First in Fly: Drosophila Research and Biological Discovery

Science ◽  
2018 ◽  
Vol 359 (6383) ◽  
pp. 1474.2-1474
Keyword(s):  
Drosophila Research ◽  
Biological Discovery

scholarly journals Molecular Phenomic Approaches to Deconvolving the Systemic Effects of SARS-CoV-2 Infection and Post-acute COVID-19 Syndrome

Phenomics ◽  
2021 ◽  
Author(s):  
Jeremy K. Nicholson
Keyword(s):  
Molecular Level ◽  
Organ Damage ◽  
Zoonotic Diseases ◽  
Mitigation Strategies ◽  
Health Consequence ◽  
Metabolic Phenotyping ◽  
Persistent Symptoms ◽  
Biological Discovery ◽  
New Biomarkers

AbstractSARS COV-2 infection causes acute and frequently severe respiratory disease with associated multi-organ damage and systemic disturbances in many biochemical pathways. Metabolic phenotyping provides deep insights into the complex immunopathological problems that drive the resulting COVID-19 disease and is also a source of novel metrics for assessing patient recovery. A multiplatform metabolic phenotyping approach to studying the pathology and systemic metabolic sequelae of COVID-19 is considered here, together with a framework for assessing post-acute COVID-19 Syndrome (PACS) that is a major long-term health consequence for many patients. The sudden emergence of the disease presents a biological discovery challenge as we try to understand the pathological mechanisms of the disease and develop effective mitigation strategies. This requires technologies to measure objectively the extent and sub-phenotypes of the disease at the molecular level. Spectroscopic methods can reveal metabolic sub-phenotypes and new biomarkers that can be monitored during the acute disease phase and beyond. This approach is scalable and translatable to other pathologies and provides as an exemplar strategy for the investigation of other emergent zoonotic diseases with complex immunological drivers, multi-system involvements and diverse persistent symptoms.

RPPAware: A software suite to preprocess, analyze and visualize reverse phase protein array data

2018 ◽  
Vol 16 (03) ◽  
pp. 1850001 ◽  
Author(s):  
A. Ranjitha Dhanasekaran ◽  
Katheleen J. Gardiner
Keyword(s):  
Brain Regions ◽  
Large Datasets ◽  
Ease Of Use ◽  
Statistical Features ◽  
Software Suite ◽  
Reverse Phase ◽  
High Throughput Technology ◽  
Phase Protein ◽  
Biological Discovery

Reverse Phase Protein Arrays (RPPA) is a high-throughput technology used to profile levels of protein expression. Handling the large datasets generated by RPPA can be facilitated by appropriate software tools. Here, we describe RPPAware, a free and intuitive software suite that was developed specifically for analysis and visualization of RPPA data. RPPAware is a portable tool that requires no installation and was built using Java. Many modules of the tool invoke R to utilize the statistical features. To demonstrate the utility of RPPAware, data generated from screening brain regions of a mouse model of Down syndrome with 62 antibodies were used as a case study. The ease of use and efficiency of RPPAware can accelerate data analysis to facilitate biological discovery. RPPAware 1.0 is freely available under GNU General Public License from the project website at http://downsyndrome.ucdenver.edu/iddrc/rppaware/home.htm along with a full documentation of the tool.

scholarly journals Widespread Prevalence of Wolbachia in Laboratory Stocks and the Implications for Drosophila Research

Genetics ◽  
2005 ◽  
Vol 170 (4) ◽  
pp. 1667-1675 ◽  
Author(s):  
Michael E. Clark ◽  
Cort L. Anderson ◽  
Jessica Cande ◽  
Timothy L. Karr
Keyword(s):  
Drosophila Research

scholarly journals Gene Expression Imputation with Generative Adversarial Imputation Nets

2020 ◽  
Author(s):  
Ramon Viñas ◽  
Tiago Azevedo ◽  
Eric R. Gamazon ◽  
Pietro Liò
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Biological Significance ◽  
Predictive Performance ◽  
Cost Effective ◽  
Rna Seq ◽  
Comprehensive Collection ◽  
Genomic Studies ◽  
Biological Discovery ◽  
Cancer Types

