S. Agarwal, C. Vaz, A. Bhattacharya, and A. Et-srinivasan, Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM), BMC Bioinformatics, vol.11, issue.Suppl 1, pp.11-29, 2010.
DOI : 10.1186/1471-2105-11-S1-S29

T. Akutsu, Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots, Discrete Applied Mathematics, vol.104, issue.1-3, pp.45-62, 2000.
DOI : 10.1016/S0166-218X(00)00186-4

S. Aldridge and J. Et-hadfield, Introduction to miRNA Profiling Technologies and Cross-Platform Comparison, Next-Generation MicroRNA Expression Profiling Technology, pp.19-31, 2012.
DOI : 10.1007/978-1-61779-427-8_2

M. I. Almeida, R. M. Reis, and G. A. Et-calin, MicroRNA history: Discovery, recent applications, and next frontiers, Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, vol.717, issue.1-2, pp.1-8, 2011.
DOI : 10.1016/j.mrfmmm.2011.03.009

URL : http://repositorium.sdum.uminho.pt/bitstream/1822/18666/1/Almeida%20MI_Mutation%20Res%20Fund%20%26%20Molec%20Mech%20Mutag-pp%202011.pdf

S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Et-lipman, Basic local alignment search tool, Journal of Molecular Biology, vol.215, issue.3, pp.403-410, 1990.
DOI : 10.1016/S0022-2836(05)80360-2

A. Ammar, Z. Elouedi, and P. Et-lingras, RPKM: The Rough Possibilistic K-Modes, Foundations of Intelligent Systems, pp.81-86, 2012.
DOI : 10.1007/978-3-642-34624-8_9

M. J. Axtell, J. O. Westholm, and E. C. Lai, Vive la diff??rence: biogenesis and evolution of microRNAs in plants and animals, Genome Biology, vol.12, issue.4, p.221, 2011.
DOI : 10.1101/gr.101386.109

P. J. Batista and H. Y. Chang, Long Noncoding RNAs: Cellular Address Codes in Development and Disease, Cell, vol.152, issue.6, pp.1298-1307, 2013.
DOI : 10.1016/j.cell.2013.02.012

J. Bentley, Programming pearls: algorithm design techniques, Communications of the ACM, vol.27, issue.9, pp.865-873, 1984.
DOI : 10.1145/358234.381162

E. Berezikov, E. Cuppen, and R. H. Et-plasterk, Approaches to microRNA discovery, Nature Genetics, vol.12, issue.6s, pp.2-7, 2006.
DOI : 10.1126/science.1109020

P. Bieganski, J. Riedl, J. Cartis, and E. F. Et-retzel, Generalized suffix trees for biological sequence data: applications and implementation, Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences HICSS-94, pp.35-44, 1994.
DOI : 10.1109/HICSS.1994.323593

J. Brennecke, D. R. Hipfner, A. Stark, R. B. Russell, and S. M. Et-cohen, bantam Encodes a Developmentally Regulated microRNA that Controls Cell Proliferation and Regulates the Proapoptotic Gene hid in Drosophila, Cell, vol.113, issue.1, pp.25-36, 2003.
DOI : 10.1016/S0092-8674(03)00231-9

J. Brennecke, A. Stark, R. B. Russell, and S. M. Et-cohen, Principles of MicroRNA???Target Recognition, PLoS Biology, vol.5, issue.3, p.85, 2005.
DOI : 10.1371/journal.pbio.0030085.g007

S. L. Brockmeier, P. G. Halbur, and E. L. Et-thacker, Polymicrobial Diseases, chapitre 13, Porcine respiratory disease complex, pp.231-258, 2002.
DOI : 10.1128/9781555817947.ch13

C. Bron and J. Et-kerbosch, Algorithm 457: finding all cliques of an undirected graph, Communications of the ACM, vol.16, issue.9, pp.575-577, 1973.
DOI : 10.1145/362342.362367

B. , D. Heap, P. Et, P. , and A. , Effect of enzootic pneumonia of pigs on growth performance, Australian veterinary journal, vol.62, issue.1, pp.13-18, 1985.

S. E. Castel and R. A. Et-martienssen, RNA interference in the nucleus: roles for small RNAs in transcription, epigenetics and beyond, Nature Reviews Genetics, vol.6, issue.2, pp.100-112, 2013.
DOI : 10.1038/nrg3355

K. Chaudhuri and R. Et-chatterjee, MicroRNA Detection and Target Prediction: Integration of Computational and Experimental Approaches, DNA and Cell Biology, vol.26, issue.5, pp.321-337, 2007.
DOI : 10.1089/dna.2006.0549

K. Chen and N. Et-rajewsky, The evolution of gene regulation by transcription factors and microRNAs, Nature Reviews Genetics, vol.433, issue.2, pp.93-103, 2007.
DOI : 10.1038/nrg1990

X. Darzacq, B. E. Jády, C. Verheggen, A. M. Kiss, E. Bertrand et al., Cajal body-specific small nuclear RNAs: a novel class of 2'-O-methylation and pseudouridylation guide RNAs, The EMBO Journal, vol.21, issue.11, pp.2746-2756, 2002.
DOI : 10.1093/emboj/21.11.2746

B. L. Davidson and P. B. Et-mccray, Current prospects for RNA interference-based therapies, Nature Reviews Genetics, vol.107, issue.5, pp.329-340, 2011.
DOI : 10.1038/nrg2968

M. De-planell-saguer and M. C. Et-rodicio, Analytical aspects of microRNA in diagnostics: A review, Analytica Chimica Acta, vol.699, issue.2, pp.134-152, 2011.
DOI : 10.1016/j.aca.2011.05.025

M. C. Debey and R. F. Et-ross, Ciliostasis and loss of cilia induced by Mycoplasma hyopneumoniae in porcine tracheal organ cultures, Infection and immunity, vol.62, issue.12, pp.5312-5318, 1994.

