Integrated RNA-seq and sRNA-seq analysis identifies novel nitrate-responsive genes in Arabidopsis thaliana roots
- Elena A Vidal†1,
- Tomás C Moyano†1,
- Gabriel Krouk†2, 3,
- Manpreet S Katari†3,
- Milos Tanurdzic†4, 5,
- W Richard McCombie4,
- Gloria M Coruzzi3Email author and
- Rodrigo A Gutiérrez1Email author
© Vidal et al.; licensee BioMed Central Ltd. 2013
Received: 5 February 2013
Accepted: 10 June 2013
Published: 11 October 2013
Nitrate and other nitrogen metabolites can act as signals that regulate global gene expression in plants. Adaptive changes in plant morphology and physiology triggered by changes in nitrate availability are partly explained by these changes in gene expression. Despite several genome-wide efforts to identify nitrate-regulated genes, no comprehensive study of the Arabidopsis root transcriptome under contrasting nitrate conditions has been carried out.
In this work, we employed the Illumina high throughput sequencing technology to perform an integrated analysis of the poly-A + enriched and the small RNA fractions of the Arabidopsis thaliana root transcriptome in response to nitrate treatments. Our sequencing strategy identified new nitrate-regulated genes including 40 genes not represented in the ATH1 Affymetrix GeneChip, a novel nitrate-responsive antisense transcript and a new nitrate responsive miRNA/TARGET module consisting of a novel microRNA, miR5640 and its target, AtPPC3.
Sequencing of small RNAs and mRNAs uncovered new genes, and enabled us to develop new hypotheses for nitrate regulation and coordination of carbon and nitrogen metabolism.
KeywordsArabidopsis Nitrate RNA-seq Roots MicroRNA Transcriptomics
Nitrogen (N) is an essential macronutrient and a key factor controlling plant growth and development. Nitrate is the main form of N available in agricultural soils [1–3]. Nitrate is taken up by the cell by specific nitrate transporters and is reduced to nitrite in the cytoplasm by nitrate reductase. Nitrite is reduced to ammonium in the plastid by nitrite reductase and is incorporated into amino acids by the glutamate synthase/glutamine synthetase cycle (GS/GOGAT cycle). Nitrate metabolism is tightly coordinated with carbon metabolism, since carbon skeletons in the form of 2-oxoglutarate are required for ammonium assimilation [1, 4]. One of the most striking examples of plant plasticity in response to changing environmental conditions is root system architecture modulation by changes in nitrate availability (for reviews see [5–7]). In order to identify molecular mechanisms underlying these changes, transcriptomics analyses of the nitrate response of Arabidopsis have been performed, most of them utilizing the Affymetrix ATH1 GeneChip. Analyses with the ATH1 chip showed that nitrate is able to regulate more than 2,000 genes in roots, some of them responding as fast as 3–6 minutes after nitrate exposure  and including genes involved in nitrate transport, reduction and assimilation, hormone signaling pathways, transcription factors, kinases and phosphatases, among others [8–12]. However, a detailed view of the transcriptomics changes triggered by nitrate has been limited by the representation of genes in the ATH1 microarray. ATH1 contains probe sets representing approximately 21,000 genes allowing for the detection of only 71% of the genes annotated in the Arabidopsis genome v.10. Moreover, these probes do not include important regulatory elements of the genome such as small (sRNAs).
High-throughput sequencing technologies allow for quantitative determination of RNA levels and RNA sequencing (RNA-seq) is becoming the technology of choice to investigate the transcriptome. RNA-seq offers several advantages over hybridization-based techniques like microarrays [13–18]. RNA-seq is not limited to detection of transcripts that correspond to annotated genes, thus it allows for identification of new genes. RNA sequencing can also be utilized to analyze the sRNA component of the transcriptome when libraries are prepared from low-molecular weight RNA fractions [19–24]. microRNAs (miRNAs), short interfering RNAs (siRNAs) and other types of sRNAs have been shown to play important roles in a broad range of biological processes, such as plant development and response to biotic and abiotic stresses [25–29], including plant responses to various nutrients [30–37].
