scholarly journals Deep learning identifies and quantifies recombination hotspot determinants

2021 ◽  
Author(s):  
Yu Li ◽  
Siyuan Chen ◽  
Trisevgeni Rapakoulia ◽  
Hiroyuki Kuwahara ◽  
Kevin Yip ◽  
...  
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Computational Method ◽  
Data Driven ◽  
Binding Motifs ◽  
Recombination Hotspot ◽  
Recombination Hotspots ◽  
The Cross ◽  
The Difference

Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we develop a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes, and species. In addition to being able to identify hotspot regions and the well-known determinants accurately, RHSNet is sensitive to the difference between different PRDM9 alleles and different sexes, and can generalize to PRDM9-lacking species. The cross-sex, cross-population, and cross-species studies suggest that the proposed method has the potential to identify and quantify the evolutionary determinant motifs.

scholarly journals Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line

2021 ◽  
Vol 11 (19) ◽  
pp. 8967
Author(s):  
Lin Song ◽  
Liping Wang ◽  
Jun Wu ◽  
Jianhong Liang ◽  
Zhigui Liu
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Condition Monitoring ◽  
Physical System ◽  
Production Line ◽  
Learning Algorithm ◽  
Data Driven ◽  
Equation Modeling ◽  
Cyber Physical System ◽  
Deep Learning Algorithm

In response to the lack of a unified cyber–physical system framework, which combined the Internet of Things, industrial big data, and deep learning algorithms for the condition monitoring of critical transmission components in a smart production line. In this study, based on the conceptualization of the layers, a novel five-layer cyber–physical systems framework for smart production lines is proposed. This architecture integrates physics and is data-driven. The smart connection layer collects and transmits data, the physical equation modeling layer converts low-value raw data into high-value feature information via signal processing, the machine learning modeling layer realizes condition prediction through a deep learning algorithm, and scientific decision-making and predictive maintenance are completed through a cognition layer and a configuration layer. Case studies on three critical transmission components—spindles, bearings, and gears—are carried out to validate the effectiveness of the proposed framework and hybrid model for condition monitoring. The prediction results of the three datasets show that the system is successful in distinguishing condition, while the short time Fourier transform signal processing and deep residual network deep learning algorithm is superior to that of other models. The proposed framework and approach are scalable and generalizable and lay the foundation for the extension of the model.

scholarly journals MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs

2020 ◽  
Vol 36 (9) ◽  
pp. 2690-2696
Author(s):  
Jarkko Toivonen ◽  
Pratyush K Das ◽  
Jussi Taipale ◽  
Esko Ukkonen
Keyword(s):  
Markov Models ◽  
Expectation Maximization Algorithm ◽  
Software Tool ◽  
Specific Weight ◽  
Training Data ◽  
Supplementary Information ◽  
Markov Modeling ◽  
Binding Motifs ◽  
The Difference ◽  
Probability Matrices

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Active and inactive transplacement of the M26 recombination hotspot in Schizosaccharomyces pombe.

Genetics ◽  
1995 ◽  
Vol 141 (1) ◽  
pp. 33-48
Author(s):  
J B Virgin ◽  
J Metzger ◽  
G R Smith
Keyword(s):  
Schizosaccharomyces Pombe ◽  
Meiotic Recombination ◽  
Plasmid Dna ◽  
Context Effect ◽  
Intragenic Recombination ◽  
Multicopy Plasmid ◽  
Chromosomal Locus ◽  
Sequence Element ◽  
Recombination Hotspot ◽  
Recombination Hotspots

Abstract The ade6-M26 mutation of the fission yeast Schizosaccharomyces pombe creates a meiotic recombination hotspot that elevates ade6 intragenic recombination approximately 10-15-fold. A heptanucleotide sequence including the M26 point mutation is required but not sufficient for hotspot activity. We studied the effects of plasmid and chromosomal context on M26 hotspot activity. The M26 hotspot was inactive on a multicopy plasmid containing M26 embedded within 3.0 or 5.9 kb of ade6 DNA. Random S. pombe genomic fragments totaling approximately 7 Mb did not activate the M26 hotspot on a plasmid. M26 hotspot activity was maintained when 3.0-, 4.4-, and 5.9-kb ade6-M26 DNA fragments, with various amounts of non-S. pombe plasmid DNA, were integrated at the ura4 chromosomal locus, but only in certain configurations relative to the ura4 gene and the cointegrated plasmid DNA. Several integrations created new M26-independent recombination hotspots. In all cases the non-ade6 DNA was located > 1 kb from the M26 site, and in some cases > 2 kb. Because the chromosomal context effect was transmitted over large distances, and did not appear to be mediated by a single discrete DNA sequence element, we infer that the local chromatin structure has a pronounced effect on M26 hotspot activity.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

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