G. Dieci, M. Preti, and B. Et-montanini, Eukaryotic snoRNAs: A paradigm for gene expression flexibility, Genomics, vol.94, issue.2, pp.83-88, 2009.
DOI : 10.1016/j.ygeno.2009.05.002

S. R. Eddy, How do RNA folding algorithms work?, Nature Biotechnology, vol.22, issue.11, pp.1457-1458, 2004.
DOI : 10.1016/S0959-440X(00)00088-9

A. J. Enright, B. John, U. Gaul, T. Tuschl, C. Sander et al., MicroRNA targets in drosophila, Genome Biology, vol.5, issue.1, pp.1-1, 2004.
DOI : 10.1186/gb-2003-5-1-r1

Z. Fang and N. Et-rajewsky, The Impact of miRNA Target Sites in Coding Sequences and in 3???UTRs, PLoS ONE, vol.17, issue.3, p.18067, 2011.
DOI : 10.1371/journal.pone.0018067.s006

W. Filipowicz and V. Et-poga?i?, Biogenesis of small nucleolar ribonucleoproteins. Current opinion in cell biology, pp.319-327, 2002.

A. Fire, S. Xu, M. K. Montgomery, S. A. Kostas, S. E. Driver et al., Potent and specific genetic interference by double-stranded RNA in caenorhabditis elegans, nature, issue.6669, pp.391806-811, 1998.

M. R. Friedländer, W. Chen, C. Adamidi, J. Maaskola, R. Einspanier et al., Discovering microRNAs from deep sequencing data using miRDeep, Nature Biotechnology, vol.29, issue.4, pp.407-415, 2008.
DOI : 10.1038/nbt1394

M. R. Friedländer, S. D. Mackowiak, N. Li, W. Chen, and N. Et-rajewsky, miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades, Nucleic Acids Research, vol.40, issue.1, pp.37-52, 2012.
DOI : 10.1093/nar/gkr688

S. W. Gardner and F. C. Et-minion, Detection and quantification of intergenic transcription in Mycoplasma hyopneumoniae, Microbiology, vol.156, issue.8, pp.1562305-2315, 2010.
DOI : 10.1099/mic.0.038760-0

S. Geisler and J. Et-coller, RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts, Nature Reviews Molecular Cell Biology, vol.472, issue.11, pp.699-712, 2013.
DOI : 10.1038/nrm3679

M. A. German, S. Luo, G. Schroth, B. C. Meyers, and P. J. Et-green, Construction of Parallel Analysis of RNA Ends (PARE) libraries for the study of cleaved miRNA targets and the RNA degradome, Nature Protocols, vol.4, issue.3, pp.356-362, 2009.
DOI : 10.1126/science.1097434

R. Gesteland and J. Et-atkins, The RNA world, 1993.

Z. Ghosh and B. Et-mallick, Renaissance of the Regulatory RNAs, Regulatory RNAs, pp.3-22, 2012.
DOI : 10.1007/978-3-662-45801-3_1

C. P. Godinho, S. Higashi, M. Sagot, A. Zaha, and A. T. Et-vasconcelos, Prediction and experimental validation of non-coding RNAs in the bacteria Mycoplasma hyopneumoniae

W. M. Gommans and E. Et-berezikov, Controlling miRNA Regulation in Disease, Next-Generation MicroRNA Expression Profiling Technology, pp.1-18, 2012.
DOI : 10.1007/978-1-61779-427-8_1

S. Gottesman and G. Et-storz, Bacterial Small RNA Regulators: Versatile Roles and Rapidly Evolving Variations, Cold Spring Harbor Perspectives in Biology, vol.3, issue.12, p.3798, 2011.
DOI : 10.1101/cshperspect.a003798

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3225950

S. Griffiths-jones, R. J. Grocock, S. Van-dongen, A. Bateman, and A. J. Enright, miRBase: microRNA sequences, targets and gene nomenclature, Nucleic Acids Research, vol.34, issue.90001, pp.140-144, 2006.
DOI : 10.1093/nar/gkj112

URL : http://doi.org/10.1093/nar/gkj112

S. Griffiths-jones, H. K. Saini, S. Van-dongen, and A. J. Enright, miRBase: tools for microRNA genomics, Nucleic Acids Research, vol.36, issue.Database, pp.154-158, 2008.
DOI : 10.1093/nar/gkm952

A. Grimson, K. K. Farh, W. K. Johnston, P. Garrett-engele, L. P. Lim et al., MicroRNA Targeting Specificity in Mammals: Determinants beyond Seed Pairing, Molecular Cell, vol.27, issue.1, pp.91-105, 2007.
DOI : 10.1016/j.molcel.2007.06.017