In plants, the sRNA transcriptome is primarily composed of 23–24 nt siRNAs and 21–22 nt miRNAs [36, 38, 39]. Since miRNA precursors have distinctive secondary structures, many bioinformatics programs have been developed to predict new miRNAs based on sequencing of a sRNA in a library and inspection of the genome sequence containing this sequence for putative miRNA precursors [40–42]. Combination of deep sequencing approaches and bioinformatics predictions have identified 19,724 miRNAs related sequences across different phyla out of which 266 correspond to Arabidopsis miRNAs in miRBase v.17 .
miRNA regulation of nitrate-responsive genes has been shown to be a key mechanism of plant responses coordinating nitrate availability and root developmental responses. miR167 is down-regulated by nitrate treatments in pericycle cells and this leads to an induction of its target, the auxin response factor ARF8 . Regulation of ARF8 by miR167 causes a change in the ratio of initiating and emerging lateral roots in response to nitrate . Another nitrate regulatory module, consisting of miR393 and the AFB3 auxin receptor has been shown to control root system architecture in response to external and internal nitrate availability . Microarray analysis suggests that other miRNAs can be involved in root responses to nitrate, since several miRNA targets are regulated by nitrate .
In this paper, we used Illumina sequencing technology to characterize the poly-A + and sRNA component of nitrate- and control-treated Arabidopsis roots to identify new nitrate-responsive genes. Using bioinformatics analysis of our libraries and miRNA prediction algorithms we were able to find new root expressed genes including new mRNAs and miRNAs. We discovered a new miRNA/target module that might act as an integrator of N and carbon metabolism in Arabidopsis roots.
Deep sequence analysis of the root transcriptome
In order to determine poly-A + and sRNA expression of Arabidopsis roots and their changes in response to nitrate, we grew plants in hydroponic nitrate-free medium with 0.5 mM ammonium succinate as the only N-source for two weeks and treated them with 5 mM KNO3, or 5 mM KCl as control, for 2 hours. These experimental conditions have been previously shown to elicit robust gene expression responses to nitrate [10, 44, 45]. Total RNA from two independent sets of plants (biological replicates) was extracted from roots, and poly-A + enriched and sRNA fractions were used to construct libraries for Illumina sequencing (see Methods for details). The sequencing yielded ~5 to 8 million 35 bp long (sRNA libraries) or 50 bp long (poly-A + libraries) raw reads per sample library. After quality control filtering and trimming adaptor sequences (see Methods), the reads were mapped to the Arabidopsis thaliana genome using the Arabidopsis genome annotation available at The Arabidopsis Information Resource (TAIR) v.10 (http://www.arabidopsis.org). Approximately two thirds of the total Illumina reads perfectly matched the genome and were used for further analysis (Additional file 1).
For Illumina libraries made from poly-A + RNA, a considerable amount of sequences map to intergenic regions (9,542,618 sequences, 55% of the reads) (Figure 1B). Inspection of sequences matching intergenic regions showed that most of them arise from telomeric or centromeric regions. Transcription from intergenic zones has been reported in previous high-throughput sequencing and tiling array experiments [48–51]. When we considered sequences with a unique match to the genome, only 732,226 sequences (22%) mapped to intergenic regions (Figure 1C). A high proportion of these sequences is supported by Arabidopsis ESTs or cDNAs (710,814 sequences, 97%) obtained from TAIR.
As shown in Additional file 4, most of these sequences are located near the 5’ or 3’ of annotated genes. We found sequences matching intergenic regions from poly-A + enriched libraries matching the same strand as annotated genes (Additional file 4 A,C). Interestingly, we also found sequences near annotated genes in antisense orientation (Additional file 4 B,D). These could represent novel transcripts that could have a role in controlling the expression of corresponding genes.
Reads matching protein coding genes (2,094,509 sequences) represent ~60% of the unique reads in poly-A + libraries (Figure 1C). The number of expressed protein coding genes detected unambiguously (19,979 protein coding genes) represents 73% of the total annotated in the Arabidopsis genome. Similar to sRNAs, a considerable proportion of genes are expressed in a cell-specific manner [52, 53], thus some of the low-expressed transcripts detected under our experimental conditions might be developmentally controlled and/or expressed in specific cell-types of the root.
