Seven Bridges Publications

Latest articles from our scientists

The Cancer Genomics Cloud: collaborative, reproducible, and democratized—a new paradigm in large-scale computational research

Lau JW, Lehnert E, Sethi A, Malhotra R, Kaushik G, Onder Z, Groves-Kirkby N, Mihajlovic A, DiGiovanna J, Srdic M, Bajcic D, Radenkovic J, Mladenovic V, Krstanovic D, Arsenijevic V, Klisic D, Mitrovic M, Bogicevic I, Kural D, Davis-Dusenbery B; for The Seven Bridges CGC Team
Cancer Research (2017) 77 (21): e3–6. doi: 10.1158/0008-5472.CAN-17-0387

This publication describes the Seven Bridges Cancer Genomics Cloud, a cloud-based system that enables researchers to rapidly access and collaborate on massive public cancer genomics datasets, including The Cancer Genome Atlas.

Using Semantic Web technologies to enable cancer genomics discovery at petabyte scale

Cejovic J, Radenkovic J, Mladenovic V, Stanojevic A, Miletic M, Radanovic S, Bajcic D, Djordjevic D, Jelic F, Nesic M, Lau J, Grady P, Groves-Kirkby N, Kural D, Davis-Dusenbery B
Cancer Informatics. In Press.

This paper describes a Semantic-Web-based Data Browser for the Cancer Genomics Cloud, which allows users to visually build and execute ontology-driven queries, improving data access and usability.

Collaborating to compete: Blood Profiling Atlas in Cancer (BloodPAC) Consortium

Grossman RL, Abel B, Angiuoli S, Barrett JC, Bassett D, Bramlett K, Blumenthal GM, Carlsson A, Cortese R, DiGiovanna J, Davis-Dusenbery B, Dittamore R, Eberhard DA, Febbo P, Fitzsimons M, Flamig Z, Godsey J, Goswami J, Gruen A, Ortuño F, Han J, Hayes D, Hicks J, Holloway D, Hovelson D, Johnson J, Juhl H, Kalamegham R, Kamal R, Kang Q, Kelloff GJ, Klozenbuecher M, Kolatkar A, Kuhn P, Langone K, Leary R, Loverso P, Manmathan H, Martin A-M, Martini J, Miller D, Mitchell M, Morgan T, Mulpuri R, Nguyen T, Otto G, Pathak A, Peters E, Philip R, Posadas E, Reese D, Reese MG, Robinson D, Dei Rossi A, Sakul H, Schageman J, Singh S, Scher HI, Schmitt K, Silvestro A, Simmons J, Simmons T, Sislow J, Talasaz A, Tang P, Tewari M, Tomlins S, Toukhy H, Tseng HR, Tuck M, Tzou A, Vinson J, Wang Y, Wells W, Welsh A, Wilbanks J, Wolf J, Young L, Lee JSH, Leiman LC
Clin Pharmacol Ther (2017) 101 (5): 589–592. doi: 10.1002/cpt.666

This article introduces the The Blood Profiling Atlas in Cancer (BloodPAC), which works to harmonize and make available data, to accelerate the development of minimally invasive blood profiling assays for cancer assessment and monitoring.

Large-scale uniform analysis of cancer whole genomes in multiple computing environments

Yung CK, O’Connor BD, Yakneen S, Zhang J, Ellrott K, Kleinheinz K, Miyoshi N, Raine KM, Royo R, Saksena GB, Schlesner M, Shorser SI, Vazquez M, Weischenfeldt J, Yuen D, Butler AP, Davis-Dusenbery BN, Eils R, Ferretti V, Grossman RL, Harismendy O, Kim Y, Nakagawa H, Newhouse SJ, Torrents D, Stein LD; PCAWG Technical Working Group
bioRxiv 161638; doi:

This preprint describes Seven Bridges’ role as part of the PCAWG Technical Working Group to generate high-quality validated consensus variants, forming the basis of the highest resolution collection of cancer genomes to date.

Rabix: an open-source workflow executor supporting recomputability and interoperability of workflow descriptions

Kaushik G, Ivkovic S, Simonovic J, Tijanic N, Davis-Dusenbery B, Kural D
Pac Symp Biocomput (2016) 22: 154–165

This paper describes the Rabix Executor, an open-source workflow engine designed to improve computational reproducibility through reusability and interoperability of workflow descriptions.

Reproducible, Scalable Fusion Gene Detection from RNA-Seq

Arsenijevic V, Davis-Dusenbery BN
Methods Mol Biol (2016) 1381: 223–237. doi: 10.1007/978-1-4939-3204-7_13

This chapter describes an approach to leverage cloud computing technology for fusion detection from RNA-sequencing data at any scale.

