biocintegrativecancervis
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- Owner: seandavi
- Language: R
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BiocIntegrativeCancerVis
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Owner
- Name: Sean Davis
- Login: seandavi
- Kind: user
- Location: Bethesda, MD, 20892, USA
- Company: National Cancer Institute, National Institutes of Health
- Website: https://seandavi.github.io/
- Repositories: 87
- Profile: https://github.com/seandavi
- Pediatric oncologist - Cancer researcher - Data scientist - Community organizer
Citation (citations.bib)
@ARTICLE{Wang2016-sz,
title = "A Practical Guide to The Cancer Genome Atlas ({TCGA})",
author = "Wang, Zhining and Jensen, Mark A and Zenklusen, Jean Claude",
affiliation = "Center for Cancer Genomics, National Cancer Institute,
National Institutes of Health, 31 Center Drive, Bethesda, MD,
USA. zhining.wang@nih.gov. Research Administration
Directorate, Leidos Biomedical Research Inc., Frederick
National Laboratory for Cancer Research, 8560 Progress Drive,
Frederick, 21701, MD, USA. Center for Cancer Genomics,
National Cancer Institute, National Institutes of Health, 31
Center Drive, Bethesda, MD, USA.",
abstract = "The Cancer Genome Atlas (TCGA) is one of the most ambitious
and successful cancer genomics programs to date. The TCGA
program has generated, analyzed, and made available genomic
sequence, expression, methylation, and copy number variation
data on over 11,000 individuals who represent over 30
different types of cancer. This chapter provides a brief
overview of the TCGA program and detailed instructions and
tips for investigators on how to find, access, and download
this data.",
journal = "Methods Mol. Biol.",
volume = 1418,
pages = "111--141",
year = 2016,
keywords = "Cancer genomics; Copy number; Methylation; Mutation;
Next-generation sequencing; Proteomics; TCGA; The Cancer
Genome Atlas; Transcriptome; miRNA"
}
@misc{Zenklusen2016-ab,
title = {The Cancer Genome Atlas: More Than a Large Collection of Data},
author = "Jean Claude Zencklsen",
howpublished = {\url{http://http://cancergenome.nih.gov/researchhighlights/leadershipupdate/Impact\_JC\_Zenklusen.}},
note = {Accessed: May 5, 2016}
}
@ARTICLE{McGranahan2016-tt,
title = "Clonal neoantigens elicit {T} cell immunoreactivity and
sensitivity to immune checkpoint blockade",
author = "McGranahan, Nicholas and Furness, Andrew J S and Rosenthal,
Rachel and Ramskov, Sofie and Lyngaa, Rikke and Saini, Sunil
Kumar and Jamal-Hanjani, Mariam and Wilson, Gareth A and
Birkbak, Nicolai J and Hiley, Crispin T and Watkins, Thomas B
K and Shafi, Seema and Murugaesu, Nirupa and Mitter, Richard
and Akarca, Ayse U and Linares, Joseph and Marafioti, Teresa
and Henry, Jake Y and Van Allen, Eliezer M and Miao, Diana and
Schilling, Bastian and Schadendorf, Dirk and Garraway, Levi A
and Makarov, Vladimir and Rizvi, Naiyer A and Snyder,
Alexandra and Hellmann, Matthew D and Merghoub, Taha and
Wolchok, Jedd D and Shukla, Sachet A and Wu, Catherine J and
Peggs, Karl S and Chan, Timothy A and Hadrup, Sine R and
Quezada, Sergio A and Swanton, Charles",
affiliation = "The Francis Crick Institute, London WC2A 3LY, UK. Centre for
Mathematics and Physics in the Life Sciences and Experimental
Biology (CoMPLEX), University College London (UCL), London
WC1E 6BT, UK. Cancer Research UK Lung Cancer Centre of
Excellence, UCL Cancer Institute, London WC1E 6BT, UK. Cancer
Research UK Lung Cancer Centre of Excellence, UCL Cancer
Institute, London WC1E 6BT, UK. Cancer Immunology Unit, UCL
Cancer Institute, UCL, London WC1E 6BT, UK. Cancer Research UK
Lung Cancer Centre of Excellence, UCL Cancer Institute, London
WC1E 6BT, UK. Section for Immunology and Vaccinology, National
Veterinary Institute, Technical University of Denmark, 1970
Frederiksberg C, Denmark. Section for Immunology and
Vaccinology, National Veterinary Institute, Technical
University of Denmark, 1970 Frederiksberg C, Denmark. Section
for Immunology and Vaccinology, National Veterinary Institute,
Technical University of Denmark, 1970 Frederiksberg C,
Denmark. Cancer Research UK Lung Cancer Centre of Excellence,
UCL Cancer Institute, London WC1E 6BT, UK. The Francis Crick
Institute, London WC2A 3LY, UK. Cancer Research UK Lung Cancer
Centre of Excellence, UCL Cancer Institute, London WC1E 6BT,
UK. The Francis Crick Institute, London WC2A 3LY, UK. Cancer
Research UK Lung Cancer Centre of Excellence, UCL Cancer
Institute, London WC1E 6BT, UK. The Francis Crick Institute,
London WC2A 3LY, UK. Cancer Research UK Lung Cancer Centre of
Excellence, UCL Cancer Institute, London WC1E 6BT, UK. The
Francis Crick Institute, London WC2A 3LY, UK. Cancer Research
UK Lung Cancer Centre of Excellence, UCL Cancer Institute,
London WC1E 6BT, UK. Cancer Research UK Lung Cancer Centre of
Excellence, UCL Cancer Institute, London WC1E 6BT, UK. Cancer
Research UK Lung Cancer Centre of Excellence, UCL Cancer
Institute, London WC1E 6BT, UK. The Francis Crick Institute,
London WC2A 3LY, UK. Cancer Immunology Unit, UCL Cancer
Institute, UCL, London WC1E 6BT, UK. Department of Cellular
Pathology, UCL, London WC1E 6BT, UK. Cancer Immunology Unit,
UCL Cancer Institute, UCL, London WC1E 6BT, UK. Department of
Cellular Pathology, UCL, London WC1E 6BT, UK. Cancer
Immunology Unit, UCL Cancer Institute, UCL, London WC1E 6BT,
UK. Department of Cellular Pathology, UCL, London WC1E 6BT,
UK. Cancer Research UK Lung Cancer Centre of Excellence, UCL
Cancer Institute, London WC1E 6BT, UK. Cancer Immunology Unit,
UCL Cancer Institute, UCL, London WC1E 6BT, UK. Department of
Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
02215, USA. Broad Institute of MIT and Harvard, Cambridge, MA
02142, USA. Center for Cancer Precision Medicine, Dana-Farber
Cancer Institute, Boston, MA 02215, USA. Department of Medical
Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Department of Dermatology, University Hospital, University
Duisburg-Essen, 45147 Essen, Germany. German Cancer Consortium
(DKTK), 69121 Heidelberg, Germany. Department of Dermatology,
University Hospital, University Duisburg-Essen, 45147 Essen,
Germany. German Cancer Consortium (DKTK), 69121 Heidelberg,
Germany. Department of Medical Oncology, Dana-Farber Cancer
Institute, Boston, MA 02215, USA. Broad Institute of MIT and
Harvard, Cambridge, MA 02142, USA. Center for Cancer Precision
Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Human Oncology and Pathogenesis Program, Memorial Sloan
Kettering Cancer Center, New York, NY 10065, USA.
Hematology/Oncology Division, 177 Fort Washington Avenue,
Columbia University, New York, NY 10032, USA. Department of
Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
10065, USA. Weill Cornell Medical College, New York, NY 10065,
USA. Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, NY 10065, USA. Weill Cornell Medical
College, New York, NY 10065, USA. Department of Medicine,
Memorial Sloan Kettering Cancer Center, New York, NY 10065,
USA. Ludwig Collaborative Laboratory, Memorial Sloan Kettering
Cancer Center, New York, NY 10065, USA. Department of
Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
10065, USA. Weill Cornell Medical College, New York, NY 10065,
USA. Ludwig Collaborative Laboratory, Memorial Sloan Kettering
Cancer Center, New York, NY 10065, USA. Department of Medical
Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Department of Medical Oncology, Dana-Farber Cancer Institute,
Boston, MA 02215, USA. Broad Institute of MIT and Harvard,
Cambridge, MA 02142, USA. Department of Medicine, Harvard
Medical School, Boston, MA 02115, USA. Department of Internal
Medicine, Brigham and Woman's Hospital, Boston, MA 02115, USA.
Cancer Research UK Lung Cancer Centre of Excellence, UCL
Cancer Institute, London WC1E 6BT, UK. Cancer Immunology Unit,
UCL Cancer Institute, UCL, London WC1E 6BT, UK. Human Oncology
and Pathogenesis Program, Memorial Sloan Kettering Cancer
Center, New York, NY 10065, USA. Section for Immunology and
Vaccinology, National Veterinary Institute, Technical
University of Denmark, 1970 Frederiksberg C, Denmark. Cancer
Research UK Lung Cancer Centre of Excellence, UCL Cancer
Institute, London WC1E 6BT, UK. Cancer Immunology Unit, UCL
Cancer Institute, UCL, London WC1E 6BT, UK.
s.quezada@ucl.ac.uk charles.swanton@crick.ac.uk. The Francis
Crick Institute, London WC2A 3LY, UK. Cancer Research UK Lung
Cancer Centre of Excellence, UCL Cancer Institute, London WC1E
6BT, UK. s.quezada@ucl.ac.uk charles.swanton@crick.ac.uk.",
abstract = "As tumors grow, they acquire mutations, some of which create
neoantigens that influence the response of patients to immune
checkpoint inhibitors. We explored the impact of neoantigen
intratumor heterogeneity (ITH) on antitumor immunity. Through
integrated analysis of ITH and neoantigen burden, we
demonstrate a relationship between clonal neoantigen burden
and overall survival in primary lung adenocarcinomas.
CD8(+)tumor-infiltrating lymphocytes reactive to clonal
neoantigens were identified in early-stage non-small cell lung
cancer and expressed high levels of PD-1. Sensitivity to PD-1
and CTLA-4 blockade in patients with advanced NSCLC and
melanoma was enhanced in tumors enriched for clonal
neoantigens. T cells recognizing clonal neoantigens were
detectable in patients with durable clinical benefit.