S. Guo and K. J. Et-kemphues, par-1, a gene required for establishing polarity in C. elegans embryos, encodes a putative Ser/Thr kinase that is asymmetrically distributed, Cell, vol.81, issue.4, pp.611-620, 1995.
DOI : 10.1016/0092-8674(95)90082-9

D. Gusfield, Algorithms on strings, trees and sequences: computer science and computational biology, 1997.
DOI : 10.1017/CBO9780511574931

C. R. Hale, P. Zhao, S. Olson, M. O. Duff, B. R. Graveley et al., RNA-Guided RNA Cleavage by a CRISPR RNA-Cas Protein Complex, Cell, vol.139, issue.5, pp.139945-956, 2009.
DOI : 10.1016/j.cell.2009.07.040

A. K. Hansen and P. H. Et-degnan, Widespread expression of conserved small RNAs in small symbiont genomes, The ISME Journal, vol.4, issue.12, 2014.
DOI : 10.1126/science.1177263

L. He and G. J. Et-hannon, MicroRNAs: small RNAs with a big role in gene regulation, Nature Reviews Genetics, vol.1, issue.7, pp.522-531, 2004.
DOI : 10.1038/nature02363

J. Hertel and P. F. Et-stadler, Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data, Bioinformatics, vol.22, issue.14, pp.22-197, 2006.
DOI : 10.1093/bioinformatics/btl257

S. Higashi, C. Fournier, C. Gautier, C. Gaspin, and M. Et-sagot, Mirinho: An efficient and general plant and animal pre-miRNA predictor for genomic and deep sequencing data, BMC Bioinformatics, vol.43, issue.1
DOI : 10.1093/nar/gkt1181

URL : https://hal.archives-ouvertes.fr/hal-01166487

S. Higashi, O. Rue, K. Gaget, G. Duport, H. Charles et al., MicroRNA identification from small RNA sequencing data in four developmental stages of A

I. L. Hofacker, W. Fontana, P. F. Stadler, L. S. Bonhoeffer, M. Tacker et al., Schnelle Faltung und Vergleich von Sekund???rstrukturen von RNA, Monatshefte f???r Chemie Chemical Monthly, vol.157, issue.2, pp.167-188, 1994.
DOI : 10.1007/BF00818163

T. Hsu and F. C. Et-minion, Molecular analysis of the P97 cilium adhesin operon of Mycoplasma hyopneumoniae, Gene, vol.214, issue.1-2, pp.13-23, 1998.
DOI : 10.1016/S0378-1119(98)00247-9

A. Jha, R. Chauhan, M. Mehra, H. R. Singh, and R. Et-shankar, miR-BAG: Bagging Based Identification of MicroRNA Precursors, PLoS ONE, vol.7, issue.9, p.45782, 2012.
DOI : 10.1371/journal.pone.0045782.s008

P. Jiang, H. Wu, W. Wang, W. Ma, X. Sun et al., MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features, Nucleic Acids Research, vol.35, issue.Web Server, pp.35-339, 2007.
DOI : 10.1093/nar/gkm368

Q. Jing, S. Huang, S. Guth, T. Zarubin, A. Motoyama et al., Involvement of MicroRNA in AU-Rich Element-Mediated mRNA Instability, Cell, vol.120, issue.5, pp.623-634, 2005.
DOI : 10.1016/j.cell.2004.12.038

M. W. Jones-rhoades, D. P. Bartel, and B. Bartel, MicroRNAs AND THEIR REGULATORY ROLES IN PLANTS, Annual Review of Plant Biology, vol.57, issue.1, pp.19-53, 2006.
DOI : 10.1146/annurev.arplant.57.032905.105218

S. Kadri, V. Hinman, and P. Et-benos, HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models, BMC Bioinformatics, vol.10, issue.Suppl 1, p.35, 2009.
DOI : 10.1186/1471-2105-10-S1-S35

P. Kapranov, J. Cheng, S. Dike, D. A. Nix, R. Duttagupta et al., RNA Maps Reveal New RNA Classes and a Possible Function for Pervasive Transcription, Science, vol.316, issue.5830, pp.3161484-1488, 2007.
DOI : 10.1126/science.1138341

S. Karlin and S. F. Et-altschul, Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes., Proceedings of the National Academy of Sciences, pp.2264-2268, 1990.
DOI : 10.1073/pnas.87.6.2264

S. Karlin and A. Et-dembo, Limit distributions of maximal segmental score among Markov-dependent partial sums, Advances in Applied Probability, vol.16, issue.01, pp.113-140, 1992.
DOI : 10.1214/aop/1176992160

T. Kawamata and Y. Et-tomari, Making RISC, Trends in Biochemical Sciences, vol.35, issue.7, pp.368-376, 2010.
DOI : 10.1016/j.tibs.2010.03.009

T. Kelesidis, The Cross-Talk between Spirochetal Lipoproteins and Immunity, Frontiers in Immunology, vol.102, issue.1????????2, 2014.
DOI : 10.1016/j.vetmic.2004.06.004

