bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data
Authors: Mao Weiguang , Pouyan Maziyar Baran , Kostka Dennis , Chikina Maria 
Published: February 2020

AbstractMotivationSingle-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, dimensionality reduction should preserve the biological information while discarding the noise. However if the dimensionality reduction is to be used directly to gain biological insight it must also be interpretable – that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical.ResultsWe present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that incorporates different interpretability inducing assumptions into a single modeling framework. The key advantage of our NIFA model is that it simultaneously models uni- and multi-modal latent factors, and thus isolates discrete cell-type identity and continuous pathway activity into separate components. We apply our approach to a range of datasets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA, NMF and scCoGAPS (an NMF method designed for single-cell data) in terms of disentangling biological sources of variation. Studying an immunotherapy dataset in detail, we show that NIFA is able to reproduce and refine previous findings in a single analysis framework and enables the discovery of new clinically relevant cell states.AvailabilityNFIA is a R package which is freely available at GitHub (https://github.com/wgmao/NIFA).Contactmchikina@pitt.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Complex networks of miRNA-transcription factors mediate gene dosage compensation in aneuploid cancer
Authors: Acón ManSai , Oviedo Guillermo , Baéz Edwin , Vásquez-Vargas Gloriana , Guevara-Coto José , Segura-Castillo Andrés , Siles-Canales Francisco , Quirós-Barrantes Steve , Mendes Pedro , Mora-Rodríguez Rodrigo 
Published: February 2020

AbstractCancer complexity is consequence of enormous genomic instability leading to aneuploidy, a hallmark of most cancers. We hypothesize that dosage compensation of critical genes could arise from systems-level properties of complex networks of microRNAs (miRNA) and transcription factors (TF) as a way for cancer cells to withstand the negative effects of aneuploidy. We studied gene dosage compensation at the transcriptional level on data of the NCI-60 cancer cell line panel with the aid of computational models to identify candidate genes with low tolerance to variation in gene expression despite high variation in copy numbers. We identified a network of TF and miRNAs validated interactions with those genes to construct a mathematical model where the property of dosage compensation emerged for MYC and STAT3. Compensation was mediated by feedback and feed-forward motifs with 4 miRNAs and was dependent on the kinetic parameters of these TF-miRNA interactions, indicating that network analysis was not enough to identify this emergent property. The inhibition of miRNAs compensating MYC suggest a therapeutic potential of targeting gene dosage compensation against aneuploid cancer.
QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
Authors: Shuvo Md Hossain , Bhattacharya Sutanu , Bhattacharya Debswapna 
Published: February 2020

AbstractMotivationProtein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction.ResultsWe present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently out-performs existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep.Availabilityhttps://github.com/Bhattacharya-Lab/QDeepContactbhattacharyad@auburn.edu
Transcriptomic analysis of dystonia-associated genes reveals functional convergence within specific cell types and shared neurobiology with psychiatric disorders
Authors: Mencacci Niccolò E. , Reynolds Regina , Ruiz Sonia Garcia , Vandrovcova Jana , Forabosco Paola ,   UK Brain Expression Consortium,   International Parkinson’s Disease Genomics Consortium, Weale Michael E. , Bhatia Kailash P. , Hardy John , Botía Juan A , Ryten Mina 
Published: February 2020

