- -

Undisclosed, unmet and neglected challenges in multi-omics studies

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Undisclosed, unmet and neglected challenges in multi-omics studies

Mostrar el registro completo del ítem

Tarazona, S.; Arzalluz-Luque, Á.; 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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/190870

Ficheros en el ítem

Metadatos del ítem

Título: Undisclosed, unmet and neglected challenges in multi-omics studies
Autor: Tarazona, Sonia Arzalluz-Luque, Ángeles Conesa, Ana
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[EN] Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently ...[+]
Derechos de uso: Reserva de todos los derechos
Fuente:
Nature Computational Science. (issn: 2662-8457 )
DOI: 10.1038/s43588-021-00086-z
Editorial:
Nature Publishing Group
Versión del editor: https://doi.org/10.1038/s43588-021-00086-z
Código del Proyecto:
info:eu-repo/grantAgreement/MICINN//BES-2016-076994/
Agradecimientos:
This work has been funded by the Spanish Ministry of Science and Innovation with grant number BES-2016-076994 to A.A.-L.
Tipo: Artículo

References

Fan, T. W. M., Bandura, L. L., Higashi, R. M. & Lane, A. N. Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells. Metabolomics 1, 325–339 (2005).

Panguluri, S. K. et al. Genomic profiling of messenger RNAs and microRNAs reveals potential mechanisms of TWEAK-induced skeletal muscle wasting in mice. PLoS ONE 5, e8760 (2010).

Song, L. et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res. 21, 1757–1767 (2011). [+]
Fan, T. W. M., Bandura, L. L., Higashi, R. M. & Lane, A. N. Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells. Metabolomics 1, 325–339 (2005).

Panguluri, S. K. et al. Genomic profiling of messenger RNAs and microRNAs reveals potential mechanisms of TWEAK-induced skeletal muscle wasting in mice. PLoS ONE 5, e8760 (2010).

Song, L. et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res. 21, 1757–1767 (2011).

Kim, S., Jhong, J.-H., Lee, J. & Koo, J.-Y. Meta-analytic support vector machine for integrating multiple omics data. BioData Min. 10, 2 (2017).

Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–i245 (2010).

Mo, Q. et al. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19, 71–86 (2017).

Argelaguet, R. et al. Multi-omics factor analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).

Rohart, F., Gautier, B., Singh, A. & Lê Cao, K.-A. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).

Zhang, L. et al. Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma. Front. Genet. 9, 477 (2018).

Ma, T. & Zhang, A. Integrate multi-omics data with biological interaction networks using multi-view factorization autoencoder (MAE). BMC Genomics 20, 944 (2019).

Huang, Z. et al. SALMON: survival analysis learning with multi-omics neural networks on breast cancer. Front. Genet. 10, 166 (2019).

Bersanelli, M. et al. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17, 15 (2016).

De Bin, R., Boulesteix, A.-L., Benner, A., Becker, N. & Sauerbrei, W. Combining clinical and molecular data in regression prediction models: insights from a simulation study. Brief. Bioinform. 21, 1904–1919 (2020).

Pierre-Jean, M., Deleuze, J.-F., Le Floch, E. & Mauger, F. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration. Brief. Bioinform. 21, 2011–2030 (2020).

Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628–641 (2016).

Buescher, J. M. & Driggers, E. M. Integration of omics: more than the sum of its parts. Cancer Metab. 4, 4 (2016).

Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).

Kristensen, V. N. et al. Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer 14, 299–313 (2014).

Sathyanarayanan, A. et al. A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping. Brief. Bioinform. 21, 1920–1936 (2020).

Zeng, H. et al. Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma. Aging 13, 9960–9975 (2021).

Kirienko, M. et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur. J. Nucl. Med. Mol. Imaging https://doi.org/10.1007/s00259-021-05371-7 (2021).

Zielinski, J. M., Luke, J. J., Guglietta, S. & Krieg, C. High throughput multi-omics approaches for clinical trial evaluation and drug discovery. Front. Immunol. 12, 590742 (2021).

Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. Rev. Genet. 11, 855–866 (2010).

van Bezouw, R. F. H. M., Keurentjes, J. J. B., Harbinson, J. & Aarts, M. G. M. Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. Plant J. Cell Mol. Biol. 97, 112–133 (2019).

Zhu, R., Zhao, Q., Zhao, H. & Ma, S. Integrating multidimensional omics data for cancer outcome. Biostatistics 17, 605–618 (2016).

