scholarly journals FiberAI: A Deep Learning model for automated analysis of nascent DNA Fibers

2020 ◽  
Author(s):  
Azam Mohsin ◽  
Stephen Arnovitz ◽  
Aly A Khan ◽  
Fotini Gounari
Keyword(s):  
Deep Learning ◽  
Dna Replication ◽  
High Throughput ◽  
Genome Stability ◽  
Molecular Mechanisms ◽  
Quantitative Information ◽  
Average Precision ◽  
Extrinsic Factors ◽  
Replication Process ◽  
Microscopy Images

AbstractAll life forms undergo cell division and are dependent on faithful DNA replication to maintain the stability of their genomes. Both intrinsic and extrinsic factors can stress the replication process and multiple checkpoint mechanisms have evolved to ensure genome stability. Understanding these molecular mechanisms is crucial for preventing and treating genomic instability associated diseases including cancer. DNA replicating fiber fluorography is a powerful technique that directly visualizes the replication process and a cell’s response to replication stress. Analysis of DNA-fiber microscopy images provides quantitative information about replication fitness. However, a bottleneck for high throughput DNA-fiber studies is that quantitative measurements are laborious when performed manually. Here we introduce FiberAI, which uses state-of-the art deep learning frameworks to detect and quantify DNA-fibers in high throughput microscopy images. FiberAI efficiently detects DNA fibers, achieving a bounding box average precision score of 0.91 and a segmentation average precision score of 0.90. We then use FiberAI to measure the integrity of replication checkpoints. FiberAI is publicly available and allows users to view model predicted selections, add their own manual selections, and easily analyze multiple image sets. Thus, FiberAI can help elucidate DNA replication processes by streamlining DNA-fiber analyses.

Control of genome stability by Slx protein complexes

2009 ◽  
Vol 37 (3) ◽  
pp. 495-510 ◽  
Author(s):  
John Rouse
Keyword(s):  
Dna Damage ◽  
Dna Replication ◽  
Protein Interactions ◽  
Cellular Response ◽  
Genome Stability ◽  
Molecular Mechanisms ◽  
Excision Repair ◽  
Protein Complexes ◽  
Alkylating Agents ◽  
Strand Break

The six Saccharomyces cerevisiae SLX genes were identified in a screen for factors required for the viability of cells lacking Sgs1, a member of the RecQ helicase family involved in processing stalled replisomes and in the maintenance of genome stability. The six SLX gene products form three distinct heterodimeric complexes, and all three have catalytic activity. Slx3–Slx2 (also known as Mus81–Mms4) and Slx1–Slx4 are both heterodimeric endonucleases with a marked specificity for branched replication fork-like DNA species, whereas Slx5–Slx8 is a SUMO (small ubiquitin-related modifier)-targeted E3 ubiquitin ligase. All three complexes play important, but distinct, roles in different aspects of the cellular response to DNA damage and perturbed DNA replication. Slx4 interacts physically not only with Slx1, but also with Rad1–Rad10 [XPF (xeroderma pigmentosum complementation group F)–ERCC1 (excision repair cross-complementing 1) in humans], another structure-specific endonuclease that participates in the repair of UV-induced DNA damage and in a subpathway of recombinational DNA DSB (double-strand break) repair. Curiously, Slx4 is essential for repair of DSBs by Rad1–Rad10, but is not required for repair of UV damage. Slx4 also promotes cellular resistance to DNA-alkylating agents that block the progression of replisomes during DNA replication, by facilitating the error-free mode of lesion bypass. This does not require Slx1 or Rad1–Rad10, and so Slx4 has several distinct roles in protecting genome stability. In the present article, I provide an overview of our current understanding of the cellular roles of the Slx proteins, paying particular attention to the advances that have been made in understanding the cellular roles of Slx4. In particular, protein–protein interactions and underlying molecular mechanisms are discussed and I draw attention to the many questions that have yet to be answered.