AbstractDystonia is a neurological disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and postures, often occurring in absence of any structural brain abnormality. Psychiatric comorbidities, including anxiety, depression, obsessive-compulsive disorder and schizophrenia, are frequent in dystonia patients. While mutations in a fast-growing number of genes have been linked to Mendelian forms of dystonia, the cellular, anatomical, and molecular basis remains unknown for most genetic forms of dystonia, as does its genetic and biological relationship to neuropsychiatric disorders. Here we applied an unbiased systems-biology approach to explore the cellular specificity of all currently known dystonia-associated genes, predict their functional relationships, and test whether dystonia and neuropsychiatric disorders share a genetic relationship. To determine the cellular specificity of dystonia-associated genes in the brain, single-nuclear transcriptomic data derived from mouse brain was used together with expression-weighted cell-type enrichment. To identify functional relationships amongst dystonia-associated genes, we determined the enrichment of these genes in co-expression networks constructed from ten human brain regions. Stratified linkage-disequilibrium score regression was used to test whether co-expression modules enriched for dystonia-associated genes significantly contribute to the heritability of anxiety, major depressive disorder, obsessive-compulsive disorder, schizophrenia, and Parkinson’s disease. Dystonia-associated genes were significantly enriched in adult nigral dopaminergic neurons and striatal medium spiny neurons. Furthermore, four of the 220 gene co-expression modules tested were significantly enriched for the dystonia-associated genes. The identified modules were derived from the substantia nigra, putamen, frontal cortex, and white matter, and were all significantly enriched for genes associated with synaptic function. Finally, we demonstrated significant enrichments of the heritability of depression, obsessive-compulsive disorder and schizophrenia, but not anxiety and Parkinson’s disease, within the putamen and white matter modules. In conclusion, multiple dystonia-associated genes interact and contribute to pathogenesis likely through dysregulation of synaptic signalling in striatal medium spiny neurons, adult nigral dopaminergic neurons and frontal cortical neurons. Furthermore, the enrichment of the heritability of psychiatric disorders in the co-expression modules enriched for dystonia-associated genes indicates that psychiatric symptoms associated with dystonia are likely to be intrinsic to its pathophysiology.
A Novel Multiplex qPCR Assay for Detection ofPlasmodium falciparumwithHistidine-rich Protein 2 and 3 (pfhrp2 and pfhrp3)Deletions in Polyclonal Infections
Authors: Grignard Lynn , Nolder Debbie , Sepúlveda Nuno , Berhane Araia , Mihreteab Selam , Kaaya Robert , Phelan Jody , Moser Kara , van Schalkwyk Donelly A. , Campino Susana , Parr Jonathan B. , Juliano Jonathan J. , Chiodini Peter , Cunningham Jane , Sutherland Colin J. , Drakeley Chris , Beshir Khalid B. 
Published: February 2020

AbstractBackgroundRapid diagnostic tests (RDTs) that detect the malaria antigen histidine-rich protein 2 (HRP2) are widely used in endemic areas globally to confirmPlasmodium falciparuminfection in febrile patients. The emergence of parasites lacking the gene encoding HRP2 and escaping RDT detection threatens progress in malaria control and elimination. Many health facilities in malaria endemic countries are dependent on RDTs for diagnosis and some National Health Service hospitals without expert microscopists rely on them for diagnosis out of hours. It is vital to study the emergence and the extent of such parasites globally to guide diagnostic policy. Currently, verification of the presence of such parasites in a blood sample requires a series of PCR assays to confirm the presence ofP. falciparumand in the absence of amplicons frompfhrp2and/orpfhrp3, which encodes a cross-reactive protein isoform. These tests have different limits of detection and many laboratories have reported difficulty in confirming the absence ofpfhrp2andpfhrp3with certainty.MethodsWe developed and validated a novel and rapid multiplex real time quantitative (qPCR) assay to detectpfhrp2, pfhrp3,confirmatory parasite and human reference genes simultaneously. We also applied the assay to detectpfhrp2andpfhrp3deletion in 462 field samples from different endemic countries and UK travellers.ResultsThe qPCR assay showed limit of detection and quantification of 0.76-1.5 parasites per μl. The amplification efficiency, coefficient of determination (R2) and slope for the genes were 96-1.07%, 0.96-0.98 and −3.375 2 to −3.416 respectively. The assay demonstrated diagnostic sensitivity of 100% (n=19, 95% CI= (82.3%; 100%)) and diagnostic specificity of 100% (n=31; 95% CI= (88.8%; 100%)) in detectingpfhrp2andpfhrp3in. In addition, the qPCR assay estimatesP. falciparumparasite density and can detectpfhrp2andpfhrp3deletions masked in polyclonal infections. We reportpfhrp2andpfhrp3deletions in parasite isolates from Kenya, Tanzania and in UK travellers.ConclusionThe new qPCR assay is simple to use and offers significant advantages in speed and ease of interpretation. It is easily scalable to routine surveillance studies in countries whereP. falciparumparasites lackingpfhrp2andpfhrp3are a threat to malaria control.
A Pathoconnectome of Early Neurodegeneration
Authors: Pfeiffer Rebecca L. , Anderson James R. , Dahal Jeebika , Garcia Jessica C. , Yang Jia-Hui , Sigulinsky Crystal L. , Rapp Kevin , Emrich Daniel P. , Watt Carl B. , Morrison Hope , Houser Alexis R. , Marc Robert E. , Jones Bryan W. 
Published: February 2020

