Faculdade

Departamento

Ana Rita Grosso

Investigador
406
11114

Interesses Científicos

Our mission is to understand the (epi)genome biology and its impact on cancer and other diseases using computational multi-omics approaches. Such methods rely on the statistical analysis and integration of large scale data (high-throughput sequencing, microarrays, proteomics, high-throughput screening) and clinical/phenotypic data. Given the accumulation of this huge amount of biological information, handling and mining big data is becoming increasingly mandatory for major research. However, more than data processing and integration, the key challenge relies on extracting information and generating scientific knowledge. Therefore, we aim to achieve biomedical advances through novel ways of combining large multi-omics and phenotypic data. Hence, we integrate large-scale profiles at different levels: genome (information within the DNA sequence and mutations), epigenome (DNA methylation, histone modifications, nucleosome positioning and chromosome conformation), transcriptome (RNA expression and isoforms), proteomics (protein levels). Integration and visualization of such complex data sets is crucial for interpretation and decoding of the underlying biology associated to pathological conditions. Such approaches can depict molecular events to be further used as biomarkers and therapeutic targets.

Publicações Representativas

Bonnet A, Grosso AR, Elkaoutari A, Coleno E, Presle A, Sridhara SC, Janbon G, Géli V, de Almeida SF, Palancade B.. 2017. Introns Protect Eukaryotic Genomes from Transcription-Associated Genetic Instability.. MOLECULAR CELL, 67(4), DOI: 10.1016/j.molcel.2017.07.002

Sridhara, SC; Carvalho, S; Grosso, AR; Gallego-Paez, LM; Carmo-Fonseca, M; de Almeida, SF. 2017. Transcription Dynamics Prevent RNA-Mediated Genomic Instability through SRPK2-Dependent DDX23 Phosphorylation. Cell Reports, 18(2), DOI: 10.1016/j.celrep.2016.12.050

Posa, I; Carvalho, S; Tavares, J; Grosso, AR. 2016. A pan-cancer analysis of MYC-PVT1 reveals CNV-unmediated deregulation and poor prognosis in renal carcinoma. Oncotarget, 7(30), DOI: 10.18632/oncotarget.9487

Nojima T, Gomes T, Grosso ARF, Kimura H, Dye MJ, Dhir S, Carmo-Fonseca M, Proudfoot NJ.. 2015. Mammalian NET-Seq Reveals Genome-wide Nascent Transcription Coupled to RNA Processing. CELL, 161(3), DOI: 10.1016/j.cell.2015.03.027

Grosso, AR; Leite, AP; Carvalho, S; Matos, MR; Martins, FB; Vítor, AC; Desterro, JMP; Carmo-Fonseca, M; de Almeida, SF. 2015. Pervasive transcription read-through promotes aberrant expression of oncogenes and RNA chimeras in renal carcinoma. eLife, 4(NOVEMBER2015), DOI: 10.7554/eLife.09214

Websites

Computational Multi-Omics Lab

CompMultOmics@UCIBIO