Research done with our products

Mammary tumor-associated RNAs impact tumor cell proliferation, invasion, and migration

Diermeier SD, Chang KC, Freier SM, Song J, El Demerdash O, Krasnitz A,
Rigo F, Bennett CF, Spector D
Cell Rep (2016) 17 (1): 261–274. doi: 10.1016/j.celrep.2016.08.081

Reproducible RNA-seq analysis using recount2

Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD,
Jaffe AE, Langmead B, Leek JT
Nat Biotechnol (2017) 35 (4): 319–321. doi: 10.1038/nbt.3838

Variant analysis of LY6 genes in TCGA ovarian cancer

Bhuvaneshwar K, Al Hossiny M, Gusev Y, Madhavan S,
Upadhyay G
In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017
Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr
3568. doi:10.1158/1538-7445.AM2017-3568

CloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens

Bais P, Namburi S, Gatti DM, Zhang X, Chuang JH
Bioinformatics (2017) 33 (19): 3110–3112. doi: 10.1093/bioinformatics/btx375

Our recent conference presentations

Precision medicine in the million genome era

Jack DiGiovanna. Keynote. Festival of Genomics Boston 2016

Activating the immune system: population responses to immunotherapy and novel workflows for neoantigen prioritization

Jack DiGiovanna. Keystone Symposia: Cancer Immunology and Immunotherapy 2017

Toward a practical implementation of graph representation of the genome

Deniz Kural. Festival of Genomics, London 2017

Improving algorithms & research results with large scale data in genomics

Deniz Kural. Keynote. Proventa Bioinformatics 2016

Further reading: a selection of our favorite articles

Precision medicine in the million genome era

Davis-Dusenbery B
GEN (2017) 37 (2): January 15

In this article, Seven Bridges CEO Brandi Davis-Dusenbery explains how the volume of NGS data gathered by massive genomics projects is driving a fundamental rethinking of data management and analysis methods.

Big data: astronomical or genomical?

Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, Iyer R, Schatz MC, Sinha S, Robinson GE
PLoS Biol (2015) 13 (7): e1002195. doi: 10.1371/journal.pbio.1002195

This paper shows how genomics is on par with astronomy, YouTube, and Twitter in terms of data acquisition, storage, distribution and analysis, and how new technologies are needed to meet these computational challenges.

Discovery and saturation analysis of cancer genes across 21 tumour types

Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, Meyerson M, Gabriel SB, Lander ES, Getz G
Nature (2014) 505 (7484): 495–501. doi: 10.1038/nature12912

This paper illustrates how large-scale genomic analysis contributes to identifying cancer genes across multiple tumor types.

Data analysis: create a cloud commons

Stein LD, Knoppers BM, Campbell P, Getz G, Korbel JO
Nature (2015) 523 (7559): 149–151. doi: 10.1038/523149a

This paper sets out the benefits of making large biological data sets available via cloud services to enable easy access and fast analysis.

Genome graphs

Novak AM, Hickey G, Garrison E, Blum S, Connelly A, Dilthey A, Eizenga J, Elmohamed MAS, Guthrie S, Kahles A, Keenan S, Kelleher J, Kural D, Li H, Lin MF, Miga K, Ouyang N, Rakocevic G, Smuga-Otto M, Zaranek AW, Durbin R, McVean G, Haussler D, Paten B
bioRxiv 101378; doi:

This preprint from leaders in graph genome research discusses how representing variation as part of a directed acyclic graph-based reference structure can reduce reference bias and improve the accuracy of alignment and variant calling.

The support of human genetic evidence for approved drug indications

Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, Floratos A, Sham PC, Li MJ, Wang J, Cardon LR, Whittaker JC, Sanseau P
Nat Genet (2015) 47: 856–860. doi: 10.1038/ng.3314

This article, led by researchers at GlaxoSmithKline, shows how obtaining genetic support for targets can double the success rate in clinical development.

The druggable genome and support for target identification and validation in drug development

Finan C, Gaulton A, Kruger FA, Lumbers RT, Shah T, Engmann J, Galver L, Kelley R, Karlsson A, Santos R, Overington JP, Hingorani AD, Casas JP
Sci Translat Med (2017) 9: eaag1166. doi:10.1126/scitranslmed.aag1166

In this paper, researchers from University College London combine data from numerous existing genome-wide association studies to identify and connect druggable proteins and known drugs across multiple diseases, facilitating the design of new targeted therapeutics.

Open access policy

We are committed to supporting the rapid dissemination of scientific knowledge. Seven Bridges pays the open access fees for publications our customers write based on research they did using our software.

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