1. Computational and Systems Biology
  2. Chromosomes and Gene Expression
Download icon

Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling

  1. Anil Raj  Is a corresponding author
  2. Sidney H Wang
  3. Heejung Shim
  4. Arbel Harpak
  5. Yang I Li
  6. Brett Engelmann
  7. Matthew Stephens
  8. Yoav Gilad
  9. Jonathan K Pritchard
  1. Stanford University, United States
  2. University of Chicago, United States
  3. Purdue University, United States
Research Article
  • Cited 67
  • Views 6,446
  • Annotations
Cite this article as: eLife 2016;5:e13328 doi: 10.7554/eLife.13328

Abstract

Accurate annotation of protein coding regions is essential for understanding how genetic information is translated into function. We describe riboHMM, a new method that uses ribosome footprint data to accurately infer translated sequences. Applying riboHMM to human lymphoblastoid cell lines, we identified 7,273 novel coding sequences, including 2,442 translated upstream open reading frames. We observed an enrichment of footprints at inferred initiation sites after drug-induced arrest of translation initiation, validating many of the novel coding sequences. The novel proteins exhibit significant selective constraint in the inferred reading frames, suggesting that many are functional. Moreover, ~40% of bicistronic transcripts showed negative correlation in the translation levels of their two coding sequences, suggesting a potential regulatory role for these novel regions. Despite known limitations of mass spectrometry to detect protein expressed at low level, we estimated a 14% validation rate. Our work significantly expands the set of known coding regions in humans.

Article and author information

Author details

  1. Anil Raj

    Department of Genetics, Stanford University, Stanford, United States
    For correspondence
    rajanil@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. Sidney H Wang

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Heejung Shim

    Department of Statistics, Purdue University, West Lafayette, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Arbel Harpak

    Department of Biology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Yang I Li

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Brett Engelmann

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Matthew Stephens

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Yoav Gilad

    Department of Human Genetics, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Jonathan K Pritchard

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Nicholas T Ingolia, University of California, Berkeley, United States

Publication history

  1. Received: November 25, 2015
  2. Accepted: May 26, 2016
  3. Accepted Manuscript published: May 27, 2016 (version 1)
  4. Version of Record published: July 11, 2016 (version 2)
  5. Version of Record updated: July 12, 2016 (version 3)

Copyright

© 2016, Raj et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 6,446
    Page views
  • 1,497
    Downloads
  • 67
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    2. Epidemiology and Global Health
    Hannah R Meredith et al.
    Research Article

    Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

    1. Computational and Systems Biology
    Daniel Griffith, Alex S Holehouse
    Tools and Resources

    The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.