M. Kertesz, N. Iovino, U. Unnerstall, U. Gaul, and E. Segal, The role of site accessibility in microRNA target recognition, Nature Genetics, vol.26, issue.10, pp.391278-1284, 2007.
DOI : 10.1038/ng2135

V. N. Kim, Small RNAs: classification, biogenesis, and function, Mol cells, vol.19, issue.1, pp.1-15, 2005.

M. Kiriakidou, P. T. Nelson, A. Kouranov, P. Fitziev, C. Bouyioukos et al., A combined computational-experimental approach predicts human microRNA targets, Genes & Development, vol.18, issue.10, pp.181165-1178, 2004.
DOI : 10.1101/gad.1184704

A. Kozomara and S. Et-griffiths-jones, miRBase: integrating microRNA annotation and deep-sequencing data, Nucleic Acids Research, vol.39, issue.Database, pp.152-157, 2011.
DOI : 10.1093/nar/gkq1027

URL : http://doi.org/10.1093/nar/gkq1027

A. Kozomara and S. Et-griffiths-jones, miRBase, p.1181, 2013.
DOI : 10.1002/9780471650126.dob0993

A. Krek, D. Grün, M. N. Poy, R. Wolf, L. Rosenberg et al., Combinatorial microRNA target predictions, Nature Genetics, vol.432, issue.5, pp.495-500, 2005.
DOI : 10.1093/nar/gkh023

J. Krüger and M. Et-rehmsmeier, RNAhybrid: microRNA target prediction easy, fast and flexible, Nucleic Acids Research, vol.34, issue.Web Server, pp.451-454, 2006.
DOI : 10.1093/nar/gkl243

T. Kunej, I. Godnic, S. Horvat, M. Zorc, and G. A. Et-calin, Cross Talk Between MicroRNA and Coding Cancer Genes, The Cancer Journal, vol.18, issue.3, p.18223, 2012.
DOI : 10.1097/PPO.0b013e318258b771

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3389046

B. Langmead, C. Trapnell, M. Pop, and S. L. Salzberg, Ultrafast and memory-efficient alignment of short DNA sequences to the human genome, Genome Biology, vol.10, issue.3, p.25, 2009.
DOI : 10.1186/gb-2009-10-3-r25

P. Larsson, A. Hinas, D. H. Ardell, L. A. Kirsebom, A. Virtanen et al., De novo search for non-coding RNA genes in the AT-rich genome of Dictyostelium discoideum: Performance of Markov-dependent genome feature scoring, Genome Research, vol.18, issue.6, pp.888-899, 2008.
DOI : 10.1101/gr.069104.107

R. C. Lee, R. L. Feinbaum, and V. Et-ambros, The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14, Cell, vol.75, issue.5, pp.75843-854, 1993.
DOI : 10.1016/0092-8674(93)90529-Y

URL : https://hal.archives-ouvertes.fr/in2p3-00597159

F. Legeai, G. Rizk, T. Walsh, O. Edwards, K. Gordon et al., Bioinformatic prediction, deep sequencing of microRNAs and expression analysis during phenotypic plasticity in the pea aphid, Acyrthosiphon pisum, BMC Genomics, vol.11, issue.1, p.281, 2010.
DOI : 10.1186/1471-2164-11-281

URL : https://hal.archives-ouvertes.fr/inserm-00482283

F. Legeai, S. Shigenobu, J. Gauthier, J. Colbourne, C. Rispe et al., AphidBase: a centralized bioinformatic resource for annotation of the pea aphid genome, Insect Molecular Biology, vol.21, issue.1, pp.195-207, 2010.
DOI : 10.1111/j.1365-2583.2009.00930.x

URL : https://hal.archives-ouvertes.fr/inria-00531562

B. P. Lewis, C. B. Burge, and D. P. Bartel, Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets, Cell, vol.120, issue.1, pp.15-20, 2005.
DOI : 10.1016/j.cell.2004.12.035

L. Li, D. Huang, M. K. Cheung, W. Nong, Q. Huang et al., BSRD: a repository for bacterial small regulatory RNA, Nucleic Acids Research, vol.41, issue.D1, pp.41-233, 2013.
DOI : 10.1093/nar/gks1264

L. P. Lim, N. C. Lau, E. G. Weinstein, A. Abdelhakim, S. Yekta et al., The microRNAs of Caenorhabditis elegans, Genes & Development, vol.17, issue.8, pp.17991-1008, 2003.
DOI : 10.1101/gad.1074403

B. Liu, J. Li, and M. J. Et-cairns, Identifying miRNAs, targets and functions, Briefings in Bioinformatics, vol.15, issue.1, pp.1-19, 2014.
DOI : 10.1093/bib/bbs075

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896928

C. Liu, G. A. Calin, S. Volinia, and C. M. Et-croce, MicroRNA expression profiling using microarrays, Nature Protocols, vol.3, issue.4, pp.563-578, 2008.
DOI : 10.1016/j.ccr.2007.07.027

H. F. Lodish, A. Berk, S. L. Zipursky, P. Matsudaira, D. Baltimore et al., Molecular cell biology, 2000.

R. Lorenz, S. H. Bernhart, C. H. Zu-siederdissen, H. Tafer, C. Flamm et al., ViennaRNA Package 2.0, Algorithms for Molecular Biology, vol.6, issue.1, p.26, 2011.
DOI : 10.1016/0005-2795(75)90109-9