Balzano-Nogueira, L. et al. Integrative analyses of TEDDY omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes. Genome Biol. 22, 39 (2021).

Shen, R., Olshen, A. B. & Ladanyi, M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906–2912 (2009).

Yener, B. et al. Multiway modeling and analysis in stem cell systems biology. BMC Syst. Biol. 2, 63 (2008).

Conesa, A., Prats-Montalbán, J. M., Tarazona, S., Nueda, M. J. & Ferrer, A. A multiway approach to data integration in systems biology based on Tucker3 and N-PLS. Chemom. Intell. Lab. Syst. 104, 101–111 (2010).

Meng, C., Kuster, B., Culhane, A. C. & Gholami, A. M. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 15, 162 (2014).

van der Kloet, F. M., Sebastián-León, P., Conesa, A., Smilde, A. K. & Westerhuis, J. A. Separating common from distinctive variation. BMC Bioinformatics 17, 195 (2016).

O’Connell, M. J. & Lock, E. F. R.JIVE for exploration of multi-source molecular data. Bioinformatics 32, 2877–2879 (2016).

Bouhaddani, S. E. et al. Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics 19, 371 (2018).

Planell, N. et al. STATegra: multi-omics data integration—a conceptual scheme with a bioinformatics pipeline. Front. Genet. 12, 143 (2021).

Boulesteix, A.-L., De Bin, R., Jiang, X. & Fuchs, M. IPF-LASSO: integrative L(1)-penalized regression with penalty factors for prediction based on multi-omics data. Comput. Math. Methods Med. 2017, 7691937 (2017).

Kennedy, E. M. et al. An integrated -omics analysis of the epigenetic landscape of gene expression in human blood cells. BMC Genomics 19, 476 (2018).

Wu, M.-Y. et al. Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer. BMC Bioinformatics 17, 108 (2016).

Wu, C. et al. A selective review of multi-level omics data integration using variable selection. High Throughput 8, 4 (2019).

Lagani, V., Kortas, G. & Tsamardinos, I. Biomarker signature identification in ‘omics’ data with multi-class outcome. Comput. Struct. Biotechnol. J. 6, e201303004 (2013).

Le, D.-H. Machine learning-based approaches for disease gene prediction. Brief. Funct. Genomics 19, 350–363 (2020).

Fang, H., Huang, C., Zhao, H. & Deng, M. CCLasso: correlation inference for compositional data through Lasso. Bioinformatics 31, 3172–3180 (2015).

Klau, S., Jurinovic, V., Hornung, R., Herold, T. & Boulesteix, A.-L. Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC Bioinformatics 19, 322 (2018).

Li, J., Lu, Q. & Wen, Y. Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data. Bioinformatics 36, 1785–1794 (2020).

Park, H., Niida, A., Miyano, S. & Imoto, S. Sparse overlapping group lasso for integrative multi-omics analysis. J. Comput. Biol. 22, 73–84 (2015).

Singh, A. et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35, 3055–3062 (2019).

Patel-Murray, N. L. et al. A multi-omics interpretable machine learning model reveals modes of action of small molecules. Sci. Rep. 10, 954 (2020).

Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

Rubio, T. et al. Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients. Sci. Rep. 11, 1907 (2021).

Cai, X., Bazerque, J. A. & Giannakis, G. B. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations. PLoS Comput. Biol. 9, e1003068 (2013).

Oberhardt, M. A., Chavali, A. K. & Papin, J. A. Flux balance analysis: interrogating genome-scale metabolic networks. Methods Mol. Biol. 500, 61–80 (2009).

Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

Covert, M. W., Schilling, C. H. & Palsson, B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213, 73–88 (2001).

Tzika, E., Dreker, T. & Imhof, A. Epigenetics and metabolism in health and disease. Front. Genet. 9, 361 (2018).

Siebert, J. C. et al. CANTARE: finding and visualizing network-based multi-omic predictive models. BMC Bioinformatics 22, 80 (2021).

Tarazona, S. et al. Harmonization of quality metrics and power calculation in multi-omic studies. Nat. Commun. 11, 3092 (2020).

Soerensen, M. et al. A genome-wide integrative association study of DNA methylation and gene expression data and later life cognitive functioning in monozygotic twins. Front. Neurosci. 14, https://doi.org/10.3389/fnins.2020.00233 (2020).

