scholarly journals Using experimental evolution and next-generation sequencing to teach bench and bioinformatic skills

2015 ◽  
Author(s):  
Alexander S Mikheyev ◽  
Jigyasa Arora
Keyword(s):  
Data Analysis ◽  
Next Generation Sequencing ◽  
Experimental Evolution ◽  
Mutational Analysis ◽  
Deep Understanding ◽  
Bioinformatic Analysis ◽  
Library Preparation ◽  
Next Generation ◽  
Resistance Mutations ◽  
Generation Sequencing

Advances in sequencing technology have exponentially increased data-generating capabilities, and data analysis has now become the major hurdle in many research programs. As sequencing tools become more accessible and automated, experimental design and data analysis skills become the key factors in determining the success of a study. However, proper bioinformatic analysis also relies on a deep understanding of laboratory workflow, in order to prevent biases in the data. This is particularly true if commercial kits are used, as proprietary reagents frequently obfuscate underlying reactions and their conditions. Here we present a training module that seamlessly combines laboratory components (experimental evolution of T5 bacteriophage resistance by Escherichia coli, and library preparation), with bioinformatic analysis of the resulting data. Students conduct a simple genetic variant discovery experiment in the course of about a week. The module uses mature Illumina chemistry for both library preparation and sequencing, though it can be modified for use with any sequencing platform. Because most students do not use Linux, the bioinformatic pipeline is available inside a cross-platform virtual machine, simplifying software installation, and providing a non-threatening introduction to the command line. The analysis, which is made simpler by the fact that most resistance mutations occur in one gene, making them easier to find, emphasizes the potential pitfalls of using short-read data for mutational analysis, and explores biases inherent to the methodology. This module can fill an existing training gap in advanced undergraduate, or early graduate education, allowing student to experience first-hand design, execution, and analysis of next-generation sequencing experiments.

scholarly journals Using experimental evolution and next-generation sequencing to teach bench and bioinformatic skills

2015 ◽  
Author(s):  
Alexander S Mikheyev ◽  
Jigyasa Arora
Keyword(s):  
Data Analysis ◽  
Next Generation Sequencing ◽  
Experimental Evolution ◽  
Mutational Analysis ◽  
Deep Understanding ◽  
Bioinformatic Analysis ◽  
Library Preparation ◽  
Next Generation ◽  
Resistance Mutations ◽  
Generation Sequencing

Advances in sequencing technology have exponentially increased data-generating capabilities, and data analysis has now become the major hurdle in many research programs. As sequencing tools become more accessible and automated, experimental design and data analysis skills become the key factors in determining the success of a study. However, proper bioinformatic analysis also relies on a deep understanding of laboratory workflow, in order to prevent biases in the data. This is particularly true if commercial kits are used, as proprietary reagents frequently obfuscate underlying reactions and their conditions. Here we present a training module that seamlessly combines laboratory components (experimental evolution of T5 bacteriophage resistance by Escherichia coli, and library preparation), with bioinformatic analysis of the resulting data. Students conduct a simple genetic variant discovery experiment in the course of about a week. The module uses mature Illumina chemistry for both library preparation and sequencing, though it can be modified for use with any sequencing platform. Because most students do not use Linux, the bioinformatic pipeline is available inside a cross-platform virtual machine, simplifying software installation, and providing a non-threatening introduction to the command line. The analysis, which is made simpler by the fact that most resistance mutations occur in one gene, making them easier to find, emphasizes the potential pitfalls of using short-read data for mutational analysis, and explores biases inherent to the methodology. This module can fill an existing training gap in advanced undergraduate, or early graduate education, allowing student to experience first-hand design, execution, and analysis of next-generation sequencing experiments.

scholarly journals HIV Drug Resistance Mutations Detection by Next-Generation Sequencing during Antiretroviral Therapy Interruption in China

Pathogens ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 264
Author(s):  
Miaomiao Li ◽  
Shujia Liang ◽  
Chao Zhou ◽  
Min Chen ◽  
Shu Liang ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Next Generation Sequencing ◽  
Antiretroviral Therapy ◽  
Detection Threshold ◽  
Next Generation ◽  
Resistance Mutations ◽  
Hiv Drug Resistance ◽  
Drug Resistance Mutations ◽  
Therapy Interruption ◽  
Generation Sequencing

