scholarly journals Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements

Biology ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 896
Author(s):  
Ilektra-Chara Giassa ◽  
Panagiotis Alexiou
Keyword(s):  
Machine Learning ◽  
Chromatin Modification ◽  
Mobile Genetic Elements ◽  
Learning Approaches ◽  
Sequencing Data ◽  
Methylation Analysis ◽  
Developmental Defects ◽  
Genetic Elements ◽  
Bisulfite Sequencing Data ◽  
And Function

Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.

scholarly journals Setting the stage for the machine intelligence era in marine science

2020 ◽  
Vol 77 (4) ◽  
pp. 1267-1273
Author(s):  
Cigdem Beyan ◽  
Howard I Browman
Keyword(s):  
Machine Learning ◽  
De Novo ◽  
Machine Intelligence ◽  
Sensor Technology ◽  
Marine Science ◽  
Learning Approaches ◽  
Marine Research ◽  
And Function ◽  
Short Time ◽  
Automated Decision Making

Abstract Machine learning, a subfield of artificial intelligence, offers various methods that can be applied in marine science. It supports data-driven learning, which can result in automated decision making of de novo data. It has significant advantages compared with manual analyses that are labour intensive and require considerable time. Machine learning approaches have great potential to improve the quality and extent of marine research by identifying latent patterns and hidden trends, particularly in large datasets that are intractable using other approaches. New sensor technology supports collection of large amounts of data from the marine environment. The rapidly developing machine learning subfield known as deep learning—which applies algorithms (artificial neural networks) inspired by the structure and function of the brain—is able to solve very complex problems by processing big datasets in a short time, sometimes achieving better performance than human experts. Given the opportunities that machine learning can provide, its integration into marine science and marine resource management is inevitable. The purpose of this themed set of articles is to provide as wide a selection as possible of case studies that demonstrate the applications, utility, and promise of machine learning in marine science. We also provide a forward-look by envisioning a marine science of the future into which machine learning has been fully incorporated.

scholarly journals Evidence of an epidemic spread of KPC-producing Enterobacterales in Czech hospitals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lucie Kraftova ◽  
Marc Finianos ◽  
Vendula Studentova ◽  
Katerina Chudejova ◽  
Vladislav Jakubu ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Infection Control ◽  
Mobile Genetic Elements ◽  
Epidemic Spread ◽  
Sequencing Data ◽  
Genetic Elements ◽  
Long Read ◽  
Genetic Rearrangements

AbstractThe aim of the present study is to describe the ongoing spread of the KPC-producing strains, which is evolving to an epidemic in Czech hospitals. During the period of 2018–2019, a total of 108 KPC-producing Enterobacterales were recovered from 20 hospitals. Analysis of long-read sequencing data revealed the presence of several types of blaKPC-carrying plasmids; 19 out of 25 blaKPC-carrying plasmids could be assigned to R (n = 12), N (n = 5), C (n = 1) and P6 (n = 1) incompatibility (Inc) groups. Five of the remaining blaKPC-carrying plasmids were multireplicon, while one plasmid couldn’t be typed. Additionally, phylogenetic analysis confirmed the spread of blaKPC-carrying plasmids among different clones of diverse Enterobacterales species. Our findings demonstrated that the increased prevalence of KPC-producing isolates was due to plasmids spreading among different species. In some districts, the local dissemination of IncR and IncN plasmids was observed. Additionally, the ongoing evolution of blaKPC-carrying plasmids, through genetic rearrangements, favours the preservation and further dissemination of these mobile genetic elements. Therefore, the situation should be monitored, and immediate infection control should be implemented in hospitals reporting KPC-producing strains.

scholarly journals CRISPR-Cas systems are widespread accessory elements across bacterial and archaeal plasmids

2021 ◽  
Author(s):  
Rafael Pinilla-Redondo ◽  
Jakob Russel ◽  
David Mayo-Muñoz ◽  
Shiraz A Shah ◽  
Roger A Garrett ◽  
...  
Keyword(s):  
Mobile Genetic Elements ◽  
Comprehensive Analysis ◽  
Immune Protection ◽  
Genetic Elements ◽  
And Function

