We are interested in understanding functional aspects of gene expression by combining a wide variety of high-throughput molecular techniques, including transcriptomics, epigenomics, proteomics, metabolomics, metagenomics and single-cell data, both for model and non-model species. Our lab develops statistical methods and user-friendly software tools to analyze these multi-omics data. Our most current interest is the application of long reads sequencing technologies for transcriptome analysis and the integration of multi-omics data to model chromatin-metabolome regulation.
Analysis packages (R, Python, etc.), Animal genomics, Annotation tools, Applications of Computational Biology, Artificial Intelligence, Behavioral Ecology, Benchmarking, Bioinformatics education, Bioinformatics enabling techniques, Bioinformatics Software and Tools, Clinical data, Cloud Computing, Computational Methods, Computational Techniques, Data Analysis, Data Visualization, Deep Learning in Biology, Desktop application, Epigenomics, Expression Profiling, FAIRfication, Function prediction, Functional annotation, Functional genomics, Gene regulatory networks, Genomic Annotation, Genomics, Green Computing, High-performance Computing, Integrative Analysis, Large language models, Machine Learning in Biology, Metabolic modelling, Metabolome profiling, Model organisms, Multivariate statistics, Network Biology, Omics, Pathway analysis, Pathway analysis, Personal medicine, Regulatory biology, Resources, RNA Sequencing (RNA-seq), Sequence error correction, Single-cell (scRNA-seq), Single-cell omics, Spatial transcriptomics, Standards in Computational Biology, Transcriptomics, Web-service
Publications
Liu, T., & Conesa, A. (2025). Profiling the epigenome using long-read sequencing. Nature Genetics, 57(1), 27–41. https://doi.org/10.1038/s41588-024-02038-5
Monzó, C., Aguerralde-Martin, M., Martínez-Mira, C., Arzalluz-Luque, Á., Conesa, A., & Tarazona, S. (2025). MOSim: bulk and single-cell multilayer regulatory network simulator. Briefings in Bioinformatics, 26(2). https://doi.org/10.1093/bib/bbaf110
Monzó, C., Liu, T., & Conesa, A. (2025). Transcriptomics in the era of long-read sequencing. Nature Reviews Genetics. https://doi.org/10.1038/s41576-025-00828-z
Keil, N., Monzó, C., McIntyre, L., & Conesa, A. (2025). Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-reads. Genome Research, 35(4), 987–998. https://doi.org/10.1101/gr.280021.124
Monzó, C., Frankish, A., & Conesa, A. (2025). Notable challenges posed by long-read sequencing for the study of transcriptional diversity and genome annotation. Genome Research, 35(4), 583–592. https://doi.org/10.1101/gr.279865.124
Gaya Martín, V. Á., Tarazona, S., & Conesa, A. (2025). BENCHMARKING OF IMAGE ALIGNMENT METHODS FOR SPATIAL TRANSCRIPTOMICS DATA OF SPINAL CORD. <i>Zenodo</i>. https://doi.org/10.5281/ZENODO.15118981
Aguerralde-Martin, M., Clemente-Císcar, M., Conesa, A., & Tarazona, S. (2024). MORE interpretable multi-omic regulatory networks to characterize phenotypes. https://doi.org/10.1101/2024.01.25.577162
Nanni, A., Titus-McQuillan, J., Bankole, K. S., Pardo-Palacios, F., Signor, S., Vlaho, S., Moskalenko, O., Morse, A. M., Rogers, R. L., Conesa, A., & McIntyre, L. M. (2024). Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD. Nucleic Acids Research, 52(5), e28–e28. https://doi.org/10.1093/nar/gkae056
Srivastava, P., Benegas Coll, M., Götz, S., Nueda, M. J., & Conesa, A. (2024). scMaSigPro: differential expression analysis along single-cell trajectories. Bioinformatics, 40(7). https://doi.org/10.1093/bioinformatics/btae443
Pop, M., Attwood, T. K., Blake, J. A., Bourne, P. E., Conesa, A., Gaasterland, T., Hunter, L., Kingsford, C., Kohlbacher, O., Lengauer, T., Markel, S., Moreau, Y., Noble, W. S., Orengo, C., Ouellette, B. F. F., Parida, L., Przulj, N., Przytycka, T. M., Ranganathan, S., … Warnow, T. (2024). Biological databases in the age of generative artificial intelligence. Bioinformatics Advances, 5(1). https://doi.org/10.1093/bioadv/vbaf044
Paniagua, A., Agustín-García, C., Pardo-Palacios, F. J., Brown, T., De Maria, M., Denslow, N. D., Mazzoni, C. J., & Conesa, A. (2024). Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq. Genome Research, 35(4), 1053–1064. https://doi.org/10.1101/gr.279864.124
Mestre-Tomás, J., Liu, T., Pardo-Palacios, F., & Conesa, A. (2023). SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark. https://doi.org/10.1101/2023.08.23.554392
Arzalluz-Luque, A., Salguero, P., Tarazona, S., & Conesa, A. (2022). acorde unravels functionally interpretable networks of isoform co-usage from single cell data. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-29497-w
Liu, T., Salguero, P., Petek, M., Martinez-Mira, C., Balzano-Nogueira, L., Ramšak, Ž., McIntyre, L., Gruden, K., Tarazona, S., & Conesa, A. (2022). PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases. Nucleic Acids Research, 50(W1), W551–W559. https://doi.org/10.1093/nar/gkac352
Nanni, A. V., Morse, A. M., Newman, J. R. B., Choquette, N. E., Wedow, J. M., Liu, Z., Leakey, A. D. B., Conesa, A., Ainsworth, E. A., & McIntyre, L. M. (2022). Variation in leaf transcriptome responses to elevated ozone corresponds with physiological sensitivity to ozone across maize inbred lines. Genetics, 221(4). https://doi.org/10.1093/genetics/iyac080