scholarly journals A deep learning framework combined with word embedding to identify DNA replication origins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Feng Wu ◽  
Runtao Yang ◽  
Chengjin Zhang ◽  
Lina Zhang
Keyword(s):  
Deep Learning ◽  
Dna Replication ◽  
Machine Learning Algorithms ◽  
Word Embedding ◽  
Correct Identification ◽  
Replication Origins ◽  
Gene Sequences ◽  
Replication Process ◽  
Similarity Reduction ◽  
Learning Framework

AbstractThe DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful understanding of DNA replication mechanisms and the regulatory mechanisms of genetic expressions. For eukaryotes in particular, multiple ORIs exist in each of their gene sequences to complete the replication in a reasonable period of time. To simplify the identification process of eukaryote’s ORIs, most of existing methods are developed by traditional machine learning algorithms, and target to the gene sequences with a fixed length. Consequently, the identification results are not satisfying, i.e. there is still great room for improvement. To break through the limitations in previous studies, this paper develops sequence segmentation methods, and employs the word embedding technique, ‘Word2vec’, to convert gene sequences into word vectors, thereby grasping the inner correlations of gene sequences with different lengths. Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew’s correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis.

scholarly journals H3.1K27me1 maintains transcriptional silencing and genome stability by preventing GCN5-mediated histone acetylation

2020 ◽  
Author(s):  
Jie Dong ◽  
Chantal LeBlanc ◽  
Axel Poulet ◽  
Benoit Mermaz ◽  
Gonzalo Villarino ◽  
...  
Keyword(s):  
Dna Replication ◽  
Histone Acetylation ◽  
Genomic Instability ◽  
Genome Stability ◽  
Molecular Mechanisms ◽  
Histone Acetyltransferase ◽  
Transcriptional Silencing ◽  
Histone Acetyltransferase Gcn5 ◽  
Lysine Residues

AbstractIn plants, genome stability is maintained during DNA replication by the H3.1K27 methyltransferases ATXR5 and ATXR6, which catalyze the deposition of K27me1 on replicationdependent H3.1 variants. Loss of H3.1K27me1 in atxr5 atxr6 double mutants leads to heterochromatin defects, including transcriptional de-repression and genomic instability, but the molecular mechanisms involved remain largely unknown. In this study, we identified the conserved histone acetyltransferase GCN5 as a mediator of transcriptional de-repression and genomic instability in the absence of H3.1K27me1. GCN5 is part of a SAGA-like complex in plants that requires ADA2b and CHR6 to mediate the heterochromatic defects of atxr5 atxr6 mutants. Our results show that Arabidopsis GCN5 acetylates multiple lysine residues on H3.1 variants in vitro, but that H3.1K27 and H3.1K36 play key roles in inducing genomic instability in the absence of H3.1K27me1. Overall, this work reveals a key molecular role for H3.1K27me1 in maintaining genome stability by restricting histone acetylation in plants.

scholarly journals Post-Translational Modifications of the Mini-Chromosome Maintenance Proteins in DNA Replication

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 331 ◽  
Author(s):  
Li ◽  
Xu
Keyword(s):  
Cell Division ◽  
Dna Replication ◽  
Genome Stability ◽  
Origin Recognition Complex ◽  
Replication Process ◽  
Post Translational Modifications ◽  
Replication Complex ◽  
Therapeutic Implications ◽  
Therapeutic Development ◽  
Mcm Proteins

