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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Science. Author manuscript; available in PMC Jan 29, 2012.
Published in final edited form as:
PMCID: PMC3170756
NIHMSID: NIHMS316063

Evidence for Network Evolution in an Arabidopsis Interactome Map

Arabidopsis Interactome Mapping Consortium*

Abstract

Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing ~6,200 highly reliable interactions between ~2,700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops.

Classical genetic and molecular approaches have provided fundamental understanding of processes such as growth control or development, and molecular descriptions of genotype-to-phenotype relationships for a variety of plant systems. Yet more than 60% of the protein-coding genes of the model plant Arabidopsis thaliana (hereafter Arabidopsis) remain functionally uncharacterized. Knowledge about the biological organization of macromolecules in complex and dynamic “interactome” networks is lacking for Arabidopsis (fig. S1, tables S1, S2), depriving us of an understanding of how genotype-to-phenotype relationships are mediated at the systems level (1).

A high-quality binary protein-protein interactome map for Arabidopsis

To generate a map of the Arabidopsis interactome network, we used a collection of ~8,000 open reading frames representing ~30% of its predicted protein-coding genes (table S3) (2, 3). We tested all pair-wise combinations of proteins encoded by these constructs (space 1) with an improved high-throughput binary interactome mapping pipeline based on the yeast two-hybrid (Y2H) system (fig. S2) (3, 4). Confirmed pairs were assembled into a dataset of 5,664 binary interactions between 2,661 proteins, called Arabidopsis Interactome version 1 “main screen” (AI-1MAIN) (table S4).

The quality of AI-1MAIN was evaluated against a positive reference set (PRS) of 118 well-documented, manually re-curated (5) Arabidopsis protein-protein interactions and a random reference set (RRS) of 146 random protein pairs (fig. S3; table S5) (3, 59). We determined the fraction of true biophysical interactions in AI-1MAIN, its “precision”, to be ~80%, by comparing the validation rates of a random sample of 249 interactions from AI-1MAIN to those of the PRS and RRS in a “well-Nucleic Acid Programmable Protein Array” (wNAPPA) protein-protein interaction assay (Fig. 1A; fig. S4; table S5) (3, 8).

Fig. 1Fig. 1
Quality of AI-1MAIN. (A) Fraction of PRS, RRS or AI-1MAIN sample pairs positive in Y2H or in wNAPPA at a scoring threshold of 1.5. Error bars: standard error of the proportion. P-values: one-sided two-sample t-tests (3). PRS pairs are more often detected ...

To estimate the size of the complete Arabidopsis protein-protein interactome network and the proportion covered by AI-1MAIN, its “coverage”, we calculated the screening completeness, the percentage of all possible Arabidopsis pair-wise protein combinations screened in space 1 (~10%) (fig. S2), and the overall sensitivity, a parameter that combines both the assay sensitivity of our Y2H version (Fig. 1A) and the sampling sensitivity of our screens (~16%) (fig. S5; table S5) (3, 6, 7, 9). Since AI-1MAIN contains 5,664 interactions, we estimate that the complete Arabidopsis biophysical binary protein-protein interactome, excluding isoforms, is 299,000 ± 79,000 binary interactions (mean ± standard deviation) (3), of which AI-1MAIN represents ~2%. While the Arabidopsis interactome is estimated to be larger than those of yeast, worm or human (6, 7, 9) the number of interactions per possible protein pairs is similar in all four species (510 per 10,000). The overall topology of AI-1MAIN is qualitatively similar to that observed for interactome maps of these other species (fig. S6) (6, 7, 9, 10). While all global network analyses were performed with AI-1MAIN, local network analyses were done with AI-1 (http://interactome.dfci.harvard.edu/A_thaliana/index.php?page=2010anm_download and http://interactome/A_thaliana/index.php?page=display; table S4), a dataset combining AI-1MAIN and interactions identified in repeated screens on the subspace indicated in fig. S2, performed to estimate sampling sensitivity (tables S4, S6, S7) (3).

