Translating Ribosome Affinity Purification (TRAP) Followed by RNA Sequencing Technology (TRAP-SEQ) for Quantitative Assessment of Plant Translatomes

RNA sequencing technology is providing data of unprecedented throughput, resolution, and accuracy. Although there are many different computational tools for processing these data, there are a limited number of statistical methods for analyzing them, and even fewer that acknowledge the unique nature of individual gene transcription. We introduce a simple and powerful statistical approach, based on a two-stage Poisson model, for modeling RNA sequencing data and testing for biologically important changes in gene expression. The advantages of this approach are demonstrated through simulations and real data applications.

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Global gene expression response of a population exposed to benzene: A pilot study exploring the use of RNA-sequencing technology

Environmental and Molecular Mutagenesis ◽

10.1002/em.21801 ◽

2013 ◽

Vol 54 (7) ◽

pp. 566-573 ◽

Cited By ~ 7

Author(s):

Reuben Thomas ◽

Cliona M. McHale ◽

Qing Lan ◽

Alan E. Hubbard ◽

Luoping Zhang ◽

...

Keyword(s):

Gene Expression ◽

Pilot Study ◽

Rna Sequencing ◽

Global Gene Expression ◽

Sequencing Technology ◽

Gene Expression Response ◽

Expression Response

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Quantitative assessment of single-cell RNA-sequencing methods

Nature Methods ◽

10.1038/nmeth.2694 ◽

2013 ◽

Vol 11 (1) ◽

pp. 41-46 ◽

Cited By ~ 464

Author(s):

Angela R Wu ◽

Norma F Neff ◽

Tomer Kalisky ◽

Piero Dalerba ◽

Barbara Treutlein ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Quantitative Assessment ◽

Single Cell Rna Sequencing

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BioTime’s bid to end age-related disease: A look at CEO Michael West’s Vision

Journal of Commercial Biotechnology ◽

10.5912/jcb706 ◽

2015 ◽

Vol 21 (3) ◽

Author(s):

Ian C. Clift

Keyword(s):

Regenerative Medicine ◽

Rna Sequencing ◽

Cell Lines ◽

Molecular Basis ◽

Sequencing Technology ◽

Related Disease ◽

Age Related ◽

Pure Cell ◽

The Common ◽

Future Growth

The regenerative medicine space is one that is set to explode with considerable innovation and profitability for shrewd biotechnologists. I had the opportunity to speak with Michael West, PhD., CEO of BioTime (BTX) and found a man passionate about regenerative medicine and of course passionate about his role in its future. In this conversation I learned of West's vision, which I think provides some powerful clues as to areas of future growth in the biotech sector. He points to three scientific advances that make this vision actualizable. First, sequencing technology that allows us to perform RNA sequencing for around 300 dollars or less, second the common and reversible molecular basis for age-related diseases, and finally industrial scaling of pure cell lines like the ones manufactured by BTX. Let's look at the three enabling technologies that West touched on and examine how they are being utilized to achieve West's vision within BTX and others involved in the anti-aging revolution.

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miRquant 2.0: an Expanded Tool for Accurate Annotation and Quantification of MicroRNAs and their isomiRs from Small RNA-Sequencing Data

Journal of Integrative Bioinformatics ◽

10.1515/jib-2016-307 ◽

2016 ◽

Vol 13 (5) ◽

Cited By ~ 6

Author(s):

Matthew Kanke ◽

Jeanette Baran-Gale ◽

Jonathan Villanueva ◽

Praveen Sethupathy

Keyword(s):

Rna Sequencing ◽

Small Rna ◽

Cell Types ◽

Small Rna Sequencing ◽

Sequencing Data ◽

Sequencing Technology ◽

Additional Information ◽

Disease States ◽

Non Coding Rnas ◽

The Rich

SummarySmall non-coding RNAs, in particular microRNAs, are critical for normal physiology and are candidate biomarkers, regulators, and therapeutic targets for a wide variety of diseases. There is an ever-growing interest in the comprehensive and accurate annotation of microRNAs across diverse cell types, conditions, species, and disease states. Highthroughput sequencing technology has emerged as the method of choice for profiling microRNAs. Specialized bioinformatic strategies are required to mine as much meaningful information as possible from the sequencing data to provide a comprehensive view of the microRNA landscape. Here we present miRquant 2.0, an expanded bioinformatics tool for accurate annotation and quantification of microRNAs and their isoforms (termed isomiRs) from small RNA-sequencing data. We anticipate that miRquant 2.0 will be useful for researchers interested not only in quantifying known microRNAs but also mining the rich well of additional information embedded in small RNA-sequencing data.

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Massively differential bias between two widely used Illumina library preparation methods for small RNA sequencing

10.1101/001479 ◽

2013 ◽

Cited By ~ 3

Author(s):

Jeanette Baran-Gale ◽

Michael R Erdos ◽

Christina Sison ◽

Alice Young ◽

Emily E Fannin ◽

...

Keyword(s):

Rna Sequencing ◽

Small Rna ◽

Small Rnas ◽

Small Rna Sequencing ◽

Library Preparation ◽

Sequencing Technology ◽

Preparation Methods ◽

Sequencing Platforms ◽

Small Rna Library ◽

Illumina Library

Recent advances in sequencing technology have helped unveil the unexpected complexity and diversity of small RNAs. A critical step in small RNA library preparation for sequencing is the ligation of adapter sequences to both the 5’ and 3’ ends of small RNAs. Two widely used protocols for small RNA library preparation, Illumina v1.5 and Illumina TruSeq, use different pairs of adapter sequences. In this study, we compare the results of small RNA-sequencing between v1.5 and TruSeq and observe a striking differential bias. Nearly 100 highly expressed microRNAs (miRNAs) are >5-fold differentially detected and 48 miRNAs are >10-fold differentially detected between the two methods of library preparation. In fact, some miRNAs, such as miR-24-3p, are over 30-fold differentially detected. The results are reproducible across different sequencing centers (NIH and UNC) and both major Illumina sequencing platforms, GAIIx and HiSeq. While some level of bias in library preparation is not surprising, the apparent massive differential bias between these two widely used adapter sets is not well appreciated. As increasingly more laboratories transition to the newer TruSeq-based library preparation for small RNAs, researchers should be aware of the extent to which the results may differ from previously published results using v1.5.

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