Ugidos, M., Nuño-Cabanes, C., Tarazona, S., Ferrer, A., Nielsen, L. K., Rodríguez-Navarro, S., Marín de Mas, I., & Conesa, A. (2022). MAMBA: a model-driven, constraint-based multiomic integration method. https://doi.org/10.1101/2022.10.09.511458
Pérez-Benavente, B., Fathinajafabadi, A., de la Fuente, L., Gandía, C., Martínez-Férriz, A., Pardo-Sánchez, J. M., Milián, L., Conesa, A., Romero, O. A., Carretero, J., Matthiesen, R., Jariel-Encontre, I., Piechaczyk, M., & Farràs, R. (2022). New roles for AP-1/JUNB in cell cycle control and tumorigenic cell invasion via regulation of cyclin E1 and TGF-β2. Genome Biology, 23(1). https://doi.org/10.1186/s13059-022-02800-0
Rubio, T., Felipo, V., Tarazona, S., Pastorelli, R., Escudero-García, D., Tosca, J., Urios, A., Conesa, A., & Montoliu, C. (2021). Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-020-80941-7
Tarazona, S., Carmona, H., Conesa, A., Llansola, M., & Felipo, V. (2021). A multi-omic study for uncovering molecular mechanisms associated with hyperammonemia-induced cerebellar function impairment in rats. Cell Biology and Toxicology, 37(1), 129–149. https://doi.org/10.1007/s10565-020-09572-y
Planell, N., Lagani, V., Sebastian-Leon, P., van der Kloet, F., Ewing, E., Karathanasis, N., Urdangarin, A., Arozarena, I., Jagodic, M., Tsamardinos, I., Tarazona, S., Conesa, A., Tegner, J., & Gomez-Cabrero, D. (2021). STATegra: Multi-Omics Data Integration – A Conceptual Scheme With a Bioinformatics Pipeline. Frontiers in Genetics, 12. https://doi.org/10.3389/fgene.2021.620453
Nanni, A. V., Morse, A. M., Newman, J. R. B., Choquette, N. E., Wedow, J. M., Liu, Z., Leakey, A. D. B., Conesa, A., Ainsworth, E. A., & McIntyre, L. M. (2021). Ozone sensitivity of diverse maize genotypes is associated with differences in gene regulation, not gene content. https://doi.org/10.1101/2021.05.06.442991
Arzalluz-Luque, A., Salguero, P., Tarazona, S., & Conesa, A. (2021). Acorde: unraveling functionally-interpretable networks of isoform co-usage from single cell data. https://doi.org/10.1101/2021.05.07.441841
Betegón‐Putze, I., Mercadal, J., Bosch, N., Planas‐Riverola, A., Marquès‐Bueno, M., Vilarrasa‐Blasi, J., Frigola, D., Burkart, R. C., Martínez, C., Conesa, A., Sozzani, R., Stahl, Y., Prat, S., Ibañes, M., & Caño‐Delgado, A. I. (2021). Precise transcriptional control of cellular quiescence by BRAVO/WOX5 complex in
Arabidopsis
roots. Molecular Systems Biology, 17(6). Portico. https://doi.org/10.15252/msb.20209864
Tarazona, S., Arzalluz-Luque, A., & Conesa, A. (2021). Undisclosed, unmet and neglected challenges in multi-omics studies. Nature Computational Science, 1(6), 395–402. https://doi.org/10.1038/s43588-021-00086-z
Turpín-Sevilla, M. del C., Pérez-Sanz, F., García-Solano, J., Sebastián-León, P., Trujillo-Santos, J., Carbonell, P., Estrada, E., Tuomisto, A., Herruzo, I., Fennell, L. J., Mäkinen, M. J., Rodríguez-Braun, E., Whitehall, V. L. J., Conesa, A., & Conesa-Zamora, P. (2021). Global Methylome Scores Correlate with Histological Subtypes of Colorectal Carcinoma and Show Different Associations with Common Clinical and Molecular Features. Cancers, 13(20), 5165. https://doi.org/10.3390/cancers13205165
Zamkovaya, T., Foster, J. S., de Crécy-Lagard, V., & Conesa, A. (2020). A network approach to elucidate and prioritize microbial dark matter in microbial communities. The ISME Journal, 15(1), 228–244. https://doi.org/10.1038/s41396-020-00777-x
Ugidos, M., Tarazona, S., Prats-Montalbán, J. M., Ferrer, A., & Conesa, A. (2020). MultiBaC: A strategy to remove batch effects between different omic data types. Statistical Methods in Medical Research, 29(10), 2851–2864. https://doi.org/10.1177/0962280220907365
Liu, T., Nogueira, L. B., Lleo, A., & Conesa, A. (2020). Transcriptional differences for COVID-19 Disease Map genes between males and females indicate a different basal immunophenotype relevant to the disease. https://doi.org/10.1101/2020.09.30.321059
Tarazona, S., Balzano-Nogueira, L., Gómez-Cabrero, D., Schmidt, A., Imhof, A., Hankemeier, T., Tegnér, J., Westerhuis, J. A., & Conesa, A. (2020). Harmonization of quality metrics and power calculation in multi-omic studies. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-16937-8
Gardner, C. L., da Silva, D. R., Pagliai, F. A., Pan, L., Padgett-Pagliai, K. A., Blaustein, R. A., Merli, M. L., Zhang, D., Pereira, C., Teplitski, M., Chaparro, J. X., Folimonova, S. Y., Conesa, A., Gezan, S., Lorca, G. L., & Gonzalez, C. F. (2020). Assessment of unconventional antimicrobial compounds for the control of ‘Candidatus Liberibacter asiaticus’, the causative agent of citrus greening disease. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-62246-x
Nuño-Cabanes, C., Ugidos, M., Tarazona, S., Martín-Expósito, M., Ferrer, A., Rodríguez-Navarro, S., & Conesa, A. (2020). A multi-omics dataset of heat-shock response in the yeast RNA binding protein Mip6. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0412-z
Casaní-Galdón, S., Pereira, C., & Conesa, A. (2020). Padhoc: a computational pipeline for pathway reconstruction on the fly. Bioinformatics, 36(Supplement_2), i795–i803. https://doi.org/10.1093/bioinformatics/btaa811
Ylla, G., Liu, T., & Conesa, A. (2020). MirCure: a tool for quality control, filter and curation of microRNAs of animals and plants. Bioinformatics, 36(Supplement_2), i618–i624. https://doi.org/10.1093/bioinformatics/btaa889