Comparing AI-1MAIN to a network of Arabidopsis literature-curated interactions

We assembled 4,252 literature-curated binary interactions between 2,160 Arabidopsis proteins (LCIBINARY) (fig. S1; tables S1, S4) (3). The observed overlap with AI-1MAIN lies within the range expected given the AI-1MAIN coverage (Fig. 1B) (3). With similar numbers of proteins (nodes) and interactions (edges), AI-1MAIN and LCIBINARY are both small-world networks (fig. S6). However, LCIBINARY shows longer distances between nodes and a higher tendency to form clusters of highly interacting nodes (Fig. 1B) (fig. S6). This is likely due to biases inherent to literature-curated datasets, as hypothesis-driven research focuses on few proteins designated to be important (57, 911). AI-1MAIN and LCIBINARY contain similar fractions of plant-specific proteins (19% and 14%, respectively; fig. S6; table S8) (3), but the presence of several highly connected plant-specific hubs in AI-1MAIN results in twice as many plant-specific interactions (40% and 20%; fig. S6; table S9).

Overlap of AI-1 with other biological relationships

To estimate the overall biological relevance of AI-1 interactions, we used statistical correlations with genome-wide functional information available for Arabidopsis (7, 9). We observed a significantly higher co-expression correlation for pairs of transcripts encoding interacting proteins than for control pairs (fig. S7) (3). Interacting proteins are also enriched in common Gene Ontology (GO) annotations, particularly those describing specific biological functions and thus assigned to only a few proteins, which we refer to as “precise” (fig. S7) (3). This enrichment holds true for GO annotations based strictly on genetic experiments (fig. S7) (3). Protein pairs that do not directly interact but share interactors are also enriched in common precise GO annotations (fig. S7) (3). Similar to the whole Arabidopsis proteome, but in contrast to proteins involved in literature-curated interactions, two-thirds of proteins in AI-1 lack any or precise GO annotations; for these AI-1 provides starting points for hypothesis development (fig. S7; tables S8, S9).

Plant signaling networks in AI-1

Integration of biophysical interactions with orthogonal functional data can uncover novel biological relationships at the scale of individual proteins, pathways, and networks (1). We examined ubiquitination enzymes and their substrates, an expanded system in plants relative to other species (12). The specific targets of most ubiquitination enzymes remain elusive and a systems level understanding of ubiquitin signaling is missing. We identified 32 interactions between E3 proteins and potential target proteins shown to be ubiquitinated in biochemical experiments (tables S8, S9) (3). Many E3 proteins showed interactions with the same putative target, and conversely, several putative targets interacted with a single common E3 (Fig. 2A) (3). Thus, our data support a high combinatorial complexity within the ubiquitination system and, with similar analyses of phosphorylation signaling cascades (fig. S8; tables S8, S9) (3), provide starting points for analysis of directional information flow through protein-protein interactome networks.

Fig. 2Fig. 2
Plant signaling networks in AI-1. (A) Putative ubiquitination subnetwork extracted from LCIBINARY and AI-1. Bar plot: number of protein-protein interactions between proteins in the ubiquitination cascade in LCIBINARY and AI-1 (outside and within space ...

Plant hormones regulate developmental processes and mediate responses to environmental stimuli. In the auxin signaling pathway, auxin/indole-3-acetic acid (AUX/IAA) proteins mediate transcriptional repression of response genes via physical interactions between their ethylene-response-factor-associated amphiphilic repression (EAR) motifs and the co-repressor TOPLESS (TPL) (13). Twelve interactions between AUX/IAA and TPL or TPL-related3 (TPR3) were observed in AI-1, including six novel ones (fig. S8). While two non-AUX/IAA interactors of TPL have been reported so far (14, 15), there are 21 such interactors in AI-1, of which 15 contain a predicted EAR motif (16) (P < 10−24, hypergeometric test). TPL interactors include ZIM-domain transcriptional repressors (JAZ5, JAZ8), regulators of salicylic acid signaling (NIMIN2, NIMIN3), and a transcriptional regulator of ethylene response (ERF9) (Fig. 2B, fig. S8). AI-1 also reveals direct interactions among co-repressors, similar to the recently described crosstalk between JAZ proteins and gibberellin-related DELLA proteins (17), as well as shared transcription factor targets of JAZ and jasmonic acid insensitive ZIM related family members (Fig. 2B; fig. S8). These observations suggest that transcriptional co-repressors and adaptors assemble in a modular way to integrate simultaneous inputs from several hormone pathways and that TPL plays a central role in this process.

