport delays as well as response latencies throughout the central nervous system? To a great extent, our highly-skilled motor responses are a result of a reactive feedback system, originating in the brain-stem and spinal cord, combined with a feed-forward anticipatory system, that is adaptively fine-tuned by sensory experience and originates in the cerebellum. Based on that interaction we design the counterfactual predictive control (CFPC) architecture, an anticipatory adaptive motor control scheme, in which a feed-forward module, based on the cerebellum, steers an error feedback controller withcounterfactualerror signals. Those are signals that trigger reactions as actual errors would, but that do not code for any current of forthcoming errors. In order to determine the optimal learning strategy, we derive a novel learning rule for the feed-forward module that involves an eligibility trace and operates at the synaptic level. In particular, our eligibility trace provides a mechanism beyond co-incidence detection in that it convolves a history of prior synaptic inputs with error signals. In the context of cerebellar physiology, this solution implies that Purkinje cell synapses should generate eligibility traces using a forward model of the system being controlled. From an engineering perspective, CFPC provides a general-purpose anticipatory control architecture equipped with a learning rule that exploits the full dynamics of the closed-loop system.
Adaptive potential of a drug-targeted viral protein as a function of environmental stress
Authors: Dai Lei , Du Yushen , Qi Hangfei , Huber Christian D. , Wu Nicholas C. , Wang Ergang , Lloyd-Smith James O. , Sun Ren 
Published: October 2016

AbstractRNA viruses are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes. Here we systematically quantified the distribution of fitness effects (DFE) of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of Hepatitis C Virus (HCV). We found that the majority of non-synonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized in natural conditions. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug Daclatasvir. Both the selection coefficient and the number of beneficial mutations are found to increase with the level of environmental stress, which is modulated by the concentration of Daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. We test theoretical predictions regarding the distribution of beneficial fitness effects of mutations. We also interpret the data in the context of Fisher’s Geometric Model and find an increased distance to optimum as a function of environmental stress. Finally, we show that replication fitness of viruses is correlated with the pattern of sequence conservation in nature and viral evolution is constrained by the need to maintain protein stability.
Quantitative Seq-LGS – Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites
Authors: Abkallo Hussein M. , Martinelli Axel , Inoue Megumi , Ramaprasad Abhinay , Xangsayarath Phonepadith , Gitaka Jesse , Tang Jianxia , Yahata Kazuhide , Zoungrana Augustin , Mitaka Hayato , Hunt Paul , Carter Richard , Kaneko Osamu , Mustonen Ville , Illingworth Christopher J. R. , Pain Arnab , Culleton Richard 
Published: October 2016

ABSTRACTIdentifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Traditionally, such discovery has relied on labor-intensive approaches that require significant investments of time and resources. By combining Linkage Group Selection (LGS), quantitative whole genome population sequencing and a novel mathematical modeling approach (qSeq-LGS), we simultaneously identified multiple genes underlying two distinct phenotypes, identifying novel alleles for growth rate and strain specific immunity (SSI), while removing the need for traditionally required steps such as cloning, individual progeny phenotyping and marker generation. The detection of novel variants, verified by experimental phenotyping methods, demonstrates the remarkable potential of this approach for the identification of genes controlling selectable phenotypes in malaria and other apicomplexan parasites for which experimental genetic crosses are amenable.Significance StatementThis paper describes a powerful and rapid approach to the discovery of genes underlying medically important phenotypes in malaria parasites. This is crucial for the design of new drug and vaccine interventions. The approach bypasses the most time-consuming steps required by traditional genetic linkage studies and combines Mendelian genetics, quantitative deep sequencing technologies, genome analysis and mathematical modeling. We demonstrate that the approach can simultaneously identify multigenic drivers of multiple phenotypes, thus allowing complex genotyping studies to be conducted concomitantly. This methodology will be particularly useful for discovering the genetic basis of medically important phenotypes such as drug resistance and virulence in malaria and other apicomplexan parasites, as well as potentially in any organism undergoing sexual recombination.
Human enhancers harboring specific sequence composition, activity, and genome organization are linked to the immune response
Authors: Lecellier Charles-Henri , Wasserman Wyeth W. , Mathelier Anthony 
Published: October 2016

AbstractThe FANTOM5 consortium recently characterized 65,423 human enhancers from 1,829 cell and tissue samples using the Cap Analysis of Gene Expression technology. We showed that the guanine and cytosine content at enhancer regions distinguishes two classes of enhancers harboring distinct DNA structural properties at flanking regions. A functional analysis of their predicted gene targets highlighted one class of enhancers as significantly enriched for associations with immune response genes. Moreover, these enhancers were specifically enriched for regulatory motifs recognized by TFs involved in immune response. We observed that enhancers enriched for links to immune response genes were more cell type specific, preferentially activated upon bacterial infection, and with specific response activity. Looking at chromatin capture data, we found that the two classes of enhancers were lying in distinct topologically-associated domains and chromatin loops. Our results suggest that specific nucleotide compositions encode for classes of enhancers that are functionally distinct and specifically organized in the human genome.
Accuracy of demographic inferences from site frequency spectrum: the case of the yoruba population
Authors: Lapierre Marguerite , Lambert Amaury , Achaz Guillaume 
Published: October 2016

AbstractSome methods for demographic inference based on the observed genetic diversity of current populations rely on the use of summary statistics such as the Site Frequency Spectrum (SFS). Demographic models can be either model-constrained with numerous parameters such as growth rates, timing of demographic events and migration rates, or model-flexible, with an unbounded collection of piecewise constant sizes. It is still debated whether demographic histories can be accurately inferred based on the SFS. Here we illustrate this theoretical issue on an example of demographic inference for an African population. The SFS of the Yoruba population (data from the 1000 Genomes Project) is fit to a simple model of population growth described with a single parameter (e.g., founding time). We infer a time to the most recent common ancestor of 1.7 million years for this population. However, we show that the Yoruba SFS is not informative enough to discriminate between several different models of growth. We also show that for such simple demographies, the fit of one-parameter models outperforms the model-flexible method recently developed by Liu and Fu. The use of this method on simulated data suggests that it is biased by the noise intrinsically present in the data.
A Machine Learning-based Framework to Identify Type 2 Diabetes through Electronic Health Records
Authors: Zheng Tao , Xie Wei , Xu Liling , He Xiaoying , Zhang Ya , You Mingrong , Yang Gong , Chen You 
Published: October 2016

AbstractObjectiveTo discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate.Materials and MethodsWe propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014.ResultsWe apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (~0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC).DiscussionExpert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls.ConclusionsOur proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.
Comparing multi- and single-sample variant calls to improve variant call sets from deep coverage whole-genome sequencing data
Authors: Shringarpure Suyash S. , Mathias Rasika A. , Hernandez Ryan D. , O’Connor Timothy D. , Szpiech Zachary A. , Torres Raul , De La Vega Francisco M. , Bustamante Carlos D. , Barnes Kathleen C. , Taub Margaret A. ,   Behalf of the CAAPA consortium
Published: October 2016

ABSTRACTMotivationVariant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X).ResultsWe have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illumina’s single-sample caller CASAVA, Real Time Genomics’ multisample variant caller, and the GATK Unified Genotyper, respectively. Since most NGS sequencing data is accompanied by genotype data for the same samples, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g., a different set of criteria to determine quality for rare vs. common variants) and thereby provides insight into sequencing characteristics that indicate data quality for variants of different frequencies.AvailabilityCode will be made available prior to publication on Github.