Patients with antiretroviral therapy interruption have a high risk of virological failure when re-initiating antiretroviral therapy (ART), especially those with HIV drug resistance. Next-generation sequencing may provide close scrutiny on their minority drug resistance variant. A cross-sectional study was conducted in patients with ART interruption in five regions in China in 2016. Through Sanger and next-generation sequencing in parallel, HIV drug resistance was genotyped on their plasma samples. Rates of HIV drug resistance were compared by the McNemar tests. In total, 174 patients were included in this study, with a median 12 (interquartile range (IQR), 6–24) months of ART interruption. Most (86.2%) of them had received efavirenz (EFV)/nevirapine (NVP)-based first-line therapy for a median 16 (IQR, 7–26) months before ART interruption. Sixty-one (35.1%) patients had CRF07_BC HIV-1 strains, 58 (33.3%) CRF08_BC and 35 (20.1%) CRF01_AE. Thirty-four (19.5%) of the 174 patients were detected to harbor HIV drug-resistant variants on Sanger sequencing. Thirty-six (20.7%), 37 (21.3%), 42 (24.1%), 79 (45.4%) and 139 (79.9) patients were identified to have HIV drug resistance by next-generation sequencing at 20% (v.s. Sanger, p = 0.317), 10% (v.s. Sanger, p = 0.180), 5% (v.s. Sanger, p = 0.011), 2% (v.s. Sanger, p < 0.001) and 1% (v.s. Sanger, p < 0.001) of detection thresholds, respectively. K65R was the most common minority mutation, of 95.1% (58/61) and 93.1% (54/58) in CRF07_BC and CRF08_BC, respectively, when compared with 5.7% (2/35) in CRF01_AE (p < 0.001). In 49 patients that followed-up a median 10 months later, HIV drug resistance mutations at >20% frequency such as K103N, M184VI and P225H still existed, but with decreased frequencies. The prevalence of HIV drug resistance in ART interruption was higher than 15% in the survey. Next-generation sequencing was able to detect more minority drug resistance variants than Sanger. There was a sharp increase in minority drug resistance variants when the detection threshold was below 5%.

scholarly journals Uncontrolled Diabetes Mellitus Has No Major Influence on the Platelet Transcriptome

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Thomas G. Nührenberg ◽  
Marco Cederqvist ◽  
Federico Marini ◽  
Christian Stratz ◽  
Björn A. Grüning ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Next Generation Sequencing ◽  
Platelet Reactivity ◽  
Platelet Rich Plasma ◽  
Expression Profiles ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Major Influence ◽  
Next Generation ◽  
Generation Sequencing

Background. Diabetes mellitus (DM) has been associated with increased platelet reactivity as well as increased levels of platelet RNAs in plasma. Here, we sought to evaluate whether the platelet transcriptome is altered in the presence of uncontrolled DM. Methods. Next-generation sequencing (NGS) was performed on platelet RNA for 5 patients with uncontrolled DM (HbA1c 9.0%) and 5 control patients (HbA1c 5.5%) with otherwise similar clinical characteristics. RNA was isolated from leucocyte-depleted platelet-rich plasma. Libraries of platelet RNAs were created separately for long RNAs after ribosomal depletion and for small RNAs from total RNA, followed by next-generation sequencing. Results. Platelets in both groups demonstrated RNA expression profiles characterized by absence of leukocyte-specific transcripts, high expression of well-known platelet transcripts, and in total 6,343 consistently detectable transcripts. Extensive statistical bioinformatic analysis yielded 12 genes with consistently differential expression at a lenient FDR < 0.1, thereof 8 protein-coding genes and 2 genes with known expression in platelets (MACF1 and ITGB3BP). Three of the four differentially expressed noncoding genes were YRNAs (RNY1, RNY3, and RNY4) which were all downregulated in DM. 23 miRNAs were differentially expressed between the two groups. Of the 13 miRNAs with decreased expression in the diabetic group, 8 belonged to the DLK1–DIO3 gene region on chromosome 14q32.2. Conclusions. In this study, uncontrolled DM had a remote impact on different components of the platelet transcriptome. Increased expression of MACF1, together with supporting predicted mRNA-miRNA interactions as well as reduced expression of RNYs in platelets, may reflect subclinical platelet activation in uncontrolled DM.

scholarly journals Robust Sub-nanomolar Library Preparation for High Throughput Next Generation Sequencing

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Wells W. Wu ◽  
Je-Nie Phue ◽  
Chun-Ting Lee ◽  
Changyi Lin ◽  
Lai Xu ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput ◽  
Library Preparation ◽  
Next Generation ◽  
Generation Sequencing

Big Data Analysis Techniques for Visualization of Genomics in Medicinal Plants

Biotechnology ◽  
2019 ◽  
pp. 804-837
Author(s):  
Hithesh Kumar ◽  
Vivek Chandramohan ◽  
Smrithy M. Simon ◽  
Rahul Yadav ◽  
Shashi Kumar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Next Generation Sequencing ◽  
Medicinal Plants ◽  
Big Data Analysis ◽  
Next Generation ◽  
Genome Data ◽  
Huge Data ◽  
Transcriptomics Data ◽  
Generation Sequencing

In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.

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