Brown, P., Tan, A.-C., El-Esawi, M. A., Liehr, T., Blanck, O., Gladue, D. P., Almeida, G. M. F., Cernava, T., Sorzano, C. O., Yeung, A. W. K., Engel, M. S., Chandrasekaran, A. R., Muth, T., Staege, M. S., Daulatabad, S. V., Widera, D., Zhang, J., Meule, A., … Zhou, Y. (2019). Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database, 2019. https://doi.org/10.1093/database/baz085
Ferreirós-Vidal, I., Carroll, T., Zhang, T., Lagani, V., Ramirez, R. N., Ing-Simmons, E., Gómez-Valadés, A. G., Cooper, L., Liang, Z., Papoutsoglou, G., Dharmalingam, G., Guo, Y., Tarazona, S., Fernandes, S. J., Noori, P., Silberberg, G., Fisher, A. G., Tsamardinos, I., Mortazavi, A., … Gomez-Cabrero, D. (2019). Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation. PLOS Biology, 17(4), e2006506. https://doi.org/10.1371/journal.pbio.2006506
García‐Solano, J., Turpin‐Sevilla, M. del C., García‐García, F., Carbonell‐Muñoz, R., Torres‐Moreno, D., Conesa, A., & Conesa‐Zamora, P. (2019). Differences in gene expression profiling and biomarkers between histological colorectal carcinoma subsets from the serrated pathway. Histopathology, 75(4), 496–507. Portico. https://doi.org/10.1111/his.13889
Tarazona, S., Bernabeu, E., Carmona, H., Gómez-Giménez, B., García-Planells, J., Leonards, P. E. G., Jung, S., Conesa, A., Felipo, V., & Llansola, M. (2019). A Multiomics Study To Unravel the Effects of Developmental Exposure to Endosulfan in Rats: Molecular Explanation for Sex-Dependent Effects. ACS Chemical Neuroscience, 10(10), 4264–4279. https://doi.org/10.1021/acschemneuro.9b00304
Sana, T. G., Lomas, R., Gimenez, M. R., Laubier, A., Soscia, C., Chauvet, C., Conesa, A., Voulhoux, R., Ize, B., & Bleves, S. (2019). Differential Modulation of Quorum Sensing Signaling through QslA in Pseudomonas aeruginosa Strains PAO1 and PA14. Journal of Bacteriology, 201(21). https://doi.org/10.1128/jb.00362-19
Jansen, C., Ramirez, R. N., El-Ali, N. C., Gomez-Cabrero, D., Tegner, J., Merkenschlager, M., Conesa, A., & Mortazavi, A. (2019). Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps. PLOS Computational Biology, 15(11), e1006555. https://doi.org/10.1371/journal.pcbi.1006555
Gomez-Cabrero, D., Tarazona, S., Ferreirós-Vidal, I., Ramirez, R. N., Company, C., Schmidt, A., Reijmers, T., Paul, V. von S., Marabita, F., Rodríguez-Ubreva, J., Garcia-Gomez, A., Carroll, T., Cooper, L., Liang, Z., Dharmalingam, G., van der Kloet, F., Harms, A. C., Balzano-Nogueira, L., Lagani, V., … Conesa, A. (2019). STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0202-7
Conesa, A., & Beck, S. (2019). Making multi-omics data accessible to researchers. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0258-4
Martín‐Expósito, M., Gas, M., Mohamad, N., Nuño‐Cabanes, C., Tejada‐Colón, A., Pascual‐García, P., de la Fuente, L., Chaves‐Arquero, B., Merran, J., Corden, J., Conesa, A., Pérez‐Cañadillas, J. M., Bravo, J., & Rodríguez‐Navarro, S. (2019). Mip6 binds directly to the Mex67 UBA domain to maintain low levels of Msn2/4 stress‐dependent mRNAs. EMBO Reports, 20(12). Portico. https://doi.org/10.15252/embr.201947964
Tarazona, S., Balzano-Nogueira, L., & Conesa, A. (2018). Multiomics Data Integration in Time Series Experiments. Data Analysis for Omic Sciences: Methods and Applications, 505–532. https://doi.org/10.1016/bs.coac.2018.06.005
Tardaguila, M., de la Fuente, L., Marti, C., Pereira, C., Pardo-Palacios, F. J., del Risco, H., Ferrell, M., Mellado, M., Macchietto, M., Verheggen, K., Edelmann, M., Ezkurdia, I., Vazquez, J., Tress, M., Mortazavi, A., Martens, L., Rodriguez-Navarro, S., Moreno-Manzano, V., & Conesa, A. (2018). SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Research, 28(3), 396–411. https://doi.org/10.1101/gr.222976.117
Hernández-de-Diego, R., Tarazona, S., Martínez-Mira, C., Balzano-Nogueira, L., Furió-Tarí, P., Pappas, G. J., & Conesa, A. (2018). PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data. https://doi.org/10.1101/281295
Delaye, L., Ruiz-Ruiz, S., Calderon, E., Tarazona, S., Conesa, A., & Moya, A. (2018). Evidence of the Red-Queen Hypothesis from Accelerated Rates of Evolution of Genes Involved in Biotic Interactions in Pneumocystis. Genome Biology and Evolution, 10(6), 1596–1606. https://doi.org/10.1093/gbe/evy116
Babilonia, J., Conesa, A., Casaburi, G., Pereira, C., Louyakis, A. S., Reid, R. P., & Foster, J. S. (2018). Comparative Metagenomics Provides Insight Into the Ecosystem Functioning of the Shark Bay Stromatolites, Western Australia. Frontiers in Microbiology, 9. https://doi.org/10.3389/fmicb.2018.01359
Colli-Dula, R. C., Fang, X., Moraga-Amador, D., Albornoz-Abud, N., Zamora-Bustillos, R., Conesa, A., Zapata-Perez, O., Moreno, D., & Hernandez-Nuñez, E. (2018). Transcriptome analysis reveals novel insights into the response of low-dose benzo(a)pyrene exposure in male tilapia. Aquatic Toxicology, 201, 162–173. https://doi.org/10.1016/j.aquatox.2018.06.005
Newman, J. R. B., Concannon, P., Tardaguila, M., Conesa, A., & McIntyre, L. M. (2018). Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data. G3 Genes|Genomes|Genetics, 8(9), 2923–2940. https://doi.org/10.1534/g3.118.200373
Monzó, C., Aguerralde-Martin, M., Martínez-Mira, C., Arzalluz-Luque, Á., Conesa, A., & Tarazona, S. (2018). MOSim: bulk and single-cell multi-layer regulatory network simulator. https://doi.org/10.1101/421834