Cytotoxic chemotherapy-induced subclonal neoantigens,
contributing to an increased mutational load, were enriched in
certain poor responders. These data suggest that neoantigen
heterogeneity may influence immune surveillance and support
therapeutic developments targeting clonal neoantigens.",
journal = "Science",
volume = 351,
number = 6280,
pages = "1463--1469",
month = "25~" # mar,
year = 2016,
url = "http://dx.doi.org/10.1126/science.aaf1490",
language = "en",
issn = "0036-8075, 1095-9203",
pmid = "26940869",
doi = "10.1126/science.aaf1490"
}
@ARTICLE{Fumagalli2015-tl,
title = "Principles Governing {A-to-I} {RNA} Editing in the Breast
Cancer Transcriptome",
author = "Fumagalli, Debora and Gacquer, David and Roth\'{e},
Fran\c{c}oise and Lefort, Anne and Libert, Frederick and
Brown, David and Kheddoumi, Naima and Shlien, Adam and
Konopka, Tomasz and Salgado, Roberto and Larsimont, Denis and
Polyak, Kornelia and Willard-Gallo, Karen and Desmedt,
Christine and Piccart, Martine and Abramowicz, Marc and
Campbell, Peter J and Sotiriou, Christos and Detours, Vincent",
affiliation = "Breast Cancer Translational Research Laboratory, Jules Bordet
Institute, Universit\'{e} Libre de Bruxelles (ULB), Boulevard
de Waterloo, 125-1000 Brussels, Belgium. IRIBHM,
Universit\'{e} Libre de Bruxelles (ULB), Route de Lennik,
808-1070 Brussels, Belgium. Breast Cancer Translational
Research Laboratory, Jules Bordet Institute, Universit\'{e}
Libre de Bruxelles (ULB), Boulevard de Waterloo, 125-1000
Brussels, Belgium. IRIBHM, Universit\'{e} Libre de Bruxelles
(ULB), Route de Lennik, 808-1070 Brussels, Belgium. IRIBHM,
Universit\'{e} Libre de Bruxelles (ULB), Route de Lennik,
808-1070 Brussels, Belgium. Breast Cancer Translational
Research Laboratory, Jules Bordet Institute, Universit\'{e}
Libre de Bruxelles (ULB), Boulevard de Waterloo, 125-1000
Brussels, Belgium. Breast Cancer Translational Research
Laboratory, Jules Bordet Institute, Universit\'{e} Libre de
Bruxelles (ULB), Boulevard de Waterloo, 125-1000 Brussels,
Belgium. Cancer Genome Project, Wellcome Trust Sanger
Institute, Wellcome Trust Genome Campus, Hinxton,
Cambridgeshire CB10 1SA, UK. IRIBHM, Universit\'{e} Libre de
Bruxelles (ULB), Route de Lennik, 808-1070 Brussels, Belgium.
Breast Cancer Translational Research Laboratory, Jules Bordet
Institute, Universit\'{e} Libre de Bruxelles (ULB), Boulevard
de Waterloo, 125-1000 Brussels, Belgium. Department of
Pathology, Jules Bordet Institute, Universit\'{e} Libre de
Bruxelles (ULB), Boulevard de Waterloo, 125-1000 Brussels,
Belgium. Department of Medical Oncology, Dana-Farber Cancer
Institute, 450 Brookline Avenue, Boston, MA 02215, USA.
Molecular Immunology Unit, Jules Bordet Institute,
Universit\'{e} Libre de Bruxelles (ULB), Boulevard de
Waterloo, 125-1000 Brussels, Belgium. Breast Cancer
Translational Research Laboratory, Jules Bordet Institute,
Universit\'{e} Libre de Bruxelles (ULB), Boulevard de
Waterloo, 125-1000 Brussels, Belgium. Department of Medicine,
Jules Bordet Institute, Universit\'{e} Libre de Bruxelles
(ULB), Boulevard de Waterloo, 125-1000 Brussels, Belgium.
Department of Genetics, H\^{o}pital Erasme, Route de Lennik,
808-1070 Brussels, Belgium. Cancer Genome Project, Wellcome
Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton,
Cambridgeshire CB10 1SA, UK. Breast Cancer Translational
Research Laboratory, Jules Bordet Institute, Universit\'{e}
Libre de Bruxelles (ULB), Boulevard de Waterloo, 125-1000
Brussels, Belgium; Department of Medicine, Jules Bordet
Institute, Universit\'{e} Libre de Bruxelles (ULB), Boulevard
de Waterloo, 125-1000 Brussels, Belgium. Electronic address:
christos.sotiriou@bordet.be. IRIBHM, Universit\'{e} Libre de
Bruxelles (ULB), Route de Lennik, 808-1070 Brussels, Belgium;
WELBIO, Route de Lennik, 808-1070 Brussels, Belgium.
Electronic address: vdetours@ulb.ac.be.",
abstract = "Little is known about how RNA editing operates in cancer.
Transcriptome analysis of 68 normal and cancerous breast
tissues revealed that the editing enzyme ADAR acts uniformly,
on the same loci, across tissues. In controlled ADAR
expression experiments, the editing frequency increased at all
loci with ADAR expression levels according to the logistic
model. Loci-specific ``editabilities,'' i.e., propensities to
be edited by ADAR, were quantifiable by fitting the logistic
function to dose-response data. The editing frequency was
increased in tumor cells in comparison to normal controls.
Type I interferon response and ADAR DNA copy number together
explained 53\% of ADAR expression variance in breast cancers.
ADAR silencing using small hairpin RNA lentivirus transduction
in breast cancer cell lines led to less cell proliferation and
more apoptosis. A-to-I editing is a pervasive, yet
reproducible, source of variation that is globally controlled
by 1q amplification and inflammation, both of which are highly
prevalent among human cancers.",
journal = "Cell reports",
volume = 13,
number = 2,
pages = "277--289",
month = "13~" # oct,
year = 2015,
url = "http://dx.doi.org/10.1016/j.celrep.2015.09.032",
file = "All Papers/F/Fumagalli et al. 2015 - Principles Governing A-to-I RNA Editing in the Breast Cancer Transcriptome.pdf",
language = "en",
issn = "2211-1247",
pmid = "26440892",
doi = "10.1016/j.celrep.2015.09.032"
}
@ARTICLE{Han2015-hl,
title = "The Genomic Landscape and Clinical Relevance of {A-to-I} {RNA}
Editing in Human Cancers",
author = "Han, Leng and Diao, Lixia and Yu, Shuangxing and Xu, Xiaoyan
and Li, Jie and Zhang, Rui and Yang, Yang and Werner, Henrica
M J and Eterovic, A Karina and Yuan, Yuan and Li, Jun and
Nair, Nikitha and Minelli, Rosalba and Tsang, Yiu Huen and
Cheung, Lydia W T and Jeong, Kang Jin and Roszik, Jason and
Ju, Zhenlin and Woodman, Scott E and Lu, Yiling and Scott,
Kenneth L and Li, Jin Billy and Mills, Gordon B and Liang, Han",
affiliation = "Department of Bioinformatics and Computational Biology, The
University of Texas MD Anderson Cancer Center, Houston, TX
77030, USA. Department of Bioinformatics and Computational
Biology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA. Department of Systems Biology, The
University of Texas MD Anderson Cancer Center, Houston, TX
77030, USA. Department of Bioinformatics and Computational
Biology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA; Department of Pathophysiology, College
of Basic Medicine, China Medical University, Shenyang City,
Liaoning Province 110001, China. Department of Systems
Biology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA. Department of Genetics, Stanford
University, Stanford, CA 94305, USA. Department of
Bioinformatics and Computational Biology, The University of
Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
Division of Biostatistics, The University of Texas Health
Science Center at Houston, School of Public Health, Houston TX
77030, USA. Department of Systems Biology, The University of
Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
Women's Department, Haukeland University Hospital, Jonas
Liesvei 72, 5053 Bergen, Norway. Department of Systems
Biology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA. Department of Bioinformatics and
Computational Biology, The University of Texas MD Anderson
Cancer Center, Houston, TX 77030, USA. Department of
Bioinformatics and Computational Biology, The University of
Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Molecular and Human Genetics, Baylor College of
Medicine, Houston, TX 77030, USA. Department of Molecular and
Human Genetics, Baylor College of Medicine, Houston, TX 77030,
USA. Department of Molecular and Human Genetics, Baylor
College of Medicine, Houston, TX 77030, USA. Department of
Systems Biology, The University of Texas MD Anderson Cancer
Center, Houston, TX 77030, USA. Department of Systems Biology,
The University of Texas MD Anderson Cancer Center, Houston, TX
77030, USA. Department of Melanoma Medical Oncology, The
University of Texas MD Anderson Cancer Center, Houston, TX
77030, USA. Department of Bioinformatics and Computational
Biology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA. Department of Melanoma Medical
Oncology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA. Department of Systems Biology, The
University of Texas MD Anderson Cancer Center, Houston, TX
77030, USA. Department of Molecular and Human Genetics, Baylor
College of Medicine, Houston, TX 77030, USA. Department of
Genetics, Stanford University, Stanford, CA 94305, USA.
