Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

Nellore, Abhinav; Collado‐Torres, Leonardo; Jaffe, Andrew E.; Alquicira-Hernández, José; Pritt, Jacob; Morton, James T.; Leek, Jeffrey T.; Langmead, Ben

doi:10.1101/019067

Cited by 29 publications

(31 citation statements)

References 54 publications

(77 reference statements)

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“…GENCODE version 28 annotations (39) were downloaded and parsed to collect full coordinates and left and right splice sites of junctions from annotated transcripts. The TCGA phenotype file from Rail-RNA (40) was parsed to collect sample type (primary, recurrent, or metastatic tumor vs. matched normal). A new SQLite3 database was created to index all GTEx and TCGA junctions, with linked tables containing 1) sample ids and associated junction ids; 2) sample ids and phenotype information for each sample; and 3) junction ids and junction information including GENCODE annotation status and location within protein coding gene boundaries.…”

Section: Rna Variant Identificationmentioning

confidence: 99%

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

Wood

Weeder

David

et al. 2019

Preprint

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Background: Tumor mutational burden (TMB, the quantity of aberrant nucleotide sequences a given tumor may harbor) has been associated with response to immune checkpoint inhibitor therapy and is gaining broad acceptance as a result. However, TMB harbors intrinsic variability across cancer types, and its assessment and interpretation are poorly standardized. Methods: Using a standardized approach, we quantify the robustness of TMB as a metric and its potential as a predictor of immunotherapy response and survival among a diverse cohort of cancer patients. We also explore the additive predictive potential of RNA-derived variants and neoepitope burden, incorporating several novel metrics of immunogenic potential. Results: We find that TMB is a partial predictor of immunotherapy response in melanoma and non-small cell lung cancer, but not renal cell carcinoma. We find that TMB is predictive of overall survival in melanoma patients receiving immunotherapy, but not in an immunotherapy-naive population. We also find that it is an unstable metric with potentially problematic repercussions for clinical cohort classification. We finally note minimal additional predictive benefit to assessing neoepitope burden or its bulk derivatives, including RNA-derived sources of neoepitopes. Conclusions: We find sufficient cause to suggest that the predictive clinical value of TMB should not be overstated or oversimplified. While it is readily quantified, TMB is at best a limited surrogate biomarker of immunotherapy response. The data do not support isolated use of TMB in renal cell carcinoma. KEYWORDStumor mutational burden, TMB, neoepitopes, neoepitope burden, neoantigens, splice junctions, retained introns, tumor variant burden, immunotherapy response BACKGROUNDThe advent of immunotherapy as a promising form of cancer treatment has been accompanied by a parallel effort to explore potential mechanisms and drivers of therapeutic response. For instance, tumor mutational burden (TMB, the overall quantity of aberrant nucleotide sequences a given tumor may harbor) has been associated with response to immune checkpoint inhibitor therapy (1) and overall survival (2) . Similarly, the quantity of non-synonymous single nucleotide variants was shown to be associated with immunotherapy response in several independent clinical cohorts (3-6) . Other sources of sequence variation such as frameshifting insertions/deletions (7) and tumor-specific alternative splicing (e.g. intron retention (8) ) have also been found to correlate with immunotherapy response. These phenomena are widely accepted and appear to be particularly pronounced in patients harboring DNA repair deficiencies (9) . Indeed, the checkpoint inhibitor, pembrolizumab, was granted accelerated disease-agnostic approval by the FDA on this basis for any cancer patient harboring deficiencies in their capacity to perform DNA mismatch repair (10) . Moreover, an expanding cohort of clinical immunotherapy trials (e.g. NCT03668119, NCT03178552, NCT03519412) are actively utilizing TMB status as a k...

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Section: Rna Variant Identificationmentioning

confidence: 99%

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

Wood

Weeder

David

et al. 2019

Preprint

Self Cite

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“…We simulated reads from 18,303 transcripts using all annotated features on chromosomes 1 and 14, including 10 scenarios of varying readlength and paired-end status, using the polyester R package [14]. Our method was run on the reduced-representation output from applying the aligner Rail-RNA [5] to the simulated FASTA files. All other methods extracted information from the full simulated FASTA files.…”

Section: Performance On Dirichlet-negative Binomial Simulated Datamentioning

confidence: 99%

“…For Cufflinks [6], we provided the fragment length ditsribution, and used --total-hits-norm --no-effective-length-correction --no-length-correction options. For our linear model, we utilized Rail-RNA [5] to process the FASTA files in the same manner as in recount2 [4]. For evaluation, each method's abundance estimates (est) were compared to the true number (truth) using mean absolute error (MAE):…”

Section: Dirichlet-negative Binomial Simulation Scenariosmentioning

confidence: 99%

“…The recently published recount2 project [4] is the result of such an undertaking. All raw data from the thousands of sequencing studies were aligned to a common reference genome using a scalable and reproducible aligner Rail-RNA [5]. Summary measures (gene, exon, junction, and base-pair level coverage) were derived from the Rail-RNA output and made available in an R package and through a web portal (https://jhubiostatistics.shinyapps.io/recount/).…”

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RNA-seq transcript quantification from reduced-representation data in recount2

Kammers

Nellore

et al. 2018

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More than 70,000 short-read RNA-sequencing samples are publicly available through the recount2 project, a curated database of summary coverage data. However, no current methods can estimate transcript-level abundances using the reduced-representation information stored in this database. Here we present a linear model utilizing coverage of junctions and subdivided exons to generate transcript abundance estimates of comparable accuracy to those obtained from methods requiring read-level data. Our approach flexibly models bias, produces standard errors, and is easy to refresh given updated annotation. We illustrate our method on simulated and real data and release transcript abundance estimates for the samples in recount2.

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“…to calculate the percentage of genome with at least 30X read depth) or to identify genomic regions overlapped by insucient number of reads for reliable variant calling [4]. Finally, depth of coverage is one of the most computationally intensive parts of di erential expression analysis using RNA-seq data at single-base resolution [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

Wiewiórka

Szmurło

Kuśmirek

et al. 2018

Preprint

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BackgroundDepth of coverage calculation is an important and computationally intensive preprocessing step in a variety of next generation sequencing pipelines, including the analyses of RNA-seq data, detection of copy number variants, or quality control procedures. Results Building upon big data technologies, we have developed SeQuiLa-cov, an extension to the recently released SeQuiLa platform, which provides e cient depth of coverage calculations, reaching more than 100x speedup over the state-of-the-art tools. Performance and scalability of our solution allows for exome and genome-wide calculations running locally or on a cluster while hiding the complexity of the distributed computing with Structured Query Language Application Programming Interface. Conclusions SeQuiLa-cov provides signi cant performance gain in depth of coverage calculations streamlining the widely used bioinformatic processing pipelines.• SeQuiLa-cov allows for high-coverage (∼60x) genome-wide depth of coverage calculations in less than one minute. • SeQuiLa-cov provides ANSI SQL compliant API for accessing and analyzing of aligned sequencing reads data.

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Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

Cited by 29 publications

References 54 publications

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

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

RNA-seq transcript quantification from reduced-representation data in recount2

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

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