Publications by authors named "Tiago C Silva"

17 Publications

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A pan-cancer analysis of CpG Island gene regulation reveals extensive plasticity within Polycomb target genes.

Nat Commun 2021 04 30;12(1):2485. Epub 2021 Apr 30.

Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University-Hadassah Medical School, Jerusalem, Israel.

CpG Island promoter genes make up more than half of human genes, and a subset regulated by Polycomb-Repressive Complex 2 (PRC2-CGI) become DNA hypermethylated and silenced in cancer. Here, we perform a systematic analysis of CGI genes across TCGA cancer types, finding that PRC2-CGI genes are frequently prone to transcriptional upregulation as well. These upregulated PRC2-CGI genes control important pathways such as Epithelial-Mesenchymal Transition (EMT) and TNFα-associated inflammatory response, and have greater cancer-type specificity than other CGI genes. Using publicly available chromatin datasets and genetic perturbations, we show that transcription factor binding sites (TFBSs) within distal enhancers underlie transcriptional activation of PRC2-CGI genes, coinciding with loss of the PRC2-associated mark H3K27me3 at the linked promoter. In contrast, PRC2-free CGI genes are predominantly regulated by promoter TFBSs which are common to most cancer types. Surprisingly, a large subset of PRC2-CGI genes that are upregulated in one cancer type are also hypermethylated/silenced in at least one other cancer type, underscoring the high degree of regulatory plasticity of these genes, likely derived from their complex regulatory control during normal development.
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http://dx.doi.org/10.1038/s41467-021-22720-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087678PMC
April 2021

Sex-specific DNA methylation differences in Alzheimer's disease pathology.

Acta Neuropathol Commun 2021 Apr 26;9(1):77. Epub 2021 Apr 26.

Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA.

Sex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer's disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation differences in AD neuropathology, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort, we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such as TMEM39A and TNXB that are associated with the AD Braak stage in a sex-specific manner. TMEM39A is known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes. TNXB encodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated genes in AD neuropathology, such as MBP and AZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation differences were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Our study implicated multiple new loci and biological processes that affected AD neuropathology in a sex-specific manner.
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http://dx.doi.org/10.1186/s40478-021-01177-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074512PMC
April 2021

White paper on high-throughput process development for integrated continuous biomanufacturing.

Biotechnol Bioeng 2021 Mar 22. Epub 2021 Mar 22.

Department of Biotechnology, Delft University of Technology, Delft, The Netherlands.

Continuous manufacturing is an indicator of a maturing industry, as can be seen by the example of the petrochemical industry. Patent expiry promotes a price competition between manufacturing companies, and more efficient and cheaper processes are needed to achieve lower production costs. Over the last decade, continuous biomanufacturing has had significant breakthroughs, with regulatory agencies encouraging the industry to implement this processing mode. Process development is resource and time consuming and, although it is increasingly becoming less expensive and faster through high-throughput process development (HTPD) implementation, reliable HTPD technology for integrated and continuous biomanufacturing is still lacking and is considered to be an emerging field. Therefore, this paper aims to illustrate the major gaps in HTPD and to discuss the major needs and possible solutions to achieve an end-to-end Integrated Continuous Biomanufacturing, as discussed in the context of the 2019 Integrated Continuous Biomanufacturing conference. The current HTPD state-of-the-art for several unit operations is discussed, as well as the emerging technologies which will expedite a shift to continuous biomanufacturing.
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http://dx.doi.org/10.1002/bit.27757DOI Listing
March 2021

DNA Methylation-based Signatures Classify Sporadic Pituitary Tumors According to Clinicopathological Features.

Neuro Oncol 2021 Feb 25. Epub 2021 Feb 25.

Department of Neurosurgery, Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, MI, USA.

Background: Distinct genome-wide methylation patterns cluster pituitary neuroendocrine tumors (PitNETs) into molecular groups associated with specific clinicopathological features. Here we aim to identify, characterize and validate methylation signatures that objectively classify PitNET into clinicopathological groups.

Methods: Combining in-house and publicly available data, we conducted an analysis of the methylome profile of a comprehensive cohort of 177 tumors (Panpit cohort) and 20 nontumor specimens from the pituitary gland. We also retrieved methylome data from an independent PitNET cohort (N=86) to validate our findings.