Department of Systems Biology, The University of Texas MD
Anderson Cancer Center, Houston, TX 77030, USA. Electronic
address: gmills@mdanderson.org. Department of Bioinformatics
and Computational Biology, The University of Texas MD Anderson
Cancer Center, Houston, TX 77030, USA; Department of Systems
Biology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA. Electronic address:
hliang1@mdanderson.org.",
abstract = "Adenosine-to-inosine (A-to-I) RNA editing is a widespread
post-transcriptional mechanism, but its genomic landscape and
clinical relevance in cancer have not been investigated
systematically. We characterized the global A-to-I RNA editing
profiles of 6,236 patient samples of 17 cancer types from The
Cancer Genome Atlas and revealed a striking diversity of
altered RNA-editing patterns in tumors relative to normal
tissues. We identified an appreciable number of clinically
relevant editing events, many of which are in noncoding
regions. We experimentally demonstrated the effects of several
cross-tumor nonsynonymous RNA editing events on cell viability
and provide the evidence that RNA editing could selectively
affect drug sensitivity. These results highlight RNA editing
as an exciting theme for investigating cancer mechanisms,
biomarkers, and treatments.",
journal = "Cancer cell",
volume = 28,
number = 4,
pages = "515--528",
month = "12~" # oct,
year = 2015,
url = "http://dx.doi.org/10.1016/j.ccell.2015.08.013",
language = "en",
issn = "1535-6108, 1878-3686",
pmid = "26439496",
doi = "10.1016/j.ccell.2015.08.013",
pmc = "PMC4605878"
}
@ARTICLE{Paz-Yaacov2015-gg,
title = "Elevated {RNA} Editing Activity Is a Major Contributor to
Transcriptomic Diversity in Tumors",
author = "Paz-Yaacov, Nurit and Bazak, Lily and Buchumenski, Ilana and
Porath, Hagit T and Danan-Gotthold, Miri and Knisbacher,
Binyamin A and Eisenberg, Eli and Levanon, Erez Y",
affiliation = "The Mina and Everard Goodman Faculty of Life Sciences,
Bar-Ilan University, Ramat Gan 52900, Israel. The Mina and
Everard Goodman Faculty of Life Sciences, Bar-Ilan University,
Ramat Gan 52900, Israel. The Mina and Everard Goodman Faculty
of Life Sciences, Bar-Ilan University, Ramat Gan 52900,
Israel. The Mina and Everard Goodman Faculty of Life Sciences,
Bar-Ilan University, Ramat Gan 52900, Israel. The Mina and
Everard Goodman Faculty of Life Sciences, Bar-Ilan University,
Ramat Gan 52900, Israel. The Mina and Everard Goodman Faculty
of Life Sciences, Bar-Ilan University, Ramat Gan 52900,
Israel. School of Physics and Astronomy, Raymond and Beverly
Sackler Faculty of Exact Sciences, Tel Aviv University, Tel
Aviv 69978, Israel. Electronic address: elieis@post.tau.ac.il.
The Mina and Everard Goodman Faculty of Life Sciences,
Bar-Ilan University, Ramat Gan 52900, Israel. Electronic
address: erez.levanon@biu.ac.il.",
abstract = "Genomic mutations in key genes are known to drive
tumorigenesis and have been the focus of much attention in
recent years. However, genetic content also may change farther
downstream. RNA editing alters the mRNA sequence from its
genomic blueprint in a dynamic and flexible way. A few
isolated cases of editing alterations in cancer have been
reported previously. Here, we provide a transcriptome-wide
characterization of RNA editing across hundreds of cancer
samples from multiple cancer tissues, and we show that A-to-I
editing and the enzymes mediating this modification are
significantly altered, usually elevated, in most cancer types.
Increased editing activity is found to be associated with
patient survival. As is the case with somatic mutations in
DNA, most of these newly introduced RNA mutations are likely
passengers, but a few may serve as drivers that may be novel
candidates for therapeutic and diagnostic purposes.",
journal = "Cell reports",
volume = 13,
number = 2,
pages = "267--276",
month = "13~" # oct,
year = 2015,
url = "http://dx.doi.org/10.1016/j.celrep.2015.08.080",
file = "All Papers/P/Paz-Yaacov et al. 2015 - Elevated RNA Editing Activity Is a Major Contributor to Transcriptomic Diversity in Tumors.pdf",
language = "en",
issn = "2211-1247",
pmid = "26440895",
doi = "10.1016/j.celrep.2015.08.080"
}
@ARTICLE{Grieb2014-xs,
title = "{MTBP} is Over-expressed in Triple Negative Breast Cancer and
Contributes to its Growth and Survival",
author = "Grieb, Brian C and Chen, Xi and Eischen, Christine M",
abstract = "Triple negative breast cancer (TNBC) is a clinically aggressive
subtype of breast cancer commonly resistant to therapeutics that
have been successful in increasing survival in ER+ and HER2+
breast cancer patients. As such, identifying factors that
contribute to poor patient outcomes and mediate the growth and
survival of TNBC cells remain important areas of investigation.
MTBP (MDM2 Binding Protein), a gene linked to cellular
proliferation and a transcriptional target of the MYC oncogene,
is over-expressed in human malignancies, yet its contribution to
cancer remains unresolved. Evaluation of mRNA expression and copy
number variation data from The Cancer Genome Atlas (TCGA)
revealed MTBP is commonly overexpressed in breast cancer and 19\%
show amplification of MTBP. Increased transcript or gene
amplification of MTBP significantly correlated with reduced
breast cancer patient survival. Further analysis revealed that
while MTBP mRNA is over-expressed in both ER+ and HER2+ breast
cancers, its expression is highest in TNBC. MTBP mRNA and protein
levels were also significantly elevated in a panel of human TNBC
cell lines. Knockdown of MTBP in TNBC model systems induced
apoptosis and significantly reduced TNBC cell growth and soft
agar colony formation, which was rescued by expression of
shRNA-resistant Mtbp. Notably, inducible knockdown of MTBP
expression significantly impaired TNBC tumor growth, in vivo,
including in established tumors. Thus, these data emphasize MTBP
is important for the growth and survival of TNBC and warrants
further investigation as a potential novel therapeutic target.
Implications: MTBP significantly contributes to breast cancer
survival and is a potential novel therapeutic target in TNBC.",
journal = "Molecular cancer research: MCR",
month = "27~" # may,
year = 2014,
url = "http://mcr.aacrjournals.org/content/early/2014/05/24/1541-7786.MCR-14-0069.abstract",
annote = "10.1158/1541-7786.MCR-14-0069",
file = "All Papers/G/Grieb et al. 2014 - MTBP is Over-expressed in Triple Negative Breast Cancer and Contributes to its Growth and Survival.pdf",
issn = "1541-7786",
doi = "10.1158/1541-7786.MCR-14-0069"
}
@ARTICLE{Grabiner2014-go,
title = "A diverse array of cancer-associated {MTOR} mutations are
hyperactivating and can predict rapamycin sensitivity",
author = "Grabiner, Brian C and Nardi, Valentina and Birsoy, K\i{}vanc
and Possemato, Richard and Shen, Kuang and Sinha, Sumi and
Jordan, Alexander and Beck, Andrew H and Sabatini, David M",
affiliation = "1Whitehead Institute for Biomedical Research; 2Howard Hughes
Medical Institute and Department of Biology, MIT; 3Broad
Institute of Harvard and MIT; 4The David H. Koch Institute for
Integrative Cancer Research at MIT, Cambridge; 5Department of
Pathology, Massachusetts General Hospital Cancer Center; and
6Department of Pathology, Beth Israel Deaconess Medical
Center, Harvard Medical School, Boston, Massachusetts.",
abstract = "Genes encoding components of the PI3K-AKT-mTOR signaling axis
are frequently mutated in cancer, but few mutations have been
characterized in MTOR, the gene encoding the mTOR kinase.
Using publicly available tumor genome sequencing data, we
generated a comprehensive catalog of mTOR pathway mutations in
cancer, identifying 33 MTOR mutations that confer pathway
hyperactivation. The mutations cluster in six distinct regions
in the C-terminal half of mTOR and occur in multiple cancer
types, with one cluster particularly prominent in kidney
cancer. The activating mutations do not affect mTOR complex
assembly, but a subset reduces binding to the mTOR inhibitor
DEPTOR. mTOR complex 1 (mTORC1) signaling in cells expressing
various activating mutations remains sensitive to
pharmacologic mTOR inhibition, but is partially resistant to
nutrient deprivation. Finally, cancer cell lines with
hyperactivating MTOR mutations display heightened sensitivity
to rapamycin both in culture and in vivo xenografts,
suggesting that such mutations confer mTOR pathway dependency.",
journal = "Cancer discovery",
volume = 4,
number = 5,
pages = "554--563",
month = may,
year = 2014,
url = "http://dx.doi.org/10.1158/2159-8290.CD-13-0929",
file = "All Papers/G/Grabiner et al. 2014 - A diverse array of cancer-associated MTOR mutations are hyperactivating and can predict rapamycin sensitivity.pdf",
language = "en",
issn = "2159-8274, 2159-8290",
pmid = "24631838",
doi = "10.1158/2159-8290.CD-13-0929",
pmc = "PMC4012430"
}
@ARTICLE{Wagle2014-cl,
title = "Activating {mTOR} mutations in a patient with an extraordinary
response on a phase {I} trial of everolimus and pazopanib",
author = "Wagle, Nikhil and Grabiner, Brian C and Van Allen, Eliezer M
and Hodis, Eran and Jacobus, Susanna and Supko, Jeffrey G and
Stewart, Michelle and Choueiri, Toni K and Gandhi, Leena and
Cleary, James M and Elfiky, Aymen A and Taplin, Mary Ellen and
Stack, Edward C and Signoretti, Sabina and Loda, Massimo and
Shapiro, Geoffrey I and Sabatini, David M and Lander, Eric S
and Gabriel, Stacey B and Kantoff, Philip W and Garraway, Levi
A and Rosenberg, Jonathan E",
affiliation = "Departments of 1Medical Oncology and 2Biostatistics and
Computational Biology, 3Center for Molecular Oncologic
Pathology, Dana-Farber Cancer Institute; Departments of
4Medicine and 5Pathology, Brigham and Women's Hospital,
Harvard Medical School; 6Division of Hematology/Oncology,
Massachusetts General Hospital, Boston; 7Broad Institute of
Harvard and MIT; 8Department of Biology, Whitehead Institute
for Biomedical Research; 9Howard Hughes Medical Institute,
Massachusetts Institute of Technology, Cambridge,
Massachusetts; and 10Department of Medicine, Memorial
Sloan-Kettering Cancer Center, New York, New York.",
abstract = "Understanding the genetic mechanisms of sensitivity to
targeted anticancer therapies may improve patient selection,
response to therapy, and rational treatment designs. One
approach to increase this understanding involves detailed
studies of exceptional responders: rare patients with
unexpected exquisite sensitivity or durable responses to
therapy. We identified an exceptional responder in a phase I
study of pazopanib and everolimus in advanced solid tumors.