To date, most transcriptomics studies on the root nitrate response have been performed using the Affymetrix ATH1 GeneChip [8–11, 44, 45, 54]. In order to determine how our sequencing data compares with data obtained with the Affymetrix ATH1 GeneChip, we used the same RNA samples for Illumina library preparation and ATH1 microarray hybridization. We used the affy package library from Bioconductor (http://www.bioconductor.org) to determine the number of present calls in the ATH1 microarrays as a measure of gene detection. We were able to find 13,964 probes with a present call, approximately 67% of the gene specific probes that are present in the ATH1 microarray (Additional file 5). The Illumina sequencing data detected 13,411 of these genes (96%, at least one read matching the gene) and 3,022 annotated elements that were called absent in the ATH1 array. We found that these 3,022 elements had low expression values when compared with the 13,411 Illumina-detected elements that had present calls in Affymetrix (Additional file 6A,B). Additionally, Illumina was able to detect 4,215 elements that had no probe on the ATH1 microarray (Additional file 5).
In order to determine how data on nitrate-responsive genes obtained with RNA-seq and Affymetrix ATH1 chips correlated, we calculated the correlation between the KNO3/KCl ratio for RMA normalized Affymetrix gene expression and the KNO3/KCl ratio obtained for normalized libraries at different average gene coverages (AGCs). We defined AGC as the number of reads matching a gene multiplied by read length and divided by gene length. We found correlation between KNO3/KCl ratios increase hyperbolically as average gene coverage increases (Additional file 7). This indicates correlation between the two techniques depends on gene expression levels. We found excellent correlation (r2 ≥ 0.9) between RNA-seq and ATH1 arrays when gene coverage was 0.8 or higher (reads matching the gene represent 80% or more of the gene length) (Additional file 7). These results highlight the potential of the sequencing strategy to identify novel nitrate-responsive genes in Arabidopsis roots.
Deep sequencing reveals a new nitrate-responsive component of the arabidopsis root transcriptome
Illumina sequencing of poly-A + RNA enriched fraction identifies new nitrate responsive genes
BT1, BTB and TAZ domain protein 1
nodulin MtN21 /EamA-like transporter family protein
Homeodomain-like superfamily protein
C2H2-like zinc finger protein
SAUR-like auxin-responsive protein family
Cysteine proteinases superfamily protein
Nodulin MtN21 /EamA-like transporter family protein
BAL, BAP2, BON association protein 2
Calcium-dependent lipid-binding (CaLB domain) family protein
AGG2, G-protein gamma subunit 2
CPuORF29, conserved peptide upstream open reading frame 29
Auxin efflux carrier family protein
Protein of Unknown function (DUF567)
DVL6, RTFL16, ROTUNDIFOLIA like 16
DVL4, RTFL17, ROTUNDIFOLIA like 17
DVL5, RTFL15, ROTUNDIFOLIA like 15
Kinase interacting (KIP1-like) family protein
basic helix-loop-helix (bHLH) DNA-binding superfamily protein
ABF2, abscisic acid responsive elements-binding factor 2
Class I glutamine amidotransferase-like superfamily protein
P-loop containing nucleoside triphosphate hydrolases superfamily protein
ARM-repeat/Tetratricopeptide repeat (TPR)-like protein
P-loop containing nucleoside triphosphate hydrolases superfamily protein
Cysteine proteinases superfamily protein
Protein of Unknown function (DUF607)
Mannose-binding lectin superfamily protein
Protein of Unknown function (DUF581)
O-acetyltransferase family protein
Prediction of new genes
Prediction of novel miRNA genes
Illumina sequencing identifies novel miRNAs
miRNA previoulsy reported
miRNA located in
ath-MIR472 stem loop
Intergenic region AT1G13240-AT1G13245
ath-MIR829 stem loop
ath-MIR840 stem loop
ath-MIR398a stem loop
Intergenic region AT2G05580-AT2G05590
Intergenic region AT2G09880-AT2G09890
ath-MIR396a stem loop
Intergenic region AT2G12490-AT2G12500
ath-MIR5632 stem loop
Intergenic region AT2G20620-AT2G20625
Intergenic region AT2G23540-AT2G23550
ath-MIR831 stem loop
Intergenic region AT2G28620-AT2G28625
ath-MIR160a stem loop
ath-MIR166 stem loop
ath-MIR408 stem loop
ath-miR173-5p stem loop
Intergenic region AT3G24340-AT3G24350
Intergenic region AT3G45170-AT3G45180
Intergenic region AT5G38460-AT5G38470