AbstractA question of fundamental biological significance is to what extent the expression of a subset of genes can be used to recover the full transcriptome, with important implications for biological discovery and clinical application. To address this challenge, we present GAIN-GTEx, a method for gene expression imputation based on Generative Adversarial Imputation Networks. In order to increase the applicability of our approach, we leverage data from GTEx v8, a reference resource that has generated a comprehensive collection of transcriptomes from a diverse set of human tissues. We compare our model to several standard and state-of-the-art imputation methods and show that GAIN-GTEx is significantly superior in terms of predictive performance and runtime. Furthermore, our results indicate strong generalisation on RNA-Seq data from 3 cancer types across varying levels of missingness. Our work can facilitate a cost-effective integration of large-scale RNA biorepositories into genomic studies of disease, with high applicability across diverse tissue types.

scholarly journals The fractured landscape of RNA-seq alignment: The default in our STARs

2017 ◽  
Author(s):  
Sara Ballouz ◽  
Alexander Dobin ◽  
Thomas Gingeras ◽  
Jesse Gillis
Keyword(s):  
Expression Profile ◽  
Rna Seq ◽  
Model Data ◽  
Mhc Genes ◽  
Wide Range ◽  
Biological Discovery ◽  
Biological Performance ◽  
Expression Quantification

ABSTRACTMany tools are available for RNA-seq alignment and expression quantification, with comparative value being hard to establish. Benchmarking assessments often highlight methods’ good performance, but are focused on either model data or fail to explain variation in performance. This leaves us to ask, what is the most meaningful way to assess different alignment choices? And importantly, where is there room for progress? In this work, we explore the answers to these two questions by performing an exhaustive assessment of the STAR aligner. We assess STAR’s performance across a range of alignment parameters using common metrics, and then on biologically focused tasks. We find technical metrics such as fraction mapping or expression profile correlation to be uninformative, capturing properties unlikely to have any role in biological discovery. Surprisingly, we find that changes in alignment parameters within a wide range have little impact on both technical and biological performance. Yet, when performance finally does break, it happens in difficult regions, such as X-Y paralogs and MHC genes. We believe improved reporting by developers will help establish where results are likely to be robust or fragile, providing a better baseline to establish where methodological progress can still occur.

scholarly journals A Solanum lycopersicoides reference genome facilitates biological discovery in tomato

2020 ◽  
Author(s):  
Adrian F. Powell ◽  
Lance E. Courtney ◽  
Maximilian H.-W. Schmidt ◽  
Ari Feder ◽  
Alexander Vogel ◽  
...  
Keyword(s):  
Pathogen Resistance ◽  
Wild Relatives ◽  
Introgression Lines ◽  
Tomato Genome ◽  
Nutrient Quality ◽  
Solanum Lycopersicoides ◽  
Genomic Studies ◽  
Biological Discovery ◽  
Cultivated Species ◽  
Tomato Breeding

SummaryWild relatives of tomato are a valuable source of natural variation in tomato breeding, as many can be hybridized to the cultivated species (Solanum lycopersicum). Several, including Solanum lycopersicoides, have been crossed to S. lycopersicum for the development of ordered introgression lines (ILs). Despite the utility of these wild relatives and their associated ILs, limited finished genomes have been produced to aid genetic and genomic studies. We have generated a chromosome-scale genome assembly for Solanum lycopersicoides LA2951 using PacBio sequencing, Illumina, and Hi-C. We identified 37,938 genes based on Illumina and Isoseq and compared gene function to the available cultivated tomato genome resources, in addition to mapping the boundaries of the S. lycopersicoides introgressions in a set of cv. VF36 x LA2951 introgression lines (IL). The genome sequence and IL map will support the development of S. lycopersicoides as a model for studying fruit nutrient/quality, pathogen resistance, and environmental stress tolerance traits that we have identified in the IL population and are known to exist in S. lycopersicoides.

scholarly journals Designing Visualizations For Biological Data

Leonardo ◽  
2013 ◽  
Vol 46 (3) ◽  
pp. 270-271 ◽  
Author(s):  
Miriah Meyer
Keyword(s):  
Biological Data ◽  
Discovery Process ◽  
Design Studies ◽  
Biological Discovery ◽  
Vital Component ◽  
Visualization Tools ◽  
Visualization Design ◽  
Effective Visualization

Visualization is now a vital component of the biological discovery process. This article presents visualization design studies as a promising approach for creating effective, visualization tools for biological data.

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