scholarly journals IPCO: Inference of Pathways from Co-variance analysis

2019 ◽  
Author(s):  
Mrinmoy Das ◽  
Tarini Shankar Ghosh ◽  
Ian B. Jeffery
Keyword(s):  
Variance Analysis ◽  
Functional Prediction ◽  
Accurate Identification ◽  
Prediction Tools ◽  
Taxonomic Profile ◽  
Metabolite Profiles ◽  
Microbiome Research ◽  
Key Aspects ◽  
Functional Profiles ◽  
Reference Genomes

AbstractKey aspects of microbiome research are accurate identification of taxa followed by the profiling of their functionality. Amplicon profiling based on the 16S ribosomal DNA sequence is a ubiquitous technique to identify and profile the abundances of the various taxa. However, it does not provide information on their encoded functionality. Predictive tools which can accurately extrapolate the functional information of a microbiome based on taxonomic profile composition is essential. At present the applicability of these tools is however limited due to requirement of reference genomes from known species. We present IPCO (Inference of Pathways from Co-variance analysis), a new method of inferring functionality for 16S-based microbiome profiles independent of reference genomes. IPCO utilises the biological co-variance observed between paired taxonomic and functional profiles and co-varies it with the queried dataset. It outperforms other established methods both in terms of sample and feature profile prediction. Validation results confirmed that IPCO can replicate observed biological signals seen within shotgun and metabolite profiles. Comparative analysis of predicted functionality profiles with other popular 16S-based functional prediction tools indicates significantly lower performance with predicted functionality showing little to no correlation with paired shotgun features across samples. IPCO is implemented in R and available from https://github.com/IPCO-Rlibrary/IPCO.

scholarly journals miRador: a fast and precise tool for the prediction of plant miRNAs

2021 ◽  
Author(s):  
Reza K Hammond ◽  
Parth Patel ◽  
Pallavi Gupta ◽  
Blake C. Meyers
Keyword(s):  
Stress Responses ◽  
Target Prediction ◽  
Accurate Identification ◽  
Rna Molecules ◽  
Prediction Tools ◽  
Novel Mirna ◽  
Precise Tool ◽  
Computationally Intensive ◽  
And Function ◽  
Post Transcriptional Regulation

Plant microRNAs (miRNAs) are short, non-coding RNA molecules that restrict gene expression via post-transcriptional regulation and function in several essential pathways including development, growth, and stress responses. Accurately identifying miRNAs in populations of small RNA (sRNA) sequencing libraries is a computationally intensive process which has resulted in the misidentification of inaccurately annotated miRNA sequences. In recent years, criteria for miRNA annotation have been refined to reduce these misannotations. Here, we describe miRador, a novel miRNA identification tool that utilizes the most up-to-date, community-established criteria for accurate identification of miRNAs in plants. We combine target prediction and Parallel Analysis of RNA Ends (PARE) data to assess the precision of the miRNAs identified by miRador. We compare miRador to other commonly used miRNA prediction tools and we find that miRador is at least as precise as other prediction tools while being significantly faster than other tools.

Design of Computer Vision and Image Processing Technology Based Car Keys Tooth Identification Detection System

2014 ◽  
Vol 1079-1080 ◽  
pp. 1061-1063 ◽  
Author(s):  
Hong Ying Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Processing Technology ◽  
Direct Impact ◽  
Assembly Process ◽  
Accurate Identification ◽  
Detection Technology ◽  
Key Aspects ◽  
The Cost

This paper can be used as acar key toothed recognition and detection technology and computer vision, imageprocessing technology combined with interdisciplinary applications. Car lockassembly complicated procedures, identification and car keys tooth detection isone of the key aspects of automotive lock assembly, lock a direct impact on theefficiency of the assembly process. The system can effectively improve theexisting car key tooth detection technology to reduce the cost of car keystooth detection recognition, while also rapid and accurate identification, sothat the entire lock assembly process much more efficient.

scholarly journals Inference based accuracy of metagenome prediction tools varies across sample types and functional categories

