Predicting Chromosome Flexibility from the Genomic Sequence Based on Deep Learning Neural Networks

2021 ◽  
Vol 16 ◽  
Author(s):  
Jinghao Peng ◽  
Jiajie Peng ◽  
Haiyin Piao ◽  
Zhang Luo ◽  
Kelin Xia ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Function Analysis ◽  
Double Helix ◽  
Gm12878 Cell ◽  
Genomic Sequence Analysis ◽  
And Function ◽  
Nuclear Processes

Background: The open and accessible regions of the chromosome are more likely to be bound by transcription factors which are important for nuclear processes and biological functions. Studying the change of chromosome flexibility can help to discover and analyze disease markers and improve the efficiency of clinical diagnosis. Current methods for predicting chromosome flexibility based on Hi-C data include the flexibility-rigidity index (FRI) and the Gaussian network model (GNM), which have been proposed to characterize chromosome flexibility. However, these methods require the chromosome structure data based on 3D biological experiments, which is time-consuming and expensive. Objective: Generally, the folding and curling of the double helix sequence of DNA have a great impact on chromosome flexibility and function. Motivated by the success of genomic sequence analysis in biomolecular function analysis, we hope to propose a method to predict chromosome flexibility only based on genomic sequence data. Method: We propose a new method (named "DeepCFP") using deep learning models to predict chromosome flexibility based on only genomic sequence features. The model has been tested in the GM12878 cell line. Results: The maximum accuracy of our model has reached 91%. The performance of DeepCFP is close to FRI and GNM. Conclusion: The DeepCFP can achieve high performance only based on genomic sequence.

scholarly journals Proteomic Analysis of Liver Proteins in a Rat Model of Chronic Restraint Stress-Induced Depression

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Cong Li ◽  
Zhengguang Guo ◽  
Ronghua Zhao ◽  
Wei Sun ◽  
Ming Xie
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Restraint Stress ◽  
High Performance ◽  
Function Analysis ◽  
Differentially Expressed Proteins ◽  
Chronic Restraint Stress ◽  
Proteomics Analysis ◽  
And Function ◽  
Canonical Pathway Analysis

Depression is a global mental disorder disease and greatly threatened human health and stress is considered to be one of the important factors that lead to depression. In this study, we used newly developed iTRAQ labeling and high performance liquid chromatography (HPLC) and mass spectrum united analysis technology obtained the 2176 accurate proteins. Successively, we used the GO analysis and IPA software to analyze the 98 differentially expressed proteins of liver in depression rats due to chronic restraint stress, showing a map of proteomics analysis of liver proteins from the aspects of related functions, disease and function analysis, canonical pathway analysis, and associated network. This study provide important information for comprehensively understanding the mechanisms of dysfunction or injury in the liver in depression.

scholarly journals Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Akihito Kikuchi ◽  
Toshimichi Ikemura ◽  
Takashi Abe
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Bacterial Genome ◽  
Computation Time ◽  
Comprehensive Analysis ◽  
Self Organizing Map ◽  
Genome Sequences ◽  
Oligonucleotide Composition

With the remarkable increase in genomic sequence data from various organisms, novel tools are needed for comprehensive analyses of available big sequence data. We previously developed a Batch-Learning Self-Organizing Map (BLSOM), which can cluster genomic fragment sequences according to phylotype solely dependent on oligonucleotide composition and applied to genome and metagenomic studies. BLSOM is suitable for high-performance parallel-computing and can analyze big data simultaneously, but a large-scale BLSOM needs a large computational resource. We have developed Self-Compressing BLSOM (SC-BLSOM) for reduction of computation time, which allows us to carry out comprehensive analysis of big sequence data without the use of high-performance supercomputers. The strategy of SC-BLSOM is to hierarchically construct BLSOMs according to data class, such as phylotype. The first-layer BLSOM was constructed with each of the divided input data pieces that represents the data subclass, such as phylotype division, resulting in compression of the number of data pieces. The second BLSOM was constructed with a total of weight vectors obtained in the first-layer BLSOMs. We compared SC-BLSOM with the conventional BLSOM by analyzing bacterial genome sequences. SC-BLSOM could be constructed faster than BLSOM and cluster the sequences according to phylotype with high accuracy, showing the method’s suitability for efficient knowledge discovery from big sequence data.

scholarly journals DeepMicrobes: taxonomic classification for metagenomics with deep learning

2019 ◽  
Author(s):  
Qiaoxing Liang ◽  
Paul W. Bible ◽  
Yu Liu ◽  
Bin Zou ◽  
Lai Wei
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Genomic Sequence ◽  
Taxonomic Classification ◽  
Sequencing Data ◽  
Computational Framework ◽  
Genome Wide ◽  
Disease Diagnostics ◽  
Genomic Sequence Analysis ◽  
Microbial Genomic

