Isolation, Library Preparation, and Bioinformatic Analysis of Historical and Ancient Plant DNA

Abstract Background With rapidly dropping sequencing cost, the popularity of whole-genome DNA methylation sequencing has been on the rise. Multiple library preparation protocols currently exist. We have performed 22 whole-genome DNA methylation sequencing experiments on snap frozen human samples, and extensively benchmarked common library preparation protocols for whole-genome DNA methylation sequencing, including three traditional bisulfite-based protocols and a new enzyme-based protocol. In addition, different input DNA quantities were compared for two kits compatible with a reduced starting quantity. In addition, we also present bioinformatic analysis pipelines for sequencing data from each of these library types. Results An assortment of metrics were collected for each kit, including raw read statistics, library quality and uniformity metrics, cytosine retention, and CpG beta value consistency between technical replicates. Overall, the NEBNext Enzymatic Methyl-seq and Swift Accel-NGS Methyl-Seq kits performed quantitatively better than the other two protocols. In addition, the NEB and Swift kits performed well at low-input amounts, validating their utility in applications where DNA is the limiting factor. Results The NEBNext Enzymatic Methyl-seq kit appeared to be the best option for whole-genome DNA methylation sequencing of high-quality DNA, closely followed by the Swift kit, which potentially works better for degraded samples. Further, a general bioinformatic pipeline is applicable across the four protocols, with the exception of extra trimming needed for the Swift Biosciences’s Accel-NGS Methyl-Seq protocol to remove the Adaptase sequence.

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

10.7287/peerj.preprints.1356v1 ◽

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.

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

10.7287/peerj.preprints.1356 ◽

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.

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The Genomic Landscape of Serrated Lesion of the Colorectum: Similarities and Differences With Tubular and Tubulovillous Adenomas

Frontiers in Oncology ◽

10.3389/fonc.2021.668466 ◽

2021 ◽

Vol 11 ◽

Author(s):

Luigi Tornillo ◽

Frank Serge Lehmann ◽

Andrea Garofoli ◽

Viola Paradiso ◽

Charlotte K. Y. Ng ◽

...

Keyword(s):

Somatic Mutations ◽

Bioinformatic Analysis ◽

Genetic Alterations ◽

University Hospital ◽

Library Preparation ◽

Loss Of Function ◽

Driver Genes ◽

Cancer Driver ◽

Genomic Landscape ◽

Serrated Lesions

Serrated lesions of the colorectum are the precursors of 15–30% of colorectal cancers (CRCs). These lesions have a peculiar morphological appearance, and they are more difficult to detect than conventional adenomatous polyps. In this study, we sought to define the genomic landscape of these lesions using high-depth targeted sequencing. Eight sessile serrated lesions without dysplasia (SSL), three sessile serrated lesions with dysplasia (SSL/D), two traditional serrated adenomas (TSA), and three tubular adenomas (TA) were retrieved from the files of the Institute of Pathology of the University Hospital Basel and from the GILAB AG, Allschwil, Switzerland. Samples were microdissected together with the matched normal counterpart, and DNA was extracted for library preparation. Library preparation was performed using the Oncomine Comprehensive Assay targeting 161 common cancer driver genes. Somatic genetic alterations were defined using state-of-the-art bioinformatic analysis. Most SSLs, as well as all SSL/Ds and TSAs, showed the classical BRAF p.V600E mutation. The BRAF-mutant TSAs showed additional alterations in CTNNB1, NF1, TP53, NRAS, PIK3CA, while TA showed a consistently different profile, with mutations in ARID1A (two cases), SMAD4, CDK12, ERBB3, and KRAS. In conclusion, our results provide evidence that SSL/D and TSA are similar in somatic mutations with the BRAF hotspot somatic mutation as a major driver of the disease. On the other hand, TAs show a different constellation of somatic mutations such as ARID1A loss of function.

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Evaluation of Whole-Genome DNA Methylation Sequencing Library Preparation Protocols

10.21203/rs.3.rs-249202/v1 ◽

2021 ◽

Author(s):

Jacob Morrison ◽

Julie M. Koeman ◽

Benjamin K. Johnson ◽

Kelly K. Foy ◽

Wanding Zhou ◽

...

Keyword(s):

Dna Methylation ◽

Bioinformatic Analysis ◽

Systematic Evaluation ◽

Whole Genome ◽

Library Preparation ◽

Sequencing Data ◽

Sequencing Cost ◽

Sequencing Library ◽

Fresh Frozen ◽

Sequencing Library Preparation

Abstract Background: With rapidly dropping sequencing cost, the popularity of whole-genome DNA methylation sequencing has been on the rise. Multiple library preparation protocols exist, but a systematic evaluation and benchmarking of their performance against each other is currently lacking. We have performed 22 whole-genome DNA methylation sequencing experiments on fresh frozen human samples, and extensively benchmarked common library preparation protocols for whole-genome DNA methylation sequencing, including three traditional bisulfite-based protocols and a new enzyme-based protocol. Additionally, different input DNA quantities were compared for two kits compatible with a reduced starting quantity. In addition, we also present bioinformatic analysis pipelines for sequencing data from each of these library types. Results: An assortment of metrics were collected for each kit, including raw read statistics, library quality and uniformity metrics, cytosine retention, and CpG beta value consistency between technical replicates. Overall, the NEBNext Enzymatic Methyl-seq kit performed quantitatively better than the other three protocols at two different DNA input amounts. Additionally, the results for the different input amounts were generally consistent across all metrics. Conclusions: Based on these results, we recommend use of the NEBNext Enzymatic Methyl-seq kit for whole-genome DNA methylation sequencing. Further, a general bioinformatic pipeline is applicable across the four protocols, with the exception of extra trimming needed for the Swift Bioscience's Accel-NGS Methyl-Seq protocol to remove the Adaptase sequence.

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