Dai, Y., Pei, G., Zhao, Z. & Jia, P. A convergent study of genetic variants associated with Crohn’s disease: evidence from GWAS, gene expression, methylation, eQTL and TWAS. Front. Genet. 10, https://doi.org/10.3389/fgene.2019.00318 (2019).

Karathanasis, N., Tsamardinos, I. & Lagani, V. omicsNPC: applying the non-parametric combination methodology to the integrative analysis of heterogeneous omics data. PLoS ONE 11, e0165545 (2016).

Garcia-Alcalde, F., Garcia-Lopez, F., Dopazo, J. & Conesa, A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 27, 137–139 (2011).

Voillet, V., Besse, P., Liaubet, L., San Cristobal, M. & González, I. Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework. BMC Bioinformatics 17, 402 (2016).

Kuo, R. I. et al. Normalized long read RNA sequencing in chicken reveals transcriptome complexity similar to human. BMC Genomics 18, 323 (2017).

Conesa, A. & Beck, S. Making multi-omics data accessible to researchers. Sci. Data 6, 251 (2019).

Dong, X. et al. TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach. Bioinformatics 35, 1278–1283 (2019).

Zhou, X., Chai, H., Zhao, H., Luo, C.-H. & Yang, Y. Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning–based neural network. GigaScience 9, https://doi.org/10.1093/gigascience/giaa076 (2020).

Ugidos, M., Tarazona, S., Prats-Montalbán, J. M., Ferrer, A. & Conesa, A. MultiBaC: a strategy to remove batch effects between different omic data types. Stat. Methods Med. Res. 29, 2851–2864 (2020).

Messer, K., Vaida, F. & Hogan, C. Robust analysis of biomarker data with informative missingness using a two-stage hypothesis test in an HIV treatment interruption trial: AIEDRP AIN503/ACTG A5217. Contemp. Clin. Trials 27, 506–517 (2006).

Hong, M.-G., Pawitan, Y., Magnusson, P. K. E. & Prince, J. A. Strategies and issues in the detection of pathway enrichment in genome-wide association studies. Hum. Genet. 126, 289–301 (2009).

Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

Arneson, D., Bhattacharya, A., Shu, L., Mäkinen, V.-P. & Yang, X. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration. BMC Genomics 17, 722 (2016).

Welch, R. P. et al. ChIP-enrich: gene set enrichment testing for ChIP-seq data. Nucleic Acids Res. 42, e105 (2014).

Canzler, S. & Hackermüller, J. multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinformatics 21, 561 (2020).

Long, Y., Lu, M., Cheng, T., Zhan, X. & Zhan, X. Multiomics-based signaling pathway network alterations in human non-functional pituitary adenomas. Front. Endocrinol. 10, https://doi.org/10.3389/fendo.2019.00835 (2019).

Hernández-de-Diego, R. et al. PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data. Nucleic Acids Res. 46, W503–W509 (2018).

Sakurai, N. et al. KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data. Nucleic Acids Res. 39, D677–D684 (2011).

Su, G., Morris, J. H., Demchak, B. & Bader, G. D. Biological network exploration with Cytoscape 3. Curr. Protoc. Bioinformatics 47, 8.13.11–18.13.24 (2014).

Kuo, T. C., Tian, T. F. & Tseng, Y. J. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst. Biol. 7, https://doi.org/10.1186/1752-0509-7-64 (2013).

Miller, J. J. Graph database applications and concepts with Neo4j. In Proc. Southern Association for Information Systems Conference (AIS, 2013).

Yoon, B.-H., Kim, S.-K. & Kim, S.-Y. Use of graph database for the integration of heterogeneous biological data. Genomics Inform. 15, 19–27 (2017).

Consortium, T. I. Hi. R. N. The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289 (2014).

ICGC Data Portal (The International Cancer Genome Consortium, 2021); https://dcc.icgc.org/

Human Microbiome Project Data Portal (Human Microbiome Project, 2021); https://portal.hmpdacc.org/

Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

Kodama, Y., Shumway, M. & Leinonen, R. The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res. 40, D54–D56 (2012).

Tryka, K. A. et al. NCBI’s Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 42, D975–D979 (2014).

Lappalainen, I. et al. The European Genome-phenome Archive of human data consented for biomedical research. Nat. Genet. 47, 692–695 (2015).

