@article{Langmead2018,
author = {Langmead, Ben and Nellore, Abhinav},
doi = {10.1038/nrg.2017.113},
file = {:Users/stvjc/PDFS/nrg.2017.113.pdf:pdf},
issn = {1471-0056},
journal = {Nature Reviews Genetics},
publisher = {Nature Publishing Group},
title = {{Cloud computing for genomic data analysis and collaboration}},
url = {http://www.nature.com/doifinder/10.1038/nrg.2017.113},
year = {2018}
}


@article{Collado-Torres2017,
abstract = {To the Editor:
RNA sequencing (RNA-seq) is used to measure gene expression levels across the transcriptome for a huge variety of samples. For example, RNA-seq has been applied to study gene expression in individuals with rare diseases, in hard-to-obtain tissues or for rare forms of cancer. Recently, enormous RNA-seq datasets have{\ldots}},
author = {Collado-Torres, Leonardo and Nellore, Abhinav and Kammers, Kai and Ellis, Shannon E. and Taub, Margaret A. and Hansen, Kasper D. and Jaffe, Andrew E. and Langmead, Ben and Leek, Jeffrey T.},
doi = {10.1038/nbt.3838},
file = {:Users/stvjc/PDFS/recount2NBT.pdf:pdf},
isbn = {1546-1696 (Electronic)
1087-0156 (Linking)},
issn = {15461696},
journal = {Nature Biotechnology},
number = {4},
pages = {319--321},
pmid = {28398307},
title = {{Reproducible RNA-seq analysis using recount2}},
volume = {35},
year = {2017}
}


@article{Quake2017,
abstract = {We have created a compendium of single cell transcriptome data from the model organism Mus musculus comprising nearly 100,000 cells from 20 organs and tissues. These data reveal many new aspects of cell biology, including gene expression in poorly characterized cell populations and the characterization of new populations of cells in many tissues. Furthermore, they allow for direct and controlled comparison of gene expression in cell types shared between tissues, such as immune cells from distinct anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3-end counting, enabled the survey of thousands of cells at relatively low coverage, while the other, FACS-based full length transcript analysis, enabled characterization of cell types with high sensitivity and coverage. The cumulative resource provides the foundation for a comprehensive resource of transcriptomic cell biology.},
author = {Quake, Stephen R. and Wyss-Coray, Tony and Darmanis, Spyros},
doi = {10.1101/237446},
file = {:Users/stvjc/PDFS/tabulaMuris.pdf:pdf},
journal = {bioRxiv},
pages = {237446},
title = {{Transcriptomic characterization of 20 organs and tissues from mouse at single cell resolution creates a Tabula Muris}},
url = {https://www.biorxiv.org/content/early/2017/12/20/237446},
year = {2017}
}
@article{Soneson2018,
abstract = {An extensive evaluation of differential expression methods applied to single-cell expression data, using uniformly processed public data in the new conquer resource.},
author = {Soneson, Charlotte and Robinson, Mark D.},
doi = {10.1038/nmeth.4612},
file = {:Users/stvjc/PDFS/conquerNMeth.pdf:pdf},
issn = {15487105},
journal = {Nature Methods},
number = {4},
pages = {255--261},
pmid = {29481549},
publisher = {Nature Publishing Group},
title = {{Bias, robustness and scalability in single-cell differential expression analysis}},
url = {http://dx.doi.org/10.1038/nmeth.4612},
volume = {15},
year = {2018}
}
@article{Darmanis2017,
abstract = {Glioblastoma (GBM) is the most common primary brain cancer in adults and is notoriously difficult to treat because of its diffuse nature. We performed single-cell RNA sequencing (RNA-seq) on 3,589 cells in a cohort of four patients. We obtained cells from the tumor core as well as surrounding peripheral tissue. Our analysis revealed cellular variation in the tumor's genome and transcriptome. We were also able to identify infiltrating neoplastic cells in regions peripheral to the core lesions. Despite the existence of significant heterogeneity among neoplastic cells, we found that infiltrating GBM cells share a consistent gene signature between patients, suggesting a common mechanism of infiltration. Additionally, in investigating the immunological response to the tumors, we found transcriptionally distinct myeloid cell populations residing in the tumor core and the surrounding peritumoral space. Our data provide a detailed dissection of GBM cell types, revealing an abundance of information about tumor formation and migration. Darmanis et al. perform single-cell transcriptomic analyses of neoplastic and stromal cells within and proximal to primary glioblastomas. The authors describe a population of neoplastic-infiltrating glioblastoma cells as well as a putative role of tumor-infiltrating immune cells in supporting tumor growth.},
author = {Darmanis, Spyros and Sloan, Steven A. and Croote, Derek and Mignardi, Marco and Chernikova, Sophia and Samghababi, Peyman and Zhang, Ye and Neff, Norma and Kowarsky, Mark and Caneda, Christine and Li, Gordon and Chang, Steven D. and Connolly, Ian David and Li, Yingmei and Barres, Ben A. and Gephart, Melanie Hayden and Quake, Stephen R.},
doi = {10.1016/j.celrep.2017.10.030},
file = {:Users/stvjc/PDFS/mmc7.pdf:pdf},
isbn = {1336716436},
issn = {22111247},
journal = {Cell Reports},
keywords = {GBM,RNA-seq,brain,checkpoint,diffuse,glioblastoma,glioma,heterogeneity,infiltrating,single cell},
number = {5},
pages = {1399--1410},
pmid = {29091775},
publisher = {ElsevierCompany.},
title = {{Single-Cell RNA-Seq Analysis of Infiltrating Neoplastic Cells at the Migrating Front of Human Glioblastoma}},
url = {https://doi.org/10.1016/j.celrep.2017.10.030},
volume = {21},
year = {2017}
}
@article{Patel2014,
author = {Patel, Anoop P and Tirosh, Itay and Trombetta, John J and Shalek, Alex K and Shawn, M and Wakimoto, Hiroaki and Cahill, Daniel P and Nahed, Brian V and Curry, William T and Martuza, Robert L and Louis, David N and Rozenblatt-rosen, Orit and Suv{\`{a}}, Mario L and Regev, Aviv and Bernstein, Bradley E.},
doi = {10.1126/science.1254257.Single-cell},
file = {:Users/stvjc/PDFS/nihms612317.pdf:pdf},
journal = {Science},
number = {6190},
pages = {1396--1401},
title = {{Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma}},
volume = {344},
year = {2014}
}