Colli-Dula, R. C., Fang, X., Moraga-Amador, D., Albornoz-Abud, N., Zamora-Bustillos, R., Conesa, A., Zapata-Perez, O., Moreno, D., & Hernandez-Nuñez, E. (2018). Gene expression profile and molecular pathway datasets resulting from benzo(a)pyrene exposure in the liver and testis of adult tilapia. Data in Brief, 20, 1500–1509. https://doi.org/10.1016/j.dib.2018.08.206
Sánchez-Gaya, V., Casaní-Galdón, S., Ugidos, M., Kuang, Z., Mellor, J., Conesa, A., & Tarazona, S. (2018). Elucidating the Role of Chromatin State and Transcription Factors on the Regulation of the Yeast Metabolic Cycle: A Multi-Omic Integrative Approach. Frontiers in Genetics, 9. https://doi.org/10.3389/fgene.2018.00578
García-Solano, J., Turpin, M. C., Torres-Moreno, D., Huertas-López, F., Tuomisto, A., Mäkinen, M. J., Conesa, A., & Conesa-Zamora, P. (2018). Two histologically colorectal carcinomas subsets from the serrated pathway show different methylome signatures and diagnostic biomarkers. Clinical Epigenetics, 10(1). https://doi.org/10.1186/s13148-018-0571-3
Arroyo-Crespo, J. J., Armiñán, A., Charbonnier, D., Balzano-Nogueira, L., Huertas-López, F., Martí, C., Tarazona, S., Forteza, J., Conesa, A., & Vicent, M. J. (2018). Tumor microenvironment-targeted poly-L-glutamic acid-based combination conjugate for enhanced triple negative breast cancer treatment. Biomaterials, 186, 8–21. https://doi.org/10.1016/j.biomaterials.2018.09.023
Duscher, A. A., Conesa, A., Bishop, M., Vroom, M. M., Zubizarreta, S. D., & Foster, J. S. (2018). Transcriptional profiling of the mutualistic bacterium Vibrio fischeri and an hfq mutant under modeled microgravity. Npj Microgravity, 4(1). https://doi.org/10.1038/s41526-018-0060-1
García-Molinero, V., García-Martínez, J., Reja, R., Furió-Tarí, P., Antúnez, O., Vinayachandran, V., Conesa, A., Pugh, B. F., Pérez-Ortín, J. E., & Rodríguez-Navarro, S. (2018). The SAGA/TREX-2 subunit Sus1 binds widely to transcribed genes and affects mRNA turnover globally. Epigenetics & Chromatin, 11(1). https://doi.org/10.1186/s13072-018-0184-2
Arzalluz-Luque, Á., & Conesa, A. (2018). Single-cell RNAseq for the study of isoforms—how is that possible? Genome Biology, 19(1). https://doi.org/10.1186/s13059-018-1496-z
Fàbregas, N., Lozano-Elena, F., Blasco-Escámez, D., Tohge, T., Martínez-Andújar, C., Albacete, A., Osorio, S., Bustamante, M., Riechmann, J. L., Nomura, T., Yokota, T., Conesa, A., Alfocea, F. P., Fernie, A. R., & Caño-Delgado, A. I. (2018). Overexpression of the vascular brassinosteroid receptor BRL3 confers drought resistance without penalizing plant growth. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-06861-3
Tríbulo, P., Balzano‐Nogueira, L., Conesa, A., Siqueira, L. G., & Hansen, P. J. (2018). Changes in the uterine metabolome of the cow during the first 7 days after estrus. Molecular Reproduction and Development, 86(1), 75–87. Portico. https://doi.org/10.1002/mrd.23082
Ordóñez-Baquera, P. L., González-Rodríguez, E., Aguado-Santacruz, G. A., Rascón-Cruz, Q., Conesa, A., Moreno-Brito, V., Echavarria, R., & Dominguez-Viveros, J. (2017). Identification of miRNA from Bouteloua gracilis, a drought tolerant grass, by deep sequencing and their in silico analysis. Computational Biology and Chemistry, 66, 26–35. https://doi.org/10.1016/j.compbiolchem.2016.11.001
Ramirez, R. N., El-Ali, N. C., Mager, M. A., Wyman, D., Conesa, A., & Mortazavi, A. (2017). Dynamic Gene Regulatory Networks of Human Myeloid Differentiation. Cell Systems, 4(4), 416-429.e3. https://doi.org/10.1016/j.cels.2017.03.005
Merino, G. A., Conesa, A., & Fernández, E. A. (2017). A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies. https://doi.org/10.1101/156752
Gomez-Cabrero, D., Marabita, F., Tarazona, S., Cano, I., Roca, J., Conesa, A., Sabatier, P., & Tegnér, J. (2017). Guidelines for Developing Successful Short Advanced Courses in Systems Medicine and Systems Biology. Cell Systems, 5(3), 168–175. https://doi.org/10.1016/j.cels.2017.05.013
Hernández-de-Diego, R., de Villiers, E. P., Klingström, T., Gourlé, H., Conesa, A., & Bongcam-Rudloff, E. (2017). The eBioKit, a stand-alone educational platform for bioinformatics. PLOS Computational Biology, 13(9), e1005616. https://doi.org/10.1371/journal.pcbi.1005616
Newman, J. R. B., Conesa, A., Mika, M., New, F. N., Onengut-Gumuscu, S., Atkinson, M. A., Rich, S. S., McIntyre, L. M., & Concannon, P. (2017). Disease-specific biases in alternative splicing and tissue-specific dysregulation revealed by multitissue profiling of lymphocyte gene expression in type 1 diabetes. Genome Research, 27(11), 1807–1815. https://doi.org/10.1101/gr.217984.116
Käding, N., Kaufhold, I., Müller, C., Szaszák, M., Shima, K., Weinmaier, T., Lomas, R., Conesa, A., Schmitt-Kopplin, P., Rattei, T., & Rupp, J. (2017). Growth of Chlamydia pneumoniae Is Enhanced in Cells with Impaired Mitochondrial Function. Frontiers in Cellular and Infection Microbiology, 7. https://doi.org/10.3389/fcimb.2017.00499
Nueda, M. J., Martorell-Marugan, J., Martí, C., Tarazona, S., & Conesa, A. (2017). Identification and visualization of differential isoform expression in RNA-seq time series. Bioinformatics, 34(3), 524–526. https://doi.org/10.1093/bioinformatics/btx578
Zhu, Q., Li, X., Conesa, A., & Pereira, C. (2017). GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text. Bioinformatics, 34(9), 1547–1554. https://doi.org/10.1093/bioinformatics/btx815