Haug, K. et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48, D440–D444 (2020).

Deutsch, E. W. et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res. 45, D1100–D1106 (2017).

Byrd, J. B., Greene, A. C., Prasad, D. V., Jiang, X. & Greene, C. S. Responsible, practical genomic data sharing that accelerates research. Nat. Rev. Genet. 21, 615–629 (2020).

Hernandez-de-Diego, R. et al. STATegra EMS: an experiment management system for complex next-generation omics experiments. BMC Syst. Biol. 8, S9 (2014).

Lin, K. et al. MADMAX—management and analysis database for multiple ~omics experiments. J. Integr. Bioinform. 8, 59–74 (2011).

Venco, F., Vaskin, Y., Ceol, A. & Muller, H. SMITH: a LIMS for handling next-generation sequencing workflows. BMC Bioinformatics 15, S3 (2014).

Perez-Riverol, Y. et al. Discovering and linking public omics data sets using the Omics Discovery index. Nat. Biotechnol. 35, 406–409 (2017).

Chervitz, S. A. et al. in Bioinformatics for Omics Data: Methods and Protocols (ed. Mayer, B.) 31–69 (Humana Press, 2011).

Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018).

van Karnebeek, C. D. M. et al. The role of the clinician in the multi-omics era: are you ready? J. Inherit. Metab. Dis. 41, 571–582 (2018).

Angione, C. Human systems biology and metabolic modelling: a review—from disease metabolism to precision medicine. Biomed. Res. Int. 2019, 8304260 (2019).

Hériché, J.-K., Alexander, S. & Ellenberg, J. Integrating imaging and omics: computational methods and challenges. Annu. Rev. Biomed. Data Sci. 2, 175–197 (2019).

Lee, J., Hyeon, D. Y. & Hwang, D. Single-cell multiomics: technologies and data analysis methods. Exp. Mol. Med. 52, 1428–1442 (2020).

Stein, L. D. The case for cloud computing in genome informatics. Genome Biol. 11, 207 (2010).

Oh, M., Park, S., Kim, S. & Chae, H. Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations. Brief. Bioinform. 22, 66–76 (2020).

Solomonik, E., Carson, E., Knight, N. & Demmel, J. Trade-offs between synchronization, communication, and computation in parallel linear algebra computations. ACM Trans. Parallel Comput. 3, 1–47 (2016).

Berger, B., Peng, J. & Singh, M. Computational solutions for omics data. Nat. Rev. Genet. 14, 333–346 (2013).

Alyass, A., Turcotte, M. & Meyre, D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med. Genet. 8, 33 (2015).

Chen, X.-W. & Lin, X. Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014).

Fan, J., Slowikowski, K. & Zhang, F. Single-cell transcriptomics in cancer: computational challenges and opportunities. Exp. Mol. Med. 52, 1452–1465 (2020).

Armand, E. J., Li, J., Xie, F., Luo, C. & Mukamel, E. A. Single-cell sequencing of brain cell transcriptomes and epigenomes. Neuron 109, 11–26 (2021).

Zhu, C., Preissl, S. & Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods 17, 11–14 (2020).

Forcato, M., Romano, O. & Bicciato, S. Computational methods for the integrative analysis of single-cell data. Brief. Bioinform. 22, 20–29 (2021).

Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e1821 (2016).

Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).

Trapnell, C. & Cacchiarelli, D. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).

Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019).

Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

Welch, J. D., Hartemink, A. J. & Prins, J. F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 18, 138 (2017).

Campbell, K. R. et al. clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome Biol. 20, 54 (2019).

Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).

Van Der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493–497 (2018).

La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

Bray, M.-A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

Sedgewick, A. J., Benz, S. C., Rabizadeh, S., Soon-Shiong, P. & Vaske, C. J. Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM. Bioinformatics 29, i62–i70 (2013).

Gomez-Cabrero, D. et al. STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Sci. Data 6, 256 (2019).

Hutter, C. & Zenklusen, J. C. The Cancer Genome Atlas: creating lasting value beyond its data. Cell 173, 283–285 (2018).

Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

Moore, J. E. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).

Mergner, J. et al. Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 579, 409–414 (2020).

O’Connor, T. R., Dyreson, C. & Wyrick, J. J. Athena: a resource for rapid visualization and systematic analysis of Arabidopsis promoter sequences. Bioinformatics 21, 4411–4413 (2005).

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem