scholarly journals Nanopore Technology and Its Applications in Gene Sequencing

Biosensors ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 214
Author(s):  
Bo Lin ◽  
Jianan Hui ◽  
Hongju Mao
Keyword(s):  
Cancer Detection ◽  
Life Science ◽  
Gene Sequencing ◽  
Fourth Generation ◽  
Thin Membrane ◽  
Sequencing Technology ◽  
Biomedical Sensing ◽  
Oxford Nanopore ◽  
Electrochemical Signal ◽  
Diagnosis Of Cancer

In recent years, nanopore technology has become increasingly important in the field of life science and biomedical research. By embedding a nano-scale hole in a thin membrane and measuring the electrochemical signal, nanopore technology can be used to investigate the nucleic acids and other biomacromolecules. One of the most successful applications of nanopore technology, the Oxford Nanopore Technology, marks the beginning of the fourth generation of gene sequencing technology. In this review, the operational principle and the technology for signal processing of the nanopore gene sequencing are documented. Moreover, this review focuses on the applications using nanopore gene sequencing technology, including the diagnosis of cancer, detection of viruses and other microbes, and the assembly of genomes. These applications show that nanopore technology is promising in the field of biological and biomedical sensing.

MP-03.21 Prostate Cancer Detection Using Cadence Contrast Pulse Sequencing Technology for Targeted Biopsies

Urology ◽  
2011 ◽  
Vol 78 (3) ◽  
pp. S48
Author(s):  
V. Jinga ◽  
M. Budau ◽  
B. Braticevici ◽  
D. Radavoi ◽  
C. Calin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Sequencing Technology ◽  
Prostate Cancer Detection ◽  
Targeted Biopsies

A Survey on Deep Learning Techniques for Prognosis and Diagnosis of Cancer from Microarray Gene Expression Data

2019 ◽  
Vol 16 (12) ◽  
pp. 5078-5088 ◽  
Author(s):  
Rahul Shahane ◽  
Md. Ismail ◽  
C. S. R. Prabhu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Cancer Detection ◽  
Gene Expression Analysis ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Cancer Subtypes ◽  
Diagnosis Of Cancer ◽  
Microarray Gene

The gene expression classification and identification from DNA microarray data is efficient technique for cancer diagnosis and prognosis for specific cancer subtypes. DNA microarray technology has great potential to discover information from expression levels of thousands of gene. The collection of significant genes which can improve the accuracy can give proper direction in early diagnosis of cancer. Cancer may be of different subtypes. Cancer detection from microarray gene expression data has major challenge of low sample size, high dimensionality and complexity of the data. There is a need for fast and computationally efficient method to deal with these kind of challenges. Deep Learning has succeeded in numerous fields such as image, video, speech, and text processing. Gene expression analysis is a unique challenge to Deep Learning for various cancer detection and prediction tasks in order to set specific biomarkers for different cancer subtypes. In this paper, we briefly discuss the strengths of different Deep Learning architectures for a cancer detection and prediction of various types of cancer through gene expression analysis.

Towards the electrochemical diagnosis of cancer: nanomaterial-based immunosensors and cytosensors

RSC Advances ◽  
2016 ◽  
Vol 6 (113) ◽  
pp. 111831-111841 ◽  
Author(s):  
Ülkü Anik ◽  
Suna Timur
Keyword(s):  
Cancer Detection ◽  
Electrochemical Biosensors ◽  
Electrochemical Immunosensors ◽  
Diagnosis Of Cancer

In this review, nanomaterial based electrochemical biosensors including electrochemical immunosensors and cytosensors towards cancer detection are covered.

scholarly journals Is oxford nanopore sequencing ready for analyzing complex microbiomes?