Communities in AI-1MAIN

In many networks, communities can be identified with densely interconnected components that function together (18). We applied an edge clustering approach (19) to identify communities in AI-1MAIN and investigated their biological relevance. We identified 26 communities containing more than five proteins in AI-1MAIN (Fig. 3; fig. S9) (3). Approximately 25% of AI-1MAIN proteins (661/2,661) could be assigned to one community, while ~1% (23/2,661) belong to more than one community. We found that ~90% of these communities are enriched in at least one GO annotation (Fig. 3; table S10) (3), whereas negative control networks randomized by degree-preserving edge shuffling showed fewer communities and little GO annotation enrichment (P < 0.01; Fig. 3). Detailed inspection of AI-1MAIN communities (figs. S10–35) both recapitulated available biological information and suggested new hypotheses. For example, the “brassinosteroid signaling/phosphoprotein-binding” community contains several 14-3-3 proteins known to regulate brassinosteroid signaling (fig. S10). Consistent with the tendency of 14-3-3 proteins to interact with phosphorylated partners (20), this community is enriched in experimentally identified phosphoproteins (P = 0.005, Fisher’s exact test). The interactions between the 14-3-3 proteins and the abscisic acid-responsive element binding transcription factor AREB3 are corroborated by previous findings in barley (21), and suggest that plant 14-3-3 proteins mediate multiple hormone signaling pathways.

Fig. 3
Communities in AI-1MAIN (bottom) and in a typical randomized network (top left; fig. S9). Only largest connected component of networks are shown. Colored regions indicate communities enriched in GO annotations summarized by the indicated terms (table ...

Several communities, such as “transcription” and “nucleosome assembly”, share proteins indicating linked biological processes (fig. S36). Particularly striking is the large “transmembrane transport” community sharing 13 proteins with the “vesicle mediated transport” community and six with the “water transport” community (fig. S36). These shared proteins are bridged via four well-connected proteins within the “transmembrane transport” community, including two membrane-tethered NAC-type transcription factors, ANAC089 and NTL9 (fig. S36). Transcription factors in this plant-specific protein family are activated by release from the cellular membrane by endopeptidase- or ubiquitin-mediated cleavage (22). Interactions corresponding to both mechanisms are found in the “transmembrane transport” community (fig. S37).

Four distinct communities correspond to “ubiquitination”. The largest is predominantly composed of interactions between 36 F-box proteins and two Skp proteins, known to form degradative SCF (Skp1, Cullin, F-box) ubiquitin ligase complexes (fig. S27). Two others are composed of shared E2 ubiquitin conjugating enzymes and distinct RING-finger family E3 ligases (figs. S12, S16). The “ubiquitination and DNA repair” community includes the UBC13 and MMS2/UEV E2 ubiquitin conjugating enzymes, which participate in non-proteolytic polyubiquitination (fig. S13) (23). Distinct types of ubiquitin-related processes were thus identified in AI-1.

Our analyses support the relevance of communities identified in AI-1MAIN and we anticipate that with increasing coverage interactome network maps will improve our understanding of the systems-level molecular organization of plants.

Evidence for network evolution

Whether or not natural selection shapes the evolution of interactome networks remains unclear. Gene duplication, a major driving force of evolutionary novelty, has been studied in yeast providing a framework for understanding subsequent protein-protein interaction rewiring (Fig. 4A) (24). However, the difficulty to date ancient gene duplication events and the low coverage of available protein-protein interaction datasets limit the interpretation of these studies (3, 2427). The high fraction of duplicated genes in the Arabidopsis genome compared to non-plant species, combined with the relatively large size of AI-1MAIN, provides interactome data for 1,882 paralogous pairs (fig. S38). These pairs span a wide range of apparent interaction rewiring, as measured by the fraction of shared interactors for each pair (fig. S38).

Fig. 4Fig. 4Fig. 4Fig. 4Fig. 4
Evidence for network evolution in AI-1MAIN. (A) Interaction rewiring over time according to the duplication-divergence model (24). (B) Average fraction of interactors shared between pairs of paralogous proteins with no (n=4), low (n=10), and high (n=3) ...

To verify that the apparent interaction rewiring in AI-1MAIN reflects functional divergence, we focused on paralogous pairs classified as having “no”, “low”, or “high” functional divergence on the basis of morphological consequences observed in functionally null mutants of single or pairs of paralogous genes (28). For the 17 pairs in AI-1MAIN for which comparative phenotypic data is available, the fraction of shared interactors accurately predicted this functional divergence classification (Fig. 4B).

