scholarly journals GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss

2021 ◽  
Author(s):  
Damianos Melidis ◽  
Christian Landgraf ◽  
Anja Schoener-Heinisch ◽  
Gunnar Schmidt ◽  
Sandra von Hardenberg ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Human Genetics ◽  
Sequence Variants ◽  
Data Sets ◽  
Command Line ◽  
Loss Of Function ◽  
Large Gene ◽  
Average Accuracy ◽  
Gene Panels ◽  
Improved Accuracy

Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and often not comparable across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) introduced standards and guidelines for the interpretation of sequencing variants. Further gene- and disease-specific refinements regarding hereditary hearing loss have been developed since then. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is especially difficult and time consuming. We developed an open-source bioinformatics tool GenOtoScope, which automates all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion (“PVS1”). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets respectively. The web interface is freely accessible. The command line application along with all source code, documentation and example outputs can be found via the project GitHub page.

scholarly journals Production, characterization, and cross-reactivity of a polyclonal antibody against Arabidopsis TARGET OF RAPAMYCIN

2019 ◽  
Vol 62 (1) ◽  
Author(s):  
Gyeong-Im Shin ◽  
Sun Young Moon ◽  
Song Yi Jeong ◽  
Myung Geun Ji ◽  
Joon-Yung Cha ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Cross Reactivity ◽  
Target Of Rapamycin ◽  
Loss Of Function ◽  
Anticancer Activities ◽  
Purification Method ◽  
E Coli ◽  
Related Family ◽  
Large Gene ◽  
Truncated Variants

AbstractTARGET OF RAPAMYCIN (TOR), a member of the phosphatidylinositol 3-kinase-related family of protein kinases, is encoded by a single, large gene and is evolutionarily conserved in all eukaryotes. TOR plays a role as a master regulator that integrates nutrient, energy, and stress signaling to orchestrate development. TOR was first identified in yeast mutant screens, as its mutants conferred resistance to rapamycin, an antibiotic with immunosuppressive and anticancer activities. In Arabidopsis thaliana, the loss-of-function tor mutant displays embryo lethality, but the precise mechanisms of TOR function are still unknown. Moreover, a lack of reliable molecular and biochemical assay tools limits our ability to explore TOR functions in plants. Here, we produced a polyclonal α-TOR antibody using two truncated variants of TOR (1–200 and 1113–1304 amino acids) as antigens because recombinant full-length TOR is challenging to express in Escherichia coli. Recombinant His-TOR1−200 and His-TOR1113−1304 proteins were individually expressed in E. coli, and a mixture of proteins (at a 1:1 ratio) was used for immunizing rabbits. Antiserum was purified by an antigen-specific purification method, and the purified polyclonal α-TOR antibody successfully detected endogenous TOR proteins in wild-type Arabidopsis and TOR orthologous in major crop plants, including tomato, maize, and alfalfa. Moreover, our α-TOR antibody is useful for coimmunoprecipitation assays. In summary, we generated a polyclonal α-TOR antibody that detects endogenous TOR in various plant species. Our antibody could be used in future studies to determine the precise molecular mechanisms of TOR, which has largely unknown multifunctional roles in plants.

scholarly journals Entropy Based Features Distribution for Anti-DDoS Model in SDN

2021 ◽  
Vol 13 (3) ◽  
pp. 1522
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Wajahat Ali Khan ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Network Security ◽  
False Positive ◽  
Denial Of Service ◽  
Network Services ◽  
Detection Accuracy ◽  
Data Sets ◽  
Traffic Patterns ◽  
Average Accuracy ◽  
Ddos Detection ◽  
Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

scholarly journals Inner Ear and Muscle Developmental Defects in Smpx-Deficient Zebrafish Embryos

2021 ◽  
Vol 22 (12) ◽  
pp. 6497
Author(s):  
Anna Ghilardi ◽  
Alberto Diana ◽  
Renato Bacchetta ◽  
Nadia Santo ◽  
Miriam Ascagni ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Inner Ear ◽  
Hair Cells ◽  
Muscle Fibers ◽  
Underlying Mechanism ◽  
Loss Of Function ◽  
Zebrafish Model ◽  
Developmental Defects ◽  
Inner Ear Development ◽  
Syndromic Hearing Loss