Whole-exome sequencing of a patient with a 14-month complete
response on this trial revealed two concurrent mutations in
mTOR, the target of everolimus. In vitro experiments
demonstrate that both mutations are activating, suggesting a
biologic mechanism for exquisite sensitivity to everolimus in
this patient. The use of precision (or ``personalized'')
medicine approaches to screen patients with cancer for
alterations in the mTOR pathway may help to identify subsets
of patients who may benefit from targeted therapies directed
against mTOR.",
journal = "Cancer discovery",
volume = 4,
number = 5,
pages = "546--553",
month = may,
year = 2014,
url = "http://dx.doi.org/10.1158/2159-8290.CD-13-0353",
file = "All Papers/W/Wagle et al. 2014 - Activating mTOR mutations in a patient with an extraordinary response on a phase I trial of everolimus and pazopanib.pdf",
language = "en",
issn = "2159-8274, 2159-8290",
pmid = "24625776",
doi = "10.1158/2159-8290.CD-13-0353",
pmc = "PMC4122326"
}
@ARTICLE{Cancer_Genome_Atlas_Network2012-nz,
title = "Comprehensive molecular portraits of human breast tumours",
author = "{Cancer Genome Atlas Network}",
abstract = "We analysed primary breast cancers by genomic DNA copy number
arrays, DNA methylation, exome sequencing, messenger RNA arrays,
microRNA sequencing and reverse-phase protein arrays. Our ability
to integrate information across platforms provided key insights
into previously defined gene expression subtypes and demonstrated
the existence of four main breast cancer classes when combining
data from five platforms, each of which shows significant
molecular heterogeneity. Somatic mutations in only three genes
(TP53, PIK3CA and GATA3) occurred at >10\% incidence across all
breast cancers; however, there were numerous subtype-associated
and novel gene mutations including the enrichment of specific
mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype.
We identified two novel protein-expression-defined subgroups,
possibly produced by stromal/microenvironmental elements, and
integrated analyses identified specific signalling pathways
dominant in each molecular subtype including a
HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature
within the HER2-enriched expression subtype. Comparison of
basal-like breast tumours with high-grade serous ovarian tumours
showed many molecular commonalities, indicating a related
aetiology and similar therapeutic opportunities. The biological
finding of the four main breast cancer subtypes caused by
different subsets of genetic and epigenetic abnormalities raises
the hypothesis that much of the clinically observable plasticity
and heterogeneity occurs within, and not across, these major
biological subtypes of breast cancer.",
journal = "Nature",
volume = 490,
number = 7418,
pages = "61--70",
month = "4~" # oct,
year = 2012,
url = "http://dx.doi.org/10.1038/nature11412",
file = "All Papers/C/Cancer Genome Atlas Network 2012 - Comprehensive molecular portraits of human breast tumours.pdf",
language = "en",
issn = "0028-0836, 1476-4687",
pmid = "23000897",
doi = "10.1038/nature11412",
pmc = "PMC3465532"
}
@ARTICLE{Schroeder2013-hd,
title = "Visualizing multidimensional cancer genomics data",
author = "Schroeder, Michael P and Gonzalez-Perez, Abel and Lopez-Bigas,
Nuria",
abstract = "Cancer genomics projects employ high-throughput technologies to
identify the complete catalog of somatic alterations that
characterize the genome, transcriptome and epigenome of cohorts
of tumor samples. Examples include projects carried out by the
International Cancer Genome Consortium (ICGC) and The Cancer
Genome Atlas (TCGA). A crucial step in the extraction of
knowledge from the data is the exploration by experts of the
different alterations, as well as the multiple relationships
between them. To that end, the use of intuitive visualization
tools that can integrate different types of alterations with
clinical data is essential to the field of cancer genomics. Here,
we review effective and common visualization techniques for
exploring oncogenomics data and discuss a selection of tools that
allow researchers to effectively visualize multidimensional
oncogenomics datasets. The review covers visualization methods
employed by tools such as Circos, Gitools, the Integrative
Genomics Viewer, Cytoscape, Savant Genome Browser, StratomeX and
platforms such as cBio Cancer Genomics Portal, IntOGen, the UCSC
Cancer Genomics Browser, the Regulome Explorer and the Cancer
Genome Workbench.",
journal = "Genome medicine",
volume = 5,
number = 1,
pages = "9",
month = jan,
year = 2013,
url = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3706894&tool=pmcentrez&rendertype=abstract",
file = "All Papers/S/Schroeder et al. 2013 - Visualizing multidimensional cancer genomics data.pdf",
issn = "1756-994X",
pmid = "23363777",
doi = "10.1186/gm413"
}
@ARTICLE{Nielsen2010-ch,
title = "Visualizing genomes: techniques and challenges",
author = "Nielsen, Cydney B and Cantor, Michael and Dubchak, Inna and
Gordon, David and Wang, Ting",
abstract = "As our ability to generate sequencing data continues to increase,
data analysis is replacing data generation as the rate-limiting
step in genomics studies. Here we provide a guide to genomic data
visualization tools that facilitate analysis tasks by enabling
researchers to explore, interpret and manipulate their data, and
in some cases perform on-the-fly computations. We will discuss
graphical methods designed for the analysis of de novo sequencing
assemblies and read alignments, genome browsing, and comparative
genomics, highlighting the strengths and limitations of these
approaches and the challenges ahead.",
journal = "Nature methods",
volume = 7,
number = "3 Suppl",
pages = "S5--S15",
month = mar,
year = 2010,
url = "http://www.ncbi.nlm.nih.gov/pubmed/20195257",
keywords = "Computer Graphics; Genome; Image Processing, Computer-Assisted",
issn = "1548-7091, 1548-7105",
pmid = "20195257",
doi = "10.1038/nmeth.1422"
}
@ARTICLE{Wang2015-xx,
title = "Open source libraries and frameworks for biological data
visualisation: a guide for developers",
author = "Wang, Rui and Perez-Riverol, Yasset and Hermjakob, Henning and
Vizca\'{\i}no, Juan Antonio",
affiliation = "European Molecular Biology Laboratory, European Bioinformatics
Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton,
Cambridge, UK.",
abstract = "Recent advances in high-throughput experimental techniques
have led to an exponential increase in both the size and the
complexity of the data sets commonly studied in biology. Data
visualisation is increasingly used as the key to unlock this
data, going from hypothesis generation to model evaluation and
tool implementation. It is becoming more and more the heart of
bioinformatics workflows, enabling scientists to reason and
communicate more effectively. In parallel, there has been a
corresponding trend towards the development of related
software, which has triggered the maturation of different
visualisation libraries and frameworks. For bioinformaticians,
scientific programmers and software developers, the main
challenge is to pick out the most fitting one(s) to create
clear, meaningful and integrated data visualisation for their
particular use cases. In this review, we introduce a
collection of open source or free to use libraries and
frameworks for creating data visualisation, covering the
generation of a wide variety of charts and graphs. We will
focus on software written in Java, JavaScript or Python. We
truly believe this software offers the potential to turn
tedious data into exciting visual stories.",
journal = "Proteomics",
volume = 15,
number = 8,
pages = "1356--1374",
month = apr,
year = 2015,
url = "http://dx.doi.org/10.1002/pmic.201400377",
file = "All Papers/W/Wang et al. 2015 - Open source libraries and frameworks for biological data visualisation - a guide for developers.pdf",
keywords = "Bioinformatics; Chart; Hierarchy; Network; Software library",
language = "en",
issn = "1615-9853, 1615-9861",
pmid = "25475079",
doi = "10.1002/pmic.201400377",
pmc = "PMC4409855"
}
@ARTICLE{Zhang2013-ul,
title = "{RCircos}: an {R} package for Circos {2D} track plots",
author = "Zhang, Hongen and Meltzer, Paul and Davis, Sean",
abstract = "BACKGROUND: Circos is a Perl language based software package for
visualizing similarities and differences of genome structure and
positional relationships between genomic intervals. Running
Circos requires extra data processing procedures to prepare plot
data files and configure files from datasets, which limits its
capability of integrating directly with other software tools such
as R. Recently published R Bioconductor package ggbio provides a
function to display genomic data in circular layout based on
multiple other packages, which increases its complexity of usage
and decreased the flexibility in integrating with other R
pipelines. RESULTS: We implemented an R package, RCircos, using
only R packages that come with R base installation. The package
supports Circos 2D data track plots such as scatter, line,
histogram, heatmap, tile, connectors, links, and text labels.
