scholarly journals Summarizing Performance for Genome Scale Measurement of miRNA: Reference Samples and Metrics

2017 ◽  
Author(s):  
PS Pine ◽  
SP Lund ◽  
JR Parsons ◽  
LK Vang ◽  
AA Mahabal ◽  
...  
Keyword(s):  
In Silico ◽  
Opportunity To Learn ◽  
Online Data ◽  
Mirna Profiling ◽  
Reverse Transcription Pcr ◽  
Measurement Assurance ◽  
Critical Components ◽  
Next Generation Sequencing Ngs ◽  
Genome Scale ◽  
Reference Samples

ABSTRACTBackgroundThe potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls.ResultsThree pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes.ConclusionsThe set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

scholarly journals Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
Author(s):  
Yilin Fang ◽  
Michael J. Wilkins ◽  
Steven B. Yabusaki ◽  
Mary S. Lipton ◽  
Philip E. Long
Keyword(s):  
Proteomic Analysis ◽  
In Silico ◽  
Field Experiments ◽  
Metabolic Model ◽  
Geobacter Metallireducens ◽  
Content Type ◽  
Proteomic Data ◽  
Subsurface Environment ◽  
A Genome ◽  
Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

scholarly journals In silico promoter and expression analyses of rice Auxin Binding Protein 57 (ABP57)

2021 ◽  
Vol 8 (3) ◽  
pp. 741-748
Author(s):  
Farah Afiqah Baharuddin ◽  
Zhan Xuan Khong ◽  
Zamri Zainal ◽  
Noor Liyana Sukiran
Keyword(s):  
Abiotic Stress ◽  
In Silico ◽  
Binding Protein ◽  
Transcript Level ◽  
Regulatory Elements ◽  
Biochemical Properties ◽  
Auxin Receptor ◽  
Reverse Transcription Pcr ◽  
Auxin Binding ◽  
Auxin Binding Protein

Auxin Binding Protein 57 (ABP57) is one of the molecular components involved in rice response to abiotic stress. The ABP57 gene encodes an auxin receptor which functions in activating the plasma membrane H+-ATPase. Biochemical properties of ABP57 have been characterized; however, the function of ABP57, particularly on stress and hormone responses is still limited. This study was conducted to understand the regulation of ABP57 expression under abiotic stress. Thus, in silico identification of cis-acting regulatory elements (CAREs) in the promoter region of ABP57 was performed. Several motifs and transcription factor binding site (TFBS) that are involved in abiotic stress such as ABRE, DRE, AP2/EREBP, WRKY and NAC were identified. Next, expression analysis of ABP57 under drought, salt, auxin (IAA) and abscisic acid (ABA) was conducted by reverse transcription-PCR (RT-PCR) to verify the effect of these treatments on ABP57 transcript level. ABP57 was expressed at different levels in the shoot and root under drought conditions, and its expression was increased under IAA and ABA treatments. Moreover, our results showed that ABP57 expression in the root was more responsive to drought, auxin and ABA treatments compared to its transcript in the shoot. This finding suggests that ABP57 is a drought-responsive gene and possibly regulated by IAA and ABA.

Exons sequencing identifies a novel mutation in GPR157 in a high myopia family

2020 ◽  
Author(s):  
Tao Tang ◽  
Mohan Liu ◽  
Ting Wei ◽  
Lin Deng ◽  
Yueyang Zhang ◽  
...  
Keyword(s):  
Nonsense Mutation ◽  
High Myopia ◽  
Sanger Sequencing ◽  
Novel Mutation ◽  
Rt Pcr ◽  
Genetic Characteristics ◽  
Reverse Transcription Pcr ◽  
Whole Exome ◽  
Reduced Penetrance ◽  
Next Generation Sequencing Ngs

Abstract Background Next-generation sequencing (NGS) and whole exome sequencing (WES) have identified many potential disease-causing loci and genetic mutations of high myopia(HM). However, these known genes can only explain the heritability of a small proportion of HM patients. A large proportion of variants have yet to be discovered. Herein we aimed to investigate the genetic characteristics of HM through a Chinese HM family(the inheritance pattern unknown) . Methods We performed WES on the parent-offspring trio and identified mutations by Sanger sequencing. All the members in this family were sequenced to validate phenotype co-segregated with candidate genes via Sanger sequencing as well. Besides, mutations detected were further evaluated in a cohort of 110 sporadic high myopia controls and 200 unrelated ethically-matched controls. And reverse transcription PCR(RT-PCR) was applied to measure the mRNA expression levels of GPR157 in the 4-week-old KM mice. Results A novel heterozygous nonsense mutation, c.859C>T (p.Arg287*) of GPR157 gene, was detected in the proband and her father by WES. And this disease-associated mutation was not found in 310 control individuals. For the family under study, HM was classified as autosomal dominant inheritance with reduced penetrance. And RT-PCR results showed GPR157 was abundantly expressed in the eye. Conclusion The hybrid nonsense mutation of the GPR157 gene identified in this study may constitute a novel genetic cause of HM. Keywords :high myopia, WES, GPR157

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

scholarly journals Building, Hosting and Recruiting: A Brief Introduction to Running Behavioral Experiments Online

2020 ◽  
Vol 10 (4) ◽  
pp. 251 ◽  
Author(s):  
Marian Sauter ◽  
Dejan Draschkow ◽  
Wolfgang Mack
Keyword(s):  
Data Acquisition ◽  
Experimental Studies ◽  
Behavioral Experiments ◽  
Online Data ◽  
Online Experimentation ◽  
Critical Components

Researchers have ample reasons to take their experimental studies out of the lab and into the online wilderness. For some, it is out of necessity, due to an unforeseen laboratory closure or difficulties in recruiting on-site participants. Others want to benefit from the large and diverse online population. However, the transition from in-lab to online data acquisition is not trivial and might seem overwhelming at first. To facilitate this transition, we present an overview of actively maintained solutions for the critical components of successful online data acquisition: creating, hosting and recruiting. Our aim is to provide a brief introductory resource and discuss important considerations for researchers who are taking their first steps towards online experimentation.

scholarly journals Genome‐scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae

2020 ◽  
Vol 21 (4) ◽  
pp. 527-540 ◽  
Author(s):  
Lokanand Koduru ◽  
Hyang Yeon Kim ◽  
Meiyappan Lakshmanan ◽  
Bijayalaxmi Mohanty ◽  
Yi Qing Lee ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Leaf Blight ◽  
Xanthomonas Oryzae ◽  
Metabolic Reconstruction ◽  
Rice Leaf ◽  
Genome Scale ◽  
Silico Analysis

scholarly journals Improvement of l-arginine production by in silico genome-scale metabolic network model guided genetic engineering

3 Biotech ◽  
2020 ◽  
Vol 10 (3) ◽  
Author(s):  
Mingzhu Huang ◽  
Yue Zhao ◽  
Rong Li ◽  
Weihua Huang ◽  
Xuelan Chen
Keyword(s):  
Genetic Engineering ◽  
Metabolic Network ◽  
Network Model ◽  
In Silico ◽  
Metabolic Network Model ◽  
Genome Scale
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