URL : http://doi.org/10.1186/1748-7188-6-26

D. Lutter, C. Marr, J. Krumsiek, E. W. Lang, and F. J. Et-theis, Intronic microRNAs support their host genes by mediating synergistic and antagonistic regulatory effects, BMC Genomics, vol.11, issue.1, p.224, 2010.
DOI : 10.1186/1471-2164-11-224

J. R. Lytle, T. A. Yario, and J. A. Et-steitz, Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5' UTR as in the 3' UTR, Proceedings of the National Academy of Sciences, vol.104, issue.23, pp.9667-9672, 2007.
DOI : 10.1073/pnas.0703820104

M. L. Madsen, S. Puttamreddy, E. L. Thacker, M. D. Carruthers, and F. C. Et-minion, Transcriptome Changes in Mycoplasma hyopneumoniae during Infection, Infection and Immunity, vol.76, issue.2, pp.658-663, 2008.
DOI : 10.1128/IAI.01291-07

L. Marsan and M. Et-sagot, Algorithms for Extracting Structured Motifs Using a Suffix Tree with an Application to Promoter and Regulatory Site Consensus Identification, Journal of Computational Biology, vol.7, issue.3-4, pp.3-4345, 2000.
DOI : 10.1089/106652700750050826

A. Mathelier and A. Et-carbone, MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data, Bioinformatics, vol.26, issue.18, pp.262226-2234, 2010.
DOI : 10.1093/bioinformatics/btq329

D. H. Mathews, M. D. Disney, J. L. Childs, S. J. Schroeder, M. Zuker et al., Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure, Proceedings of the National Academy of Sciences of the United States of America, pp.7287-7292, 2004.
DOI : 10.1073/pnas.0401799101

D. H. Mathews, S. J. Schroeder, D. H. Turner, and M. Et-zuker, Predicting RNA secondary structure. Cold Spring Harbor Monograph Archive, pp.631-657, 2006.

D. H. Mathews and D. H. Turner, Prediction of RNA secondary structure by free energy minimization, Current Opinion in Structural Biology, vol.16, issue.3, pp.270-278, 2006.
DOI : 10.1016/j.sbi.2006.05.010

J. S. Mccaskill, The equilibrium partition function and base pair binding probabilities for RNA secondary structure, Biopolymers, vol.24, issue.6-7, pp.6-71105, 1990.
DOI : 10.1002/bip.360290621

E. M. Mccreight, A Space-Economical Suffix Tree Construction Algorithm, Journal of the ACM, vol.23, issue.2, pp.262-272, 1976.
DOI : 10.1145/321941.321946

G. Meister, Argonaute proteins: functional insights and emerging roles, Nature Reviews Genetics, vol.136, issue.7, pp.447-459, 2013.
DOI : 10.1038/nrg3462

N. Mendes, A. T. Freitas, and M. Et-sagot, Current tools for the identification of miRNA genes and their targets, Nucleic Acids Research, vol.37, issue.8, pp.2419-2433, 2009.
DOI : 10.1093/nar/gkp145

URL : https://hal.archives-ouvertes.fr/hal-00539410

T. Miura, C. Braendle, A. Shingleton, G. Sisk, S. Kambhampati et al., A comparison of parthenogenetic and sexual embryogenesis of the pea aphid acyrthosiphon pisum (hemiptera: Aphidoidea), Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, vol.295, issue.1, pp.59-81, 2003.

F. Moretti, R. Thermann, and M. W. Et-hentze, Mechanism of translational regulation by miR-2 from sites in the 5' untranslated region or the open reading frame, RNA, vol.16, issue.12, pp.162493-2502, 2010.
DOI : 10.1261/rna.2384610

F. Murtagh, A Survey of Recent Advances in Hierarchical Clustering Algorithms, The Computer Journal, vol.26, issue.4, pp.354-359, 1983.
DOI : 10.1093/comjnl/26.4.354

F. Murtagh, Complexities of hierarchic clustering algorithms: State of the art, Computational Statistics Quarterly, vol.1, issue.2, pp.101-113, 1984.

J. Nam, J. Kim, S. Kim, and B. Et-zhang, ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs, Nucleic Acids Research, vol.34, issue.Web Server, pp.34-455, 2006.
DOI : 10.1093/nar/gkl321

J. Nam, K. Shin, J. Han, Y. Lee, V. N. Kim et al., Human microRNA prediction through a probabilistic co-learning model of sequence and structure, Nucleic Acids Research, vol.33, issue.11, pp.333570-3581, 2005.
DOI : 10.1093/nar/gki668

C. Napoli, C. Lemieux, and R. Et-jorgensen, Introduction of a Chimeric Chalcone Synthase Gene into Petunia Results in Reversible Co-Suppression of Homologous Genes in trans, THE PLANT CELL ONLINE, vol.2, issue.4, pp.279-289, 1990.
DOI : 10.1105/tpc.2.4.279

S. B. Bibliography-needleman and C. D. Et-wunsch, A general method applicable to the search for similarities in the amino acid sequence of two proteins, Journal of Molecular Biology, vol.48, issue.3, pp.443-453, 1970.
DOI : 10.1016/0022-2836(70)90057-4