The last decade has witnessed the identification of several families affected by hereditary non-syndromic hearing loss (NSHL) caused by mutations in the SMPX gene and the loss of function has been suggested as the underlying mechanism. In the attempt to confirm this hypothesis we generated an Smpx-deficient zebrafish model, pointing out its crucial role in proper inner ear development. Indeed, a marked decrease in the number of kinocilia together with structural alterations of the stereocilia and the kinocilium itself in the hair cells of the inner ear were observed. We also report the impairment of the mechanotransduction by the hair cells, making SMPX a potential key player in the construction of the machinery necessary for sound detection. This wealth of evidence provides the first possible explanation for hearing loss in SMPX-mutated patients. Additionally, we observed a clear muscular phenotype consisting of the defective organization and functioning of muscle fibers, strongly suggesting a potential role for the protein in the development of muscle fibers. This piece of evidence highlights the need for more in-depth analyses in search for possible correlations between SMPX mutations and muscular disorders in humans, thus potentially turning this non-syndromic hearing loss-associated gene into the genetic cause of dysfunctions characterized by more than one symptom, making SMPX a novel syndromic gene.

scholarly journals Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps

2021 ◽  
Author(s):  
Ali H. Al-Timemy ◽  
Nebras H. Ghaeb ◽  
Zahraa M. Mosa ◽  
Javier Escudero
Keyword(s):  
Transfer Learning ◽  
Learning Strategy ◽  
Clinical Decision ◽  
Topographic Maps ◽  
Computer Assisted ◽  
Clinical Knowledge ◽  
Average Accuracy ◽  
Assisted Diagnosis ◽  
Improved Accuracy ◽  
Fine Tune

Abstract Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.

scholarly journals APOL1 polymorphisms and kidney disease: loss-of-function or gain-of-function?

2019 ◽  
Vol 316 (1) ◽  
pp. F1-F8 ◽  
Author(s):  
Leslie A. Bruggeman ◽  
John F. O’Toole ◽  
John R. Sedor
Keyword(s):  
Kidney Disease ◽  
Human Genetics ◽  
Disease Risk ◽  
Kidney Diseases ◽  
Expression Patterns ◽  
Model Systems ◽  
Recessive Trait ◽  
Loss Of Function ◽  
Allele Expression ◽  
Basic Biology

The mechanism that explains the association of APOL1 variants with nondiabetic kidney diseases in African Americans remains unclear. Kidney disease risk is inherited as a recessive trait, and many studies investigating the intracellular function of APOL1 have indicated the APOL1 variants G1 and G2 are associated with cytotoxicity. Whether cytotoxicity results from the absence of a protective effect conferred by the G0 allele or is induced by a deleterious effect of variant allele expression has not be conclusively established. A central issue hampering basic biology studies is the lack of model systems that authentically replicate APOL1 expression patterns. APOL1 is present in humans and a few other primates and appears to have important functions in the kidney, as the kidney is the primary target for disease associated with the genetic variance. There have been no studies to date assessing the function of untagged APOL1 protein under native expression in human or primate kidney cells, and no studies have examined the heterozygous state, a disease-free condition in humans. A second major issue is the chronic kidney disease (CKD)-associated APOL1 variants are conditional mutations, where the disease-inducing function is only evident under the appropriate environmental stimulus. In addition, it is possible there may be more than one mechanism of pathogenesis that is dependent on the nature of the stressor or other genetic variabilities. Studies addressing the function of APOL1 and how the CKD-associated APOL1 variants cause kidney disease are challenging and remain to be fully investigated under conditions that faithfully model known human genetics and physiology.

scholarly journals Rare variant pathogenicity triage and inclusion of synonymous variants improves analysis of disease associations

2018 ◽  
Author(s):  
Ridge Dershem ◽  
Raghu P.R. Metpally ◽  
Kirk Jeffreys ◽  
Sarathbabu Krishnamurthy ◽  
Diane T. Smelser ◽  
...  
Keyword(s):  
Rare Variant ◽  
Functional Characterization ◽  
Data Sets ◽  
Common Variants ◽  
Loss Of Function ◽  
Altered Expression ◽  
Missense Variants ◽  
Association Testing ◽  
Disease Associations ◽  
Causes Of Disease

