valr: Reproducible genome interval analysis in R

Riemondy, Kent; Sheridan, Ryan M.; Gillen, Austin E.; Yu, Yinni; Bennett, Christopher; Hesselberth, Jay R.

doi:10.12688/f1000research.11997.1

Cited by 72 publications

(63 citation statements)

References 12 publications

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“…Poisson test for enrichment in hotspots was performed as for super-enhancers. We also simulated 1000 sets of hotspots of identical size using a bed shuffle implementation in R 67 , estimating the probability under the null hypothesis to obtain a more extreme result than the observed data. As a final control measure, we repeated the same analysis for loci associated with development of a completely unrelated condition.…”

Section: Methodsmentioning

confidence: 99%

Revealing the impact of recurrent and rare structural variants in multiple myeloma

Rustad

Yellapantula

Glodzik

et al. 2019

Preprint

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53The landscape of structural variants (SVs) in multiple myeloma remains poorly 54 understood. Here, we performed comprehensive classification and analysis of SVs in 55 multiple myeloma, interrogating a large cohort of 762 patients with whole genome and 56 RNA sequencing. We identified 100 SV hotspots involving 31 new candidate driver 57 genes, including drug targets BCMA (TNFRSF17) and SLAMF7. Complex SVs, 58 including chromothripsis and templated insertions, were present in 61 % of patients and 59 frequently resulted in the simultaneous acquisition of multiple drivers. After accounting 60 for all recurrent events, 63 % of SVs remained unexplained. Intriguingly, these rare SVs 61were associated with up to 7-fold enrichment for outlier gene expression, indicating that 62 many rare driver SVs remain unrecognized and are likely important in the biology of 63 individual tumors. 64 65 whole chromosome gains later in tumor evolution 13,20 . The second category is made up of 89 all other recurrent copy number alterations (CNAs), one or more of which are present in 90 virtually all patients 19,20 . Among these, gain of chromosome 1q21 often occurs early in 91 tumor evolution, while loss of tumor suppressor genes tends occur later 20-23 . The 92 structural basis of these established drivers remains poorly understood, and there is a lack 93 of data about the role of SVs beyond the most recurrent aberrations. 94We recently reported the first comprehensive SV characterization using WGS of 95 sequential samples from 30 multiple myeloma patients 20 . Despite the small sample set, 96SVs emerged as key drivers during all evolutionary phases of disease, and we identified a 97 high prevalence of three main classes of complex SVs: chromothripsis, templated 98 insertions and chromoplexy 20 . In chromothripsis, chromosomal shattering and random 99 rejoining results in a pattern of tens to hundreds of breakpoints with oscillating copy 100 number across one or more chromosomes 24 . Templated insertions are characterized by 101 focal gains bounded by translocations, resulting in concatenation of amplified segments 102 from two or more chromosomes into a continuous stretch of DNA, which is inserted back 103 into any of the involved chromosomes 2,20 . Chromoplexy similarly connects segments 104 from multiple chromosomes, but the local footprint is characterized by copy number 105 loss 25 . Importantly, complex SVs represent large-scale genomic alterations acquired by 106 the cancer cell at a single point in time, potentially driving subsequent tumor evolution. 107Here, we present the first comprehensive study of SVs in a large series of 762 108 multiple myeloma patients. We show that recurrent and rare SVs are critical in shaping 109 the genomic landscape of multiple myeloma, including complex events simultaneously 110 causing multiple drivers. 111 6 112 Results 113 Genome-wide landscape of structural variation in multiple myeloma 114To define the landscape of simple and complex SVs in multiple myeloma, we 115 investigated 762 newly di...

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Section: Methodsmentioning

confidence: 99%

Revealing the impact of recurrent and rare structural variants in multiple myeloma

Rustad

Yellapantula

Glodzik

et al. 2019

Preprint

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“…Operations on genomic intervals in all modules is performed using either GenomicRanges (Lawrence et al, 2013) or valr (A. Riemondy et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

ChIPdig: a comprehensive user-friendly tool for mining multi-sample ChIP-seq data

Esse

Grishok

2017

Preprint

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Summary: In this article we present ChIPdig, a tool for analyzing and comparing multiple ChIP-seq data sets. ChIPdig is written in R and enables access to powerful R-based functions and packages through a simple user interface powered by the shiny package. It allows users to align reads to a reference genome, perform peak calling and differential enrichment analysis on regions of interest, annotate such regions based on available coordinates of transcription start and termination sites, exons, introns, 5' UTRs and 3' UTRs, and generate heatmaps and metaplots for visualizing coverage. Availability and implementation:ChIPdig is open source and available at https://github.com/rmesse/ChIPdig.

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“…These coordinates were converted to mm10 coordinates using UCSC liftover chain files and the liftOver tool. Squirrel RNA editing sites were converted to mm10 coordinates using the UCSC liftover chain and liftOver tool, and shared editing sites were identified using the R package valr (v0.3.1) (Riemondy et al 2017) . Editing sites conserved in mammals were taken from the supplementary data of (Pinto et al 2014) and…”

Section: Editing Site Conservationmentioning

confidence: 99%

Dynamic temperature-sensitive A-to-I RNA editing in the brain of a heterothermic mammal during hibernation

Riemondy

Gillen

White

et al. 2018

Preprint

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RNA editing diversifies genomically encoded information to expand the complexity of the transcriptome. In ectothermic organisms, including Drosophila and Cephalopoda , where body temperature mirrors ambient temperature, decreases in environmental temperature lead to increases in A-to-I RNA editing and cause amino acid recoding events that are thought to be adaptive responses to temperature fluctuations. In contrast, endothermic mammals, including humans and mice, typically maintain a constant body temperature despite environmental changes. Here, A-to-I editing primarily targets repeat elements, rarely results in the recoding of amino acids and plays a critical role in innate immune tolerance. Hibernating ground squirrels provide a unique opportunity to examine RNA editing in a heterothermic mammal whose body temperature varies over 30°C and can be maintained at 5°C for many days during torpor. We profiled the transcriptome in three brain regions at six physiological states to quantify RNA editing and determine whether cold-induced RNA editing modifies the transcriptome as a potential mechanism for neuroprotection at low temperature during hibernation. We identified 5,165 A-to-I editing sites in 1,205 genes with dynamically increased editing after prolonged cold exposure. The majority (99.6%) of the cold-increased editing sites are outside of previously annotated coding regions, 82.7% lie in SINE-derived repeats, and 12 sites are predicted to recode amino acids. Additionally, A-to-I editing frequencies increase with increasing cold-exposure demonstrating that ADAR remains active during torpor. Our findings suggest that dynamic A-to-I editing at low body temperature may provide a neuroprotective mechanism to limit aberrant dsRNA accumulation during torpor in the mammalian hibernator.

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valr: Reproducible genome interval analysis in R

Cited by 72 publications

References 12 publications

Revealing the impact of recurrent and rare structural variants in multiple myeloma

Revealing the impact of recurrent and rare structural variants in multiple myeloma

ChIPdig: a comprehensive user-friendly tool for mining multi-sample ChIP-seq data

Dynamic temperature-sensitive A-to-I RNA editing in the brain of a heterothermic mammal during hibernation

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