AbstractConnectomics has demonstrated that synaptic networks and their topologies are precise and directly correlate with physiology and behavior. The next extension of connectomics is pathoconnectomics: to map neural network synaptology and circuit topologies corrupted by neurological disease in order to identify robust targets for therapeutics. In this report, we characterize the first pathoconnectome, in this case, generated from a retina with photoreceptor degeneration. We observe aberrant connectivity in the rod-network pathway and novel synaptic connections deriving from neurite sprouting. These observations reveal principles of neuron responses to the loss of network components and can be extended to other neurodegenerative diseases.
Meso-limbic Gene Expression Findings from Mouse Cocaine Self-Administration Recapitulate Human Cocaine Use Disorder
Authors: Huggett Spencer B. , Bubier Jason A. , Chesler Elissa J. , Palmer Rohan C. 
Published: February 2020

AbstractAnimal models of drug use have been employed for over 100 years to facilitate the identification of mechanisms governing human substance use and addiction. Most cross-species research on drug use/addiction examines behavioral overlap, but studies assessing neuro-molecular correspondence are lacking. Our study utilized transcriptome-wide data from the hippocampus and ventral tegmental area (VTA)/midbrain from a total of 35 human males with cocaine use disorder/controls and 49 male C57BL/6J cocaine/saline administering/exposed mice. We hypothesized that individual genes (differential expression) and systems of co-expressed genes (gene networks) would demonstrate appreciable overlap across mouse cocaine self-administration and human cocaine use disorder. We found modest, but significant associations between differentially expressed genes associated with cocaine self-administration (short access) and cocaine use disorder within meso-limbic circuitry, but non-robust associations with mouse models of acute cocaine exposure, (cocaine) context re-exposure and cocaine + context re-exposure. Investigating systems of co-expressed genes, we also found several validated gene networks with weak to moderate conservation between cocaine/saline self-administering mice and disordered cocaine users/controls. The most conserved hippocampal and VTA gene networks demonstrated substantial overlap (2,029 common genes) and included novel and previously implicated targets of cocaine use/addiction. Lastly, we conducted expression-based phenome-wide association studies of the ninecommonhub genes across conserved gene networks and found that they were associated with dopamine/serotonin function, cocaine self-administration and other relevant mouse traits. Overall, our study identified and characterized homologous transcriptional effects between mouse models of cocaine self-administration and human cocaine use disorder that may serve as a benchmark for future research.
Functional biological paths altered in Alzheimer’s disease: from genes to bile acids
Authors: Gorijala Priyanka , Nho Kwangsik , Risacher Shannon L. , Kaddurah-Daouk Rima , Saykin Andrew J. , Yan Jingwen 
Published: February 2020

