A cloud-based workflow to quantify transcript-expression levels in public cancer compendia

Abstract

Public compendia of raw sequencing data are now measured in petabytes. Accordingly, it is becoming infeasible for individual researchers to transfer these data to local computers. Recently, the National Cancer Institute funded an initiative to explore opportunities and challenges of working with molecular data in cloud-computing environments. With data in the cloud, it becomes possible for scientists to take their tools to the data and thereby avoid large data transfers. It also becomes feasible to scale computing resources to the needs of a given analysis. To evaluate this concept, we quantified transcript-expression levels for 12,307 RNASequencing samples from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas. We used two cloud-based configurations to process the data and examined the performance and cost profiles of each configuration. Using "preemptible virtual machines", we processed the samples for as little as $0.09 (USD) per sample. In total, we processed the TCGA samples (n=11,373) for only $1,065.49 and simultaneously processed thousands of samples at a time. As the samples were being processed, we collected detailed performance metrics, which helped us to track the duration of each processing step and to identify computational resources used at different stages of sample processing. Although the computational demands of reference alignment and expression quantification have decreased considerably, there remains a critical need for researchers to optimize preprocessing steps (e.g., sorting, converting, and trimming sequencing reads). We have created open-source Docker containers that include all the software and scripts necessary to process such data in the cloud and to collect performance metrics. The processed data are available in tabular format and in Google's BigQuery database (see https://osf.io/gqrz9).

Burden of tumor mutations, neoepitopes, and other variants are dubious predictors of cancer immunotherapy response and overall survival

“…Third, neoepitope burden was calculated for each patient weighted by TCGA transcript expression of the transcript(s) of origin for each neoepitope. We identified expressed transcripts in matched TCGA cancer types for each disease type in our cohort (SKCM for melanoma, LUAD/LUSC for NSCLC, COAD for colon cancer, UCEC for endometrial cancer, THCA for thyroid cancer, PRAD for prostate cancer, and KIRC for RCC) from TPM values generated by the National Cancer Institute (52) . A transcript was considered "expressed" for a cancer type if the 75th quantile TPM value for that transcript in that disease was greater than 1 TPM.…”

Classification of clear cell renal cell carcinoma based onPKMalternative splicing

“…The TCGA transcript-expression level profiles (TPM and count values) of ccRCC and matched normal kidney samples was downloaded from https://osf.io/gqrz9 (Tatlow and Piccolo, 2016) on November 27, 2018, which was quantified by Kallisto (Bray et al, 2016) based on the GENCODE reference transcriptome (version 24). The clinical information of TCGA samples was downloaded through R package TCGAbiolinks (Colaprico et al, 2016).…”

Recounting the FANTOM Cage Associated Transcriptome

“…Although the systematic analysis of lncRNAs function is being addressed by the FANTOM consortium in loss of function studies, increasing the detection rate of these transcripts combining different studies is difficult because the heterogeneity of analytic methods employed. Current resources that apply uniform analytic methods to create expression summaries from public data do exist but can miss several lncRNAs because their dependency on a pre-existing gene annotation for creating the genes expression summaries 5,6 . We recently created recount2 7 , a collection of uniformly-processed human RNA-seq data, wherein we summarized 4.4 trillion reads from over 70,000 human samples from the Sequence Reads Archive (SRA), The Cancer Genome Atlas (TCGA) 8 , and the Genotype-Tissue Expression (GTEx) 9 projects 7 .…”