Letter | Open | Published:

Comparative analysis of the transcriptome across distant species

Nature volume 512, pages 445448 (28 August 2014) | Download Citation

Abstract

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features1,2,3,4,5,6. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters.

Main

Our comparison used the ENCODE–modENCODE RNA resource (Extended Data Fig. 1). This resource comprises: deeply sequenced RNA-sequencing (RNA-seq) data from many distinct samples from all three organisms; comprehensive annotation of transcribed elements; and uniformly processed, standardized analysis files, focusing on non-coding transcription and expression patterns. Where practical, these data sets match comparable samples across organisms and to other types of functional genomics data. In total, the resource contains 575 different experiments containing >67 billion sequence reads. It encompasses many different RNA types, including poly(A)+, poly(A)-, ribosomal-RNA-depleted, short and long RNA.

The annotation in the resource represents a capstone for the decade-long efforts in human, worm and fly. The new annotation sets have numbers, sizes and families of protein-coding genes similar to previous compilations; however, the number of pseudogenes and annotated non-coding RNAs differ (Extended Data Fig. 2, Extended Data Table 1 and Supplementary Fig. 1). Also, the number of splicing events is greatly increased, resulting in a concomitant increase in protein complexity. We find the proportion of the different types of alternative splicing (for example, exon skipping or intron retention) is generally similar across the three organisms; however, skipped exons predominate in human while retained introns are most common in worm and fly7 (Extended Data Fig. 3, Supplementary Fig. 1 and Supplementary Table 1).

A fraction of the transcription comes from genomic regions not associated with standard annotations, representing ‘non-canonical transcription’ (Supplementary Table 2)8. Using a minimum-run–maximum-gap algorithm to process reads mapping outside of protein-coding transcripts, pseudogenes and annotated non-coding RNAs, we identified read clusters; that is, transcriptionally active regions (TARs). Across all three genomes we found roughly one-third of the bases gives rise to TARs or non-canonical transcription (Extended Data Table 1). To determine the extent that this transcription represents an expansion of the current established classes of non-coding RNAs, we identified the TARs most similar to known annotated non-coding RNAs using a supervised classifier9 (Supplementary Fig. 2 and Supplementary Table 2). We validated the classifier’s predictions using RT–PCR (PCR with reverse transcription), demonstrating high accuracy. Overall, these predictions encompass only a small fraction of all TARs, suggesting that most TARs have features distinct from annotated non-coding RNAs and that the majority of non-coding RNAs of established classes have already been identified. To shed further light on the possible roles of TARs we intersected them with enhancers and HOT (high-occupancy target) regions8,10,11,12,13, finding statistically significant overlaps (Extended Data Fig. 4 and Supplementary Table 2).

Given the uniformly processed nature of the data and annotations, we were able to make comparisons across organisms. First, we built co-expression modules, extending earlier analysis14 (Fig. 1a). To detect modules consistently across the three species, we combined across-species orthology and within-species co-expression relationships. In the resulting multilayer network we searched for dense subgraphs (modules), using simulated annealing15,16. We found some modules dominated by a single species, whereas others contain genes from two or three. As expected, the modules with genes from multiple species are enriched in orthologues. Moreover, a phylogenetic analysis shows that the genes in such modules are more conserved across 56 diverse animal species (Extended Data Fig. 5 and Supplementary Fig. 3). To focus on the cross-species conserved functions, we restricted the clustering to orthologues, arriving at 16 conserved modules, which are enriched in a variety of functions, ranging from morphogenesis to chromatin remodelling (Fig. 1a and Supplementary Table 3). Finally, we annotated many TARs based on correlating their expression profiles with these modules (Extended Data Fig. 4).