Intergenic region AT3G46616-AT3G46620
Intergenic region AT3G47410-AT3G47420
Intergenic region AT3G50700-AT3G50710
Intergenic region AT3G52730-AT3G52740
ath-MIR393 stem loop
ath-MIR166 stem loop
Intergenic region AT4G11800-AT4G11810
Intergenic region AT4G22320-AT4G22330
Intergenic region AT5G10504-AT5G10510
Intergenic region AT5G11790-AT5G11800
ath-MIR865 stem loop
Intergenic region AT5G22510-AT5G22520
Intergenic region AT5G26617-AT5G26620
Intergenic region AT5G35945-AT5G35950
ath-MIR160c stem loop
ath-MIR870 stem loop
ath-MIR870 stem loop
A novel nitrate-responsive miRNA/target regulatory module (AtPPC3/miR5640)
High throughput sequencing approaches have become powerful tools to identify the transcriptome of Arabidopsis and other systems. Besides the ability to profile novel genes expressed at low levels which could not be identified by traditional cloning and sequencing approaches, the high depth of sequencing obtained by these techniques allows for the absolute quantification of genes, and the comparison of gene expression under different experimental conditions [38, 73, 74]. Our high throughput sequencing results provided a detailed view of poly-A + RNAs and sRNAs expressed in Arabidopsis roots. We found that roots express a considerable portion of known protein coding genes and miRNA genes. However, most of these genes are expressed at low levels. These transcripts might represent cell specific transcripts whose expression is diluted when considering the whole root. Transcriptomics analysis of specific root cell types has shown that gene expression has an important cell-specific component that gives rise to functional diversification of cells [52, 53].
Even though the sequencing depth used to characterize the sRNA component did not allow for accurate quantitative estimates, we were able to discover novel miRNAs that have eluded previous efforts. Our bioinformatics analysis predicted 51 putative miRNAs expressed in roots under the experimental conditions. Most of these sequences were poorly expressed with less than 1 transcript per million transcripts. A recent publication that analyzes miRNA expressed in specific developmental zones and cell types of the root shows that 9 of these new miRNAs have cell or developmental zone specific expression  which can explain their low expression in the whole root samples. We were able to validate one of the predicted miRNAs, miR5640, as a putative miRNA expressed in roots. This miRNA is located inside intron 23 of the CALLOSE SYNTHASE 1 gene (CALS1, AT1G05570). Intronic miRNAs represent the majority of the miRNAs of animal systems but there are only a few examples in Arabidopsis [75, 76]. Characterized intronic Arabidopsis miRNAs include miR162a and miR838 which are involved in the regulation of DCL1[24, 77, 78]. However, analyzing our sequencing results, we found that the CALS1 transcript was not regulated by nitrate, thus miR5640 could have an independent nitrate-responsive promoter or pri-miR5640 processing to generate the mature miRNA could be a nitrate-regulated process.
We found miR5640 targeted the transcript that codes for AtPPC3, one of the four phosphoenolpyruvate carboxylase enzymes in Arabidopsis . AtPPCs are important enzymes of carbon metabolism that catalyze the β-carboxylation of phosphoenolpyruvate to yield oxaloacetate. In C3 plants and algae, it has been shown that ATPPCs are important for the production of carbon skeletons for nitrogen assimilation [68, 80, 81]. Although there has been an extensive biochemical characterization of the AtPPCs enzymes in Arabidopsis, there are no reports of their function in N metabolism. AtPPC3 is a root specific AtPPC  and we found that it was nitrate-induced in our experiments, which is in agreement with the positive effect on nitrate assimilation predicted for this AtPPC. We also found evidence indicating that nitrate induction of AtPPC3 might depend on a miR5640-mediated post-transcriptional regulation of AtPPC3 levels in response to nitrate. Although we found AtPPC3 cleavage products that might be generated by miR5640 action over this transcript, we need further experiments to validate AtPPC3 as a miR5640 target (i.e. to analyze AtPPC3 levels in a miR5640 overexpressor plant), and to validate the role of this miRNA/TARGET module in nitrate assimilation in roots.
