X

Quality Statement

Pacific Biosciences is committed to providing high-quality products that meet customer expectations and comply with regulations. We will achieve these goals by adhering to and maintaining an effective quality-management system designed to ensure product quality, performance, and safety.

X

Image Use Agreement

By downloading, copying, or making any use of the images located on this website (“Site”) you acknowledge that you have read and understand, and agree to, the terms of this Image Usage Agreement, as well as the terms provided on the Legal Notices webpage, which together govern your use of the images as provided below. If you do not agree to such terms, do not download, copy or use the images in any way, unless you have written permission signed by an authorized Pacific Biosciences representative.

Subject to the terms of this Agreement and the terms provided on the Legal Notices webpage (to the extent they do not conflict with the terms of this Agreement), you may use the images on the Site solely for (a) editorial use by press and/or industry analysts, (b) in connection with a normal, peer-reviewed, scientific publication, book or presentation, or the like. You may not alter or modify any image, in whole or in part, for any reason. You may not use any image in a manner that misrepresents the associated Pacific Biosciences product, service or technology or any associated characteristics, data, or properties thereof. You also may not use any image in a manner that denotes some representation or warranty (express, implied or statutory) from Pacific Biosciences of the product, service or technology. The rights granted by this Agreement are personal to you and are not transferable by you to another party.

You, and not Pacific Biosciences, are responsible for your use of the images. You acknowledge and agree that any misuse of the images or breach of this Agreement will cause Pacific Biosciences irreparable harm. Pacific Biosciences is either an owner or licensee of the image, and not an agent for the owner. You agree to give Pacific Biosciences a credit line as follows: "Courtesy of Pacific Biosciences of California, Inc., Menlo Park, CA, USA" and also include any other credits or acknowledgments noted by Pacific Biosciences. You must include any copyright notice originally included with the images on all copies.

IMAGES ARE PROVIDED BY Pacific Biosciences ON AN "AS-IS" BASIS. Pacific Biosciences DISCLAIMS ALL REPRESENTATIONS AND WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, NON-INFRINGEMENT, OWNERSHIP, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. IN NO EVENT SHALL Pacific Biosciences BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES OF ANY KIND WHATSOEVER WITH RESPECT TO THE IMAGES.

You agree that Pacific Biosciences may terminate your access to and use of the images located on the PacificBiosciences.com website at any time and without prior notice, if it considers you to have violated any of the terms of this Image Use Agreement. You agree to indemnify, defend and hold harmless Pacific Biosciences, its officers, directors, employees, agents, licensors, suppliers and any third party information providers to the Site from and against all losses, expenses, damages and costs, including reasonable attorneys' fees, resulting from any violation by you of the terms of this Image Use Agreement or Pacific Biosciences' termination of your access to or use of the Site. Termination will not affect Pacific Biosciences' rights or your obligations which accrued before the termination.

I have read and understand, and agree to, the Image Usage Agreement.

I disagree and would like to return to the Pacific Biosciences home page.

Pacific Biosciences
Contact:

Authors: Newman, Jeremy R B and Concannon, Patrick and Tardaguila, Manuel and Conesa, Ana and McIntyre, Lauren M

Alternative splicing leverages genomic content by allowing the synthesis of multiple transcripts and, by implication, protein isoforms, from a single gene. However, estimating the abundance of transcripts produced in a given tissue from short sequencing reads is difficult and can result in both the construction of transcripts that do not exist, and the failure to identify true transcripts. An alternative approach is to catalog the events that make up isoforms (splice junctions and exons). We present here the Event Analysis (EA) approach, where we project transcripts onto the genome and identify overlapping/unique regions and junctions. In addition, all possible logical junctions are assembled into a catalog. Transcripts are filtered before quantitation based on simple measures: the proportion of the events detected, and the coverage. We find that mapping to a junction catalog is more efficient at detecting novel junctions than mapping in a splice aware manner. We identify 99.8% of true transcripts while iReckon identifies 82% of the true transcripts and creates more transcripts not included in the simulation than were initially used in the simulation. Using PacBio Iso-seq data from a mouse neural progenitor cell model, EA detects 60% of the novel junctions that are combinations of existing exons while only 43% are detected by STAR. EA further detects ~5,000 annotated junctions missed by STAR. Filtering transcripts based on the proportion of the transcript detected and the number of reads on average supporting that transcript captures 95% of the PacBio transcriptome. Filtering the reference transcriptome before quantitation, results in is a more stable estimate of isoform abundance, with improved correlation between replicates. This was particularly evident when EA is applied to an RNA-seq study of type 1 diabetes (T1D), where the coefficient of variation among subjects (n = 81) in the transcript abundance estimates was substantially reduced compared to the estimation using the full reference. EA focuses on individual transcriptional events. These events can be quantitate and analyzed directly or used to identify the probable set of expressed transcripts. Simple rules based on detected events and coverage used in filtering result in a dramatic improvement in isoform estimation without the use of ancillary data (e.g., ChIP, long reads) that may not be available for many studies. Copyright © 2018 Newman et al.

Journal: G3
DOI: 10.1534/g3.118.200373
Year: 2018

Read Publication

 

Stay
Current

Visit our blog »