AbstractTaxonomic classification is a crucial step for metagenomics applications including disease diagnostics, microbiome analyses, and outbreak tracing. Yet it is unknown what deep learning architecture can capture microbial genome-wide features relevant to this task. We report DeepMicrobes (https://github.com/MicrobeLab/DeepMicrobes), a computational framework that can perform large-scale training on > 10,000 RefSeq complete microbial genomes and accurately predict the species-of-origin of whole metagenome shotgun sequencing reads. We show the advantage of DeepMicrobes over state-of-the-art tools in precisely identifying species from microbial community sequencing data. Therefore, DeepMicrobes expands the toolbox of taxonomic classification for metagenomics and enables the development of further deep learning-based bioinformatics algorithms for microbial genomic sequence analysis.

scholarly journals Targeted Recovery of Mutations in Drosophila

Genetics ◽  
2000 ◽  
Vol 156 (3) ◽  
pp. 1169-1173
Author(s):  
Alyssa Bentley ◽  
Bridget MacLennan ◽  
Jonathan Calvo ◽  
Charles R Dearolf
Keyword(s):  
High Performance ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Targeted Mutagenesis ◽  
High Rate ◽  
Chemical Mutagenesis ◽  
Base Pairs ◽  
Dna Repair Mechanisms ◽  
Repair Mechanisms ◽  
Genetic Techniques

Abstract Reverse genetic techniques will be necessary to take full advantage of the genomic sequence data for Drosophila and other experimental organisms. To develop a method for the targeted recovery of mutations, we combined an EMS chemical mutagenesis regimen with mutation detection by denaturing high performance liquid chromatography (DHPLC). We recovered mutant strains at the high rate of ∼4.8 mutations/kb for every 1000 mutagenized chromosomes from a screen for new mutations in the Drosophila awd gene. Furthermore, we observed that the EMS mutational spectrum in Drosophila germ cells shows a strong preference for 5′-PuG-3′ sites, and for G/C within a stretch of three or more G/C base pairs. Our method should prove useful for targeted mutagenesis screens in Drosophila and other genetically tractable organisms and for more precise studies of mutagenesis and DNA repair mechanisms.

scholarly journals A deep learning approach to real-time HIV outbreak detection using genetic data

2021 ◽  
Author(s):  
Michael D. Kupperman ◽  
Thomas Leitner ◽  
Ruian Ke
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Drug Users ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Computational Cost ◽  
Disease Outbreaks ◽  
Distance Matrix ◽  
Outbreak Detection ◽  
Hiv 1

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0>3, and overall a specificity exceeding 85% and sensitivity better than 70%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

PoGB-Pred: Prediction of Antifreeze Proteins Sequences using Amino Acid Composition with Feature Selection followed by a Sequential based Ensemble Approach

2020 ◽  
Vol 15 ◽  
Author(s):  
Affan Alim ◽  
Abdul Rafay ◽  
Imran Naseem
Keyword(s):  
Amino Acid ◽  
Dimension Reduction ◽  
Protein Identification ◽  
Cold Water ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Antifreeze Proteins ◽  
Building Blocks ◽  
Gradient Boosting ◽  
Proposed Model

Background: Proteins contribute significantly in every task of cellular life. Their functions encompass the building and repairing of tissues in human bodies and other organisms. Hence they are the building blocks of bones, muscles, cartilage, skin, and blood. Similarly, antifreeze proteins are of prime significance for organisms that live in very cold areas. With the help of these proteins, the cold water organisms can survive below zero temperature and resist the water crystallization process which may cause the rupture in the internal cells and tissues. AFP’s have attracted attention and interest in food industries and cryopreservation. Objective: With the increase in the availability of genomic sequence data of protein, an automated and sophisticated tool for AFP recognition and identification is in dire need. The sequence and structures of AFP are highly distinct, therefore, most of the proposed methods fail to show promising results on different structures. A consolidated method is proposed to produce the competitive performance on highly distinct AFP structure. Methods: In this study, we propose to use machine learning-based algorithms Principal Component Analysis (PCA) followed by Gradient Boosting (GB) for antifreeze protein identification. To analyze the performance and validation of the proposed model, various combinations of two segments composition of amino acid and dipeptide are used. PCA, in particular, is proposed to dimension reduction and high variance retaining of data which is followed by an ensemble method named gradient boosting for modelling and classification. Results: The proposed method obtained the superfluous performance on PDB, Pfam and Uniprot dataset as compared with the RAFP-Pred method. In experiment-3, by utilizing only 150 PCA components a high accuracy of 89.63 was achieved which is superior to the 87.41 utilizing 300 significant features reported for the RAFP-Pred method. Experiment-2 is conducted using two different dataset such that non-AFP from the PISCES server and AFPs from Protein data bank. In this experiment-2, our proposed method attained high sensitivity of 79.16 which is 12.50 better than state-of-the-art the RAFP-pred method. Conclusion: AFPs have a common function with distinct structure. Therefore, the development of a single model for different sequences often fails to AFPs. A robust results have been shown by our proposed model on the diversity of training and testing dataset. The results of the proposed model outperformed compared to the previous AFPs prediction method such as RAFP-Pred. Our model consists of PCA for dimension reduction followed by gradient boosting for classification. Due to simplicity, scalability properties and high performance result our model can be easily extended for analyzing the proteomic and genomic dataset.

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