@article{Ramos2017,
abstract = {Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide all of the multiple 'omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.},
author = {Ramos, Marcel and Schiffer, Lucas and Re, Angela and Azhar, Rimsha and Basunia, Azfar and Cabrera, Rodriguez and Chan, Tiffany and Chapman, Philip and Davis, Sean and Gomez-cabrero, David},
doi = {10.1158/0008-5472.CAN-17-0344},
file = {:Users/stvjc/PDFS/e39.full.pdf:pdf},
issn = {0008-5472},
journal = {Cancer Research},
pages = {e39--e42},
title = {{Software for the integration of multi-omics experiments in Bioconductor}},
url = {http://cancerres.aacrjournals.org/content/77/21/e39},
volume = {77},
year = {2017}
}


@article{Williams2016,
abstract = {Objective: Within the information technology (IT) industry, best practices and standards are constantly evolving and being refined. In contrast, computer technology utilized within the healthcare industry often evolves at a glacial pace, with reduced opportunities for justified innovation. Although the use of timely technology refreshes within an enterprise's overall technology stack can be costly, thoughtful adoption of select technologies with a demonstrated return on investment can be very effective in increasing productivity and at the same time, reducing the burden of maintenance often associated with older and legacy systems. In this brief technical communication, we introduce the concept of microservices as applied to the ecosystem of data analysis pipelines. Microservice architecture is a framework for dividing complex systems into easily managed parts. Each individual service is limited in functional scope, thereby conferring a higher measure of functional isolation and reliability to the collective solution. Moreover, maintenance challenges are greatly simplified by virtue of the reduced architectural complexity of each constitutive module. This fact notwithstanding, rendered overall solutions utilizing a microservices‑based approach provide equal or greater levels of functionality as compared to conventional programming approaches. Bioinformatics, with its ever‑increasing demand for performance and new testing algorithms, is the perfect use‑case for such a solution. Moreover, if promulgated within the greater development community as an open‑source solution, such an approach holds potential to be transformative to current bioinformatics software development. Context: Bioinformatics relies on nimble IT framework which can adapt to changing requirements. Aims: To present a well‑established software design and deployment strategy as a solution for current challenges within bioinformatics Conclusions: Use of the microservices framework is an effective methodology for the fabrication and implementation of reliable and innovative software, made possible in a highly collaborative setting.},
author = {Williams, Christopher L and Sica, Jeffrey C and Killen, Robert T and Balis, Ulysses G. J.},
doi = {10.4103/2153-3539.194835},
file = {:Users/stvjc/PDFS/microserv.pdf:pdf},
issn = {2153-3539},
journal = {Journal of Pathology Informatics},
keywords = {bioinformatics,crowd sourcing,defect analysis,failure mode analysis,information technology,microservices,pathology,reliability engineering,software},
number = {1},
pages = {45},
pmid = {27994937},
title = {{The growing need for microservices in bioinformatics}},
url = {http://www.jpathinformatics.org/text.asp?2016/7/1/45/194835},
volume = {7},
year = {2016}
}