Each plot is implemented with a specific function and input data
for all functions are data frames which can be objects read from
text files or generated with other R pipelines. CONCLUSION:
RCircos package provides a simple and flexible way to make Circos
2D track plots with R and could be easily integrated into other R
data processing and graphic manipulation pipelines for presenting
large-scale multi-sample genomic research data. It can also serve
as a base tool to generate complex Circos images.",
journal = "BMC bioinformatics",
volume = 14,
pages = "244",
month = jan,
year = 2013,
url = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3765848&tool=pmcentrez&rendertype=abstract",
file = "All Papers/Z/Zhang et al. 2013 - RCircos - an R package for Circos 2D track plots.pdf",
keywords = "Animals; Computer Graphics; Gene Expression Profiling; Genome;
Genome: genetics; Genomics; Genomics: instrumentation; Image
Processing, Computer-Assisted; Mice; Rats; Software",
issn = "1471-2105",
pmid = "23937229",
doi = "10.1186/1471-2105-14-244"
}
@ARTICLE{Gu2016-xb,
title = "Complex heatmaps reveal patterns and correlations in
multidimensional genomic data",
author = "Gu, Zuguang and Eils, Roland and Schlesner, Matthias",
affiliation = "Division of Theoretical Bioinformatics Heidelberg Center for
Personalized Oncology (DKFZ-HIPO), German Cancer Research
Center (DKFZ), Heidelberg, Germany. Division of Theoretical
Bioinformatics Heidelberg Center for Personalized Oncology
(DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg,
Germany Department for Bioinformatics and Functional Genomics,
Institute for Pharmacy and Molecular Biotechnology (IPMB) and
BioQuant, Heidelberg University, Heidelberg, Germany. Division
of Theoretical Bioinformatics.",
abstract = "Parallel heatmaps with carefully designed annotation graphics
are powerful for efficient visualization of patterns and
relationships among high dimensional genomic data. Here we
present the ComplexHeatmap package that provides rich
functionalities for customizing heatmaps, arranging multiple
parallel heatmaps and including user-defined annotation
graphics. We demonstrate the power of ComplexHeatmap to easily
reveal patterns and correlations among multiple sources of
information with four real-world datasets. AVAILABILITY AND
IMPLEMENTATION: The ComplexHeatmap package and documentation
are freely available from the Bioconductor project:
http://www.bioconductor.org/packages/devel/bioc/html/ComplexHeatmap.html
CONTACT: m.schlesner@dkfz.deSupplementary information:
Supplementary data are available at Bioinformatics online.",
journal = "Bioinformatics",
month = "20~" # may,
year = 2016,
url = "http://dx.doi.org/10.1093/bioinformatics/btw313",
file = "All Papers/G/Gu et al. 2016 - Complex heatmaps reveal patterns and correlations in multidimensional genomic data.pdf",
language = "en",
issn = "1367-4803, 1367-4811",
pmid = "27207943",
doi = "10.1093/bioinformatics/btw313"
}
% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Gu2016-ir,
title = "gtrellis: an {R/Bioconductor} package for making genome-level
Trellis graphics",
author = "Gu, Zuguang and Eils, Roland and Schlesner, Matthias",
affiliation = "Division of Theoretical Bioinformatics (B080), German Cancer
Research Center (DKFZ), Im Neuenheimer Feld 280, 69120,
Heidelberg, Germany. Heidelberg Center for Personalized
Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Im
Neuenheimer Feld 280, 69120, Heidelberg, Germany. Division of
Theoretical Bioinformatics (B080), German Cancer Research
Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg,
Germany. Heidelberg Center for Personalized Oncology
(DKFZ-HIPO), German Cancer Research Center (DKFZ), Im
Neuenheimer Feld 280, 69120, Heidelberg, Germany. Department
for Bioinformatics and Functional Genomics, Institute for
Pharmacy and Molecular Biotechnology (IPMB) and BioQuant
Center, Heidelberg University, Im Neuenheimer Feld 267, 69120,
Heidelberg, Germany. Division of Theoretical Bioinformatics
(B080), German Cancer Research Center (DKFZ), Im Neuenheimer
Feld 280, 69120, Heidelberg, Germany. m.schlesner@dkfz.de.",
abstract = "BACKGROUND: Trellis graphics are a visualization method that
splits data by one or more categorical variables and displays
subsets of the data in a grid of panels. Trellis graphics are
broadly used in genomic data analysis to compare statistics
over different categories in parallel and reveal multivariate
relationships. However, current software packages to produce
Trellis graphics have not been designed with genomic data in
mind and lack some functionality that is required for
effective visualization of genomic data. RESULTS: Here we
introduce the gtrellis package which provides an efficient and
extensible way to visualize genomic data in a Trellis layout.
gtrellis provides highly flexible Trellis layouts which allow
efficient arrangement of genomic categories on the plot. It
supports multiple-track visualization, which makes it
straightforward to visualize several properties of genomic
data in parallel to explain complex relationships. In
addition, gtrellis provides an extensible framework that
allows adding user-defined graphics. CONCLUSIONS: The gtrellis
package provides an easy and effective way to visualize
genomic data and reveal high dimensional relationships on a
genome-wide scale. gtrellis can be flexibly extended and thus
can also serve as a base package for highly specific purposes.
gtrellis makes it easy to produce novel visualizations, which
can lead to the discovery of previously unrecognized patterns
in genomic data.",
journal = "BMC bioinformatics",
publisher = "bmcbioinformatics.biomedcentral. …",
volume = 17,
pages = "169",
month = "18~" # apr,
year = 2016,
url = "http://dx.doi.org/10.1186/s12859-016-1051-4",
file = "All Papers/G/Gu et al. 2016 - gtrellis - an R - Bioconductor package for making genome-level Trellis graphics.pdf",
keywords = "Genomic data visualization; Software; Trellis graphics",
issn = "1471-2105",
pmid = "27089965",
doi = "10.1186/s12859-016-1051-4",
pmc = "PMC4835841"
}
@ARTICLE{Zhang2016-lc,
title = "{caOmicsV}: an {R} package for visualizing multidimensional
cancer genomic data",
author = "Zhang, Hongen and Meltzer, Paul S and Davis, Sean R",
affiliation = "Genetics Branch, Center for Cancer Research, National Cancer
Institute, National Institutes of Health, Building 37, Room
6138, 37 Convent Drive, Bethesda, MD, 20892-4265, USA.
Genetics Branch, Center for Cancer Research, National Cancer
Institute, National Institutes of Health, Building 37, Room
6138, 37 Convent Drive, Bethesda, MD, 20892-4265, USA.
Genetics Branch, Center for Cancer Research, National Cancer
Institute, National Institutes of Health, Building 37, Room
6138, 37 Convent Drive, Bethesda, MD, 20892-4265, USA.
sdavis2@mail.nih.gov.",
abstract = "BACKGROUND: Translational genomics research in cancers, e.g.,
International Cancer Genome Consortium (ICGC) and The Cancer
Genome Atlas (TCGA), has generated large multidimensional
datasets from high-throughput technologies. Data analysis at
multidimensional level will greatly benefit clinical
applications of genomic information in diagnosis, prognosis
and therapeutics of cancers. To help, tools to effectively
visualize integrated multidimensional data are important for
understanding and describing the relationship between genomic
variations and cancers. RESULTS: We implemented the R package,
caOmicsV, to provide methods under R environment to visualize
multidimensional cancer genomic data in two layouts: matrix
layout and combined biological network and circular layout.
Both layouts support to display sample information, gene
expression (e.g., RNA and miRNA), DNA methylation, DNA copy
number variations, and summarized data. A set of supplemental
functions are included in the caOmicsV package to help users
in generation of plot data sets from multiple genomic datasets
with given gene names and sample names. Default plot methods
for both layouts for easy use are also implemented.
CONCLUSION: caOmicsV package provides an easy and flexible way
to visualize integrated multidimensional cancer genomic data
under R environment.",
journal = "BMC bioinformatics",
volume = 17,
pages = "141",
month = "22~" # mar,
year = 2016,
url = "http://dx.doi.org/10.1186/s12859-016-0989-6",
file = "All Papers/Z/Zhang et al. 2016 - caOmicsV - an R package for visualizing multidimensional cancer genomic data.pdf",
keywords = "Genomic data visualization; Multidimensional data
visualization; R package; Software",
language = "en",
issn = "1471-2105",
pmid = "27005934",
doi = "10.1186/s12859-016-0989-6",
pmc = "PMC4804509"
}
@ARTICLE{Colaprico2016-nj,
title = "{TCGAbiolinks}: an {R/Bioconductor} package for integrative
analysis of {TCGA} data",
author = "Colaprico, Antonio and Silva, Tiago C and Olsen, Catharina and
Garofano, Luciano and Cava, Claudia and Garolini, Davide and
Sabedot, Thais S and Malta, Tathiane M and Pagnotta, Stefano M
and Castiglioni, Isabella and Ceccarelli, Michele and
Bontempi, Gianluca and Noushmehr, Houtan",
affiliation = "Interuniversity Institute of Bioinformatics in Brussels (IB),
Brussels, Belgium Machine Learning Group (MLG), Department
d'Informatique, Universit\'{e} libre de Bruxelles (ULB),
Brussels, Belgium. Department of Genetics Ribeir\~{a}o Preto
Medical School, University of S\~{a}o Paulo, Ribeir\~{a}o
Preto, S\~{a}o Paulo, Brazil Center for Integrative Systems
Biology - CISBi, NAP/USP, Ribeir\~{a}o Preto, S\~{a}o Paulo,
Brazil. Interuniversity Institute of Bioinformatics in
Brussels (IB), Brussels, Belgium Machine Learning Group (MLG),
Department d'Informatique, Universit\'{e} libre de Bruxelles
(ULB), Brussels, Belgium. Department of Science and
Technology, University of Sannio, Benevento, Italy Unlimited
Software srl, Naples, Italy. Institute of Molecular Bioimaging
and Physiology of the National Research Council (IBFM-CNR),
Milan, Italy. Physics for Complex Systems, Department of
Physics, University of Turin, Italy. Department of Genetics
Ribeir\~{a}o Preto Medical School, University of S\~{a}o
Paulo, Ribeir\~{a}o Preto, S\~{a}o Paulo, Brazil Center for
Integrative Systems Biology - CISBi, NAP/USP, Ribeir\~{a}o
Preto, S\~{a}o Paulo, Brazil. Department of Genetics
Ribeir\~{a}o Preto Medical School, University of S\~{a}o
Paulo, Ribeir\~{a}o Preto, S\~{a}o Paulo, Brazil Center for
Integrative Systems Biology - CISBi, NAP/USP, Ribeir\~{a}o
Preto, S\~{a}o Paulo, Brazil. Department of Science and
Technology, University of Sannio, Benevento, Italy
Bioinformatics Laboratory, BIOGEM, Ariano Irpino, Avellino,
Italy. Institute of Molecular Bioimaging and Physiology of the
National Research Council (IBFM-CNR), Milan, Italy. Qatar
Computing Research Institute (QCRI), HBKU, Doha, Qatar.
