A fusion method based on alignment software with SNP and Indel detection methods

2020 ◽  
Vol 23 ◽  
Author(s):  
Guobing Chen ◽  
Chao Tang ◽  
Jun Qi ◽  
Ying Wang ◽  
Xiaolong Shi
Keyword(s):  
Sequence Alignment ◽  
Experimental Verification ◽  
Sequence Variation ◽  
Second Generation ◽  
Detection Methods ◽  
Fusion Method ◽  
Sequencing Technology ◽  
Indel Detection ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Background: With the advent of the second generation sequencing technology, the discovery of sequence alignment and sequence variation is a long-standing challenge. Results: A method based on general alignment software, SNP and Indel software tools was proposed in this paper. By comparing the advantages of traditional alignment software, we can produce the best alignment. SNP and Indel detection tools fusion research found that different depth of sequencing effect is different. When the sequence depth reaches a certain value, select one of the software for testing. Conclusions: Finally, the experimental verification shows that SNP and Indel have certain advantages in the comparison of the effects of the fusion method.

scholarly journals Exploring the Switchgrass Transcriptome Using Second-Generation Sequencing Technology

PLoS ONE ◽  
2012 ◽  
Vol 7 (3) ◽  
pp. e34225 ◽  
Author(s):  
Yixing Wang ◽  
Xin Zeng ◽  
Niranjani J. Iyer ◽  
Douglas W. Bryant ◽  
Todd C. Mockler ◽  
...  
Keyword(s):  
Second Generation ◽  
Sequencing Technology ◽  
Generation Sequencing Technology ◽  
Second Generation Sequencing ◽  
Generation Sequencing

scholarly journals Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Zhuo Liu ◽  
Gerui Zhang ◽  
Zhao Jingyuan ◽  
Liyan Yu ◽  
Junxiu Sheng ◽  
...  
Keyword(s):  
Big Data ◽  
Reinforcement Learning ◽  
Internet Of Things ◽  
Infectious Diseases ◽  
Second Generation ◽  
Lung Infection ◽  
Sequencing Technology ◽  
Generation Sequencing Technology ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of Things, is prospective for computer-aided diagnosis and therapy. In this paper, we focus on the application value of the second-generation sequencing technology in the diagnosis and treatment of pulmonary infectious diseases with the aid of the deep reinforcement learning. Specifically, the rapid, comprehensive, and accurate identification of pathogens is a prerequisite for clinicians to choose timely and targeted treatment. Thus, in this work, we present representative deep reinforcement learning methods that are potential to identify pathogens for lung infection treatment. After that, current status of pathogenic diagnosis of pulmonary infectious diseases and their main characteristics are summarized. Furthermore, we analyze the common types of second-generation sequencing technology, which can be used to diagnose lung infection as well. Finally, we point out the challenges and possible future research directions in integrating deep reinforcement learning with second-generation sequencing technology to diagnose and treat lung infection, which is prospective to accelerate the evolution of smart healthcare with medical Internet of Things and big data.

scholarly journals Precision Medication of Tacrolimus Based on the Genotype by Using Second-generation Sequencing

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Yumeng Li
Keyword(s):  
Organ Transplantation ◽  
Second Generation ◽  
Low Cost ◽  
Single Nucleotide ◽  
Factors Affecting ◽  
Key Enzyme ◽  
Second Generation Sequencing ◽  
Degree Level ◽  
Generation Sequencing ◽  
Related Mortality

Organ transplantation has become a powerful strategy for the treatment of malignant diseases. Nevertheless, graft rejection is one of the main factors affecting graft survival after organ transplantation. Under this circumstance, the transplant-related mortality still keeps up. This invention includes the precise medication guidance of Tacrolimus (FK506) inapplicable population, against the side-effects of this drug. This invention, based on second-generation sequencing, has the advantages of relatively low cost and high sequencing throughput. During the design process, we collect the data of single nucleotide polymorphism (SNP) concerning the adverse drug reactions of Tacrolimus. Then we filter and summarize fifteen SNPs basing on importance degree (level >key enzyme>race). Thenceforth, after the process of analyzing the raw extract by operating BWA, Picard-tools, GATK, and Perl, we annotate SNPs by Annovar. Through this innovation, people can obtain further feedback on drugs that targets different genes in order to achieve the purpose of precision medication and minimizing the risks of misusing Tacrolimus.

scholarly journals A case report of the differential diagnosis of Cellulosimicrobium cellulans-infected endocarditis combined with intracranial infection by conventional blood culture and second-generation sequencing

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Huifang Zhang ◽  
Chunyan He ◽  
Rui Tian ◽  
Ruilan Wang
Keyword(s):  
Second Generation ◽  
Human Infection ◽  
Sequencing Data ◽  
Case Presentation ◽  
Intracranial Infection ◽  
Cellulosimicrobium Cellulans ◽  
Two Samples ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Abstract Background Cellulosimicrobium cellulans is a gram-positive filamentous bacterium found primarily in soil and sewage that rarely causes human infection, especially in previously healthy adults, but when it does, it often indicates a poor prognosis. Case presentation We report a case of endocarditis and intracranial infection caused by C. cellulans in a 52-year-old woman with normal immune function and no implants in vivo. The patient started with a febrile headache that progressed to impaired consciousness after 20 days, and she finally died after treatment with vancomycin combined with rifampicin. C. cellulans was isolated from her blood cultures for 3 consecutive days after her admission; however, there was only evidence of C. cellulans sequences for two samples in the second-generation sequencing data generated from her peripheral blood, which were ignored by the technicians. No C. cellulans bands were detected in her cerebrospinal fluid by second-generation sequencing. Conclusions Second-generation sequencing seems to have limitations for certain specific strains of bacteria.

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