@article{Swaminathan2016,
abstract = {The constant improvement and falling prices of whole human genome Next Generation Sequencing (NGS) has resulted in rapid adoption of genomic information at both clinics and research institutions. Considered together, the complexity of genomics data, due to its large volume and diversity along with the need for genomic data sharing, has resulted in the creation of Application Programming Interface (API) for secure, modular, interoperable access to genomic data from different applications, platforms, and even organizations. The Genomics APIs are a set of special protocols that assist software developers in dealing with multiple genomic data sources for building seamless, interoperable applications leading to the advancement of both genomic and clinical research. These APIs help define a standard for retrieval of genomic data from multiple sources as well as to better package genomic information for integration with Electronic Health Records. This review covers three currently available Genomics APIs: a) Google Genomics, b) SMART Genomics, and c) 23andMe. The functionalities, reference implementations (if available) and authentication protocols of each API are reviewed. A comparative analysis of the different features across the three APIs is provided in the Discussion section. Though Genomics APIs are still under active development and have yet to reach widespread adoption, they hold the promise to make building of complicated genomics applications easier with downstream constructive effects on healthcare.},
author = {Swaminathan, Rajeswari and Huang, Yungui and Moosavinasab, Soheil and Buckley, Ronald and Bartlett, Christopher W. and Lin, Simon M.},
doi = {10.1016/j.csbj.2015.10.004},
file = {:Users/stvjc/PDFS/genomicsAPIs.pdf:pdf},
isbn = {20010370 (Electronic)},
issn = {20010370},
journal = {Computational and Structural Biotechnology Journal},
keywords = {Application Programming Interface (API),Clinical,Data sharing,Genomics,Sequencing},
pages = {8--15},
pmid = {26702340},
publisher = {Natrix Separations},
title = {{A Review on Genomics APIs}},
url = {http://dx.doi.org/10.1016/j.csbj.2015.10.004},
volume = {14},
year = {2016}
}



@article{Ding2018,
abstract = {The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing. A synthesized view on oncogenic processes based on PanCancer Atlas analyses highlights the complex impact of genome alterations on the signaling and multi-omic profiles of human cancers as well as their influence on tumor microenvironment.},
author = {Ding, Li and Bailey, Matthew H. and Porta-Pardo, Eduard and
Lee, Jung Il and Aredes, Nat{\'{a}}lia D. and Mariamidze, Armaz and others},
doi = {10.1016/j.cell.2018.03.033},
file = {:Users/stvjc/PDFS/oncoGenicProcesses.pdf:pdf},
isbn = {0092-8674},
issn = {10974172},
journal = {Cell},
keywords = {TCGA,cancer,cancer genomics,omics,oncogenic process},
number = {2},
pages = {305--320.e10},
pmid = {29625049},
title = {{Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics}},
volume = {173},
year = {2018}
}


@article{Bailey2018,
abstract = {Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified {\textgreater}3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60{\%}–85{\%} of predicted mutations as likely drivers. We found that {\textgreater}300 MSI tumors are associated with high PD-1/PD-L1, and 57{\%} of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors. A comprehensive analysis of oncogenic driver genes and mutations in {\textgreater}9,000 tumors across 33 cancer types highlights the prevalence of clinically actionable cancer driver events in TCGA tumor samples.},
author = {Bailey, Matthew H. and Tokheim, Collin and Porta-Pardo, Eduard 
and Karchin, Rachel and Ding, Li and others},
doi = {10.1016/j.cell.2018.02.060},
file = {:Users/stvjc/PDFS/baileyDrivers.pdf:pdf},
isbn = {10974172 (Electronic)},
issn = {10974172},
journal = {Cell},
keywords = {driver gene discovery,mutations of clinical relevance,oncology,structure analysis},
number = {2},
pages = {371--385.e18},
pmid = {29625053},
title = {{Comprehensive Characterization of Cancer Driver Genes and Mutations}},
volume = {173},
year = {2018}
}