J. W. Nelson, F. H. Martin, and I. Et-tinoco, DNA and RNA oligomer thermodynamics: The effect of mismatched bases on double-helix stability, Biopolymers, vol.11, issue.12, pp.202509-2531, 1981.
DOI : 10.1002/bip.1981.360201204

M. F. Nicolás, F. G. Barcellos, N. Hess, P. Et-hungria, and M. , ABC transporters in Mycoplasma hyopneumoniae and Mycoplasma synoviae: insights into evolution and pathogenicity, Genetics and Molecular Biology, vol.30, issue.1, pp.202-211, 2007.
DOI : 10.1590/S1415-47572007000200006

R. Nussinov, G. Pieczenik, J. R. Griggs, and D. J. Et-kleitman, Algorithms for Loop Matchings, SIAM Journal on Applied Mathematics, vol.35, issue.1, pp.68-82, 1978.
DOI : 10.1137/0135006

Y. Ogawa, B. K. Sun, and J. T. Et-lee, Intersection of the RNA Interference and X-Inactivation Pathways, Science, vol.320, issue.5881, pp.3201336-1341, 2008.
DOI : 10.1126/science.1157676

K. Okamura, J. W. Hagen, H. Duan, D. M. Tyler, and E. C. Lai, The Mirtron Pathway Generates microRNA-Class Regulatory RNAs in Drosophila, Cell, vol.130, issue.1, pp.89-100, 2007.
DOI : 10.1016/j.cell.2007.06.028

K. Okamura, M. D. Phillips, D. M. Tyler, H. Duan, Y. Chou et al., The regulatory activity of microRNA* species has substantial influence on microRNA and 3??? UTR evolution, Nature Structural & Molecular Biology, vol.15, issue.4, pp.354-363, 2008.
DOI : 10.1101/gad.9.23.2936

U. A. Ørom, F. C. Nielsen, and A. H. Et-lund, MicroRNA-10a Binds the 5???UTR of Ribosomal Protein mRNAs and Enhances Their Translation, Molecular Cell, vol.30, issue.4, pp.460-471, 2008.
DOI : 10.1016/j.molcel.2008.05.001

A. Oulas, A. Boutla, K. Gkirtzou, M. Reczko, K. Kalantidis et al., Prediction of novel microRNA genes in cancer-associated genomic regions?Äîa combined computational and experimental approach, Nucleic acids research, issue.10, pp.373276-3287, 2009.

A. E. Pasquinelli, MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship, Nature Reviews Genetics, vol.20, issue.4, pp.271-282, 2012.
DOI : 10.1038/nrg3162

C. P. Petersen, J. G. Doench, A. Grishok, and P. A. Sharp, 19 the biology of short RNAs. Cold Spring Harbor Monograph Archive, pp.535-565, 2006.

A. J. Pratt and I. J. Et-macrae, The RNA-induced Silencing Complex: A Versatile Gene-silencing Machine, Journal of Biological Chemistry, vol.284, issue.27, pp.17897-17901, 2009.
DOI : 10.1074/jbc.R900012200

C. C. Pritchard, H. H. Cheng, and M. Et-tewari, MicroRNA profiling: approaches and considerations, Nature Reviews Genetics, vol.667, issue.5, pp.358-369, 2012.
DOI : 10.1038/nrg3198

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517822

W. Qin, Y. Shi, B. Zhao, C. Yao, L. Jin et al., miR-24 Regulates Apoptosis by Targeting the Open Reading Frame (ORF) Region of FAF1 in Cancer Cells, PLoS ONE, vol.5, issue.2, p.9429, 2010.
DOI : 10.1371/journal.pone.0009429.g005

A. Rabatel, G. Febvay, K. Gaget, G. Duport, P. Baa-puyoulet et al., Tyrosine pathway regulation is host-mediated in the pea aphid symbiosis during late embryonic and early larval development, BMC Genomics, vol.14, issue.1, p.235, 2013.
DOI : 10.1111/j.1432-1033.1996.00779.x

URL : https://hal.archives-ouvertes.fr/hal-00824527

S. Razin, The Genus Mycoplasma and Related Genera (Class Mollicutes), The Prokaryotes, pp.836-904, 2006.
DOI : 10.1007/0-387-30744-3_29

B. J. Reinhart, F. J. Slack, M. Basson, A. E. Pasquinelli, J. C. Bettinger et al., The 21-nucleotide let-7 RNA regulates developmental timing in caenorhabditis elegans, nature, issue.6772, pp.403901-906, 2000.

A. S. Richter and R. Et-backofen, Accessibility and conservation: General features of bacterial small RNA???mRNA interactions?, RNA Biology, vol.6, issue.7, pp.954-965, 2012.
DOI : 10.4161/rna.20294

E. Rivas and S. R. Et-eddy, A dynamic programming algorithm for RNA structure prediction including pseudoknots11Edited by I. Tinoco, Journal of Molecular Biology, vol.285, issue.5, pp.2053-2068, 1999.
DOI : 10.1006/jmbi.1998.2436

G. Rizk and D. Et-lavenier, Gpu accelerated rna folding algorithm, Computational Science?ICCS 2009, pp.1004-1013, 2009.
DOI : 10.1016/b978-0-12-384988-5.00014-0

URL : https://hal.archives-ouvertes.fr/hal-00637827

K. Rogers and X. Et-chen, Biogenesis, turnover, and mode of action of plant microR- NAs. The Plant Cell Online, pp.2383-2399, 2013.