Furió-Tarí, P., Tarazona, S., Gabaldón, T., Enright, A. J., & Conesa, A. (2016). spongeScan: A web for detecting microRNA binding elements in lncRNA sequences. Nucleic Acids Research, 44(W1), W176–W180. https://doi.org/10.1093/nar/gkw443
De Panis, D. N., Padró, J., Furió‐Tarí, P., Tarazona, S., Milla Carmona, P. S., Soto, I. M., Dopazo, H., Conesa, A., & Hasson, E. (2016). Transcriptome modulation during host shift is driven by secondary metabolites in desert<scp>D</scp>rosophila. Molecular Ecology, 25(18), 4534–4550. Portico. https://doi.org/10.1111/mec.13785
Furió-Tarí, P., Conesa, A., & Tarazona, S. (2016). RGmatch: matching genomic regions to proximal genes in omics data integration. BMC Bioinformatics, 17(S15). https://doi.org/10.1186/s12859-016-1293-1
van der Kloet, F. M., Sebastián-León, P., Conesa, A., Smilde, A. K., & Westerhuis, J. A. (2016). Separating common from distinctive variation. BMC Bioinformatics, 17(S5). https://doi.org/10.1186/s12859-016-1037-2
Auffray, C., Balling, R., Barroso, I., Bencze, L., Benson, M., Bergeron, J., Bernal-Delgado, E., Blomberg, N., Bock, C., Conesa, A., Del Signore, S., Delogne, C., Devilee, P., Di Meglio, A., Eijkemans, M., Flicek, P., Graf, N., Grimm, V., Guchelaar, H.-J., … Zanetti, G. (2016). Making sense of big data in health research: Towards an EU action plan. Genome Medicine, 8(1). https://doi.org/10.1186/s13073-016-0323-y
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., & Mortazavi, A. (2016). A survey of best practices for RNA-seq data analysis. Genome Biology, 17(1). https://doi.org/10.1186/s13059-016-0881-8
Yáñez, Y., Grau, E., Rodríguez-Cortez, V. C., Hervás, D., Vidal, E., Noguera, R., Hernández, M., Segura, V., Cañete, A., Conesa, A., de Mora, J. F., & Castel, V. (2015). Two independent epigenetic biomarkers predict survival in neuroblastoma. Clinical Epigenetics, 7(1). https://doi.org/10.1186/s13148-015-0054-8
Irmer, H., Tarazona, S., Sasse, C., Olbermann, P., Loeffler, J., Krappmann, S., Conesa, A., & Braus, G. H. (2015). RNAseq analysis of Aspergillus fumigatus in blood reveals a just wait and see resting stage behavior. BMC Genomics, 16(1). https://doi.org/10.1186/s12864-015-1853-1
Conesa-Zamora, P., García-Solano, J., Turpin, M. del C., Sebastián-León, P., Torres-Moreno, D., Estrada, E., Tuomisto, A., Wilce, J., Mäkinen, M. J., Pérez-Guillermo, M., & Conesa, A. (2015). Methylome profiling reveals functions and genes which are differentially methylated in serrated compared to conventional colorectal carcinoma. Clinical Epigenetics, 7(1). https://doi.org/10.1186/s13148-015-0128-7
de la Fuente, L., Conesa, A., Lloret, A., Badenes, M. L., & Ríos, G. (2015). Genome-wide changes in histone H3 lysine 27 trimethylation associated with bud dormancy release in peach. Tree Genetics & Genomes, 11(3). https://doi.org/10.1007/s11295-015-0869-7
Atwood, T. K., Bongcam-Rudloff, E., Brazas, M. E., Corpas, M., Gaudet, P., Lewitter, F., Mulder, N., Palagi, P. M., Schneider, M. V., & van Gelder, C. W. G. (2015). GOBLET: The Global Organisation for Bioinformatics Learning, Education and Training. PLOS Computational Biology, 11(4), e1004143. https://doi.org/10.1371/journal.pcbi.1004143
Morin-Adeline, V., Mueller, K., Conesa, A., & Šlapeta, J. (2015). Comparative RNA-seq analysis of the Tritrichomonas foetus PIG30/1 isolate from pigs reveals close association with Tritrichomonas foetus BP-4 isolate ‘bovine genotype.’ Veterinary Parasitology, 212(3–4), 111–117. https://doi.org/10.1016/j.vetpar.2015.08.012
Ewing, A. D., Houlahan, K. E., Hu, Y., Ellrott, K., Caloian, C., Yamaguchi, T. N., Bare, J. C., P’ng, C., Waggott, D., Sabelnykova, V. Y., Kellen, M. R., Norman, T. C., Haussler, D., Friend, S. H., Stolovitzky, G., Margolin, A. A., Stuart, J. M., & Boutros, P. C. (2015). Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nature Methods, 12(7), 623–630. https://doi.org/10.1038/nmeth.3407
Okonechnikov, K., Conesa, A., & García-Alcalde, F. (2015). Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics, 32(2), 292–294. https://doi.org/10.1093/bioinformatics/btv566
Conesa, A., & Mortazavi, A. (2014). The common ground of genomics and systems biology. BMC Systems Biology, 8(Suppl 2), S1. https://doi.org/10.1186/1752-0509-8-s2-s1
Larriba, E., Jaime, M. D. L. A., Carbonell-Caballero, J., Conesa, A., Dopazo, J., Nislow, C., Martín-Nieto, J., & Lopez-Llorca, L. V. (2014). Sequencing and functional analysis of the genome of a nematode egg-parasitic fungus, Pochonia chlamydosporia. Fungal Genetics and Biology, 65, 69–80. https://doi.org/10.1016/j.fgb.2014.02.002
Hernández-de-Diego, R., Boix-Chova, N., Gómez-Cabrero, D., Tegner, J., Abugessaisa, I., & Conesa, A. (2014). STATegra EMS: an Experiment Management System for complex next-generation omics experiments. BMC Systems Biology, 8(S2). https://doi.org/10.1186/1752-0509-8-s2-s9
Almouzni, G., Altucci, L., Amati, B., Ashley, N., Baulcombe, D., Beaujean, N., Bock, C., Bongcam-Rudloff, E., Bousquet, J., Braun, S., Paillerets, B. B., Bussemakers, M., Clarke, L., Conesa, A., Estivill, X., Fazeli, A., Grgurević, N., Gut, I., Heijmans, B. T., … Widschwendter, M. (2014). Relationship between genome and epigenome - challenges and requirements for future research. BMC Genomics, 15(1). https://doi.org/10.1186/1471-2164-15-487
