Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,170 bioRxiv papers from 306,408 authors.

DeepBound: Accurate Identification of Transcript Boundaries via Deep Convolutional Neural Fields

By Mingfu Shao, Jianzhu Ma, Sheng Wang

Posted 07 Apr 2017
bioRxiv DOI: 10.1101/125229 (published DOI: 10.1093/bioinformatics/btx267)

Motivation: Reconstructing the full-length expressed transcripts (a.k.a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak. Results: We present DeepBound, an effective approach to identify boundaries of expressed transcripts from RNA-seq reads alignment. In its core DeepBound employs deep convolutional neural fields to learn the hidden distributions and patterns of boundaries. To accurately model the transition probabilities and to solve the label-imbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objective function. To address the issue that deep probabilistic graphical models requires large number of labeled training samples, we propose to use simulated RNA-seq datasets to train our model. Through extensive experimental studies on both simulation datasets of two species and biological datasets, we show that DeepBound consistently and significantly outperforms the two existing methods. Availability: DeepBound is freely available at https://github.com/realbigws/DeepBound. Contact: mingfu.shao@cs.cmu.edu, realbigws@gmail.com

Download data

  • Downloaded 607 times
  • Download rankings, all-time:
    • Site-wide: 17,297 out of 70,177
    • In bioinformatics: 2,641 out of 6,878
  • Year to date:
    • Site-wide: 43,716 out of 70,177
  • Since beginning of last month:
    • Site-wide: 67,722 out of 70,177

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News