Interuniversity Institute of Bioinformatics in Brussels (IB),
Brussels, Belgium Machine Learning Group (MLG), Department
d'Informatique, Universit\'{e} libre de Bruxelles (ULB),
Brussels, Belgium gbonte@ulb.ac.be. Department of Genetics
Ribeir\~{a}o Preto Medical School, University of S\~{a}o
Paulo, Ribeir\~{a}o Preto, S\~{a}o Paulo, Brazil Center for
Integrative Systems Biology - CISBi, NAP/USP, Ribeir\~{a}o
Preto, S\~{a}o Paulo, Brazil houtan@usp.br.",
abstract = "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.",
journal = "Nucleic acids research",
volume = 44,
number = 8,
pages = "e71",
month = "5~" # may,
year = 2016,
url = "http://dx.doi.org/10.1093/nar/gkv1507",
file = "All Papers/C/Colaprico et al. 2016 - TCGAbiolinks - an R - Bioconductor package for integrative analysis of TCGA data.pdf",
language = "en",
issn = "0305-1048, 1362-4962",
pmid = "26704973",
doi = "10.1093/nar/gkv1507",
pmc = "PMC4856967"
}
@ARTICLE{Samur2014-vz,
title = "{RTCGAToolbox}: a new tool for exporting {TCGA} Firehose data",
author = "Samur, Mehmet Kemal",
affiliation = "Department of Biostatistics and Computational Biology,
Dana-Farber Cancer Institute and Harvard School of Public
Health, Boston, Massachusetts, United States of America; Lebow
Institute of Myeloma Therapeutics and Jerome Lipper Multiple
Myeloma Center, Dana-Farber Cancer Institute and Harvard
Medical School, Boston, Massachusetts, United States of
America.",
abstract = "BACKGROUND \& OBJECTIVE: Managing data from large-scale
projects (such as The Cancer Genome Atlas (TCGA)) for further
analysis is an important and time consuming step for research
projects. Several efforts, such as the Firehose project, make
TCGA pre-processed data publicly available via web services
and data portals, but this information must be managed,
downloaded and prepared for subsequent steps. We have
developed an open source and extensible R based data client
for pre-processed data from the Firehouse, and demonstrate its
use with sample case studies. Results show that our
RTCGAToolbox can facilitate data management for researchers
interested in working with TCGA data. The RTCGAToolbox can
also be integrated with other analysis pipelines for further
data processing. AVAILABILITY AND IMPLEMENTATION: The
RTCGAToolbox is open-source and licensed under the GNU General
Public License Version 2.0. All documentation and source code
for RTCGAToolbox is freely available at
http://mksamur.github.io/RTCGAToolbox/ for Linux and Mac OS X
operating systems.",
journal = "PloS one",
volume = 9,
number = 9,
pages = "e106397",
month = "2~" # sep,
year = 2014,
url = "http://dx.doi.org/10.1371/journal.pone.0106397",
file = "All Papers/S/Samur 2014 - RTCGAToolbox - a new tool for exporting TCGA Firehose data.pdf",
language = "en",
issn = "1932-6203",
pmid = "25181531",
doi = "10.1371/journal.pone.0106397",
pmc = "PMC4152273"
}
@ARTICLE{Wan2016-ol,
title = "{TCGA2STAT}: simple {TCGA} data access for integrated
statistical analysis in {R}",
author = "Wan, Ying-Wooi and Allen, Genevera I and Liu, Zhandong",
affiliation = "Computational and Integrative Biomedical Research Center,
Department of Obstetrics and Gynecology, Baylor College of
Medicine, Houston, TX, USA. Department of Statistics and
Electrical \& Computer Engineering, Rice University, Houston,
TX, USA and Department of Pediatrics-Neurology, Jan and Dan
Duncan Neurological Research Institute, Texas Children's
Hospital, Baylor College of Medicine, Houston, TX, USA.
Computational and Integrative Biomedical Research Center,
Department of Pediatrics-Neurology, Jan and Dan Duncan
Neurological Research Institute, Texas Children's Hospital,
Baylor College of Medicine, Houston, TX, USA.",
abstract = "MOTIVATION: Massive amounts of high-throughput genomics data
profiled from tumor samples were made publicly available by
the Cancer Genome Atlas (TCGA). RESULTS: We have developed an
open source software package, TCGA2STAT, to obtain the TCGA
data, wrangle it, and pre-process it into a format ready for
multivariate and integrated statistical analysis in the R
environment. In a user-friendly format with one single
function call, our package downloads and fully processes the
desired TCGA data to be seamlessly integrated into a
computational analysis pipeline. No further technical or
biological knowledge is needed to utilize our software, thus
making TCGA data easily accessible to data scientists without
specific domain knowledge. AVAILABILITY AND IMPLEMENTATION:
TCGA2STAT is available from the
https://cran.r-project.org/web/packages/TCGA2STAT/index.html
SUPPLEMENTARY INFORMATION: Supplementary data are available at
Bioinformatics online. CONTACT: zhandong.liu@bcm.edu.",
journal = "Bioinformatics",
volume = 32,
number = 6,
pages = "952--954",
month = "15~" # mar,
year = 2016,
url = "http://dx.doi.org/10.1093/bioinformatics/btv677",
file = "All Papers/W/Wan et al. 2016 - TCGA2STAT - simple TCGA data access for integrated statistical analysis in R.pdf",
language = "en",
issn = "1367-4803, 1367-4811",
pmid = "26568634",
doi = "10.1093/bioinformatics/btv677"
}
@ARTICLE{Durinck2009-ge,
title = "Mapping identifiers for the integration of genomic datasets
with the {R/Bioconductor} package biomaRt",
author = "Durinck, Steffen and Spellman, Paul T and Birney, Ewan and
Huber, Wolfgang",
affiliation = "Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
steffen@stat.berkeley.edu",
abstract = "Genomic experiments produce multiple views of biological
systems, among them are DNA sequence and copy number
variation, and mRNA and protein abundance. Understanding these
systems needs integrated bioinformatic analysis. Public
databases such as Ensembl provide relationships and mappings
between the relevant sets of probe and target molecules.
However, the relationships can be biologically complex and the
content of the databases is dynamic. We demonstrate how to use
the computational environment R to integrate and jointly
analyze experimental datasets, employing BioMart web services
to provide the molecule mappings. We also discuss typical
problems that are encountered in making
gene-to-transcript-to-protein mappings. The approach provides
a flexible, programmable and reproducible basis for
state-of-the-art bioinformatic data integration.",
journal = "Nature protocols",
volume = 4,
number = 8,
pages = "1184--1191",
month = "23~" # jul,
year = 2009,
url = "http://dx.doi.org/10.1038/nprot.2009.97",
file = "All Papers/D/Durinck et al. 2009 - Mapping identifiers for the integration of genomic datasets with the R - Bioconductor package biomaRt.pdf",
language = "en",
issn = "1754-2189, 1750-2799",
pmid = "19617889",
doi = "10.1038/nprot.2009.97",
pmc = "PMC3159387"
}
@ARTICLE{Durinck2005-bm,
title = "{BioMart} and Bioconductor: a powerful link between biological
databases and microarray data analysis",
author = "Durinck, Steffen and Moreau, Yves and Kasprzyk, Arek and
Davis, Sean and De Moor, Bart and Brazma, Alvis and Huber,
Wolfgang",
affiliation = "Department of Electronical Engineering, ESAT-SCD, K.U.Leuven,
Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium.
steffen.durinck@esat.kuleuven.ac.be",
abstract = "biomaRt is a new Bioconductor package that integrates BioMart
data resources with data analysis software in Bioconductor. It
can annotate a wide range of gene or gene product identifiers
(e.g. Entrez-Gene and Affymetrix probe identifiers) with
information such as gene symbol, chromosomal coordinates, Gene
Ontology and OMIM annotation. Furthermore biomaRt enables
retrieval of genomic sequences and single nucleotide
polymorphism information, which can be used in data analysis.
Fast and up-to-date data retrieval is possible as the package
executes direct SQL queries to the BioMart databases (e.g.
Ensembl). The biomaRt package provides a tight integration of
large, public or locally installed BioMart databases with data
analysis in Bioconductor creating a powerful environment for
biological data mining.",
journal = "Bioinformatics",
volume = 21,
number = 16,
pages = "3439--3440",
month = "15~" # aug,
year = 2005,
url = "http://dx.doi.org/10.1093/bioinformatics/bti525",
file = "All Papers/D/Durinck et al. 2005 - BioMart and Bioconductor - a powerful link between biological databases and microarray data analysis.pdf",
language = "en",
issn = "1367-4803",
pmid = "16082012",
doi = "10.1093/bioinformatics/bti525"
}
@ARTICLE{Robinson2010-ja,
title = "edgeR: a Bioconductor package for differential expression
analysis of digital gene expression data",
author = "Robinson, Mark D and McCarthy, Davis J and Smyth, Gordon K",
affiliation = "Cancer Program, Garvan Institute of Medical Research, 384
Victoria Street, Darlinghurst, NSW 2010, Australia.
mrobinson@wehi.edu.au",
abstract = "SUMMARY: It is expected that emerging digital gene expression
(DGE) technologies will overtake microarray technologies in
the near future for many functional genomics applications. One
of the fundamental data analysis tasks, especially for gene
expression studies, involves determining whether there is
evidence that counts for a transcript or exon are
significantly different across experimental conditions. edgeR
is a Bioconductor software package for examining differential
expression of replicated count data. An overdispersed Poisson
model is used to account for both biological and technical
variability. Empirical Bayes methods are used to moderate the
degree of overdispersion across transcripts, improving the
reliability of inference. The methodology can be used even
with the most minimal levels of replication, provided at least
one phenotype or experimental condition is replicated. The
software may have other applications beyond sequencing data,
such as proteome peptide count data. AVAILABILITY: The package
is freely available under the LGPL licence from the
Bioconductor web site (http://bioconductor.org).",
journal = "Bioinformatics",
volume = 26,
number = 1,
pages = "139--140",
month = "1~" # jan,
year = 2010,
url = "http://dx.doi.org/10.1093/bioinformatics/btp616",
file = "All Papers/R/Robinson et al. 2010 - edgeR - a Bioconductor package for differential expression analysis of digital gene expression data.pdf",
language = "en",
issn = "1367-4803, 1367-4811",
pmid = "19910308",
doi = "10.1093/bioinformatics/btp616",
pmc = "PMC2796818"
}
@ARTICLE{McCarthy2012-df,
title = "Differential expression analysis of multifactor {RNA-Seq}
experiments with respect to biological variation",
author = "McCarthy, Davis J and Chen, Yunshun and Smyth, Gordon K",
abstract = "A flexible statistical framework is developed for the analysis of
read counts from RNA-Seq gene expression studies. It provides the
ability to analyse complex experiments involving multiple
treatment conditions and blocking variables while still taking
full account of biological variation. Biological variation
between RNA samples is estimated separately from the technical
variation associated with sequencing technologies. Novel
empirical Bayes methods allow each gene to have its own specific
variability, even when there are relatively few biological
replicates from which to estimate such variability. The pipeline
is implemented in the edgeR package of the Bioconductor project.
A case study analysis of carcinoma data demonstrates the ability
of generalized linear model methods (GLMs) to detect differential
expression in a paired design, and even to detect tumour-specific
expression changes. The case study demonstrates the need to allow
for gene-specific variability, rather than assuming a common
dispersion across genes or a fixed relationship between abundance
and variability. Genewise dispersions de-prioritize genes with
inconsistent results and allow the main analysis to focus on
changes that are consistent between biological replicates.