2020 ◽  
Author(s):  
Shan Sun ◽  
Roshonda B. Jones ◽  
Anthony A. Fodor
Keyword(s):  
16S Rrna ◽  
Gene Prediction ◽  
Amplicon Sequencing ◽  
Taxonomic Composition ◽  
Inference Models ◽  
Prediction Tools ◽  
16S Rrna Amplicon Sequencing ◽  
Metagenome Sequencing ◽  
The Cost ◽  
Functional Profiles

Abstract Background: Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2 and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments. Results: We selected 7 datasets of human, non-human animal and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2 and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to “house-keeping” functions. However, their performance degraded sharply outside of human datasets when used for inference. Conclusion: We conclude that the utility of PICRUSt, PICRUSt2 and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.

scholarly journals Salinity Drives Functional and Taxonomic Diversities in Global Water Metagenomes

2021 ◽  
Vol 12 ◽  
Author(s):  
Huaihai Chen ◽  
Kayan Ma ◽  
Yu Huang ◽  
Yuchun Yang ◽  
Zilong Ma ◽  
...  
Keyword(s):  
Aquatic Ecosystems ◽  
Comprehensive Evaluation ◽  
Saline Water ◽  
Taxonomic Diversity ◽  
Functional Redundancy ◽  
Global Scale ◽  
Metagenomic Data ◽  
Functional Prediction ◽  
Functional Profiles ◽  
Global Water

A tight association between microbial function and taxonomy is the basis of functional prediction based on taxonomy, but such associations have been controversial in water biomes largely due to the probable prevalence of functional redundancy. However, previous studies on this topic used a relatively coarse resolution of ecosystem functioning, potentially inflating the estimated functional redundancy. Thus, a comprehensive evaluation of the association between high-resolution functional traits and taxonomic diversity obtained from fresh and saline water metagenomic data is urgently needed. Here, we examined 938 functionally and taxonomically annotated water metagenomes obtained worldwide to scrutinize the connection between function and taxonomy, and to identify the key driver of water metagenomes function or taxonomic composition at a global scale. We found that pairwise similarity of function was significantly associated with taxonomy, though taxonomy had higher global dissimilarity than function. Classification into six water biomes resulted in greater variation in taxonomic compositions than functional profiles, as the key regulating factor was salinity. Fresh water microbes harbored distinct functional and taxonomic structures from microbes in saline water biomes, despite that taxonomy was more susceptible to gradient of geography and climate than function. In summary, our results find a significant relationship between taxonomic diversity and microbial functioning in global water metagenomes, although microbial taxonomic compositions vary to a larger extent than functional profiles in aquatic ecosystems, suggesting the possibility and necessity for functional prediction of microorganisms based on taxonomy in global aquatic ecosystems.

scholarly journals Active Smoking Induces Aberrations in Digestive Tract Microbiota of Rats

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiang Wang ◽  
Pei Ye ◽  
Li Fang ◽  
Sheng Ge ◽  
Fan Huang ◽  
...  
Keyword(s):  
Digestive Tract ◽  
Serum Insulin ◽  
Control Group ◽  
Active Smoking ◽  
Functional Prediction ◽  
Metabolic Factors ◽  
Multiple Regions ◽  
Smoking Group ◽  
Functional Profiles ◽  
Microbiota Structure

Cigarette smoking could have certain effects on gut microbiota. Some pioneering studies have investigated effects of active smoking on the microbiome in local segments of the digestive tract, while active smoking-induced microbiome alterations in the whole digestive tract have not been fully investigated. Here, we developed a rat model of active smoking and characterized the effects of active smoking on the microbiota within multiple regions along the digestive tract. Blood glucose and some metabolic factors levels, the microbial diversity and composition, relative abundances of taxa, bacterial network correlations and predictive functional profiles were compared between the control group and active smoking group. We found that active smoking induced hyperglycemia and significant reductions in serum insulin and leptin levels. Active smoking induced region-specific shifts in microbiota structure, composition, network correlation and metabolism function along the digestive tract. Our results demonstrated that active smoking resulted in a reduced abundance of some potentially beneficial genera (i.e. Clostridium, Turicibacter) and increased abundance of potentially harmful genera (i.e. Desulfovibrio, Bilophila). Functional prediction suggested that amino acid, lipid, propanoate metabolism function could be impaired and antioxidant activity may be triggered. Active smoking may be an overlooked risk to health through its potential effects on the digestive tract microbiota, which is involved in the cause and severity of an array of chronic diseases.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