Figure 1: Expression clustering.
Figure 1

a, Left, human, worm and fly gene–gene co-association matrix; darker colouring reflects the increased likelihood that a pair of genes are assigned to the same module. A dark block along the diagonal represents a group of genes within a species. If this is associated with an off-diagonal block then it is a cross-species module (for example, a three-species conserved module is shown with a circle and a worm–fly module, with a star). However, if a diagonal block has no off-diagonal associations, then it forms a species-specific module (for example, green pentagon). Right, the Gene Ontology functional enrichment of genes within the 16 conserved modules is shown. GF, growth factor; nuc., nuclear; proc., processing. b, Primary and secondary alignments of worm-and-fly developmental stages based on all worm–fly orthologues. Inset shows worm–fly stage alignment using only hourglass orthologues is more significant and exhibits a gap (brown) matching the phylotypic stage. The scale for the heat map in b is indicated on the left side of the scale in a (labelled stage alignment). c, Normalized expression of the conserved modules in fly shows the smallest intra-organism divergence during the phylotypic stage (brown). A representative module is indicated with a blue asterisk in a and c. (For further details see Extended Data Figs 5 and 6; ref. 20, related to the left part of a; and ref. 21, related to the bottom part of b.)

Next, we used expression profiles of orthologous genes to align the developmental stages in worm and fly (Fig. 1b and Extended Data Fig. 6). For every developmental stage, we identified stage-associated genes; that is, genes highly expressed at that particular stage but not across all stages. We then counted the number of orthologous pairs among these stage-associated genes for each possible worm-and-fly stage correspondence, aligning stages by the significance of the overlap. Notably, worm stages map to two sets of fly stages. First, they match in a co-linear fashion to the fly (that is, embryos-to-embryos, larvae-to-larvae). However, worm late embryonic stages also match fly pupal stages, suggesting a shared expression program between embryogenesis and metamorphosis. The approximately 50 stage-associated genes involved in this dual alignment are enriched in functions such as ion transport and cation-channel activity (Supplementary Table 3).

To gain further insight into the stage alignment, we examined our 16 conserved modules in terms of the ‘hourglass hypothesis’, which posits that all animals go through a particular stage in embryonic development (the tight point of the hourglass or ‘phylotypic’ stage) during which the expression divergence across species for orthologous genes is smallest4,5,17. For genes in 12 of the 16 modules, we observed canonical hourglass behaviour; that is, inter-organism expression divergence across closely related fly species during development is minimal5 (Supplementary Fig. 3). Moreover, we find a subset of TARs also exhibit this hourglass behaviour (Supplementary Fig. 2). Beyond looking at inter-species divergence, we also investigated the intra-species divergence within just Drosophila melanogaster and Caenorhabditis elegans. Notably, we observed that divergence of gene expression between modules is minimized during the worm and fly phylotypic stages (Fig. 1c). This suggests, for an individual species, the expression patterns of different modules are most tightly coordinated (low divergence) during the phylotypic stage, but each module has its own expression signature before and after this. In fact it is possible to see this coordination directly as a local maximum in between-module correlations for the worm (Extended Data Fig. 5). Finally, using genes from just the 12 ‘hourglass modules’, we found that the alignment between worm and fly stages becomes stronger (Fig. 1b and Supplementary Fig. 3); in particular it shows a gap where no changes are observed, perfectly matching the phylotypic stage.

The uniformly processed and matched nature of the transcriptome data also facilitates integration with upstream factor-binding and chromatin-modification signals. We investigated the degree to which these upstream signals can quantitatively predict gene expression and how consistent this prediction is across organisms. Similar to previous reports11,18,19, we found consistent correlations, around the transcription start site (TSS), in each of the three species between various histone-modification signals and the expression level of the downstream gene: H3K4me1, H3K4me2, H3K4me3 and H3K27ac are positively correlated, whereas H3K27me3 is negatively correlated (Fig. 2, Extended Data Fig. 7 and Supplementary Fig. 4). Then for each organism, we integrated these individual correlations into a multivariate, statistical model, obtaining high accuracy in predicting expression for protein-coding genes and non-coding RNAs. The promoter-associated marks, H3K4me2 and H3K4me3, consistently have the highest contribution to the model.

Figure 2: Histone models for gene expression.
Figure 2

Top, normalized correlations of two representative histone marks with expression. Left, relative importance of the histone marks in organism-specific models and the universal model. Right, prediction accuracies (Pearson correlations all significant, P < 1 × 10−100) of the organism-specific and universal models. (See Extended Data Figs 7 and 8 for further details.)

