RESUMO
SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Sequenciamento de Nucleotídeos em Larga Escala , Software , Análise de Dados , Reprodutibilidade dos Testes , Fluxo de TrabalhoRESUMO
Kaposi's sarcoma herpesvirus (KSHV) causes Kaposi's sarcoma and certain lymphoproliferative malignancies. Latent infection is established in the majority of tumor cells, whereas lytic replication is reactivated in a small fraction of cells, which is important for both virus spread and disease progression. A siRNA screen for novel regulators of KSHV reactivation identified the E3 ubiquitin ligase MDM2 as a negative regulator of viral reactivation. Depletion of MDM2, a repressor of p53, favored efficient activation of the viral lytic transcription program and viral reactivation. During lytic replication cells activated a p53 response, accumulated DNA damage and arrested at G2-phase. Depletion of p21, a p53 target gene, restored cell cycle progression and thereby impaired the virus reactivation cascade delaying the onset of virus replication induced cytopathic effect. Herpesviruses are known to reactivate in response to different kinds of stress, and our study now highlights the molecular events in the stressed host cell that KSHV has evolved to utilize to ensure efficient viral lytic replication.
Assuntos
Pontos de Checagem do Ciclo Celular/genética , Regulação Viral da Expressão Gênica/genética , Herpesvirus Humano 8/genética , Estresse Fisiológico/genética , Replicação Viral , Linhagem Celular Tumoral , Replicação do DNA , Humanos , RNA Interferente Pequeno/genética , Sarcoma de Kaposi/metabolismo , Sarcoma de Kaposi/virologia , Ativação Viral/fisiologia , Latência Viral/genética , Replicação Viral/genéticaRESUMO
Androgen receptor (AR) binds male sex steroids and mediates physiological androgen actions in target tissues. ChIP-seq analyses of AR-binding events in murine prostate, kidney and epididymis show that in vivo AR cistromes and their respective androgen-dependent transcription programs are highly tissue specific mediating distinct biological pathways. This high order of tissue specificity is achieved by the use of exclusive collaborating factors in the three androgen-responsive tissues. We find two novel collaborating factors for AR signaling in vivo--Hnf4α (hepatocyte nuclear factor 4α) in mouse kidney and AP-2α (activating enhancer binding protein 2α) in mouse epididymis--that define tissue-specific AR recruitment. In mouse prostate, FoxA1 serves for the same purpose. FoxA1, Hnf4α and AP-2α motifs are over-represented within unique AR-binding loci, and the cistromes of these factors show substantial overlap with AR-binding events distinct to each tissue type. These licensing or pioneering factors are constitutively bound to chromatin and guide AR to specific genomic loci upon hormone exposure. Collectively, liganded receptor and its DNA-response elements are required but not sufficient for establishment of tissue-specific transcription programs.
Assuntos
Epididimo/metabolismo , Regulação da Expressão Gênica/efeitos dos fármacos , Fator 3-alfa Nuclear de Hepatócito/fisiologia , Fator 4 Nuclear de Hepatócito/fisiologia , Rim/metabolismo , Próstata/metabolismo , Receptores Androgênicos/metabolismo , Testosterona/farmacologia , Fator de Transcrição AP-2/fisiologia , Animais , Linhagem Celular , Cromatina/metabolismo , Imunoprecipitação da Cromatina , Epididimo/citologia , Perfilação da Expressão Gênica , Masculino , Camundongos , Camundongos Endogâmicos ICR , Orquiectomia , Especificidade de Órgãos , Ligação Proteica , Interferência de RNA , RNA Interferente Pequeno/farmacologia , Proteínas de Ligação a Tacrolimo/biossíntese , Proteínas de Ligação a Tacrolimo/genética , Testosterona/fisiologia , Transcrição GênicaRESUMO
Computational analysis of data produced in deep sequencing (DS) experiments is challenging due to large data volumes and requirements for flexible analysis approaches. Here, we present a mathematical formalism based on set algebra for frequently performed operations in DS data analysis to facilitate translation of biomedical research questions to language amenable for computational analysis. With the help of this formalism, we implemented the Genomic Region Operation Kit (GROK), which supports various DS-related operations such as preprocessing, filtering, file conversion, and sample comparison. GROK provides high-level interfaces for R, Python, Lua, and command line, as well as an extension C++ API. It supports major genomic file formats and allows storing custom genomic regions in efficient data structures such as red-black trees and SQL databases. To demonstrate the utility of GROK, we have characterized the roles of two major transcription factors (TFs) in prostate cancer using data from 10 DS experiments. GROK is freely available with a user guide from >http://csbi.ltdk.helsinki.fi/grok/.
