High-Throughput Protein Engineering by Massively Parallel Combinatorial Mutagenesis

2020 ◽  
pp. 3-12
Author(s):  
Yuk Kei Wan ◽  
Gigi C. G. Choi ◽  
Alan S. L. Wong
Keyword(s):  
Protein Engineering ◽  
High Throughput ◽  
Massively Parallel ◽  
Combinatorial Mutagenesis

scholarly journals Assessing genotyping errors in mammalian museum study skins using high-throughput genotyping-by-sequencing

2021 ◽  
Author(s):  
Stella C. Yuan ◽  
Eric Malekos ◽  
Melissa T. R. Hawkins
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Massively Parallel Sequencing ◽  
Massively Parallel ◽  
Museum Specimens ◽  
Museum Specimen ◽  
Genotyping Errors ◽  
Allelic Dropout ◽  
Parallel Sequencing ◽  
Sequencing Technologies

AbstractThe use of museum specimens held in natural history repositories for population and conservation genetic research is increasing in tandem with the use of massively parallel sequencing technologies. Short Tandem Repeats (STRs), or microsatellite loci, are commonly used genetic markers in wildlife and population genetic studies. However, they traditionally suffered from a host of issues including length homoplasy, high costs, low throughput, and difficulties in reproducibility across laboratories. Massively parallel sequencing technologies can address these problems, but the incorporation of museum specimen derived DNA suffers from significant fragmentation and exogenous DNA contamination. Combatting these issues requires extra measures of stringency in the lab and during data analysis, yet there have not been any high-throughput sequencing studies evaluating microsatellite allelic dropout from museum specimen extracted DNA. In this study, we evaluate genotyping errors derived from mammalian museum skin DNA extracts for previously characterized microsatellites across PCR replicates utilizing high-throughput sequencing. We found it useful to classify samples based on DNA concentration, which determined the rate by which genotypes were accurately recovered. Longer microsatellites performed worse in all museum specimens. Allelic dropout rates across loci were dependent on sample quantity, with high concentration museum specimens performing as well and recovering quality metrics nearly as high as the frozen tissue sample. Based on our results, we provide a set of best practices for quality assurance and incorporation of reliable genotypes from museum specimens.

scholarly journals SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

2020 ◽  
Author(s):  
Anna M. Sozanska ◽  
Charles Fletcher ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
High Throughput ◽  
R Package ◽  
Data Reuse ◽  
Massively Parallel ◽  
Data Sets ◽  
Similar Data ◽  
Data Generation ◽  
Data Repositories ◽  
Public Data ◽  
Omic Data

AbstractMore than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.Availabilityhttps://github.com/ss-lab-cancerunit/SpiderSeqR

scholarly journals Massively parallel single-molecule telomere length measurement with digital real-time PCR

2020 ◽  
Vol 6 (34) ◽  
pp. eabb7944 ◽  
Author(s):  
Yongqiang Luo ◽  
Ramya Viswanathan ◽  
Manoor Prakash Hande ◽  
Amos Hong Pheng Loh ◽  
Lih Feng Cheow
Keyword(s):  
Telomere Length ◽  
Real Time ◽  
High Throughput ◽  
Single Molecule ◽  
Real Time Pcr ◽  
Length Distribution ◽  
Telomere Maintenance ◽  
Massively Parallel ◽  
Primary Tumors ◽  
Absolute Length

Telomere length is a promising biomarker for age-associated diseases and cancer, but there are still substantial challenges to routine telomere analysis in clinics because of the lack of a simple and rapid yet scalable method for measurement. We developed the single telomere absolute-length rapid (STAR) assay, a novel high-throughput digital real-time PCR approach for rapidly measuring the absolute lengths and quantities of individual telomere molecules. We show that this technique provides the accuracy and sensitivity to uncover associations between telomere length distribution and telomere maintenance mechanisms in cancer cell lines and primary tumors. The results indicate that the STAR assay is a powerful tool to enable the use of telomere length distribution as a biomarker in disease and population-wide studies.

scholarly journals High throughput whole mitochondrial genome sequencing by two platforms of massively parallel sequencing

BMC Genomics ◽  
2014 ◽  
Vol 15 (Suppl 2) ◽  
pp. P7 ◽  
Author(s):  
Seung Seo ◽  
Xiangpei Zeng ◽  
Mourad Assidi ◽  
Bobby LaRue ◽  
Jonathan King ◽  
...  
Keyword(s):  
Mitochondrial Genome ◽  
Genome Sequencing ◽  
High Throughput ◽  
Massively Parallel Sequencing ◽  
Massively Parallel ◽  
Parallel Sequencing

Abstract LB-122: High-throughput tumor genomic profiling by massively parallel sequencing

2010 ◽  
Author(s):  
Nikhil Wagle ◽  
Matt Davis ◽  
Michael F. Berger ◽  
Brendan Blumenstiel ◽  
Matthew Defelice ◽  
...  
Keyword(s):  
High Throughput ◽  
Massively Parallel Sequencing ◽  
Massively Parallel ◽  
Genomic Profiling ◽  
Parallel Sequencing

scholarly journals An Introduction to Systems Analytics and Integration of Big Omics Data

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 245 ◽  
Author(s):  
Gary Hardiman
Keyword(s):  
High Throughput ◽  
Research Community ◽  
Massively Parallel ◽  
Omics Data ◽  
The Past ◽  
Parallel Fashion

A major technological shift in the research community in the past decade has been the adoption of high throughput (HT) technologies to interrogate the genome, epigenome, transcriptome, and proteome in a massively parallel fashion [...]

scholarly journals In-Depth High-Throughput Screening of Protein Engineering Libraries by Split-GFP Direct Crude Cell Extract Data Normalization

2015 ◽  
Vol 22 (10) ◽  
pp. 1406-1414 ◽  
Author(s):  
Javier Santos-Aberturas ◽  
Mark Dörr ◽  
Geoffrey S. Waldo ◽  
Uwe T. Bornscheuer
Keyword(s):  
Protein Engineering ◽  
High Throughput ◽  
High Throughput Screening ◽  
Cell Extract ◽  
Data Normalization ◽  
Crude Cell Extract
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