AbstractMany G protein-coupled receptors (GPCRs) lack common variants that lead to reproducible genome-wide disease associations. Here we used rare variant approaches to assess the disease associations of 85 orphan or understudied GPCRs in an unselected cohort of 51,289 individuals. Rare loss-of-function variants, missense variants predicted to be pathogenic or likely pathogenic, and a subset of rare synonymous variants were used as independent data sets for sequence kernel association testing (SKAT). Strong, phenome-wide disease associations shared by two or more variant categories were found for 39% of the GPCRs. Validating the bioinformatics and SKAT analyses, functional characterization of rare missense and synonymous variants of GPR39, a Family A GPCR, showed altered expression and/or Zn2+-mediated signaling for members of both variant classes. Results support the utility of rare variant analyses for identifying disease associations for genes that lack common variants, while also highlighting the functional importance of rare synonymous variants.Author summaryRare variant approaches have emerged as a viable way to identify disease associations for genes without clinically important common variants. Rare synonymous variants are generally considered benign. We demonstrate that rare synonymous variants represent a potentially important dataset for deriving disease associations, here applied to analysis of a set of orphan or understudied GPCRs. Synonymous variants yielded disease associations in common with loss-of-function or missense variants in the same gene. We rationalize their associations with disease by confirming their impact on expression and agonist activation of a representative example, GPR39. This study highlights the importance of rare synonymous variants in human physiology, and argues for their routine inclusion in any comprehensive analysis of genomic variants as potential causes of disease.

Hadoop-CNV-RF: a clinically validated and scalable copy number variation detection tool for next-generation sequencing data

2020 ◽  
Author(s):  
Getiria Onsongo ◽  
Ham Ching Lam ◽  
Matthew Bower ◽  
Bharat Thyagarajan
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Large Gene ◽  
Data Framework ◽  
Number Variation ◽  
Targeted Capture ◽  
Objective Detection ◽  
Gene Panels

Abstract Objective : Detection of small copy number variations (CNVs) in clinically relevant genes is routinely being used to aid diagnosis. We recently developed a tool, CNV-RF , capable of detecting small clinically relevant CNVs. CNV-RF was designed for small gene panels and did not scale well to large gene panels. On large gene panels, CNV-RF routinely failed due to memory limitations. When successful, it took about 2 days to complete a single analysis, making it impractical for routinely analyzing large gene panels. We need a reliable tool capable of detecting CNVs in the clinic that scales well to large gene panels. Results : We have developed Hadoop-CNV-RF, a scalable implementation of CNV-RF . Hadoop-CNV-RF is a freely available tool capable of rapidly analyzing large gene panels. It takes advantage of Hadoop, a big data framework developed to analyze large amounts of data. Preliminary results show it reduces analysis time from about 2 days to less than 4 hours and can seamlessly scale to large gene panels. Hadoop-CNV-RF has been clinically validated for targeted capture data and is currently being used in a CLIA molecular diagnostics laboratory. Its availability and usage instructions are publicly available at: https://github.com/getiria-onsongo/hadoop-cnvrf-public .

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

scholarly journals Notch Signaling in Skeletal Development, Homeostasis and Pathogenesis

Biomolecules ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 332 ◽  
Author(s):  
Jennifer T. Zieba ◽  
Yi-Ting Chen ◽  
Brendan H. Lee ◽  
Yangjin Bae
Keyword(s):  
Notch Signaling ◽  
Cell Fate ◽  
Human Genetics ◽  
Skeletal Development ◽  
Gene Activation ◽  
Fracture Repair ◽  
Loss Of Function ◽  
Age Related ◽  
Complex Process ◽  
And Cartilage

Skeletal development is a complex process which requires the tight regulation of gene activation and suppression in response to local signaling pathways. Among these pathways, Notch signaling is implicated in governing cell fate determination, proliferation, differentiation and apoptosis of skeletal cells-osteoblasts, osteoclasts, osteocytes and chondrocytes. Moreover, human genetic mutations in Notch components emphasize the critical roles of Notch signaling in skeletal development and homeostasis. In this review, we focus on the physiological roles of Notch signaling in skeletogenesis, postnatal bone and cartilage homeostasis and fracture repair. We also discuss the pathological gain- and loss-of-function of Notch signaling in bone and cartilage, resulting in osteosarcoma and age-related degenerative diseases, such as osteoporosis and osteoarthritis. Understanding the physiological and pathological function of Notch signaling in skeletal tissues using animal models and human genetics will provide new insights into disease pathogenesis and offer novel approaches for the treatment of bone/cartilage diseases.

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