A similar statistical analysis with transcription factors showed the correlation between gene expression and transcription-factor binding to be the greatest at the TSS, positively for activators and negatively for repressors (Extended Data Fig. 7). Integrated transcription-factor models in each organism also achieved high accuracy for protein-coding genes and non-coding RNAs, with as few as five transcription factors necessary for accurate predictions (Extended Data Fig. 8). This perhaps reflects an intricate, correlated structure to regulation. The relative importance of the upstream regions is more peaked for the transcription-factor models than for the histone ones, likely reflecting the fact that histone modifications are spread over broader regions, including the gene body, whereas most transcription factors bind near the promoter.

Finally, we constructed a ‘universal model’, containing a single set of organism-independent parameters (Fig. 2 and Supplementary Fig. 4). This achieved accuracy comparable to the organism-specific models. In the universal model, the consistently important promoter-associated marks such as H3K4me2 and H3K4me3 are weighted most highly. In contrast, the enhancer mark H3K4me1 is down-weighted, perhaps reflecting that signals for most human enhancers are not near the TSS. Using the same set of organism-independent parameters derived from training on protein-coding genes, the universal model can also accurately predict non-coding RNA expression.

Overall, our comparison of the transcriptomes of three phylogenetically distant metazoans highlights fundamental features of transcription conserved across animal phyla. First, there are ancient co-expression modules across organisms, many of which are enriched for developmentally important hourglass genes. These conserved modules have highly coordinated intra-organism expression during the phylotypic stage, but display diversified expression before and after. The expression clustering also aligns developmental stages between worm and fly, revealing shared expression programs between embryogenesis and metamorphosis. Finally, we were able to build a single model that could predict transcription in all three organisms from upstream histone marks using a single set of parameters for both protein-coding genes and non-coding RNAs. Overall, our results underscore the importance of comparing divergent model organisms to human to highlight conserved biological principles (and disentangle them from lineage-specific adaptations).

Methods Summary

Detailed methods are given in the Supplementary Information. (See the first section of the Supplementary Information for a guide.) More details on data availability are given in section F of the Supplementary Information.

Accessions

Data deposits

Data sets described here can be obtained from the ENCODE project website at http://www.encodeproject.org/comparative via accession number ENCSR145VDW (alternate URL http://cmptxn.gersteinlab.org).

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Acknowledgements

The authors thank the NHGRI and the ENCODE and modENCODE projects for support. In particular, this work was funded by a contract from the National Human Genome Research Institute modENCODE Project, contract U01 HG004271 and U54 HG006944, to S.E.C. (principal investigator) and P.C., T.R.G., R.A.H. and B.R.G. (co-principal investigators) with additional support from R01 GM076655 (S.E.C.) both under Department of Energy contract no. DE-AC02-05CH11231, and U54 HG007005 to B.R.G. J.B.B.’s work was supported by NHGRI K99 HG006698 and DOE DE-AC02-05CH11231. Work in P.J.B.’s group was supported by the modENCODE DAC sub award 5710003102, 1U01HG007031-01 and the ENCODE DAC 5U01HG004695-04. Work in M.B.G.’s group was supported by NIH grants HG007000 and HG007355. Work in Bloomington was supported in part by the Indiana METACyt Initiative of Indiana University, funded by an award from the Lilly Endowment, Inc. Work in E.C.L.’s group was supported by U01-HG004261 and RC2-HG005639. P.J.P. acknowledges support from the National Institutes of Health (grant no. U01HG004258). We thank the HAVANA team for providing annotation of the human reference genome, whose work is supported by National Institutes of Health (grant no. 5U54HG004555), the Wellcome Trust (grant no. WT098051). R.G. acknowledges support from the Spanish Ministry of Education (grant BIO2011-26205). We also acknowledge use of the Yale University Biomedical High Performance Computing Center. R.W.'s lab was supported by grant no. U01 HG 004263.