Ponzoni, I., Nueda, M. J., Tarazona, S., Götz, S., Montaner, D., Dussaut, J. S., Dopazo, J., & Conesa, A. (2014). Pathway network inference from gene expression data. BMC Systems Biology, 8(S2). https://doi.org/10.1186/1752-0509-8-s2-s7
Nueda, M. J., Tarazona, S., & Conesa, A. (2014). Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics, 30(18), 2598–2602. https://doi.org/10.1093/bioinformatics/btu333
Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., Ballestar, E., Bongcam-Rudloff, E., Conesa, A., & Tegnér, J. (2014). Data integration in the era of omics: current and future challenges. BMC Systems Biology, 8(Suppl 2), I1. https://doi.org/10.1186/1752-0509-8-s2-i1
Morin-Adeline, V., Lomas, R., O’Meally, D., Stack, C., Conesa, A., & Šlapeta, J. (2014). Comparative transcriptomics reveals striking similarities between the bovine and feline isolates of Tritrichomonas foetus: consequences for in silico drug-target identification. BMC Genomics, 15(1). https://doi.org/10.1186/1471-2164-15-955
(2014). A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature Biotechnology, 32(9), 903–914. https://doi.org/10.1038/nbt.2957
Araos, P., Pedraz, M., Serrano, A., Lucena, M., Barrios, V., García‐Marchena, N., Campos‐Cloute, R., Ruiz, J. J., Romero, P., Suárez, J., Baixeras, E., de la Torre, R., Montesinos, J., Guerri, C., Rodríguez‐Arias, M., Miñarro, J., Martínez‐Riera, R., Torrens, M., Chowen, J. A., … Rodríguez de Fonseca, F. (2014). Plasma profile of pro‐inflammatory cytokines and chemokines in cocaine users under outpatient treatment: influence of cocaine symptom severity and psychiatric co‐morbidity. Addiction Biology, 20(4), 756–772. Portico. https://doi.org/10.1111/adb.12156
Galan, A., Diaz-Gimeno, P., Poo, M. E., Valbuena, D., Sanchez, E., Ruiz, V., Dopazo, J., Montaner, D., Conesa, A., & Simon, C. (2013). Defining the Genomic Signature of Totipotency and Pluripotency during Early Human Development. PLoS ONE, 8(4), e62135. https://doi.org/10.1371/journal.pone.0062135
Tarazona, S., Prado-López, S., Dopazo, J., Ferrer, A., & Conesa, A. (2012). Variable selection for multifactorial genomic data. Chemometrics and Intelligent Laboratory Systems, 110(1), 113–122. https://doi.org/10.1016/j.chemolab.2011.10.012
Carcel-Trullols, J., Aguilar-Gallardo, C., Garcia-Alcalde, F., Pardo-Cea, M. A., Dopazo, J., Conesa, A., & Simón, C. (2012). Transdifferentiation of MALME-3M and MCF-7 Cells toward Adipocyte-like Cells is Dependent on Clathrin-mediated Endocytosis. SpringerPlus, 1(1). https://doi.org/10.1186/2193-1801-1-44
Oppert, B., Dowd, S. E., Bouffard, P., Li, L., Conesa, A., Lorenzen, M. D., Toutges, M., Marshall, J., Huestis, D. L., Fabrick, J., Oppert, C., & Jurat-Fuentes, J. L. (2012). Transcriptome Profiling of the Intoxication Response of Tenebrio molitor Larvae to Bacillus thuringiensis Cry3Aa Protoxin. PLoS ONE, 7(4), e34624. https://doi.org/10.1371/journal.pone.0034624
García-Alcalde, F., Okonechnikov, K., Carbonell, J., Cruz, L. M., Götz, S., Tarazona, S., Dopazo, J., Meyer, T. F., & Conesa, A. (2012). Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics, 28(20), 2678–2679. https://doi.org/10.1093/bioinformatics/bts503
RIZZA, S., CONESA, A., JUAREZ, J., CATARA, A., NAVARRO, L., DURAN‐VILA, N., & ANCILLO, G. (2012). Microarray analysis of Etrog citron (Citrus medica L.) reveals changes in chloroplast, cell wall, peroxidase and symporter activities in response to viroid infection. Molecular Plant Pathology, 13(8), 852–864. Portico. https://doi.org/10.1111/j.1364-3703.2012.00794.x
Jaime, M. D., Lopez-Llorca, L. V., Conesa, A., Lee, A. Y., Proctor, M., Heisler, L. E., Gebbia, M., Giaever, G., Westwood, J. T., & Nislow, C. (2012). Identification of yeast genes that confer resistance to chitosan oligosaccharide (COS) using chemogenomics. BMC Genomics, 13(1). https://doi.org/10.1186/1471-2164-13-267
Agustí, J., Gimeno, J., Merelo, P., Serrano, R., Cercós, M., Conesa, A., Talón, M., & Tadeo, F. R. (2012). Early gene expression events in the laminar abscission zone of abscission-promoted citrus leaves after a cycle of water stress/rehydration: involvement of CitbHLH1. Journal of Experimental Botany, 63(17), 6079–6091. https://doi.org/10.1093/jxb/ers270
Fernandez, P., Soria, M., Blesa, D., DiRienzo, J., Moschen, S., Rivarola, M., Clavijo, B. J., Gonzalez, S., Peluffo, L., Príncipi, D., Dosio, G., Aguirrezabal, L., García-García, F., Conesa, A., Hopp, E., Dopazo, J., Heinz, R. A., & Paniego, N. (2012). Development, Characterization and Experimental Validation of a Cultivated Sunflower (Helianthus annuus L.) Gene Expression Oligonucleotide Microarray. PLoS ONE, 7(10), e45899. https://doi.org/10.1371/journal.pone.0045899
Conesa‐Zamora, P., García‐Solano, J., García‐García, F., Turpin, M. del C., Trujillo‐Santos, J., Torres‐Moreno, D., Oviedo‐Ramírez, I., Carbonell‐Muñoz, R., Muñoz‐Delgado, E., Rodriguez‐Braun, E., Conesa, A., & Pérez‐Guillermo, M. (2012). Expression profiling shows differential molecular pathways and provides potential new diagnostic biomarkers for colorectal serrated adenocarcinoma. International Journal of Cancer, 132(2), 297–307. Portico. https://doi.org/10.1002/ijc.27674
Durban, J., Juárez, P., Angulo, Y., Lomonte, B., Flores-Diaz, M., Alape-Girón, A., Sasa, M., Sanz, L., Gutiérrez, J. M., Dopazo, J., Conesa, A., & Calvete, J. J. (2011). Profiling the venom gland transcriptomes of Costa Rican snakes by 454 pyrosequencing. BMC Genomics, 12(1). https://doi.org/10.1186/1471-2164-12-259