2021 ◽  
Author(s):  
L J Kerkhof
Keyword(s):  
Data Analysis ◽  
Microbial Ecology ◽  
Nanopore Sequencing ◽  
Sequencing Technology ◽  
Capital Costs ◽  
Target Molecule ◽  
Field Coupling ◽  
Oxford Nanopore ◽  
Additional Equipment ◽  
Attractive Option

Abstract This minireview will discuss the improvements in Oxford Nanopore sequencing technology which make the MinION a viable platform for microbial ecology studies. Specific issues being addressed are the increase in sequence accuracy from 65–96.5% during the last 5 years, the ability to obtain a quantifiable/predictive signal from the MinION with respect to target molecule abundance, simple-to-use GUI-based pathways for data analysis, and the modest additional equipment needs for sequencing in the field. Coupling these recent improvements with the low capital costs for equipment and the reasonable per sample cost makes MinION sequencing an attractive option for virtually any laboratory.

scholarly journals Lung Cancer Detection using Nearest Neighbour Classifier

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3641-3645
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Cancer Detection ◽  
Early Stage ◽  
Second Step ◽  
Nearest Neighbour ◽  
Cancer Data ◽  
Diagnosis Of Cancer ◽  
Nearest Neighbour Classifier ◽  
Lung Cancer Detection

One of the most precarious diseases is lung cancer. Lung cancer detection is one of the main challenging dilemma nowadays. Most of the cancer cells are overlies with each other. It is tough to detect the cell but also important to identify the existence of cancer cells in the early stage unless unable to prevent. According to 2018 reports, 17 million new lung cancer cases are identified worldwide. The Computer Tomography can be used for diagnosis of cancer with image processing. In this research, we proposed two steps of process for diagnosing the presence of cancer either benign or malignant. In the first step, features are extracted by using GLCM. In the second step, the lung cancer cells are classified either benign or malignant by using Nearest Neighbour classifier. Experimental results demonstrated that the proposed approach performance is 98.76% classification accuracy for diagnosing the lung cancer data.

scholarly journals First de novo draft genome sequence of Oryza coarctata, the only halophytic species in the genus Oryza

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1750 ◽  
Author(s):  
Tapan Kumar Mondal ◽  
Hukam Chand Rawal ◽  
Kishor Gaikwad ◽  
Tilak Raj Sharma ◽  
Nagendra Kumar Singh
Keyword(s):  
West Bengal ◽  
De Novo ◽  
Draft Genome ◽  
Nanopore Sequencing ◽  
Sequencing Technology ◽  
Genome Sequences ◽  
Oxford Nanopore ◽  
Hybrid Genome ◽  
Genus Oryza ◽  
First Time

Oryza coarctata plants, collected from Sundarban delta of West Bengal, India, have been used in the present study to generate draft genome sequences, employing the hybrid genome assembly with Illumina reads and third generation Oxford Nanopore sequencing technology. We report for the first time that more than 85.71 % of the genome coverage and the data have been deposited in NCBI SRA, with BioProject ID PRJNA396417.

scholarly journals The study of transcriptomes of symbiotic tissue of pea using the third-generation sequencing technology Oxford Nanopore

2020 ◽  
Author(s):  
E. S. Gribchenko
Keyword(s):  
Nanopore Sequencing ◽  
Third Generation ◽  
Nitrogen Fixing ◽  
Sequencing Technology ◽  
The Third ◽  
Third Generation Sequencing ◽  
Oxford Nanopore ◽  
Mycorrhizal Roots ◽  
Gene Isoforms ◽  
Generation Sequencing

The transcriptome profiles the cv. Frisson mycorrhizal roots and inoculated nitrogen-fixing nodules were investigated using the Oxford Nanopore sequencing technology. A database of gene isoforms and their expression has been created.

scholarly journals Comparison of Illumina versus Nanopore 16S rRNA Gene Sequencing of the Human Nasal Microbiota

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1105 ◽  
Author(s):  
Astrid P. Heikema ◽  
Deborah Horst-Kreft ◽  
Stefan A. Boers ◽  
Rick Jansen ◽  
Saskia D. Hiltemann ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Nanopore Sequencing ◽  
Oxford Nanopore ◽  
Rrna Gene Sequencing ◽  
Oxford Nanopore Technologies ◽  
Nasal Microbiota