Parallel computational approaches are developed to make
non-linear model fitting faster and more reliable, making the
application of GLMs to genomic data more convenient and
practical. Simulations demonstrate the ability of adjusted
profile likelihood estimators to return accurate estimators of
biological variability in complex situations. When variation is
gene-specific, empirical Bayes estimators provide an advantageous
compromise between the extremes of assuming common dispersion or
separate genewise dispersion. The methods developed here can also
be applied to count data arising from DNA-Seq applications,
including ChIP-Seq for epigenetic marks and DNA methylation
analyses.",
journal = "Nucleic acids research",
pages = "gks042--",
month = "6~" # feb,
year = 2012,
url = "http://nar.oxfordjournals.org/cgi/content/abstract/gks042v2",
file = "All Papers/M/McCarthy et al. 2012 - Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.pdf",
issn = "0305-1048, 1362-4962",
pmid = "22287627",
doi = "10.1093/nar/gks042"
}
@ARTICLE{Robinson2007-zi,
title = "Moderated statistical tests for assessing differences in tag
abundance",
author = "Robinson, Mark D and Smyth, Gordon K",
affiliation = "Department of Medical Biology, University of Melbourne,
Parkville, Victoria, Australia.",
abstract = "MOTIVATION: Digital gene expression (DGE) technologies measure
gene expression by counting sequence tags. They are sensitive
technologies for measuring gene expression on a genomic scale,
without the need for prior knowledge of the genome sequence.
As the cost of sequencing DNA decreases, the number of DGE
datasets is expected to grow dramatically. Various tests of
differential expression have been proposed for replicated DGE
data using binomial, Poisson, negative binomial or
pseudo-likelihood (PL) models for the counts, but none of the
these are usable when the number of replicates is very small.
RESULTS: We develop tests using the negative binomial
distribution to model overdispersion relative to the Poisson,
and use conditional weighted likelihood to moderate the level
of overdispersion across genes. Not only is our strategy
applicable even with the smallest number of libraries, but it
also proves to be more powerful than previous strategies when
more libraries are available. The methodology is equally
applicable to other counting technologies, such as proteomic
spectral counts. AVAILABILITY: An R package can be accessed
from http://bioinf.wehi.edu.au/resources/",
journal = "Bioinformatics",
volume = 23,
number = 21,
pages = "2881--2887",
month = "1~" # nov,
year = 2007,
url = "http://dx.doi.org/10.1093/bioinformatics/btm453",
language = "en",
issn = "1367-4803, 1367-4811",
pmid = "17881408",
doi = "10.1093/bioinformatics/btm453"
}
@ARTICLE{Robinson2007-zi,
title = "Moderated statistical tests for assessing differences in tag
abundance",
author = "Robinson, Mark D and Smyth, Gordon K",
affiliation = "Department of Medical Biology, University of Melbourne,
Parkville, Victoria, Australia.",
abstract = "MOTIVATION: Digital gene expression (DGE) technologies measure
gene expression by counting sequence tags. They are sensitive
technologies for measuring gene expression on a genomic scale,
without the need for prior knowledge of the genome sequence.
As the cost of sequencing DNA decreases, the number of DGE
datasets is expected to grow dramatically. Various tests of
differential expression have been proposed for replicated DGE
data using binomial, Poisson, negative binomial or
pseudo-likelihood (PL) models for the counts, but none of the
these are usable when the number of replicates is very small.
RESULTS: We develop tests using the negative binomial
distribution to model overdispersion relative to the Poisson,
and use conditional weighted likelihood to moderate the level
of overdispersion across genes. Not only is our strategy
applicable even with the smallest number of libraries, but it
also proves to be more powerful than previous strategies when
more libraries are available. The methodology is equally
applicable to other counting technologies, such as proteomic
spectral counts. AVAILABILITY: An R package can be accessed
from http://bioinf.wehi.edu.au/resources/",
journal = "Bioinformatics",
volume = 23,
number = 21,
pages = "2881--2887",
month = "1~" # nov,
year = 2007,
url = "http://dx.doi.org/10.1093/bioinformatics/btm453",
file = "All Papers/R/Robinson and Smyth 2007 - Moderated statistical tests for assessing differences in tag abundance.pdf",
language = "en",
issn = "1367-4803, 1367-4811",
pmid = "17881408",
doi = "10.1093/bioinformatics/btm453"
}
@ARTICLE{Robinson2008-km,
title = "Small-sample estimation of negative binomial dispersion, with
applications to {SAGE} data",
author = "Robinson, Mark D and Smyth, Gordon K",
affiliation = "Bioinformatics Division, The Walter and Eliza Hall Institute
of Medical Research, and Department of Medical Biology, The
University of Melbourne, Parkville, Victoria 3010, Australia.
mrobinson@wehi.edu.au",
abstract = "We derive a quantile-adjusted conditional maximum likelihood
estimator for the dispersion parameter of the negative
binomial distribution and compare its performance, in terms of
bias, to various other methods. Our estimation scheme
outperforms all other methods in very small samples, typical
of those from serial analysis of gene expression studies, the
motivating data for this study. The impact of dispersion
estimation on hypothesis testing is studied. We derive an
``exact'' test that outperforms the standard approximate
asymptotic tests.",
journal = "Biostatistics",
volume = 9,
number = 2,
pages = "321--332",
month = apr,
year = 2008,
url = "http://dx.doi.org/10.1093/biostatistics/kxm030",
file = "All Papers/R/Robinson and Smyth 2008 - Small-sample estimation of negative binomial dispersion, with applications to SAGE data.pdf",
language = "en",
issn = "1465-4644",
pmid = "17728317",
doi = "10.1093/biostatistics/kxm030"
}
@ARTICLE{Zhou2014-aj,
title = "Robustly detecting differential expression in {RNA} sequencing
data using observation weights",
author = "Zhou, Xiaobei and Lindsay, Helen and Robinson, Mark D",
affiliation = "Institute of Molecular Life Sciences, University of Zurich,
CH-8057 Zurich, Switzerland SIB Swiss Institute of
Bioinformatics, University of Zurich, CH-8057 Zurich,
Switzerland. Institute of Molecular Life Sciences, University
of Zurich, CH-8057 Zurich, Switzerland SIB Swiss Institute of
Bioinformatics, University of Zurich, CH-8057 Zurich,
Switzerland. Institute of Molecular Life Sciences, University
of Zurich, CH-8057 Zurich, Switzerland SIB Swiss Institute of
Bioinformatics, University of Zurich, CH-8057 Zurich,
Switzerland mark.robinson@imls.uzh.ch.",
abstract = "A popular approach for comparing gene expression levels
between (replicated) conditions of RNA sequencing data relies
on counting reads that map to features of interest. Within
such count-based methods, many flexible and advanced
statistical approaches now exist and offer the ability to
adjust for covariates (e.g. batch effects). Often, these
methods include some sort of 'sharing of information' across
features to improve inferences in small samples. It is
important to achieve an appropriate tradeoff between
statistical power and protection against outliers. Here, we
study the robustness of existing approaches for count-based
differential expression analysis and propose a new strategy
based on observation weights that can be used within existing
frameworks. The results suggest that outliers can have a
global effect on differential analyses. We demonstrate the
effectiveness of our new approach with real data and simulated
data that reflects properties of real datasets (e.g.
dispersion-mean trend) and develop an extensible framework for
comprehensive testing of current and future methods. In
addition, we explore the origin of such outliers, in some
cases highlighting additional biological or technical factors
within the experiment. Further details can be downloaded from
the project website:
http://imlspenticton.uzh.ch/robinson\_lab/edgeR\_robust/.",
journal = "Nucleic acids research",
volume = 42,
number = 11,
pages = "e91",
month = jun,
year = 2014,
url = "http://dx.doi.org/10.1093/nar/gku310",
file = "All Papers/Z/Zhou et al. 2014 - Robustly detecting differential expression in RNA sequencing data using observation weights.pdf",
language = "en",
issn = "0305-1048, 1362-4962",
pmid = "24753412",
doi = "10.1093/nar/gku310",
pmc = "PMC4066750"
}
@ARTICLE{Ritchie2015-hm,
title = "limma powers differential expression analyses for
{RNA-sequencing} and microarray studies",
author = "Ritchie, Matthew E and Phipson, Belinda and Wu, Di and Hu,
Yifang and Law, Charity W and Shi, Wei and Smyth, Gordon K",
affiliation = "Molecular Medicine Division, The Walter and Eliza Hall
Institute of Medical Research, 1G Royal Parade, Parkville,
Victoria 3052, Australia Department of Mathematics and
Statistics, The University of Melbourne, Parkville, Victoria
3010, Australia. Murdoch Childrens Research Institute, Royal
Children's Hospital, 50 Flemington Road, Parkville, Victoria
3052, Australia. Department of Statistics, Harvard University,
1 Oxford Street, Cambridge, MA 02138-2901, USA. Bioinformatics
Division, The Walter and Eliza Hall Institute of Medical
Research, 1G Royal Parade, Parkville, Victoria 3052,
Australia. Institute of Molecular Life Sciences, University of
Zurich, Winterthurerstrasse 190, Zurich 8057, Switzerland.
Bioinformatics Division, The Walter and Eliza Hall Institute
of Medical Research, 1G Royal Parade, Parkville, Victoria
3052, Australia Department of Computing and Information
Systems, The University of Melbourne, Parkville, Victoria
3010, Australia. Department of Mathematics and Statistics, The
University of Melbourne, Parkville, Victoria 3010, Australia
Bioinformatics Division, The Walter and Eliza Hall Institute
of Medical Research, 1G Royal Parade, Parkville, Victoria
3052, Australia smyth@wehi.edu.au.",
abstract = "limma is an R/Bioconductor software package that provides an
integrated solution for analysing data from gene expression
experiments. It contains rich features for handling complex
experimental designs and for information borrowing to overcome
the problem of small sample sizes. Over the past decade, limma
has been a popular choice for gene discovery through
differential expression analyses of microarray and
high-throughput PCR data. The package contains particularly
strong facilities for reading, normalizing and exploring such
data. Recently, the capabilities of limma have been
significantly expanded in two important directions. First, the
package can now perform both differential expression and
differential splicing analyses of RNA sequencing (RNA-seq)
data. All the downstream analysis tools previously restricted
to microarray data are now available for RNA-seq as well.