J. Ruan, G. D. Stormo, and W. Et-zhang, An Iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots, Bioinformatics, vol.20, issue.1, pp.58-66, 2004.
DOI : 10.1093/bioinformatics/btg373

M. Sagot, Spelling approximate repeated or common motifs using a suffix tree, LATIN'98: Theoretical Informatics, pp.374-390, 1998.
DOI : 10.1007/BFb0054337

URL : https://hal.archives-ouvertes.fr/hal-00428511

M. S. Scott, Diversity, Overlap, and Relationships in the Small RNA Landscape, Regulatory RNAs, pp.23-48, 2012.
DOI : 10.1007/978-3-662-45801-3_2

G. L. Sen and H. M. Et-blau, A brief history of RNAi: the silence of the genes, The FASEB Journal, vol.20, issue.9, pp.1293-1299, 2006.
DOI : 10.1096/fj.06-6014rev

H. Siomi and M. C. Et-siomi, Posttranscriptional Regulation of MicroRNA Biogenesis in Animals, Molecular Cell, vol.38, issue.3, pp.323-332, 2010.
DOI : 10.1016/j.molcel.2010.03.013

M. C. Siomi, K. Sato, D. Pezic, and A. A. Et-aravin, PIWI-interacting small RNAs: the vanguard of genome defence, Nature Reviews Molecular Cell Biology, vol.103, issue.4, pp.246-258, 2011.
DOI : 10.1038/nrm3089

F. M. Siqueira, C. E. Thompson, V. G. Virginio, T. Gonchoroski, L. Reolon et al., New insights on the biology of swine respiratory tract mycoplasmas from a comparative genome analysis, BMC Genomics, vol.14, issue.1, p.175, 2013.
DOI : 10.1093/sysbio/syp103

P. H. Sneath and R. R. Sokal, Numerical taxonomy. The principles and practice of numerical classification, 1973.

R. Sokal and C. Et-michener, A statistical method for evaluating systematic relationships . i'niv. kansas sci. bull, Primary productivity and ecological factors in Lake Maggiore, pp.1409-1438, 1958.

P. Steffen, R. Giegerich, and M. Et-giraud, GPU Parallelization of Algebraic Dynamic Programming, Parallel Processing and Applied Mathematics, pp.290-299, 2010.
DOI : 10.1007/978-3-642-14403-5_31

URL : https://hal.archives-ouvertes.fr/inria-00438219

G. Storz, J. Vogel, and K. M. Et-wassarman, Regulation by Small RNAs in Bacteria: Expanding Frontiers, Molecular Cell, vol.43, issue.6, pp.880-891, 2011.
DOI : 10.1016/j.molcel.2011.08.022

W. Sun, J. Li, Y. Huang, H. Shyy, J. Y. Et-chien et al., microRNA: A Master Regulator of Cellular Processes for Bioengineering Systems, Annual Review of Biomedical Engineering, vol.12, issue.1, pp.1-27, 2010.
DOI : 10.1146/annurev-bioeng-070909-105314

R. J. Taft, E. A. Glazov, T. Lassmann, Y. Hayashizaki, P. Carninci et al., Small RNAs derived from snoRNAs, RNA, vol.15, issue.7, pp.151233-1240, 2009.
DOI : 10.1261/rna.1528909

S. Tempel and F. Et-tahi, A fast ab-initio method for predicting miRNA precursors in genomes, Nucleic Acids Research, vol.40, issue.11, pp.80-80, 2012.
DOI : 10.1093/nar/gks146

URL : https://hal.archives-ouvertes.fr/hal-00667075

M. Thomas, J. Lieberman, and A. Et-lal, Desperately seeking microRNA targets, Nature Structural & Molecular Biology, vol.1789, issue.10, pp.1169-1174, 2010.
DOI : 10.1038/nsmb.1552

D. W. Thomson, C. P. Bracken, and G. J. Et-goodall, Experimental strategies for microRNA target identification, Nucleic Acids Research, vol.39, issue.16, pp.6845-6853, 2011.
DOI : 10.1093/nar/gkr330

D. H. Turner and D. H. Et-mathews, NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure, Nucleic Acids Research, vol.38, issue.Database, pp.280-282, 2010.
DOI : 10.1093/nar/gkp892

A. R. Van-der-krol, L. A. Mur, M. Beld, J. Mol, and A. R. Et-stuitje, Flavonoid genes in petunia: addition of a limited number of gene copies may lead to a suppression of gene expression. The Plant Cell Online, pp.291-299, 1990.