Illumina and nanopore sequencing technologies are powerful tools that can be used to determine the bacterial composition of complex microbial communities. In this study, we compared nasal microbiota results at genus level using both Illumina and nanopore 16S rRNA gene sequencing. We also monitored the progression of nanopore sequencing in the accurate identification of species, using pure, single species cultures, and evaluated the performance of the nanopore EPI2ME 16S data analysis pipeline. Fifty-nine nasal swabs were sequenced using Illumina MiSeq and Oxford Nanopore 16S rRNA gene sequencing technologies. In addition, five pure cultures of relevant bacterial species were sequenced with the nanopore sequencing technology. The Illumina MiSeq sequence data were processed using bioinformatics modules present in the Mothur software package. Albacore and Guppy base calling, a workflow in nanopore EPI2ME (Oxford Nanopore Technologies—ONT, Oxford, UK) and an in-house developed bioinformatics script were used to analyze the nanopore data. At genus level, similar bacterial diversity profiles were found, and five main and established genera were identified by both platforms. However, probably due to mismatching of the nanopore sequence primers, the nanopore sequencing platform identified Corynebacterium in much lower abundance compared to Illumina sequencing. Further, when using default settings in the EPI2ME workflow, almost all sequence reads that seem to belong to the bacterial genus Dolosigranulum and a considerable part to the genus Haemophilus were only identified at family level. Nanopore sequencing of single species cultures demonstrated at least 88% accurate identification of the species at genus and species level for 4/5 strains tested, including improvements in accurate sequence read identification when the basecaller Guppy and Albacore, and when flowcell versions R9.4 (Oxford Nanopore Technologies—ONT, Oxford, UK) and R9.2 (Oxford Nanopore Technologies—ONT, Oxford, UK) were compared. In conclusion, the current study shows that the nanopore sequencing platform is comparable with the Illumina platform in detection bacterial genera of the nasal microbiota, but the nanopore platform does have problems in detecting bacteria within the genus Corynebacterium. Although advances are being made, thorough validation of the nanopore platform is still recommendable.

EYE ON CHINA

2017 ◽  
Vol 21 (11) ◽  
pp. 6-13
Keyword(s):  
Traditional Medicine ◽  
Rapid Growth ◽  
Rice Variety ◽  
Gene Sequencing ◽  
Northern China ◽  
World Food ◽  
Sequencing Technology ◽  
Genome Sequences ◽  
Anti Cancer ◽  
Chinese Researcher

China boosts plans to motivate its scientists. Chinese researcher recognized by World Food Prize. Breakthrough new rice variety announced in Northern China. Ebola vaccine approved in China. Study reveals anti-cancer properties of a fungus used in traditional medicine. CRISPR bacon: Chinese scientists create genetically modified low-fat pigs. A new weapon of fighting Malaria. Gene sequencing technology saw rapid growth in China. Chinese scientists map genome sequences for Peony. Werum IT Solutions and Neotrident sign partnership in China.

scholarly journals OPTIMIZATION OF CANCER DETECTION

1974 ◽  
Vol 22 (7) ◽  
pp. 663-667 ◽  
Author(s):  
DAN H. MOORE
Keyword(s):  
Cancer Detection ◽  
False Positive ◽  
False Negative ◽  
Optimal Number ◽  
False Negatives ◽  
Expected Cost ◽  
Predetermined Level ◽  
Diagnosis Of Cancer ◽  
Number Of Cells ◽  
Cost Equation

A statistical model is developed that describes the population of women who are given a cytologic screening test for cervical cancer. The model is used to determine false positive and false negative rates as a function of (a) the proportion of "positive" cells in women free from cancer and in those with cancer, (b) the number of cells examined and (c) the minimal number of positive cells for a diagnosis of cancer. The model allows estimation of the minimal number of cells that must be examined in order to reduce both the false positive and the false negative rates below some predetermined levels. An expected cost equation is derived which combines the costs of examining each cell with the costs for false positives and false negatives. It is shown how cancer detection can be optimized through the use of this cost equation. The method determines both the maximal permissible cost for examining each cell and the optimal number of cells to examine in order to reduce the over-all expected cost below some predetermined level.

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