An advantage of using high throughput sequencing is the ability to interrogate gene expression without the representation bias present in microarray experiments. We discovered 40 protein-coding genes that have not been reported to be nitrate-responsive in previous transcriptomics analysis of Arabidopsis roots. Among them, we found highly responsive genes such as BT1 (At5g63160), a calmodulin-binding scaffold protein that acts redundantly with other BT proteins in female gametophyte development . The closest homolog of BT1, BT2, has been reported to be responsive to multiple hormonal, stress and nutritional signals, including nitrate . Interestingly, BT1 is only expressed when nitrate is supplied, suggesting that it might have a nitrate-specific function in roots. The AGG2 gene, one of the two genes encoding the gamma subunit of heterotrimeric G protein was also induced by nitrate. Heterotrimeric G protein in Arabidopsis has been involved in various developmental processes. In roots, it is involved in lateral root formation  and root apical meristem growth . We have found that nitrate has an effect in primary and lateral root growth , thus nitrate regulation of AGG2 might contribute to this response.
NATs are transcripts that fully or partially overlap with other transcripts. These pairs can mediate production of siRNAs to silence gene expression . Additionally, NATs can modulate transcription, can affect mRNA stability and translation and can induce chromatin and DNA epigenetic changes . Computational predictions have shown that the Arabidopsis genome potentially encodes sense-antisense transcript pairs representing approximately 7% of the protein coding genes . We were able to identify 4 putative NATs of >300 bp in our sequencing data. One of these NATs was antisense to TCP23 gene and was induced by nitrate. TCP genes are transcription factors that promote growth and proliferation . TCP23 is predicted to contain a chloroplast-targeting peptide, suggesting it might control transcription of chloroplast genes . Although TCP23 has no described function, other class I TCP factors have been shown to be expressed in meristematic tissues and to control cell cycle genes such as PCNA and CYCB1;1[91, 92]. Thus, TCP23as induction by nitrate might repress TCP23 expression, controlling meristematic activity of the primary root. However, further studies are needed to analyze TCP23as role over TCP23 expression on roots and on TCP23 regulation by nitrate.
In summary, the sequencing of small RNAs and mRNAs uncovered new genes, and enabled us to develop new hypotheses for nitrate regulation and coordination of carbon and N metabolism. A highlight is the discovery of a novel microRNA, miR5640 and its target, AtPPC3. The data suggest that the nitrate-responsive miRNA/target module might be involved in controlling carbon flux to assimilate nitrate into amino acids. These findings suggest that microRNAs can have metabolic regulatory functions, as well as previously described developmental functions [37, 44] in the nitrate response of Arabidopsis roots.
Growth and treatment conditions
Approximately 1,500 Arabidopsis seedlings were grown hydroponically on Phytatrays on MS-modified basal salt media without N (Phytotechnology Laboratories, M531) supplemented with 0.5 mM ammonium succinate and 3 mM sucrose under a photoperiod of 16 h of light and 8 h of darkness and a temperature of 22°C using a plant growth incubator (Percival Scientific, Inc.). After 2 weeks, plants were treated with 5 mM KNO3 or 5 mM KCl as control for 2 hours.
Preparation of illumina libraries
Total RNA from from nitrate-treated or control roots was extracted using Trizol® (Invitrogen, cat. Number 15596–026). For poly-A + libraries, poly-A + RNA was enriched using the Poly(A)Purist™ MAG Kit (Ambion, cat, number AM1922M). Poly-A + RNA was decapped using tobacco acid pyrophosphatase and fragmented using RNA Fragmentation Reagents (Ambion, cat. Number AM8740). Low molecular weight RNA (<40 nt) was isolated from 100 μg of total RNA by PAGE on a FlashPAGE™ fractionator (Ambion, cat. Number AM13100). For construction of the libraries, cloning linker (AMP-5’p = 5’pCTG TAG GCA CCA TCA ATdideoxyC-3’) was ligated to the 3’ end of the RNA followed by purification of the ligation product on a 15% polyacrilamide/urea gel. The 3’-ligated product was ligated to the 5’ Solexa linker (5’-rArCrA rCrUrC rUrUrU rCrCrC rUrArC rArCrG rArCrG rCrUrC rUrUrC rCrGrA rUrC-3’). RNA with ligated adaptors was reverse transcribed into DNA using Illumina specific primer (5’- CAA GCA GAA GAC GGC ATA CGA TTG ATG GTG CCT ACA G-3’) and cDNA was then PCR amplified using this primer and a specific primer (5’- AAT GAT ACG GCG ACC ACC GAA CAC TGT TTC CCT ACA CGA CG-3’). The libraries were gel purified using the QIAquick gel extraction kit (QIAGEN, cat. Number 28704). Libraries were sequenced on the Illumina 1G Genome analyzer.