To study the dynamics of interaction rewiring, we dated gene duplication events using a comparative genomics approach that brackets these events on the basis of multi-taxonomic phylogenetic trees (3). This allowed us to divide AI-1MAIN paralogous pairs into four “time-since-duplication” age groups covering up to ~700 million years (fig. S39). To account for the illusion of divergence induced by low experimental coverage, we empirically determined the average fraction of common interactors detected for a set of proteins screened twice as performed for AI-1MAIN (fig. S40) (3). We used this expected upper bound to calibrate the fraction of observed shared interactors between paralogous proteins, assuming that duplicates are identical at the time of duplication (Fig. 4C) (3). Our observations are not driven by the existence of certain large protein families in AI-1MAIN (fig. S41). As reported for yeast (24, 26, 27), the average fraction of common interactors decreases over evolutionary time, showing substantial and rapid divergence, even after correcting for the coverage of AI-1MAIN. Yet, in Arabidopsis, paralogous pairs that have been diverging for ~700 million years still share more interactors than random proteins pairs (P < 2.2 × 10−16, Mann-Whitney U-test), indicating that the long-term fate of paralogous proteins is not necessarily a complete divergence of their interaction profiles.

The proportion of shared interactors does not decay exponentially with time-since-duplication, as expected when assuming neutral evolution (3, 29, 30), i.e. random interaction rewiring, with no impact on fitness (31). Instead, the rate of rewiring appears “rapid-then-slow”, as suggested by a better fit to a power-law decay (Fig. 4C; fig. S42) (3). This trend mirrors that of protein sequence divergence for these paralogous pairs (Fig. 4C), which reflects the variation of selective pressure at different times after the duplication event. After an initial transient relaxation leading to rapid protein sequence divergence, selective pressure tightens on retained paralogs and their divergence decelerates (3, 25) (fig. S39). The fact that interactions diverge in a time-dependent manner similar to protein sequences supports the hypothesis that protein-protein interactions drive the evolution of duplicated genes.

To investigate the interplay between duplication mechanism and the fate of duplicates (32), we compared duplicates originating from whole-genome duplications (WGDs) to those from other types of gene duplications. In our most recent age group containing paralogs specific to the Arabidopsis genus, 109 paralogous pairs arose during the two most recent WGDs in the Arabidopsis lineage (α and β WGDs) (3, 33). As previously observed for yeast (34), these pairs share more interactors than other paralogous pairs in the same age group (Fig. 4D; fig. S43), but this effect could simply reflect the younger age of WGD pairs as revealed by more precise time estimates (fig. S43). While gene dosage balance has been proposed to determine loss or retention of duplicates following WGDs (33), the observed extensive rewiring reinforces previous observations pointing to functional divergence as a major feature of the long-term evolution of polyploid plants (35).

Expression profile divergence is rapid, non-random and substantial in Arabidopsis (36, 37) (fig. S44), yet appears to play a limited role in the functional divergence of paralogs (28). We tested whether the evolutionary forces acting on expression profiles and protein interaction divergence are complementary or correlated. For each duplication age group, the most co-expressed paralogous pairs tend to share more interactors than the least co-expressed ones (Fig. 4E). This suggests that selective pressures driving functional divergence concurrently act on both aspects of protein function.

With >65% sequence identity and strongly correlated expression profiles, the most recent paralogous pairs share less than half of their interactors (41%) (Fig. 4C; figs. S44, S45). This contrast is consistent with the common understanding that protein-protein interactions are only one of many constraints limiting sequence changes during evolution, allowing for small sequence changes to induce fate-determining network rewiring (38, 39). One example of interaction rewiring despite sequence conservation is observed in the actin family. Each actin protein pair shares >90% sequence identity, yet collectively the actin family exhibits time-dependent interaction rewiring (fig. S45).

Modeling interaction rewiring with non-constant rates should provide insight into the evolution of interactome networks and their topology (40). Whether this rewiring is merely a consequence of sequence divergence or is a primary driver remains an open question. Together with observations of fast rewiring of other types of biological networks (41, 42), our data invite speculation that edge-specific rewiring is faster than node evolution in biological networks.