Results: We identified three methylation clusters associated with adenohypophyseal cell lineages and functional status using an unsupervised approach. Differentially methylated CpG probes (DMP) significantly distinguished the Panpit clusters and accurately assigned the samples of the validation cohort to their corresponding lineage and functional subtypes memberships. The DMPs were annotated in regulatory regions enriched of enhancer elements, associated with pathways and genes involved in pituitary cell identity, function, tumorigenesis, invasiveness. Some DMPs correlated with genes with prognostic and therapeutic values in other intra or extracranial tumors.

Conclusions: We identified and validated methylation signatures, mainly annotated in enhancer regions that distinguished PitNETs by distinct adenohypophyseal cell lineages and functional status. These signatures provide the groundwork to develop an unbiased approach to classifying PitNETs according to the most recent classification recommended by the 2017 WHO and to explore their biological and clinical relevance in these tumors.
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http://dx.doi.org/10.1093/neuonc/noab044DOI Listing
February 2021

Epigenome-wide meta-analysis of DNA methylation differences in prefrontal cortex implicates the immune processes in Alzheimer's disease.

Nat Commun 2020 11 30;11(1):6114. Epub 2020 Nov 30.

Division of Biostatistics, Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.

DNA methylation differences in Alzheimer's disease (AD) have been reported. Here, we conducted a meta-analysis of more than 1000 prefrontal cortex brain samples to prioritize the most consistent methylation differences in multiple cohorts. Using a uniform analysis pipeline, we identified 3751 CpGs and 119 differentially methylated regions (DMRs) significantly associated with Braak stage. Our analysis identified differentially methylated genes such as MAMSTR, AGAP2, and AZU1. The most significant DMR identified is located on the MAMSTR gene, which encodes a cofactor that stimulates MEF2C. Notably, MEF2C cooperates with another transcription factor, PU.1, a central hub in the AD gene network. Our enrichment analysis highlighted the potential roles of the immune system and polycomb repressive complex 2 in pathological AD. These results may help facilitate future mechanistic and biomarker discovery studies in AD.
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http://dx.doi.org/10.1038/s41467-020-19791-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704686PMC
November 2020

Lineage-Specific Epigenomic and Genomic Activation of Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas.

Cancer Res 2020 07 24;80(13):2722-2736. Epub 2020 Apr 24.

Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.

Gastrointestinal adenocarcinomas (GIAC) of the tubular gastrointestinal (GI) tract including esophagus, stomach, colon, and rectum comprise most GI cancers and share a spectrum of genomic features. However, the unified epigenomic changes specific to GIAC are poorly characterized. Using 907 GIAC samples from The Cancer Genome Atlas, we applied mathematical algorithms to large-scale DNA methylome and transcriptome profiles to reconstruct transcription factor (TF) networks and identify a list of functionally hyperactive master regulator (MR) TF shared across different GIAC. The top candidate HNF4A exhibited prominent genomic and epigenomic activation in a GIAC-specific manner. A complex interplay between the HNF4A promoter and three distal enhancer elements was coordinated by GIAC-specific MRTF including ELF3, GATA4, GATA6, and KLF5. HNF4A also self-regulated its own promoter and enhancers. Functionally, HNF4A promoted cancer proliferation and survival by transcriptional activation of many downstream targets, including HNF1A and factors of interleukin signaling, in a lineage-specific manner. Overall, our study provides new insights into the GIAC-specific gene regulatory networks and identifies potential therapeutic strategies against these common cancers. SIGNIFICANCE: These findings show that GIAC-specific master regulatory transcription factors control HNF4A via three distal enhancers to promote GIAC cell proliferation and survival. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/13/2722/F1.large.jpg.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-0390DOI Listing
July 2020

Interpreting pathways to discover cancer driver genes with Moonlight.

Nat Commun 2020 01 3;11(1):69. Epub 2020 Jan 3.

Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark.

Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.
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http://dx.doi.org/10.1038/s41467-019-13803-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941958PMC
January 2020

GENAVi: a shiny web application for gene expression normalization, analysis and visualization.

BMC Genomics 2019 Oct 16;20(1):745. Epub 2019 Oct 16.

Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.

Background: The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge.

Results: We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis.

Conclusion: The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.
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http://dx.doi.org/10.1186/s12864-019-6073-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796420PMC
October 2019

RNA-Binding Protein Suppresses Hypoxia and Cell-Cycle Signaling.

Cancer Res 2020 01 24;80(2):219-233. Epub 2019 Sep 24.

Cancer Science Institute of Singapore, National University of Singapore, Singapore.