Author information

Author notes

    • Mark B. Gerstein
    • , Joel Rozowsky
    • , Koon-Kiu Yan
    • , Daifeng Wang
    • , Chao Cheng
    • , James B. Brown
    • , Carrie A. Davis
    • , LaDeana Hillier
    • , Cristina Sisu
    • , Jingyi Jessica Li
    • , Baikang Pei
    • , Arif O. Harmanci
    • , Michael O. Duff
    •  & Sarah Djebali

    These authors contributed equally to this work.

    • Mark B. Gerstein
    • , Steven E. Brenner
    • , Brenton R. Graveley
    • , Susan E. Celniker
    • , Thomas R. Gingeras
    •  & Robert Waterston

    These authors jointly supervised this work.

Affiliations

  1. Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA

    • Mark B. Gerstein
    • , Joel Rozowsky
    • , Koon-Kiu Yan
    • , Daifeng Wang
    • , Cristina Sisu
    • , Baikang Pei
    • , Arif O. Harmanci
    • , Roger P. Alexander
    • , Raymond Auerbach
    • , Gang Fang
    • , Robert R. Kitchen
    • , Jing Leng
    • , Anurag Sethi
    • , Yan Zhang
    •  & Henry Zheng
  2. Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA

    • Mark B. Gerstein
    • , Joel Rozowsky
    • , Koon-Kiu Yan
    • , Daifeng Wang
    • , Cristina Sisu
    • , Baikang Pei
    • , Arif O. Harmanci
    • , Roger P. Alexander
    • , Raymond Auerbach
    • , Gang Fang
    • , Robert R. Kitchen
    • , Jing Leng
    • , Anurag Sethi
    • , Yan Zhang
    •  & Henry Zheng
  3. Department of Computer Science, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA

    • Mark B. Gerstein
  4. Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA

    • Chao Cheng
  5. Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA

    • Chao Cheng
  6. Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

    • James B. Brown
    • , Nathan P. Boley
    • , Benjamin W. Booth
    • , Ann Hammonds
    • , Roger A. Hoskins
    • , Marcus H. Stoiber
    • , Kenneth H. Wan
    •  & Susan E. Celniker
  7. Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA

    • James B. Brown
    • , Jingyi Jessica Li
    • , Peter J. Bickel
    • , Haiyan Huang
    •  & Garrett Robinson
  8. Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA

    • Carrie A. Davis
    • , Kimberly Bell
    • , Alex Dobin
    • , Jorg Drenkow
    • , Megan Fastuca
    • , Sonali Jha
    • , Felix Schlesinger
    • , Huaien Wang
    • , Chenghai Xue
    • , Chris Zaleski
    •  & Thomas R. Gingeras
  9. Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA

    • LaDeana Hillier
    • , Max E. Boeck
    • , Brent Ewing
    • , Chau Huynh
    • , Michael MacCoss
    • , Gennifer Merrihew
    • , Pnina Strasbourger
    • , Owen A. Thompson
    •  & Robert Waterston
  10. Department of Statistics, University of California, Los Angeles, California 90095-1554, USA

    • Jingyi Jessica Li
  11. Department of Human Genetics, University of California, Los Angeles, California 90095-7088, USA

    • Jingyi Jessica Li
  12. Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA

    • Michael O. Duff
    • , Gemma May
    • , Sara Olson
    • , Li Yang
    •  & Brenton R. Graveley
  13. Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain

    • Sarah Djebali
    • , Roderic Guigó
    • , Julien Lagarde
    •  & Dmitri Pervouchine
  14. Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain

    • Sarah Djebali
    • , Roderic Guigó
    • , Julien Lagarde
    •  & Dmitri Pervouchine
  15. Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, Massachusetts 02115, USA

    • Burak H. Alver
    •  & Peter J. Park
  16. Department of Biostatistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA

    • Nathan P. Boley
    •  & Marcus H. Stoiber
  17. Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA

    • Lucy Cherbas
    • , Peter Cherbas
    •  & Thomas C. Kaufman
  18. Center for Genomics and Bioinformatics, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA

    • Lucy Cherbas
    •  & Peter Cherbas
  19. MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China

    • Chao Di
    • , Guanjun Gao
    • , Long Hu
    • , Zhi Lu
    •  & Kejia Wen
  20. National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA

    • Elise A. Feingold
    • , Peter J. Good
    •  & Michael J. Pazin
  21. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK

