scholarly journals Revisiting criteria for plant miRNA annotation in the era of big data

2017 ◽  
Author(s):  
Michael J. Axtell ◽  
Blake C. Meyers

AbstractMicroRNAs (miRNAs) are ~21 nucleotide-long regulatory RNAs that arise from endonucleolytic processing of hairpin precursors. Many function as essential post-transcriptional regulators of target mRNAs and long non-coding RNAs. Alongside miRNAs, plants also produce large numbers of short interfering RNAs (siRNAs), which are distinguished from miRNAs primarily by their biogenesis (typically processed from long double-stranded RNA instead of single-stranded hairpins) and functions (typically via roles in transcriptional regulation instead of post-transcriptional regulation). Next-generation DNA sequencing methods have yielded extensive datasets of plant small RNAs, resulting in many miRNA annotations, occasionally inaccurately curated. The sheer number of endogenous siRNAs compared to miRNAs has been a major factor in the erroneous annotation of siRNAs as miRNAs. Here, we provide updated criteria for the confident annotation of plant miRNAs, suitable for the era of “big data” from DNA sequencing. The updated criteria emphasize replication, the minimization of false positives, and they require next-generation sequencing of small RNAs. We argue that improved annotation systems are needed for miRNAs and all other classes of plant small RNAs. Finally, to illustrate the complexities of miRNA and siRNA annotation, we review the evolution and functions of miRNAs and siRNAs in plants.

2009 ◽  
Vol 55 (4) ◽  
pp. 641-658 ◽  
Author(s):  
Karl V Voelkerding ◽  
Shale A Dames ◽  
Jacob D Durtschi

Abstract Background: For the past 30 years, the Sanger method has been the dominant approach and gold standard for DNA sequencing. The commercial launch of the first massively parallel pyrosequencing platform in 2005 ushered in the new era of high-throughput genomic analysis now referred to as next-generation sequencing (NGS). Content: This review describes fundamental principles of commercially available NGS platforms. Although the platforms differ in their engineering configurations and sequencing chemistries, they share a technical paradigm in that sequencing of spatially separated, clonally amplified DNA templates or single DNA molecules is performed in a flow cell in a massively parallel manner. Through iterative cycles of polymerase-mediated nucleotide extensions or, in one approach, through successive oligonucleotide ligations, sequence outputs in the range of hundreds of megabases to gigabases are now obtained routinely. Highlighted in this review are the impact of NGS on basic research, bioinformatics considerations, and translation of this technology into clinical diagnostics. Also presented is a view into future technologies, including real-time single-molecule DNA sequencing and nanopore-based sequencing. Summary: In the relatively short time frame since 2005, NGS has fundamentally altered genomics research and allowed investigators to conduct experiments that were previously not technically feasible or affordable. The various technologies that constitute this new paradigm continue to evolve, and further improvements in technology robustness and process streamlining will pave the path for translation into clinical diagnostics.


2018 ◽  
Vol 46 (22) ◽  
pp. 11869-11882 ◽  
Author(s):  
Franziska Bonath ◽  
Judit Domingo-Prim ◽  
Marcel Tarbier ◽  
Marc R Friedländer ◽  
Neus Visa

2016 ◽  
Vol 125 (4) ◽  
pp. 236-244 ◽  
Author(s):  
Sinchita Roy-Chowdhuri ◽  
Somak Roy ◽  
Sara E. Monaco ◽  
Mark J. Routbort ◽  
Liron Pantanowitz

Biotechnology ◽  
2019 ◽  
pp. 804-837
Author(s):  
Hithesh Kumar ◽  
Vivek Chandramohan ◽  
Smrithy M. Simon ◽  
Rahul Yadav ◽  
Shashi Kumar

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.


BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 484 ◽  
Author(s):  
Anya Rudnicki ◽  
Ofer Isakov ◽  
Kathy Ushakov ◽  
Shaked Shivatzki ◽  
Inbal Weiss ◽  
...  

2014 ◽  
Vol 42 (S1) ◽  
pp. 22-41 ◽  
Author(s):  
Patricia A. Deverka ◽  
Jennifer C. Dreyfus

Clinical next generation sequencing (NGS) is a term that refers to a variety of technologies that permit rapid sequencing of large numbers of DNA segments, up to and including entire genomes. As an approach that is playing an increasingly important role in obtaining genetic information from patients, it may be viewed by public and private payers either positively, as an enabler of the promised benefits of personalized medicine, or as “the perfect storm” resulting from the confluence of high market demand, an uproven technology, and an unprepared delivery system. A number of recent studies have noted that coverage and reimbursement will be critical for clinical integration of NGS, yet the evidentiary pathway for payer decision-making is unclear. Although there are multiple reasons for this uncertain reimbursement environment, the situation stems in large part from a long-standing lack of alignment between the information needs of regulators and post-regulatory decision-makers such as payers.


2016 ◽  
Vol 9 (2) ◽  
pp. 119-149 ◽  
Author(s):  
Rashmi Tripathi ◽  
Pawan Sharma ◽  
Pavan Chakraborty ◽  
Pritish Kumar Varadwaj

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