The eukaryotic mini-chromosome maintenance (MCM) complex, composed of MCM proteins 2–7, is the core component of the replisome that acts as the DNA replicative helicase to unwind duplex DNA and initiate DNA replication. MCM10 tightly binds the cell division control protein 45 homolog (CDC45)/MCM2–7/ DNA replication complex Go-Ichi-Ni-San (GINS) (CMG) complex that stimulates CMG helicase activity. The MCM8–MCM9 complex may have a non-essential role in activating the pre-replicative complex in the gap 1 (G1) phase by recruiting cell division cycle 6 (CDC6) to the origin recognition complex (ORC). Each MCM subunit has a distinct function achieved by differential post-translational modifications (PTMs) in both DNA replication process and response to replication stress. Such PTMs include phosphorylation, ubiquitination, small ubiquitin-like modifier (SUMO)ylation, O-N-acetyl-D-glucosamine (GlcNAc)ylation, and acetylation. These PTMs have an important role in controlling replication progress and genome stability. Because MCM proteins are associated with various human diseases, they are regarded as potential targets for therapeutic development. In this review, we summarize the different PTMs of the MCM proteins, their involvement in DNA replication and disease development, and the potential therapeutic implications.

scholarly journals Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

2019 ◽  
Vol 11 (19) ◽  
pp. 2209 ◽  
Author(s):  
Ethan L. Stewart ◽  
Tyr Wiesner-Hanks ◽  
Nicholas Kaczmar ◽  
Chad DeChant ◽  
Harvey Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Plant Disease ◽  
Ground Truth ◽  
Leaf Blight ◽  
Northern Leaf Blight ◽  
Average Precision ◽  
Global Food Security ◽  
Serious Disease ◽  
Uav Images

Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.

scholarly journals Cyclin E/CDK2: DNA Replication, Replication Stress and Genomic Instability

2021 ◽  
Vol 9 ◽  
Author(s):  
Rafaela Fagundes ◽  
Leonardo K. Teixeira
Keyword(s):  
Cell Cycle ◽  
Dna Replication ◽  
Genomic Instability ◽  
Genome Stability ◽  
Molecular Mechanisms ◽  
Human Cancer ◽  
Cyclin E ◽  
Replication Stress ◽  
Cyclin E1 ◽  
Cdk2 Complex

DNA replication must be precisely controlled in order to maintain genome stability. Transition through cell cycle phases is regulated by a family of Cyclin-Dependent Kinases (CDKs) in association with respective cyclin regulatory subunits. In normal cell cycles, E-type cyclins (Cyclin E1 and Cyclin E2, CCNE1 and CCNE2 genes) associate with CDK2 to promote G1/S transition. Cyclin E/CDK2 complex mostly controls cell cycle progression and DNA replication through phosphorylation of specific substrates. Oncogenic activation of Cyclin E/CDK2 complex impairs normal DNA replication, causing replication stress and DNA damage. As a consequence, Cyclin E/CDK2-induced replication stress leads to genomic instability and contributes to human carcinogenesis. In this review, we focus on the main functions of Cyclin E/CDK2 complex in normal DNA replication and the molecular mechanisms by which oncogenic activation of Cyclin E/CDK2 causes replication stress and genomic instability in human cancer.

scholarly journals DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis

2021 ◽  
Author(s):  
Théo Aspert ◽  
Didier Hentsch ◽  
Gilles Charvin
Keyword(s):  
Deep Learning ◽  
Cell Division ◽  
Single Cell ◽  
High Throughput ◽  
Semantic Segmentation ◽  
Temporal Information ◽  
Biological Processes ◽  
Replicative Lifespan ◽  
Learning Platform ◽  
Microscopy Images

AbstractAutomating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. Here, we have developed DetecDiv, a microfluidic-based image acquisition platform combined with deep learning-based software for high-throughput single-cell division tracking. DetecDiv can reconstruct cellular replicative lifespans with an outstanding accuracy and provides comprehensive temporal cellular metrics using timeseries classification and image semantic segmentation.

scholarly journals DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Michael A. Boemo
Keyword(s):  
Dna Replication ◽  
High Throughput ◽  
Single Molecule ◽  
Replication Fork ◽  
Genome Stability ◽  
Brdu Incorporation ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution

Abstract Background Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target for diseases like cancer. In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software. Results This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent. Conclusions This paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair.

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