ZFP36L1 is a tandem zinc-finger RNA-binding protein that recognizes conserved adenylate-uridylate-rich elements (ARE) located in 3'untranslated regions (UTR) to mediate mRNA decay. We hypothesized that ZFP36L1 is a negative regulator of a posttranscriptional hub involved in mRNA half-life regulation of cancer-related transcripts. Analysis of data revealed that ZFP36L1 was significantly mutated, epigenetically silenced, and downregulated in a variety of cancers. Forced expression of ZFP36L1 in cancer cells markedly reduced cell proliferation and , whereas silencing of ZFP36L1 enhanced tumor cell growth. To identify direct downstream targets of ZFP36L1, systematic screening using RNA pull-down of wild-type and mutant ZFP36L1 as well as whole transcriptome sequencing of bladder cancer cells {plus minus} tet-on ZFP36L1 was performed. A network of 1,410 genes was identified as potential direct targets of ZFP36L1. These targets included a number of key oncogenic transcripts such as HIF1A, CCND1, and E2F1. ZFP36L1 specifically bound to the 3'UTRs of these targets for mRNA degradation, thus suppressing their expression. Dual luciferase reporter assays and RNA electrophoretic mobility shift assays showed that wild-type, but not zinc-finger mutant ZFP36L1, bound to HIF1A 3'UTR and mediated HIF1A mRNA degradation, leading to reduced expression of HIF1A and its downstream targets. Collectively, our findings reveal an indispensable role of ZFP36L1 as a posttranscriptional safeguard against aberrant hypoxic signaling and abnormal cell-cycle progression. SIGNIFICANCE: RNA-binding protein ZFP36L1 functions as a tumor suppressor by regulating the mRNA stability of a number of mRNAs involved in hypoxia and cell-cycle signaling.
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http://dx.doi.org/10.1158/0008-5472.CAN-18-2796DOI Listing
January 2020

Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons' Data.

Cell Syst 2019 07;9(1):24-34.e10

National Cancer Institute, Bethesda, MD 20892, USA.

We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)-mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations-comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the 'legacy' GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as 'harmonized' by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve.
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http://dx.doi.org/10.1016/j.cels.2019.06.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6707074PMC
July 2019

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx.

PLoS Comput Biol 2019 03 5;15(3):e1006701. Epub 2019 Mar 5.

Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark.

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.
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http://dx.doi.org/10.1371/journal.pcbi.1006701DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6420023PMC
March 2019

ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles.

Bioinformatics 2019 06;35(11):1974-1977

Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Motivation: DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set.

Results: We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer.

Availability And Implementation: ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty902DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546131PMC
June 2019

The chromatin accessibility landscape of primary human cancers.

Science 2018 10;362(6413)

We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas (TCGA). We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq (the assay for transposase-accessible chromatin using sequencing) with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may affect patient survival. These results suggest a systematic approach to understanding the noncoding genome in cancer to advance diagnosis and therapy.
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http://dx.doi.org/10.1126/science.aav1898DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6408149PMC
October 2018

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications.

Neuro Oncol 2018 04;20(5):608-620

Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA.

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends molecular diagnosis of isocitrate dehydrogenase (IDH) mutational status in gliomas. IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provides a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.
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http://dx.doi.org/10.1093/neuonc/nox183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5892155PMC
April 2018

: Analyze cancer genomics and epigenomics data using Bioconductor packages.

F1000Res 2016 29;5:1542. Epub 2016 Jun 29.

Department of Genetics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Brazil; Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA.

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox,  TCGAbiolinks.
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http://dx.doi.org/10.12688/f1000research.8923.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302158PMC
June 2016

SpidermiR: An R/Bioconductor Package for Integrative Analysis with miRNA Data.

Int J Mol Sci 2017 Jan 27;18(2). Epub 2017 Jan 27.

Institute of Molecular Bioimaging and Physiology National Research Council (IBFM-CNR), Segrate (Mi) 20090, Italy.

Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA-gene-gene and miRNA-protein-protein interactions, and to analyze miRNA GRNs in order to identify miRNA-gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.
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http://dx.doi.org/10.3390/ijms18020274DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5343810PMC
January 2017

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

Nucleic Acids Res 2016 05 23;44(8):e71. Epub 2015 Dec 23.

Department of Genetics Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil Center for Integrative Systems Biology - CISBi, NAP/USP, Ribeirão Preto, São Paulo, Brazil

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
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http://dx.doi.org/10.1093/nar/gkv1507DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856967PMC
May 2016