Garrido-Gomez, T., Dominguez, F., Lopez, J. A., Camafeita, E., Quiñonero, A., Martinez-Conejero, J. A., Pellicer, A., Conesa, A., & Simón, C. (2011). Modeling Human Endometrial Decidualization from the Interaction between Proteome and Secretome. The Journal of Clinical Endocrinology & Metabolism, 96(3), 706–716. https://doi.org/10.1210/jc.2010-1825
Yung, S., Ledran, M., Moreno-Gimeno, I., Conesa, A., Montaner, D., Dopazo, J., Dimmick, I., Slater, N. J., Marenah, L., Real, P. J., Paraskevopoulou, I., Bisbal, V., Burks, D., Santibanez-Koref, M., Moreno, R., Mountford, J., Menendez, P., Armstrong, L., & Lako, M. (2011). Large-scale transcriptional profiling and functional assays reveal important roles for Rho-GTPase signalling and SCL during haematopoietic differentiation of human embryonic stem cells. Human Molecular Genetics, 20(24), 4932–4946. https://doi.org/10.1093/hmg/ddr431
Khalaf, A. A., Gmitter, F. G., Conesa, A., Dopazo, J., & Moore, G. A. (2011). Fortunella margarita Transcriptional Reprogramming Triggered by Xanthomonas citri subsp. citri. BMC Plant Biology, 11(1). https://doi.org/10.1186/1471-2229-11-159
Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011). Differential expression in RNA-seq: A matter of depth. Genome Research, 21(12), 2213–2223. https://doi.org/10.1101/gr.124321.111
Götz, S., Arnold, R., Sebastián-León, P., Martín-Rodríguez, S., Tischler, P., Jehl, M.-A., Dopazo, J., Rattei, T., & Conesa, A. (2011). B2G-FAR, a species-centered GO annotation repository. Bioinformatics, 27(7), 919–924. https://doi.org/10.1093/bioinformatics/btr059
Pérez-Quintero, Á. L., Sablok, G., Tatarinova, T. V., Conesa, A., Kuo, J., & López, C. (2011). Mining of miRNAs and potential targets from gene oriented clusters of transcripts sequences of the anti-malarial plant, Artemisia annua. Biotechnology Letters, 34(4), 737–745. https://doi.org/10.1007/s10529-011-0808-0
Leida, C., Conesa, A., Llácer, G., Badenes, M. L., & Ríos, G. (2011). Histone modifications and expression of DAM6 gene in peach are modulated during bud dormancy release in a cultivar‐dependent manner. New Phytologist, 193(1), 67–80. Portico. https://doi.org/10.1111/j.1469-8137.2011.03863.x
Nueda, M. j., Ferrer, A., & Conesa, A. (2011). ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics, 13(3), 553–566. https://doi.org/10.1093/biostatistics/kxr042
Nueda, M. J., Carbonell, J., Medina, I., Dopazo, J., & Conesa, A. (2010). Serial Expression Analysis: a web tool for the analysis of serial gene expression data. Nucleic Acids Research, 38(suppl_2), W239–W245. https://doi.org/10.1093/nar/gkq488
Németh, A., Conesa, A., Santoyo-Lopez, J., Medina, I., Montaner, D., Péterfia, B., Solovei, I., Cremer, T., Dopazo, J., & Längst, G. (2010). Initial Genomics of the Human Nucleolus. PLoS Genetics, 6(3), e1000889. https://doi.org/10.1371/journal.pgen.1000889
Prado-Lopez, S., Conesa, A., Armiñán, A., Martínez-Losa, M., Escobedo-Lucea, C., Gandia, C., Tarazona, S., Melguizo, D., Blesa, D., Montaner, D., Sanz-González, S., Sepúlveda, P., Götz, S., O’Connor, J. E., Moreno, R., Dopazo, J., Burks, D. J., & Stojkovic, M. (2010). Hypoxia Promotes Efficient Differentiation of Human Embryonic Stem Cells to Functional Endothelium. Stem Cells, 28(3), 407–418. https://doi.org/10.1002/stem.295
Medina, I., Carbonell, J., Pulido, L., Madeira, S. C., Goetz, S., Conesa, A., T�rraga, J., Pascual-Montano, A., Nogales-Cadenas, R., Santoyo, J., Garc�a, F., Marb�, M., Montaner, D., & Dopazo, J. (2010). Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Research, 38(suppl_2), W210–W213. https://doi.org/10.1093/nar/gkq388
Conesa, A., Prats-Montalbán, J. M., Tarazona, S., Nueda, M. J., & Ferrer, A. (2010). A multiway approach to data integration in systems biology based on Tucker3 and N-PLS. Chemometrics and Intelligent Laboratory Systems, 104(1), 101–111. https://doi.org/10.1016/j.chemolab.2010.06.004
García-Alcalde, F., García-López, F., Dopazo, J., & Conesa, A. (2010). Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics, 27(1), 137–139. https://doi.org/10.1093/bioinformatics/btq594
Brumós, J., Colmenero-Flores, J. M., Conesa, A., Izquierdo, P., Sánchez, G., Iglesias, D. J., López-Climent, M. F., Gómez-Cadenas, A., & Talón, M. (2009). Membrane transporters and carbon metabolism implicated in chloride homeostasis differentiate salt stress responses in tolerant and sensitive Citrus rootstocks. Functional & Integrative Genomics, 9(3), 293–309. https://doi.org/10.1007/s10142-008-0107-6
Nueda, M. J., Sebastián, P., Tarazona, S., García-García, F., Dopazo, J., Ferrer, A., & Conesa, A. (2009). Functional assessment of time course microarray data. BMC Bioinformatics, 10(S6). https://doi.org/10.1186/1471-2105-10-s6-s9
Rattei, T., Tischler, P., Götz, S., Jehl, M.-A., Hoser, J., Arnold, R., Conesa, A., & Mewes, H.-W. (2009). SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters. Nucleic Acids Research, 38(suppl_1), D223–D226. https://doi.org/10.1093/nar/gkp949
Stierum, R., Conesa, A., Heijne, W., Ommen, B. van, Junker, K., Scott, M. P., Price, R. J., Meredith, C., Lake, B. G., & Groten, J. (2008). Transcriptome analysis provides new insights into liver changes induced in the rat upon dietary administration of the food additives butylated hydroxytoluene, curcumin, propyl gallate and thiabendazole. Food and Chemical Toxicology, 46(8), 2616–2628. https://doi.org/10.1016/j.fct.2008.04.019