These capabilities allow users to analyse both RNA-seq and
microarray data with very similar pipelines. Second, the
package is now able to go past the traditional gene-wise
expression analyses in a variety of ways, analysing expression
profiles in terms of co-regulated sets of genes or in terms of
higher-order expression signatures. This provides enhanced
possibilities for biological interpretation of gene expression
differences. This article reviews the philosophy and design of
the limma package, summarizing both new and historical
features, with an emphasis on recent enhancements and features
that have not been previously described.",
journal = "Nucleic acids research",
volume = 43,
number = 7,
pages = "e47",
month = "20~" # apr,
year = 2015,
url = "http://dx.doi.org/10.1093/nar/gkv007",
file = "All Papers/R/Ritchie et al. 2015 - limma powers differential expression analyses for RNA-sequencing and microarray studies.pdf",
language = "en",
issn = "0305-1048, 1362-4962",
pmid = "25605792",
doi = "10.1093/nar/gkv007",
pmc = "PMC4402510"
}
@Book{Wickham2009-ab,
author = {Hadley Wickham},
title = {ggplot2: Elegant Graphics for Data Analysis},
publisher = {Springer-Verlag New York},
year = {2009},
isbn = {978-0-387-98140-6},
url = {http://ggplot2.org},
}
@Manual{Warnes2016-ab,
title = {gplots: Various R Programming Tools for Plotting Data},
author = {Gregory R. Warnes and Ben Bolker and Lodewijk Bonebakker and Robert Gentleman and Wolfg
ang Huber Andy Liaw and Thomas Lumley and Martin Maechler and Arni Magnusson and Steffen Moeller and
Marc Schwartz and Bill Venables},
year = {2016},
note = {R package version 3.0.1},
url = {https://CRAN.R-project.org/package=gplots},
}
@ARTICLE{Hahne2016-nq,
title = "Visualizing Genomic Data Using Gviz and Bioconductor",
author = "Hahne, Florian and Ivanek, Robert",
affiliation = "Novartis Institute for Biomedical Research, WKL-135.P18,
Klybeckstrasse 141, 4057, Basel, Switzerland.
florian.hahne@novartis.com. Department of Biomedicine,
University of Basel, Basel, Switzerland.",
abstract = "The Gviz package offers a flexible framework to visualize
genomic data in the context of a variety of different genome
annotation features. Being tightly embedded in the
Bioconductor genomics landscape, it nicely integrates with the
existing infrastructure, but also provides direct data
retrieval from external sources like Ensembl and UCSC and
supports most of the commonly used annotation file types.
Through carefully chosen default settings the package greatly
facilitates the production of publication-ready figures of
genomic loci, while still maintaining high flexibility due to
its ample customization options.",
journal = "Methods in molecular biology",
volume = 1418,
pages = "335--351",
year = 2016,
url = "http://dx.doi.org/10.1007/978-1-4939-3578-9_16",
file = "All Papers/H/Hahne and Ivanek 2016 - Visualizing Genomic Data Using Gviz and Bioconductor.pdf",
keywords = "Annotation; Genomics; NGS; Visualization",
language = "en",
issn = "1064-3745, 1940-6029",
pmid = "27008022",
doi = "10.1007/978-1-4939-3578-9\_16"
}
@Manual{Zhao2015-ab,
title = {heatmap3: An Improved Heatmap Package},
author = {Shilin Zhao and Yan Guo and Quanhu Sheng and Yu Shyr},
year = {2015},
note = {R package version 1.1.1},
url = {https://CRAN.R-project.org/package=heatmap3},
}
@ARTICLE{Krzywinski2009-ku,
title = "Circos: an information aesthetic for comparative genomics",
author = "Krzywinski, Martin and Schein, Jacqueline and Birol, Inan\c{c}
and Connors, Joseph and Gascoyne, Randy and Horsman, Doug and
Jones, Steven J and Marra, Marco A",
abstract = "We created a visualization tool called Circos to facilitate the
identification and analysis of similarities and differences
arising from comparisons of genomes. Our tool is effective in
displaying variation in genome structure and, generally, any
other kind of positional relationships between genomic intervals.
Such data are routinely produced by sequence alignments,
hybridization arrays, genome mapping, and genotyping studies.
Circos uses a circular ideogram layout to facilitate the display
of relationships between pairs of positions by the use of
ribbons, which encode the position, size, and orientation of
related genomic elements. Circos is capable of displaying data as
scatter, line, and histogram plots, heat maps, tiles, connectors,
and text. Bitmap or vector images can be created from GFF-style
data inputs and hierarchical configuration files, which can be
easily generated by automated tools, making Circos suitable for
rapid deployment in data analysis and reporting pipelines.",
journal = "Genome research",
volume = 19,
number = 9,
pages = "1639--1645",
month = sep,
year = 2009,
url = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2752132&tool=pmcentrez&rendertype=abstract",
file = "All Papers/K/Krzywinski et al. 2009 - Circos - an information aesthetic for comparative genomics.pdf",
keywords = "Animals; Chromosome Mapping; Chromosomes, Artificial, Bacterial;
Chromosomes, Human, Pair 17; Chromosomes, Human, Pair 17:
genetics; Chromosomes, Human, Pair 6; Chromosomes, Human, Pair 6:
genetics; Contig Mapping; Dogs; Gene Dosage; Gene Dosage:
genetics; Genome; Genome: genetics; Genomics; Humans; Lymphoma,
Follicular; Lymphoma, Follicular: genetics; Software",
issn = "1088-9051, 1549-5469",
pmid = "19541911",
doi = "10.1101/gr.092759.109"
}
@ARTICLE{Yin2012-hx,
title = "ggbio: an {R} package for extending the grammar of graphics for
genomic data",
author = "Yin, Tengfei and Cook, Dianne and Lawrence, Michael",
abstract = "We introduce ggbio, a new methodology to visualize and explore
genomics annotations and high-throughput data. The plots provide
detailed views of genomic regions, summary views of sequence
alignments and splicing patterns, and genome-wide overviews with
karyogram, circular and grand linear layouts. The methods
leverage the statistical functionality available in R, the
grammar of graphics and the data handling capabilities of the
Bioconductor project. The plots are specified within a modular
framework that enables users to construct plots in a systematic
way, and are generated directly from Bioconductor data
structures. The ggbio R package is available at
http://www.bioconductor.org/packages/2.11/bioc/html/ggbio.html.",
journal = "Genome biology",
volume = 13,
number = 8,
pages = "R77",
month = jan,
year = 2012,
url = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053745&tool=pmcentrez&rendertype=abstract",
file = "All Papers/Y/Yin et al. 2012 - ggbio - an R package for extending the grammar of graphics for genomic data.pdf",
issn = "1465-6906, 1465-6914",
pmid = "22937822",
doi = "10.1186/gb-2012-13-8-r77"
}
@ARTICLE{Gu2014-bm,
title = "circlize Implements and enhances circular visualization in {R}",
author = "Gu, Zuguang and Gu, Lei and Eils, Roland and Schlesner, Matthias
and Brors, Benedikt",
abstract = "SUMMARY: Circular layout is an efficient way for the
visualization of huge amounts of genomic information. Here we
present the circlize package, which provides an implementation of
circular layout generation in R as well as an enhancement of
available software. The flexibility of this package is based on
the usage of low-level graphics functions such that self-defined
high-level graphics can be easily implemented by users for
specific purposes. Together with the seamless connection between
the powerful computational and visual environment in R, circlize
gives users more convenience and freedom to design figures for
better understanding genomic patterns behind multi-dimensional
data. AVAILABILITY AND IMPLEMENTATION: circlize is available at
the Comprehensive R Archive Network (CRAN):
http://cran.r-project.org/web/packages/circlize/",
journal = "Bioinformatics",
volume = 30,
number = 19,
pages = "2811--2812",
month = oct,
year = 2014,
url = "http://www.ncbi.nlm.nih.gov/pubmed/24930139",
keywords = "Algorithms; Computational Biology; Computational Biology:
methods; Computer Graphics; Genome; Genomics; Genomics: methods;
Internet; Programming Languages; Software",
issn = "1367-4803, 1367-4811",
pmid = "24930139",
doi = "10.1093/bioinformatics/btu393"
}
@ARTICLE{Hu2014-wf,
title = "{OmicCircos}: A {Simple-to-Use} {R} Package for the Circular
Visualization of Multidimensional Omics Data",
author = "Hu, Ying and Yan, Chunhua and Hsu, Chih-Hao and Chen, Qing-Rong
and Niu, Kelvin and Komatsoulis, George A and Meerzaman, Daoud",
abstract = "SUMMARY: OmicCircos is an R software package used to generate
high-quality circular plots for visualizing genomic variations,
including mutation patterns, copy number variations (CNVs),
expression patterns, and methylation patterns. Such variations
can be displayed as scatterplot, line, or text-label figures.
Relationships among genomic features in different chromosome
positions can be represented in the forms of polygons or curves.
Utilizing the statistical and graphic functions in an
R/Bioconductor environment, OmicCircos performs statistical
analyses and displays results using cluster, boxplot, histogram,
and heatmap formats. In addition, OmicCircos offers a number of
unique capabilities, including independent track drawing for easy
modification and integration, zoom functions, link-polygons, and
position-independent heatmaps supporting detailed visualization.
AVAILABILITY AND IMPLEMENTATION: OmicCircos is available through
Bioconductor at
http://www.bioconductor.org/packages/devel/bioc/html/OmicCircos.html.
An extensive vignette in the package describes installation, data
formatting, and workflow procedures. The software is open source
under the Artistic-2.0 license.",
journal = "Cancer informatics",
volume = 13,
pages = "13--20",
month = jan,
year = 2014,
url = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3921174&tool=pmcentrez&rendertype=abstract",
file = "All Papers/H/Hu et al. 2014 - OmicCircos - A Simple-to-Use R Package for the Circular Visualization of Multidimensional Omics Data.pdf",
issn = "1176-9351",
pmid = "24526832",
doi = "10.4137/CIN.S13495"
}
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