    • Adam Frankish
    • , Jen Harrow
    • , Tim J. P. Hubbard
    •  & Gary I. Saunders
  22. Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne 1015, Switzerland

    • Cédric Howald
    •  & Alexandre Reymond
  23. Swiss Institute of Bioinformatics, Genopode building, Lausanne 1015, Switzerland

    • Cédric Howald
  24. Medical and Molecular Genetics, King's College London, London WC2R 2LS, UK

    • Tim J. P. Hubbard
  25. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520-8005, USA

    • Dionna Kasper
    • , Valerie Reinke
    •  & Guilin Wang
  26. Department of Molecular, Cellular and Developmental Biology, PO Box 208103, Yale University, New Haven, Connecticut 06520, USA

    • Masaomi Kato
    •  & Frank J. Slack
  27. Sloan-Kettering Institute, 1275 York Avenue, Box 252, New York, New York 10065, USA

    • Erik Ladewig
    • , Eric Lai
    •  & Jiayu Wen
  28. Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA

    • Gemma May
  29. Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, Tennessee 37232-8240, USA

    • Rebecca McWhirter
    • , David M. Miller
    • , William C. Spencer
    •  & Kathie L. Watkins
  30. Developmental and Cell Biology, University of California, Irvine, California 92697, USA

    • Ali Mortazavi
    •  & Rabi Murad
  31. Center for Complex Biological Systems, University of California, Irvine, California 92697, USA

    • Ali Mortazavi
    •  & Rabi Murad
  32. Section of Developmental Genomics, Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA

    • Brian Oliver
  33. Department of Genetics and Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA

    • Norbert Perrimon
    •  & Anastasia Samsonova
  34. Howard Hughes Medical Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA

    • Norbert Perrimon
    •  & Anastasia Samsonova
  35. European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK

    • Gary I. Saunders
  36. Bioinformatics and Genomics Programme, Center for Genomic Regulation, Universitat Pompeu Fabra (CRG-UPF), 08003 Barcelona, Catalonia, Spain

    • Andrea Tanzer
  37. Institute for Theoretical Chemistry, Theoretical Biochemistry Group (TBI), University of Vienna, Währingerstrasse 17/3/303, A-1090 Vienna, Austria

    • Andrea Tanzer
  38. Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

    • Li Yang
  39. Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    • Kevin Yip
  40. 5 CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    • Kevin Yip
  41. Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA

    • Steven E. Brenner
  42. Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA

    • Steven E. Brenner

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Contributions

Work on the paper was divided between data production and analysis. The analysts were J.R., K.K.Y., D.W., C.C., J.B.B., C.S., J.J.L., B.P., A.O.H., M.O.D., S.D., R.P.A., B.H.A., R.K.A., P.J.B., N.P.B., C.D., A.D., G.F., A.F., R.G., J.H., L.H., H.H., T.H., R.R.K., J.L., J.L., Z.L., A.M., R.M., P.P., D.P., A.S., K.W., K.Y., Y.Z. and H.Z. (names are sorted according to their order in the author list). The data producers were C.A.D., L.H., K.B., M.E.B., B.W.B., L.C., P.C., J.D., B.E., M.F., G.G., P.G., A.H., R.A.H., C.H., C.H., S.J., D.K., M.K., T.C.K., E.L., E.L., M.M., G.M., R.M., G.M., D.M.M., B.O., S.O., N.P., V.R., A.R., G.R., A.S., G.I.S., F.S., F.J.S., W.C.S., M.H.S., P.S., K.L.W., J.W., C.X., L.Y. and C.Z. Substantially larger contributions were made by the joint first authors. The role of the NIH Project Management Group, E.A.F., P.J.G., M.J.P., was limited to coordination and scientific management of the modENCODE and ENCODE consortia. Overall project management was carried out by the senior authors M.B.G., R.W., T.R.G., S.E.C., B.R.G. and S.E.B.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Mark B. Gerstein or Rebecca McWhirter or Steven E. Brenner or Brenton R. Graveley or Susan E. Celniker or Thomas R. Gingeras.

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DOI

https://doi.org/10.1038/nature13424

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