Hoogerwerf, W. A., Sinha, M., Conesa, A., Luxon, B. A., Shahinian, V. B., Cornélissen, G., Halberg, F., Bostwick, J., Timm, J., & Cassone, V. M. (2008). Transcriptional Profiling of mRNA Expression in the Mouse Distal Colon. Gastroenterology, 135(6), 2019–2029. https://doi.org/10.1053/j.gastro.2008.08.048
Botton, A., Galla, G., Conesa, A., Bachem, C., Ramina, A., & Barcaccia, G. (2008). Large-scale Gene Ontology analysis of plant transcriptome-derived sequences retrieved by AFLP technology. BMC Genomics, 9(1). https://doi.org/10.1186/1471-2164-9-347
Gotz, S., Garcia-Gomez, J. M., Terol, J., Williams, T. D., Nagaraj, S. H., Nueda, M. J., Robles, M., Talon, M., Dopazo, J., & Conesa, A. (2008). High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Research, 36(10), 3420–3435. https://doi.org/10.1093/nar/gkn176
Tarraga, J., Medina, I., Carbonell, J., Huerta-Cepas, J., Minguez, P., Alloza, E., Al-Shahrour, F., Vegas-Azcarate, S., Goetz, S., Escobar, P., Garcia-Garcia, F., Conesa, A., Montaner, D., & Dopazo, J. (2008). GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Research, 36(Web Server), W308–W314. https://doi.org/10.1093/nar/gkn303
Conesa, A., Bro, R., García-García, F., Prats, J. M., Götz, S., Kjeldahl, K., Montaner, D., & Dopazo, J. (2008). Direct functional assessment of the composite phenotype through multivariate projection strategies. Genomics, 92(6), 373–383. https://doi.org/10.1016/j.ygeno.2008.05.015
Conesa, A., & Götz, S. (2008). Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics. International Journal of Plant Genomics, 2008, 1–12. https://doi.org/10.1155/2008/619832
Al-Shahrour, F., Carbonell, J., Minguez, P., Goetz, S., Conesa, A., Tarraga, J., Medina, I., Alloza, E., Montaner, D., & Dopazo, J. (2008). Babelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments. Nucleic Acids Research, 36(Web Server), W341–W346. https://doi.org/10.1093/nar/gkn318
Gandía, M., Conesa, A., Ancillo, G., Gadea, J., Forment, J., Pallás, V., Flores, R., Duran-Vila, N., Moreno, P., & Guerri, J. (2007). Transcriptional response of Citrus aurantifolia to infection by Citrus tristeza virus. Virology, 367(2), 298–306. https://doi.org/10.1016/j.virol.2007.05.025
Levin, A. M., de Vries, R. P., Conesa, A., de Bekker, C., Talon, M., Menke, H. H., van Peij, N. N. M. E., & Wösten, H. A. B. (2007). Spatial Differentiation in the Vegetative Mycelium of
Aspergillus niger. Eukaryotic Cell, 6(12), 2311–2322. https://doi.org/10.1128/ec.00244-07
Nueda, M. J., Conesa, A., Westerhuis, J. A., Hoefsloot, H. C. J., Smilde, A. K., Talón, M., & Ferrer, A. (2007). Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA. Bioinformatics, 23(14), 1792–1800. https://doi.org/10.1093/bioinformatics/btm251
Agustí, J., Conesa, A., Cercós, M., Talón, M., & Tadeo, F. R. (2007). Calcium signaling in water stress-induced leaf abscission in citrus plants. Advances in Plant Ethylene Research, 303–304. https://doi.org/10.1007/978-1-4020-6014-4_66
Terol, J., Conesa, A., Colmenero, J. M., Cercos, M., Tadeo, F., Agustí, J., Alós, E., Andres, F., Soler, G., Brumos, J., Iglesias, D. J., Götz, S., Legaz, F., Argout, X., Courtois, B., Ollitrault, P., Dossat, C., Wincker, P., Morillon, R., & Talon, M. (2007). Analysis of 13000 unique Citrus clusters associated with fruit quality, production and salinity tolerance. BMC Genomics, 8(1). https://doi.org/10.1186/1471-2164-8-31
Conesa, A., Nueda, M. J., Ferrer, A., & Talón, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096–1102. https://doi.org/10.1093/bioinformatics/btl056
Williams, T. D., Diab, A. M., George, S. G., Godfrey, R. E., Sabine, V., Conesa, A., Minchin, S. D., Watts, P. C., & Chipman, J. K. (2006). Development of the GENIPOL European Flounder (Platichthys flesus) Microarray and Determination of Temporal Transcriptional Responses to Cadmium at Low Dose. Environmental Science & Technology, 40(20), 6479–6488. https://doi.org/10.1021/es061142h
Forment, J., Gadea, J., Huerta, L., Abizanda, L., Agusti, J., Alamar, S., Alos, E., Andres, F., Arribas, R., Beltran, J. P., Berbel, A., Blazquez, M. A., Brumos, J., Canas, L. A., Cercos, M., Colmenero-Flores, J. M., Conesa, A., Estables, B., Gandia, M., … Conejero, V. (2005). Development of a citrus genome-wide EST collection and cDNA microarray as resources for genomic studies. Plant Molecular Biology, 57(3), 375–391. https://doi.org/10.1007/s11103-004-7926-1
Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., & Robles, M. (2005). Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics, 21(18), 3674–3676. https://doi.org/10.1093/bioinformatics/bti610
Yi, X., Conesa, A., Punt, P. J., & Hager, L. P. (2003). Examining the Role of Glutamic Acid 183 in Chloroperoxidase Catalysis. Journal of Biological Chemistry, 278(16), 13855–13859. https://doi.org/10.1074/jbc.m210906200
Conesa, A., Punt, P. J., & van den Hondel, C. A. M. J. J. (2002). Fungal peroxidases: molecular aspects and applications. Journal of Biotechnology, 93(2), 143–158. https://doi.org/10.1016/s0168-1656(01)00394-7
Punt, P. J., van Biezen, N., Conesa, A., Albers, A., Mangnus, J., & van den Hondel, C. (2002). Filamentous fungi as cell factories for heterologous protein production. Trends in Biotechnology, 20(5), 200–206. https://doi.org/10.1016/s0167-7799(02)01933-9
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