AbstractLarge-scale genome wide association studies (GWASs) have been performed in search for risk genes for Alzheimer’s disease (AD). Despite the significant progress, replicability of genetic findings and their translation into targetable mechanisms related to the disease pathogenesis remains a challenge. Given that bile acids have been suggested in recent metabolic studies as potential age-related metabolic factors associated with AD, we integrated genomic and metabolomic data together with heterogeneous biological networks and investigated the potential cascade of effect of genetic variations to proteins, bile acids and ultimately AD brain phenotypes. Particularly, we leveraged functional protein interaction networks and metabolic networks and focused on the genes directly interacting with AD-altered bile acids and their functional regulators. We examined the association of all the SNPs located in those candidate genes with AD brain imaging phenotypes, and identified multiple AD risk SNPs whose downstream genes and bile acids were also found to be altered in AD. These AD related markers span from genetics to metabolomics, forming functional biological paths connecting across multiple-omics layers, and give valuable insights into the underlying mechanism of AD.
DELPHI: accurate deep ensemble model for protein interaction sites prediction
Authors: Li Yiwei , Ilie Lucian 
Published: February 2020

AbstractMotivationProteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein-protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods.ResultsWe propose DELPHI (DEep Learning Prediction of Highly probable protein Interaction sites), a new sequence-based deep learning suite for PPI binding sites prediction. DELPHI has an ensemble structure with data augmentation and it employs novel features in addition to existing ones. We comprehensively compare DELPHI to nine state-of-the-art programs on five datasets and show that it is more accurate.AvailabilityThe trained model, source code for training, predicting, and data processing are freely available athttps://github.com/lucian-ilie/DELPHI. All datasets used in this study can be downloaded athttp://www.csd.uwo.ca/~ilie/DELPHI/.Contactilie@uwo.ca
Annotating Gene Ontology terms for protein sequences with the Transformer model
Authors: Duong Dat , Gai Lisa , Uppunda Ankith , Le Don , Eskin Eleazar , Li Jingyi Jessica , Chang Kai-Wei 
Published: February 2020

AbstractPredicting functions for novel amino acid sequences is a long-standing research problem. The Uniprot database which contains protein sequences annotated with Gene Ontology (GO) terms, is one commonly used training dataset for this problem. Predicting protein functions can then be viewed as a multi-label classification problem where the input is an amino acid sequence and the output is a set of GO terms. Recently, deep convolutional neural network (CNN) models have been introduced to annotate GO terms for protein sequences. However, the CNN architecture can only model close-range interactions between amino acids in a sequence. In this paper, first, we build a novel GO annotation model based on the Transformer neural network. Unlike the CNN architecture, the Transformer models all pairwise interactions for the amino acids within a sequence, and so can capture more relevant information from the sequences. Indeed, we show that our adaptation of Transformer yields higher classification accuracy when compared to the recent CNN-based method DeepGO. Second, we modify our model to take motifs in the protein sequences found by BLAST as additional input features. Our strategy is different from other ensemble approaches that average the outcomes of BLAST-based and machine learning predictors. Third, we integrate into our Transformer the metadata about the protein sequences such as 3D structure and protein-protein interaction (PPI) data. We show that such information can greatly improve the prediction accuracy, especially for rare GO labels.
Estimates of autozygosity through runs of homozygosity in farmed coho salmon
Authors: Yoshida Grazyella M. , Cáceres Pablo , Marín-Nahuelpi Rodrigo , Koop Ben F. , Yáñez José M. 
Published: February 2020