Conclusion

Our empirically determined high-quality protein-protein interaction map for a plant interactome network should not only hasten the functional characterization of unknown proteins, including those with potential biotechnological utility, but also enable systems level investigations of genotype-to-phenotype relationships in the plant kingdom. One example is how AI-1 illuminates mechanisms and strategies by which plants cope with pathogenic challenges (Mukhtar et al., co-submitted).

The paradigms established here are compatible with models in which the interactome network constrains and shapes sequence evolution. Studying sequence variation, conservation, mutation, and evolution rate has shed light on how natural selection drives evolution. Explorations of interaction variation will similarly broaden the understanding of network evolution whether in the context of duplication or trans-kingdom comparative interactomics.

Supplementary Material

Supplementary Text and Figures

Table S1

Table S10

Table S11

Table S12

Table S2

Table S3

Table S4

Table S5

Table S6

Table S7

Table S8

Table S9

Acknowledgments

We thank Drs. Philip Benfey, Haiyuan Yu and Magnus Nordborg as well as members of the Center for Cancer Systems Biology (CCSB) for helpful discussions. This work was supported by the following grants: NSF 0703905 to M.V., J.R.E. and D.E.H.; NHGRI R01HG001715 to M.V., D.E.H. and F.P.R.; NSF 0520253 and NSF 0313578 to J.R.E.; Canada Excellence Research Chairs (CERC) Program and Canadian Institute for Advanced Research Fellowship to F.P.R.; James S. McDonnell Foundation (JSMF) 220020084 to A.-L.B; Sixth Framework Programme LSHG-CT-2006-037704 (AGRON-OMICS) to C.L.; NIGMS R01GM066025 to J.L.D.; USDA ARS 1907-21000-030 to D.W.; NIH NRSA Fellowships F32HG004098 to M.T. and F32HG004830 to R.J.S.; and NSF 0703908 to D.W. in support of J.S. and W.S. M.V. is a “Chercheur Qualifié Honoraire” from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium).

Arabidopsis Interactome Mapping Consortium

Authorship of this paper should be cited as “Arabidopsis Interactome Mapping Consortium”.

Participants are arranged by working group then listed in alphabetical order (AO), except for Chairs, co-Chairs and Project Leaders when indicated.

Correspondence and request for materials should be addressed, to M.V. (marc_vidal/at/dfci.harvard.edu); J.R.E. (ecker/at/salk.edu); P.B. (pascal_braun/at/dfci.harvard.edu); D.E.H. (david_hill/at/dfci.harvard.edu).

Matija Dreze, Anne-Ruxandra Carvunis, Benoit Charloteaux, Mary Galli, Samuel J. Pevzner and Murat Tasan contributed equally to this work and should be considered co-first authors.

Steering group (AO): Pascal Braun1,2 (Chair), Anne-Ruxandra Carvunis,1,2,3 Benoit Charloteaux,1,2,4 Matija Dreze,1,2,5 Joseph R. Ecker,6,7 David E. Hill,1,2 Frederick P. Roth,1,8,* Marc Vidal1,2.

ORFeome group: Mary Galli6 (Project Leader), Padmavathi Balumuri,9 Vanessa Bautista,6 Jonathan D. Chesnut,9 Rosa Cheuk Kim,6, Chris de los Reyes,6 Patrick Gilles,9, Christopher J. Kim,6 Uday Matrubutham,9 Jyotika Mirchandani,9 Eric Olivares,9,§ Suswapna Patnaik,9 Rosa Quan,6 Gopalakrishna Ramaswamy,9, Paul Shinn,6 Geetha M. Swamilingiah,9 Stacy Wu,6 Joseph R. Ecker6,7 (Chair).

Interactome data acquisition group: Matija Dreze1,2,5 (Project Leader), Danielle Byrdsong,1,2 Amélie Dricot,1,2 Melissa Duarte,1,2 Fana Gebreab,1,2 Bryan J. Gutierrez,1,2 Andrew MacWilliams,1,2 Dario Monachello,12, M. Shahid Mukhtar,11,# Matthew M. Poulin,1,2 Patrick Reichert,1,2 Viviana Romero,1,2 Stanley Tam,1,2 Selma Waaijers,1,2,** Evan M. Weiner,1,2 Marc Vidal1,2 (co-Chair), David E. Hill1,2 (co-Chair), Pascal Braun1,2 (Chair).

