Barriers to accessing public cancer genomic data

Learned, Katrina; Durbin, Ann; Currie, R.; Kephart, Ellen; Beale, Holly C.; Sanders, Lauren; Pfeil, Jacob; Goldstein, Theodore C.; Salama, Sofie R.; Haussler, David; Vaske, Olena M.; Bjork, Isabel

doi:10.1038/s41597-019-0096-4

Cited by 26 publications

(24 citation statements)

References 12 publications

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“…However, even when primary genomic data are available, barriers to accessing the data still exist, often limiting reuse by the community [ 53 ]. In particular, downloading and re-reprocessing large raw sequencing datasets requires dedicated infrastructure and bioinformatics skills.…”

Section: Discussionmentioning

confidence: 99%

A molecular map of lung neuroendocrine neoplasms

et al. 2020

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Background Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids). Results To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories. Conclusions We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.

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Section: Discussionmentioning

confidence: 99%

A molecular map of lung neuroendocrine neoplasms

et al. 2020

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“…As a consequence, genomic data generated by outside institutions is often difficult to find, access, and use. The absence of data annotation standards results in confusion on data elements critical to use and analysis, such as disease designation (Learned et al 2019). Although there are internationally accepted World Health Organization International Classification of Diseases standards for the diagnostic classification of cancer types and anatomic locations, this system is insufficiently granular, quickly outdated, and not utilized by many institutions, limiting harmonization.…”

Section: The Challenges Of Genomic Data Sharing and Ways To Move Forwardmentioning

confidence: 99%

Data sharing for clinical utility

Bjork

Peralez

Haussler

et al. 2019

Cold Spring Harb Mol Case Stud

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Genomic data offer valuable insights that can be used to help find treatments and cures for disease. Precision medicine, defined by the NIH as “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person,” is gaining acceptance among physicians, who are beginning to integrate patient-centric data analysis into clinical decision-making. Although precision medicine makes use of various types of data, this piece focuses on molecular characterization data specifically, as the discoveries yielded from these data can advance thinking around clinical care for cancer patients. Our pediatrics genomics team at the University of California Santa Cruz Genomics Institute is uniquely situated to discuss the use of shared genomic data for clinical benefit because our collaborations with hospital partners in the United States and internationally rely on big-data comparative genomic analysis. Using shared data, Treehouse Childhood Cancer Initiative develops methods for comparative analysis of tumor RNA sequencing profiles from single patients for the purposes of identifying overexpressed oncogenes that could be targeted by therapies in the clinic. To enable and improve this analysis, we continuously increase the size of our data compendium by adding public pediatric tumor RNA sequencing data sets. We developed an approach for assessing the quality of shared RNA sequencing data to ensure the integrity of the data. In this approach we calculate the number of mapped exonic nonduplicate (MEND) reads, applying a 10 million MEND read minimum threshold for inclusion in our comparative analysis. In collaboration with Stanford University and Lucile Packard Children's Hospital Stanford, our team at Treehouse Childhood Cancer Initiative explores the value to researchers everywhere of shared genomic data for clinical utility and the challenges of data sharing that threaten to impede otherwise rapid advances in precision medicine. This Perspective offers recommendations for maximizing the use of genomic data to make discoveries that will benefit patients.

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“…There are several reasons why a survey of this breadth has not been previously performed. Obtaining and processing clinical datasets from multiple sources is an intensive effort [ 33 ]. Access to tumor datasets is usually controlled, and obtaining the 48 cohorts that we report on here required multiple legal agreements [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets

et al. 2021

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Background The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets used for gene expression analysis. Findings In bulk RNA-Seq datasets from 2,179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1–77% of all reads (median [IQR], 3% [3–6%]); duplicate reads constitute 3–100% of mapped reads (median [IQR], 27% [13–43%]); and non-exonic reads constitute 4–97% of mapped, non-duplicate reads (median [IQR], 25% [16–37%]). MEND reads constitute 0–79% of total reads (median [IQR], 50% [30–61%]). Conclusions Because not all reads in an RNA-Seq dataset are informative for reproducibility of gene expression measurements and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped, or exonic reads. We provide a Docker image containing (i) the existing required tools (RSeQC, sambamba, and samblaster) and (ii) a custom script to calculate MEND reads from RNA-Seq data files. We recommend that all RNA-Seq gene expression experiments, sensitivity studies, and depth recommendations use MEND units for sequencing depth.

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Barriers to accessing public cancer genomic data

Cited by 26 publications

References 12 publications

A molecular map of lung neuroendocrine neoplasms

A molecular map of lung neuroendocrine neoplasms

Data sharing for clinical utility

The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets

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