scholarly journals MaveQuest: a web resource for planning experimental tests of human variant effects

2020 ◽  
Vol 36 (12) ◽  
pp. 3938-3940 ◽  
Author(s):  
Da Kuang ◽  
Jochen Weile ◽  
Roujia Li ◽  
Tom W Ouellette ◽  
Jarry A Barber ◽  
...  
Keyword(s):  
Target Genes ◽  
Experimental Tests ◽  
Supplementary Information ◽  
Protein Coding ◽  
Web Based ◽  
Web Resource ◽  
Disease Phenotypes ◽  
Functional Assays ◽  
Variant Effect

Abstract Summary Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes. Availability and implementation MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety

2019 ◽  
Vol 35 (17) ◽  
pp. 3028-3037 ◽  
Author(s):  
Eyal Simonovsky ◽  
Ronen Schuster ◽  
Esti Yeger-Lotem
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Target Genes ◽  
Supplementary Information ◽  
Protein Coding ◽  
Expression Levels ◽  
Expression Variability ◽  
Protein Coding Genes ◽  
Approved Drugs ◽  
Variable Genes

Abstract Motivation The effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon. Results To explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development. Availability and implementation LCV is available as a python script in GitHub (https://github.com/eyalsim/LCV). Supplementary information Supplementary data are available at Bioinformatics online.

CDSfold: an algorithm for designing a protein-coding sequence with the most stable secondary structure

2015 ◽  
Vol 32 (6) ◽  
pp. 828-834 ◽  
Author(s):  
Goro Terai ◽  
Satoshi Kamegai ◽  
Kiyoshi Asai
Keyword(s):  
Secondary Structure ◽  
Protein Production ◽  
Supplementary Information ◽  
Stable Secondary Structure ◽  
Negative Effects ◽  
Protein Coding ◽  
Web Based ◽  
Potential Factor ◽  
Time And Space Complexity

Abstract Motivation: An important problem in synthetic biology is to design a nucleotide sequence of an mRNA that confers a desirable expression level of a target protein. The secondary structure of protein-coding sequences (CDSs) is one potential factor that could have both positive and negative effects on protein production. To elucidate the role of secondary structure in CDSs, algorithms for manipulating secondary structure should be developed. Results: We developed an algorithm for designing a CDS with the most stable secondary structure among all possible ones translated into the same protein, and implemented it as the program CDSfold. The algorithm runs the Zuker algorithm under the constraint of a given amino acid sequence. The time and space complexity is O(L3) and O(L2), respectively, where L is the length of the CDS to be designed. Although our algorithm is slower than the original Zuker algorithm, it could design a relatively long (2.7-kb) CDS in approximately 1 h. Availability and implementation: The CDSfold program is freely available for non-commercial users as stand-alone and web-based software from http://cdsfold.trahed.jp/cdsfold/. Contacts: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

DeepLGP: a novel deep learning method for prioritizing lncRNA target genes

2020 ◽  
Vol 36 (16) ◽  
pp. 4466-4472 ◽  
Author(s):  
Tianyi Zhao ◽  
Yang Hu ◽  
Jiajie Peng ◽  
Liang Cheng
Keyword(s):  
Target Genes ◽  
Interaction Network ◽  
Gene Interaction ◽  
Biological Data ◽  
Supplementary Information ◽  
Gene Interaction Network ◽  
Convolutional Network ◽  
Protein Coding ◽  
Lncrna Gene ◽  
Novel Target

Abstract Motivation Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data are increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA. Results In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues and miRNA-mediated lncRNA–gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high area under curves (0.90–0.98) and area under precision-recall curves (0.91–0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs. Availability and implementation https://github.com/zty2009/LncRNA-target-gene. Supplementary information Supplementary data are available at Bioinformatics online.

Modeling Affordances and Functioning for Personalized Robotic Assistance

2020 ◽  
Author(s):  
Alessandro Umbrico ◽  
Gabriella Cortellessa ◽  
Andrea Orlandini ◽  
Amedeo Cesta
Keyword(s):  
Domain Knowledge ◽  
Experimental Tests ◽  
International Classification Of Functioning ◽  
Knowledge Representation And Reasoning ◽  
Health State ◽  
Knowledge Reasoning ◽  
Cognitive Assistance ◽  
Ontological Model ◽  
And Function

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.

ZFARED: A Database of the Antioxidant Response Elements in Zebrafish

2020 ◽  
Vol 15 (5) ◽  
pp. 415-419
Author(s):  
Azhwar Raghunath ◽  
Raju Nagarajan ◽  
Ekambaram Perumal
Keyword(s):  
Target Genes ◽  
Antioxidant Response ◽  
Zebrafish Genome ◽  
Promoter Regions ◽  
Protein Coding ◽  
Response Elements ◽  
Toxic Agents ◽  
Upstream Promoter ◽  
Its Gene ◽  
Antioxidant Response Elements