Inter-individual variation shapes the human microbiome

2019 ◽  
Vol 42 ◽  
Author(s):  
Emily F. Wissel ◽  
Leigh K. Smith
Keyword(s):  
Individual Differences ◽  
Individual Variation ◽  
Human Microbiome ◽  
Individual Variability ◽  
Microbiota Composition ◽  
Target Article ◽  
Microbiome Research

Abstract The target article suggests inter-individual variability is a weakness of microbiota-gut-brain (MGB) research, but we discuss why it is actually a strength. We comment on how accounting for individual differences can help researchers systematically understand the observed variance in microbiota composition, interpret null findings, and potentially improve the efficacy of therapeutic treatments in future clinical microbiome research.

Immunolabelling of glomerular deposits in kidney biopsies routinely fixed in glutaraldehyde

1989 ◽  
Vol 47 ◽  
pp. 910-911
Author(s):  
Ś Lhoták ◽  
I. Alexopoulou ◽  
G. T. Simon
Keyword(s):  
Uv Light ◽  
Kidney Diseases ◽  
Light Chains ◽  
Microscopical Level ◽  
Accurate Identification ◽  
Light Microscopical Level ◽  
Dense Deposits ◽  
Cacodylate Buffer ◽  
Glomerular Deposits ◽  
Prognostic Importance

Various kidney diseases are characterized by the presence of dense deposits in the glomeruli. The type(s) of immunoglobulins (Igs) present in the dense deposits are characteristic of the disease. The accurate Identification of the deposits is therefore of utmost diagnostic and prognostic importance. Immunofluorescence (IF) used routinely at the light microscopical level is unable to detect and characterize small deposits found in early stages of glomerulonephritis. Although conventional TEM is able to localize such deposits, it is not capable of determining their nature. It was therefore attempted to immunolabel at EM level IgG, IgA IgM, C3, fibrinogen and kappa and lambda Ig light chains commonly found in glomerular deposits on routinely fixed ( 2% glutaraldehyde (GA) in 0.1M cacodylate buffer) kidney biopsies.The unosmicated tissue was embedded in LR White resin polymerized by UV light at -10°C. A postembedding immunogold technique was employed

Project BRAIN: Working Together to Improve Educational Outcomes for Students With Traumatic Brain Injury

2012 ◽  
Vol 22 (3) ◽  
pp. 106-118 ◽  
Author(s):  
Paula Denslow ◽  
Jean Doster ◽  
Kristin King ◽  
Jennifer Rayman
Keyword(s):  
Traumatic Brain Injury ◽  
At Risk ◽  
Brain Injury ◽  
Educational Outcomes ◽  
School Personnel ◽  
Department Of Education ◽  
Health Providers ◽  
Accurate Identification ◽  
Transition Intervention ◽  
Working Together

Children and youth who sustain traumatic brain injury (TBI) are at risk for being unidentified or misidentified and, even if appropriately identified, are at risk of encountering professionals who are ill-equipped to address their unique needs. A comparison of the number of people in Tennessee ages 3–21 years incurring brain injury compared to the number of students ages 3–21 years being categorized and served as TBI by the Department of Education (DOE) motivated us to create this program. Identified needs addressed by the program include the following: (a) accurate identification of students with TBI; (b) training of school personnel; (c) development of linkages and training of hospital personnel; and (d) hospital-school transition intervention. Funded by Health Services and Resources Administration (HRSA) grants with support from the Tennessee DOE, Project BRAIN focuses on improving educational outcomes for students with TBI through the provision of specialized group training and ongoing education for educators, families, and health professionals who support students with TBI. The program seeks to link families, hospitals, and community health providers with school professionals such as speech-language pathologists (SLPs) to identify and address the needs of students with brain injury.

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