NAPPA interactome validation group: Mary Galli6 (Project Leader), Anne-Ruxandra Carvunis,1,2,3 Michael E. Cusick,1,2 Matija Dreze,1,2,5 Viviana Romero,1,2 Frederick P. Roth,1,8,* Murat Tasan,8 Junshi Yazaki,7 Pascal Braun1,2 (co-Chair), Joseph R. Ecker6,7 (Chair).

Bioinformatics and analysis group: Anne-Ruxandra Carvunis1,2,3 (Project Leader), Yong-Yeol Ahn,1,10 Albert-László Barabási,1,10 Benoit Charloteaux,1,2,4 Huaming Chen,6 Michael E. Cusick,1,2 Jeffery L. Dangl,11 Matija Dreze,1,2,5 Joseph R. Ecker,6,7 Changyu Fan,1,2 Lantian Gai,6 Mary Galli,6 Gourab Ghoshal,1,10 Tong Hao,1,2 David E. Hill,1,2 Claire Lurin,12 Tijana Milenkovic,13 Jonathan Moore,14 M. Shahid Mukhtar,11,# Samuel J. Pevzner,1,2,15,16 Natasa Przulj,17 Sabrina Rabello,1,10 Edward A. Rietman,1,2,†† Thomas Rolland,1,2 Frederick P. Roth,1,8,* Balaji Santhanam,1,2 Robert J. Schmitz,7 William Spooner,18,19 Joshua Stein,18 Murat Tasan,8 Jean Vandenhaute,5 Doreen Ware,18,20 Pascal Braun1,2 (co-Chair), Marc Vidal1,2 (Chair).

Writing group (AO): Pascal Braun1,2 (Chair), Anne-Ruxandra Carvunis,1,2,3 Benoit Charloteaux,1,2,4 Matija Dreze,1,2,5 Mary Galli,6 Marc Vidal1,2 (co-Chair).

Footnotes

1Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

2Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.

3Computational and Mathematical Biology Group, TIMC-IMAG, CNRS UMR5525 and Université de Grenoble, Faculté de Médecine, 38706 La Tronche cedex, France.

4Unit of Animal Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Liège, 4000 Liège, Wallonia-Brussels Federation, Belgium.

5Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix, 5000 Namur, Wallonia-Brussels Federation, Belgium.

6Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.

7Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.

8Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.

9Life Technologies, Carlsbad, CA 92008, USA.

10Center for Complex Network Research (CCNR), Department of Physics, Northeastern University, Boston, MA 02115, USA.

11Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

12Unité de Recherche en Génomique Végétale (URGV), UMR INRA/UEVE - ERL CNRS 91057, Evry Cedex, France.

13Department of Computer Science and Engineering, University of Notre Dame, IN 46556, USA.

14Warwick Systems Biology Centre, Coventry House, University of Warwick, Coventry, CV4 7AL, UK.

15Biomedical Engineering Department, Boston University, Boston, MA 02215, USA.

16Boston University School of Medicine, Boston, MA 02118, USA.

17Department of Computing, Imperial College London SW7 2AZ, UK.

18Cold Spring Harbor Laboratory (CSHL), Cold Spring Harbor, NY 11724, USA.

19Eagle Genomics Ltd, Babraham Research Campus, Cambridge, CB4 1JD, UK.

20United States Department of Agriculture, Agricultural Research Service (USDA ARS), Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA.

*Present address: Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S3E1, Canada and Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Ontario M5G1X5, Canada.

Present address: Foley & Lardner LLP, 3579 Valley Centre Drive, Suite 300, San Diego, CA 92130, USA.

Deceased

§Present address: Pacific Biosciences, 940 Hamilton Drive, Menlo Park, CA 94025, USA.

Thermo Fisher Scientific, BioSciences Division, Bangalore-560011, India.

Present address: Centre de Génétique Moléculaire du C.N.R.S., 1 Avenue de la Terrasse, 91190 Gif-sur-Yvette, France.

#Present address: Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.

**Present address: University of Utrecht, 3508 TC Utrecht, The Netherlands.

††Present address: Center of Cancer Systems Biology, St. Elizabeth’s Medical Center, Tufts University School of Medicine, Boston, MA 02135, USA.

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