Background: Antioxidant Response Elements (ARE) play a key role in the expression of Nrf2 target genes by regulating the Keap1-Nrf2-ARE pathway, which offers protection against toxic agents and oxidative stress-induced diseases. Objective: To develop a database of putative AREs for all the genes in the zebrafish genome. This database will be helpful for researchers to investigate Nrf2 regulatory mechanisms in detail. Methods: To facilitate researchers functionally characterize zebrafish AREs, we have developed a database of AREs, Zebrafish Antioxidant Response Element Database (ZFARED), for all the protein-coding genes including antioxidant and mitochondrial genes in the zebrafish genome. The front end of the database was developed using HTML, JavaScript, and CSS and tested in different browsers. The back end of the database was developed using Perl scripts and Perl-CGI and Perl- DBI modules. Results: ZFARED is the first database on the AREs in zebrafish, which facilitates fast and efficient searching of AREs. AREs were identified using the in-house developed Perl algorithms and the database was developed using HTML, JavaScript, and Perl-CGI scripts. From this database, researchers can access the AREs based on chromosome number (1 to 25 and M for mitochondria), strand (positive or negative), ARE pattern and keywords. Users can also specify the size of the upstream/promoter regions (5 to 30 kb) from transcription start site to access the AREs located in those specific regions. Conclusion: ZFARED will be useful in the investigation of the Keap1-Nrf2-ARE pathway and its gene regulation. ZFARED is freely available at http://zfared.buc.edu.in/.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification and in Silico Characterization of Novel and Conserved MicroRNAs in Methyl Jasmonate-Stimulated Scots Pine (Pinus sylvestris L.) Needles

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 384
Author(s):  
Baiba Krivmane ◽  
Ilze Šņepste ◽  
Vilnis Šķipars ◽  
Igor Yakovlev ◽  
Carl Gunnar Fossdal ◽  
...  
Keyword(s):  
Methyl Jasmonate ◽  
Scots Pine ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Stem Loop ◽  
Protein Coding ◽  
Stem Loop Structure ◽  
In Silico Characterization ◽  
Potential Precursor

MicroRNAs (miRNAs) are non-protein coding RNAs of ~20–24 nucleotides in length that play an important role in many biological and metabolic processes, including the regulation of gene expression, plant growth and developmental processes, as well as responses to stress and pathogens. The aim of this study was to identify and characterize novel and conserved microRNAs expressed in methyl jasmonate-treated Scots pine needles. In addition, potential precursor sequences and target genes of the identified miRNAs were determined by alignment to the Pinus unigene set. Potential precursor sequences were identified using the miRAtool, conserved miRNA precursors were also tested for the ability to form the required stem-loop structure, and the minimal folding free energy indexes were calculated. By comparison with miRBase, 4975 annotated sequences were identified and assigned to 173 miRNA groups, belonging to a total of 60 conserved miRNA families. A total of 1029 potential novel miRNAs, grouped into 34 families were found, and 46 predicted precursor sequences were identified. A total of 136 potential target genes targeted by 28 families were identified. The majority of previously reported highly conserved plant miRNAs were identified in this study, as well as some conserved miRNAs previously reported to be monocot specific. No conserved dicot-specific miRNAs were identified. A number of potential gymnosperm or conifer specific miRNAs were found, shared among a range of conifer species.

scholarly journals Comparative Genomics: Insights on the Pathogenicity and Lifestyle of Rhizoctonia solani

2021 ◽  
Vol 22 (4) ◽  
pp. 2183
Author(s):  
Nurhani Mat Razali ◽  
Siti Norvahida Hisham ◽  
Ilakiya Sharanee Kumar ◽  
Rohit Nandan Shukla ◽  
Melvin Lee ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Rhizoctonia Solani ◽  
Abiotic Factors ◽  
Biotic Factor ◽  
Protein Coding ◽  
Sustainable Food ◽  
Repeat Elements ◽  
Gene Sets ◽  
Core Genes

Proper management of agricultural disease is important to ensure sustainable food security. Staple food crops like rice, wheat, cereals, and other cash crops hold great export value for countries. Ensuring proper supply is critical; hence any biotic or abiotic factors contributing to the shortfall in yield of these crops should be alleviated. Rhizoctonia solani is a major biotic factor that results in yield losses in many agriculturally important crops. This paper focuses on genome informatics of our Malaysian Draft R. solani AG1-IA, and the comparative genomics (inter- and intra- AG) with four AGs including China AG1-IA (AG1-IA_KB317705.1), AG1-IB, AG3, and AG8. The genomic content of repeat elements, transposable elements (TEs), syntenic genomic blocks, functions of protein-coding genes as well as core orthologous genic information that underlies R. solani’s pathogenicity strategy were investigated. Our analyses show that all studied AGs have low content and varying profiles of TEs. All AGs were dominant for Class I TE, much like other basidiomycete pathogens. All AGs demonstrate dominance in Glycoside Hydrolase protein-coding gene assignments suggesting its importance in infiltration and infection of host. Our profiling also provides a basis for further investigation on lack of correlation observed between number of pathogenicity and enzyme-related genes with host range. Despite being grouped within the same AG with China AG1-IA, our Draft AG1-IA exhibits differences in terms of protein-coding gene proportions and classifications. This implies that strains from similar AG do not necessarily have to retain similar proportions and classification of TE but must have the necessary arsenal to enable successful infiltration and colonization of host. In a larger perspective, all the studied AGs essentially share core genes that are generally involved in adhesion, penetration, and host colonization. However, the different infiltration strategies will depend on the level of host resilience where this is clearly exhibited by the gene sets encoded for the process of infiltration, infection, and protection from host.

scholarly journals Epidemiological modeling in StochSS Live!

2021 ◽  
Author(s):  
Richard Jiang ◽  
Bruno Jacob ◽  
Matthew Geiger ◽  
Sean Matthew ◽  
Bryan Rumsey ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Epidemiological Model ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Epidemiological Modeling ◽  
Modeling Simulation ◽  
Wide Range ◽  
Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

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