Abstract Many prokaryotes encode CRISPR-Cas systems as immune protection against mobile genetic elements (MGEs), yet a number of MGEs also harbor CRISPR-Cas components. With a few exceptions, CRISPR-Cas loci encoded on MGEs are uncharted and a comprehensive analysis of their distribution, prevalence, diversity, and function is lacking. Here, we systematically investigated CRISPR-Cas loci across the largest curated collection of natural bacterial and archaeal plasmids. CRISPR-Cas loci are widely but heterogeneously distributed across plasmids and, in comparison to host chromosomes, their mean prevalence per Mbp is higher and their distribution is distinct. Furthermore, the spacer content of plasmid CRISPRs exhibits a strong targeting bias towards other plasmids, while chromosomal arrays are enriched with virus-targeting spacers. These contrasting targeting preferences highlight the genetic independence of plasmids and suggest a major role for mediating plasmid-plasmid conflicts. Altogether, CRISPR-Cas are frequent accessory components of many plasmids, which is an overlooked phenomenon that possibly facilitates their dissemination across microbiomes.

scholarly journals Bisulfite sequencing Data Presentation and Compilation (BDPC) web server--a useful tool for DNA methylation analysis

2008 ◽  
Vol 36 (5) ◽  
pp. e34-e34 ◽  
Author(s):  
C. Rohde ◽  
Y. Zhang ◽  
T. P. Jurkowski ◽  
H. Stamerjohanns ◽  
R. Reinhardt ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Web Server ◽  
Sequencing Data ◽  
Methylation Analysis ◽  
Data Presentation ◽  
Bisulfite Sequencing Data ◽  
Dna Methylation Analysis

scholarly journals MethFlowVM: a virtual machine for the integral analysis of bisulfite sequencing data

2016 ◽  
Author(s):  
Ricardo Lebron ◽  
Guillermo Barturen ◽  
Cristina Gomez-Martin ◽  
Jose L Oliver ◽  
Michael Hackenberg
Keyword(s):  
Dna Methylation ◽  
Virtual Machine ◽  
Genome Stability ◽  
Regulation Of Gene Expression ◽  
Sequencing Data ◽  
Methylation Analysis ◽  
User Friendliness ◽  
Bisulfite Sequencing Data ◽  
Methylation Patterns ◽  
Privacy Issues

The analysis of whole genome DNA methylation patterns is an important first step towards the understanding on how DNA methylation is involved in the regulation of gene expression and genome stability. Previously, we published MethylExtract, a program for DNA methylation profiling and genotyping from the same sample. Over the last years we developed it further into a methylation analysis pipeline that allows to take full advantage of novel genome assembly models. The result is a new pipeline termed MethFlow which permits both, profiling of methylation levels and differential methylation analysis. Frequently DNA methylation research is carried out in the biomedical field, where privacy issues play an important role. Therefore we implemented the pipeline into a virtual machine termed MethFlowVM which shares with a web-server its user-friendliness however, the decisive advantage is that the sequencing data does not leave the user desktop or server and therefore no privacy issues do exist. The virtual machine is available at: http://bioinfo2.ugr.es:8080/MethFlow/

scholarly journals Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches

2021 ◽  
Vol 13 (22) ◽  
pp. 4572
Author(s):  
Bibek Aryal ◽  
Stephen M. Escarzaga ◽  
Sergio A. Vargas Vargas Zesati ◽  
Miguel Velez-Reyes ◽  
Olac Fuentes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Semantic Segmentation ◽  
Training Data ◽  
Supervised Machine Learning ◽  
The Arctic ◽  
Ecosystem Structure ◽  
Learning Approaches ◽  
Water Index ◽  
Shoreline Mapping ◽  
And Function

Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method).

scholarly journals Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

2021 ◽  
Vol 6 (4) ◽  
pp. 37-44
Author(s):  
Hiral Raja ◽  
Aarti Gupta ◽  
Rohit Miri
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Digit Recognition ◽  
Learning Techniques ◽  
Effective System ◽  
Handwritten Digit ◽  
Digit String ◽  
And Function

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

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