AbstractThe characterization of runs of homozygosity (ROH), using high-density single nucleotide polymorphisms (SNPs) allows inferences to be made about the past demographic history of animal populations and the genomic ROH has become a common approach to characterize the inbreeding. We aimed to analyze and characterize ROH patterns and compare different genomic and pedigree-based methods to estimate the inbreeding coefficient in two pure lines (POP A and B) and one recently admixed line (POP C) of coho salmon breeding nuclei, genotyped using a 200K Affymetrix Axiom®myDesign Custom SNP Array. A large number and greater mean length of ROH were found for the two “pure” lines and the recently admixed line (POP C) showed the lowest number and smaller mean length of ROH. The ROH analysis for different length classes suggests that all three coho salmon lines the genome is largely composed of a high number of short segments (<4 Mb), and for POP C no segment >16 Mb was found. A high variable number of ROH, mean length and inbreeding values across chromosomes; positively the consequence of artificial selection. Pedigree-based inbreeding values tended to underestimate genomic-based inbreeding levels, which in turn varied depending on the method used for estimation. The high positive correlations between different genomic-based inbreeding coefficients suggest that they are consistent and may be more accurate than pedigree-based methods, given that they capture information from past and more recent demographic events, even when there are no pedigree records available.
Fast and accurate assembly of Nanopore reads via progressive error correction and adaptive read selection
Authors: Chen Ying , Nie Fan , Xie Shang-Qian , Zheng Ying-Feng , Bray Thomas , Dai Qi , Wang Yao-Xin , Xing Jian-feng , Huang Zhi-Jian , Wang De-Peng , He Li-Juan , Luo Feng , Wang Jian-Xin , Liu Yi-Zhi , Xiao Chuan-Le 
Published: February 2020

AbstractAlthough long Nanopore reads are advantageous inde novogenome assembly, applying Nanopore reads in genomic studies is still hindered by their complex errors. Here, we developed NECAT, an error correction andde novoassembly tool designed to overcome complex errors in Nanopore reads. We proposed an adaptive read selection and two-step progressive method to quickly correct Nanopore reads to high accuracy. We introduced a two-stage assembler to utilize the full length of Nanopore reads. NECAT achieves superior performance in both error correction andde novoassembly of Nanopore reads. NECAT requires only 7,225 CPU hours to assemble a 35X coverage human genome and achieves a 2.28-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line showed an NG50 of 29 Mbp. The high-quality assembly of Nanopore reads can significantly reduce false positives in structure variation detection.
Extreme Genomic Makeover: Evolutionary History of Maternally-transmitted Clam Symbionts
Authors: Perez M , Breusing C , Angers B , Won YJ , Young CR 
Published: February 2020

AbstractGiven their recent switch to a vertically-transmitted intracellular lifestyle, the chemosynthetic bacteria associated with deep-sea vesicomyid clams are an excellent model system to study the processes underlying reductive genome evolution. In this study, we provide the first estimates of the relative contributions of drift, recombination and selection in shaping the ongoing reductive genome evolution in these symbionts. To do so, we compared the genomes of endosymbionts associated with 11 vesicomyid clam species to that of closely related free-living bacteria and their respective hosts’ mitochondria. Our investigation confirmed that neutral evolutionary processes were the dominant driver of reductive genome evolution in this group and highlighted the important role of horizontal gene transfer in mitigating genome erosion. Finally, a genome-wide screen for episodic positive selection across the symbiont phylogeny revealed the pervasive role of selective processes in maintaining symbiont functional integrity.
Radiographic and ultrasonographic imaging of a dystrophic mineralization on the oblique sesamoidean ligament in a jumping horse. Twenty months follow-up
Authors: Marsiglia Marilia Ferrari , Yamada Ana Lucia Miluzzi , Pinheiro Marcelo , Marcondes Geissiane Moraes , Paretsis Nicole Fidalgo , Da Silva Luis Cláudio Lopes Correia 
Published: February 2020

Mineralization of the anatomical structures involved in the limb movement of horses has been reported to induce lameness and interfere with athletic performance. A 4-year-old jumping horse in sport activity was reported at Veterinary Hospital of University of São Paulo, with mild lameness and a mineralized oblique distal sesamoidean ligament (ODSL) of the left forelimb, revealed by a purchase radiographic and ultrasound examination. Mineralization is more common in tendons than ligaments. This report calls for attention to these structures when performing image exams, even in mild lameness. Long-term favorable follow-up by ultrasonographic and radiographic examination showed that the severe ligament injury in this case did not decrease the athletic activity until the present day.