Raw sequences from the Illumina 1G Genome analyzer in FASTQ format were analyzed with publicly available tools. Low quality reads were extracted with fastq quality filter by FASTX toolkit version 0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit/). The Phred quality score was set to 20, a probability of incorrect base call of 1 in 100. 3’ adaptor sequences were trimmed from the Illumina reads, and then were mapped to the Arabidopsis TAIR10 genome using Novoalign version 2.05.17 (http://www.novocraft.com). Perfect match sequences having passed the quality control, polynucleotide filter, and size filter (between 18 and 28 nt for sRNA libraries and ≥18 nt for poly-A + libraries) were selected for further analysis with custom made PERL scripts.
Determination of differentially expressed genes
To evaluate differential gene expression between KNO3 and KCl treated samples, we used sequence counts corresponding to sRNAs or annotated elements as input for the DESeq package version 1.1.6  available from Bioconductor (http://www.bioconductor.org). This tool uses a negative binomial distribution model to test for differential gene expression . We found correlation values of 0.91 and 0.96 for controls and treatments respectively for sRNA-seq and of 0.99 for controls and treatments for RNA-seq data. Replicates were used independently for statistical analysis of gene expression. We adjusted for multiple testing using FDR correction  and filtered genes whose expression changed with corrected p-values ≥ 0.05.
New miRNA and target predictions
Quality filtered Illumina sequences were used as input for the MIRCAT tool , available at the University of East Anglia (UEA) sRNA toolkit (http://srna-tools.cmp.uea.ac.uk) using default parameters. To predict miRNA targets, we used the target prediction tool available from the UEA sRNA toolkit. The predicted targets, along with the putative cleavage site on these targets, were further validated using RNAhybrid version 2.1 .
Predicting novel transcribed regions
Novoalign alignments that did not overlap with annotated regions of the genome were pooled from all samples. Regions with continuous alignments in the same strand greater than 300 bp were identified as candidate novel transcribed regions.
Gene expression analysis using RT-qPCR
Gene expression analysis was carried out using the Brilliant® SYBR® Green QPCR Reagents on a Stratagene MX3000P qPCR system (Agilent) according to manufacturer’s instructions. The RNA levels were normalized relative to the Clathrin adaptor complexes medium subunit family protein (At4g24550). Quantification of microRNA levels was carried out using the High-Specificity miRNA QRT-PCR Detection Kit from Stratagene on a Stratagene MX3000P qPCR system. The RNA levels were normalized relative to U6 snRNA (At3g14735). A list of RT-qPCR primers used in this work is provided in Additional file 12.
A modified procedure for RLM-RACE  was carried out using the GeneRacer™ kit. The GeneRacer RNA Oligo adapter was directly ligated to 250 ng of Poly-A + mRNA and the GeneRacer OligodT primer was used to synthesize first strand cDNA. This cDNA was subjected to a PCR amplification procedure with the GeneRacer 5′Primer and the GeneRacer 3′Primer to generate a pool of non-genespecific RACE products. Gene-specific 5′RACE reactions were performed with the GeneRacer 5′Nested Primer and a reverse gene-specific primer. The expected size of the PCR amplicons was checked on a 3% agarose gel. PCR products were cloned and sequenced to confirm predicted miRNA-mediated cleavage of the transcripts.
Availability of supporting data
The data sets supporting the results of this article are available in the NCBI GEO database  repository, under accession GSE44062.
This work was supported by International Early Career Scientist program from Howard Hughes Medical Institute to R.A.G., Fondo de Desarrollo de Areas Prioritarias (FONDAP) Center for Genome Regulation [15090007 to R.A.G.], Millennium Nucleus Center for Plant Functional Genomics [P10-062-F to R.A.G.], Fondo Nacional de Desarrollo Científico y Tecnológico [1100698 to R.A.G.], Comisión Nacional de Investigación Científica y Tecnológica-ANR program [ANR-007 to R.A.G.], the National Institutes of Health [GM032877 to G.M.C.] and National Science Foundation [MCB-0929338 to G.M.C.], Agence Nationale de la Recherche [NitroNet: ANR 11 PDOC 020 01 to G.K.] and Centre National de la Recherche Scientifique [PEPS Bio math Info 2012–2013: SuperRegNet to G.K.]. E.A.V is supported by Proyecto de Inserción en la Academia (PSD74).
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