A. Vanet, L. Marsan, A. Labigne, and M. Sagot, Inferring regulatory elements from a whole genome. an analysis of Helicobacter pylori??80 family of promoter signals, Journal of Molecular Biology, vol.297, issue.2, pp.335-353, 2000.
DOI : 10.1006/jmbi.2000.3576

URL : https://hal.archives-ouvertes.fr/hal-00427110

A. Vanet, L. Marsan, and M. Et-sagot, Promoter sequences and algorithmical methods for identifying them, Research in Microbiology, vol.150, issue.9-10, pp.779-799, 1999.
DOI : 10.1016/S0923-2508(99)00115-1

URL : https://hal.archives-ouvertes.fr/hal-00428461

X. Wang, J. Zhang, F. Li, J. Gu, T. He et al., MicroRNA identification based on sequence and structure alignment, Bioinformatics, vol.21, issue.18, pp.3610-3614, 2005.
DOI : 10.1093/bioinformatics/bti562

B. Wightman, I. Ha, and G. Et-ruvkun, Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans, Cell, vol.75, issue.5, pp.75855-862, 1993.
DOI : 10.1016/0092-8674(93)90530-4

A. L. Williams and I. Et-tinoco, A dynamic programming algorithm for finding alternative RNA secondary structure, Nucleic Acids Research, vol.14, issue.1, pp.299-315, 1986.
DOI : 10.1093/nar/14.1.299

J. Winter and S. Et-diederichs, MicroRNA Biogenesis and Cancer, MicroRNA and Cancer, pp.3-22, 2011.
DOI : 10.1007/978-1-60761-863-8_1

J. Winter, S. Link, D. Witzigmann, C. Hildenbrand, C. Previti et al., Loop-miRs: active microRNAs generated from single-stranded loop regions, Nucleic Acids Research, vol.41, issue.10, pp.415503-5512, 2013.
DOI : 10.1093/nar/gkt251

URL : http://doi.org/10.1093/nar/gkt251

T. Witkos, E. Koscianska, and W. Et-krzyzosiak, Practical Aspects of microRNA Target Prediction, Current Molecular Medicine, vol.11, issue.2, p.93, 2011.
DOI : 10.2174/156652411794859250

Y. Wu, B. Wei, H. Liu, T. Li, and S. Et-rayner, MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences, BMC Bioinformatics, vol.12, issue.1, p.107, 2011.
DOI : 10.1016/j.cell.2006.10.040

S. Wuchty, W. Fontana, I. L. Hofacker, and P. Schuster, Complete suboptimal folding of RNA and the stability of secondary structures, Biopolymers, vol.4, issue.2, pp.145-165, 1999.
DOI : 10.1002/(SICI)1097-0282(199902)49:2<145::AID-BIP4>3.0.CO;2-G

J. Xu, C. Li, Y. Li, J. Lv, Y. Ma et al., MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features, Nucleic Acids Research, vol.39, issue.3, pp.825-836, 2011.
DOI : 10.1093/nar/gkq832

URL : http://doi.org/10.1093/nar/gkq832

P. Xu, S. Y. Vernooy, M. Guo, and B. A. Et-hay, The Drosophila MicroRNA Mir-14 Suppresses Cell Death and Is Required for Normal Fat Metabolism, Current Biology, vol.13, issue.9, pp.790-795, 2003.
DOI : 10.1016/S0960-9822(03)00250-1

Y. Xu, X. Zhou, and W. Et-zhang, MicroRNA prediction with a novel ranking algorithm based on random walks, Bioinformatics, vol.24, issue.13, pp.24-50, 2008.
DOI : 10.1093/bioinformatics/btn175

C. Xue, F. Li, T. He, G. Liu, Y. Li et al., Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine, BMC Bioinformatics, vol.6, issue.1, p.310, 2005.
DOI : 10.1186/1471-2105-6-310

J. Yang, M. D. Phillips, D. Betel, P. Mu, A. Ventura et al., Widespread regulatory activity of vertebrate microRNA* species, RNA, vol.17, issue.2, pp.312-326, 2011.
DOI : 10.1261/rna.2537911

J. Q. Yin, R. C. Zhao, and K. V. Morris, Profiling microRNA expression with microarrays, Trends in Biotechnology, vol.26, issue.2, pp.70-76, 2008.
DOI : 10.1016/j.tibtech.2007.11.007

P. D. Zamore and B. Haley, Ribo-gnome: The Big World of Small RNAs, Science, vol.309, issue.5740, pp.1519-1524, 2005.
DOI : 10.1126/science.1111444

J. H. Zar, Biostatistical analysis, 1999.

Y. Zhang, Y. Yang, H. Zhang, X. Jiang, B. Xu et al., Prediction of novel pre-microRNAs with high accuracy through boosting and SVM, Bioinformatics, vol.27, issue.10, pp.271436-1437, 2011.
DOI : 10.1093/bioinformatics/btr148

Y. Zheng and W. Et-zhang, ANIMAL MICRORNA TARGET PREDICTION USING DIVERSE SEQUENCE-SPECIFIC DETERMINANTS, Journal of Bioinformatics and Computational Biology, vol.08, issue.04, pp.763-788, 2010.
DOI : 10.1142/S0219720010004896

M. Zuker, D. H. Mathews, and D. H. Turner, Algorithms and Thermodynamics for RNA Secondary Structure Prediction: A Practical Guide, RNA biochemistry and biotechnology, pp.11-43, 1999.
DOI : 10.1007/978-94-011-4485-8_2

M. Zuker and P. Et-stiegler, Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information, Nucleic Acids Research, vol.9, issue.1, pp.133-148, 1981.
DOI : 10.1093/nar/9.1.133