DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data

Ribosome profiling brings insight to the process of translation. A basic step in profile construction at transcript level is to map Ribo-seq data to transcripts, and then assign a huge number of multiple-mapped reads to similar isoforms. Existing methods either discard the multiple mapped-reads, or allocate them randomly, or assign them proportionally according to transcript abundance estimated from RNA-seq data. Here we present DeepShape, an RNA-seq free computational method to estimate ribosome abundance of isoforms, and simultaneously compute their ribosome profiles using a deep learning model. Our simulation results demonstrate that DeepShape can provide more accurate estimations on both ribosome abundance and profiles when compared to state-of-the-art methods. We applied DeepShape to a set of Ribo-seq data from PC3 human prostate cancer cells with and without PP242 treatment. In the four cell invasion/metastasis genes that are translationally regulated by PP242 treatment, different isoforms show very different characteristics of translational efficiency and regulation patterns. Transcript level ribosome distributions were analyzed by “Codon Residence Index (CRI)” proposed in this study to investigate the relative speed that a ribosome moves on a codon compared to its synonymous codons. We observe consistent CRI patterns in PC3 cells. We found that the translation of several codons could be regulated by PP242 treatment. In summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.

[1]  Nicholas T. Ingolia.  Ribosome profiling: new views of translation, from single codons to genome scale , 2014, Nature Reviews Genetics.

[2]  Isaac Meilijson,et al.  Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model , 2011, PLoS Comput. Biol..

[3]  Nicholas T. Ingolia,et al.  The translational landscape of mTOR signalling steers cancer initiation and metastasis , 2012, Nature.

[4]  J. Weissman,et al.  Ribosome profiling reveals the what, when, where and how of protein synthesis , 2015, Nature Reviews Molecular Cell Biology.

[5]  Gong Zhang,et al.  TranslatomeDB: a comprehensive database and cloud-based analysis platform for translatome sequencing data , 2017, Nucleic Acids Res..

[6]  Rachel Green,et al.  Clarifying the Translational Pausing Landscape in Bacteria by Ribosome Profiling. , 2016, Cell reports.

[7]  Thomas J. Hardcastle,et al.  The use of duplex-specific nuclease in ribosome profiling and a user-friendly software package for Ribo-seq data analysis , 2015, RNA.

[8]  Eva Maria Novoa,et al.  Speeding with control: codon usage, tRNAs, and ribosomes. , 2012, Trends in genetics : TIG.

[9]  D. Gatfield,et al.  Translational contributions to tissue specificity in rhythmic and constitutive gene expression , 2017, Genome Biology.

[10]  Tao Pan,et al.  Tissue-Specific Differences in Human Transfer RNA Expression , 2006, PLoS genetics.

[11]  Rob Patro,et al.  Salmon provides fast and bias-aware quantification of transcript expression , 2017, Nature Methods.

[12]  Nicholas T. Ingolia,et al.  Mammalian microRNAs predominantly act to decrease target mRNA levels , 2010, Nature.

[13]  Y. Pilpel,et al.  An Evolutionarily Conserved Mechanism for Controlling the Efficiency of Protein Translation , 2010, Cell.

[14]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[15]  Nicholas T. Ingolia,et al.  Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling , 2009, Science.

[16]  Qing-Yu He,et al.  Translating mRNAs strongly correlate to proteins in a multivariate manner and their translation ratios are phenotype specific , 2013, Nucleic acids research.

[17]  Yun S. Song,et al.  Prediction of ribosome footprint profile shapes from transcript sequences , 2016, Bioinform..

[18]  W. F. Anderson,et al.  The effect of tRNA concentration on the rate of protein synthesis. , 1969, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Shintaro Iwasaki,et al.  The Translation Inhibitor Rocaglamide Targets a Bimolecular Cavity between eIF4A and Polypurine RNA. , 2019, Molecular cell.

[20]  C. Sander,et al.  Integrative genomic profiling of human prostate cancer. , 2010, Cancer cell.

[21]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[22]  Leonid Kruglyak,et al.  Genetic Influences on Translation in Yeast , 2014, bioRxiv.

[23]  Pascal Barbry,et al.  RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing , 2016, F1000Research.

[24]  David M Sabatini,et al.  Defining the role of mTOR in cancer. , 2007, Cancer cell.

[25]  Shu-Bing Qian,et al.  Cotranslational response to proteotoxic stress by elongation pausing of ribosomes. , 2013, Molecular cell.

[26]  L. Pachter,et al.  Streaming fragment assignment for real-time analysis of sequencing experiments , 2012, Nature Methods.

[27]  Cole Trapnell,et al.  Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. , 2010, Nature biotechnology.

[28]  V. Gladyshev,et al.  Genome-wide ribosome profiling reveals complex translational regulation in response to oxidative stress , 2012, Proceedings of the National Academy of Sciences.

[29]  Julie L. Chaney,et al.  Roles for Synonymous Codon Usage in Protein Biogenesis. , 2015, Annual review of biophysics.

[30]  Daniel N. Wilson,et al.  Translation regulation via nascent polypeptide-mediated ribosome stalling. , 2016, Current opinion in structural biology.

[31]  Z. Wang,et al.  Ribosome stalling is responsible for arginine-specific translational attenuation in Neurospora crassa , 1997, Molecular and cellular biology.

[32]  V. Ramakrishnan,et al.  Ribosome Structure and the Mechanism of Translation , 2002, Cell.

[33]  A. Sachs.  Cell Cycle–Dependent Translation Initiation IRES Elements Prevail , 2000, Cell.

[34]  Colin N. Dewey,et al.  RNA-Seq gene expression estimation with read mapping uncertainty , 2009, Bioinform..

[35]  Carl Kingsford,et al.  Isoform-level ribosome occupancy estimation guided by transcript abundance with Ribomap , 2015, bioRxiv.

[36]  Hunter B. Fraser,et al.  Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation , 2014, Genome research.

[37]  Nick Goldman,et al.  Realistic simulations reveal extensive sample-specificity of RNA-seq biases , 2013, 1308.3172.

[38]  Gemma E. May,et al.  Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast , 2013, Genome research.

[39]  Qing‐Yu He,et al.  Genome-Wide and Experimental Resolution of Relative Translation Elongation Speed at Individual Gene Level in Human Cells , 2016, PLoS genetics.

[40]  Jianyang Zeng,et al.  Analysis of Ribosome Stalling and Translation Elongation Dynamics by Deep Learning. , 2017, Cell systems.

[41]  M. Albà,et al.  Long non-coding RNAs as a source of new peptides , 2014, eLife.

[42]  Jonathan S. Weissman,et al.  Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data , 2016, BMC Genomics.

[43]  Audrey M. Michel,et al.  RiboGalaxy: A browser based platform for the alignment, analysis and visualization of ribosome profiling data , 2016, RNA biology.