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Impact of RNA degradation on fusion detection by RNA-seq.

ABSTRACT: RNA-seq is a well-established method for studying the transcriptome. Popular methods for library preparation in RNA-seq such as Illumina TruSeq® RNA v2 kit use a poly-A pulldown strategy. Such methods can cause loss of coverage at the 5' end of genes, impacting the ability to detect fusions when used on degraded samples. The goal of this study was to quantify the effects RNA degradation has on fusion detection when using poly-A selected mRNA and to identify the variables involved in this process.Using both artificially and naturally degraded samples, we found that there is a reduced ability to detect fusions as the distance of the breakpoint from the 3' end of the gene increases. The median transcript coverage decreases exponentially as a function of the distance from the 3' end and there is a linear relationship between the coverage decay rate and the RNA integrity number (RIN). Based on these findings we developed plots that show the probability of detecting a gene fusion ("sensitivity") as a function of the distance of the fusion breakpoint from the 3' end.This study developed a strategy to assess the impact that RNA degradation has on the ability to detect gene fusions by RNA-seq.

SUBMITTER: Davila JI

PROVIDER: S-EPMC5072325 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Impact of RNA degradation on fusion detection by RNA-seq.

Davila Jaime I JI Fadra Numrah M NM Wang Xiaoke X McDonald Amber M AM Nair Asha A AA Crusan Barbara R BR Wu Xianglin X Blommel Joseph H JH Jen Jin J Rumilla Kandelaria M KM Jenkins Robert B RB Aypar Umut U Klee Eric W EW Kipp Benjamin R BR Halling Kevin C KC

BMC genomics 20161020 1

<h4>Background</h4>RNA-seq is a well-established method for studying the transcriptome. Popular methods for library preparation in RNA-seq such as Illumina TruSeq® RNA v2 kit use a poly-A pulldown strategy. Such methods can cause loss of coverage at the 5' end of genes, impacting the ability to detect fusions when used on degraded samples. The goal of this study was to quantify the effects RNA degradation has on fusion detection when using poly-A selected mRNA and to identify the variables invol ...[more]

PMID: 27765019

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Project description:Purpose: Popular methods for library preparation in RNA-seq such as Illumina TruSeq® RNA v2 kit use a poly-A pulldown strategy. Such methods can cause loss of coverage at the 5’ end of genes, impacting the ability to detect fusions when used on degraded samples. The goal of this study was to quantify the effects RNA degradation has on fusion detection when using poly-A selected mRNA and to identify the variables involved in this process Methods: Total RNA was extracted from solid tumor tissue and whole blood using the Qiagen® miRNeasy Micro and Mini kits, respectively. The KU812 cell line was purchased from Sigma-Aldrich (St. Louis, MO) and UHR (Universal Human Reference RNA) was purchased from Agilent (Santa Clara, CA). UHR is a mixture of cell lines derived from breast adenocarcinoma, hepatoblastoma, cervix adenocarcinoma, testis embryonal carcinoma, gliobastoma, melanoma, liposarcoma, histiocytic lymphoma, lymphoblastic leukemia and plasmocytoma. For Degradation experiments, two micrograms of human universal reference RNA (UHR) (Agilent Technologies, Santa Clara, CA) and 1ug of RNA extracted from KU812 cell line (purchased from ATCC) were degraded at 74oC from 1 to 11 minutes in 1 minute intervals, using the NEBNext® Magnesium RNA Fragmentation Module Kit (NEB, Ipswich, MA). RNA was then purified and concentrated with RNeasy MinElute Cleanup Kit (Qiagen, Valencia, CA). Results: In this study, we designed experiments using artificially degraded RNA from cell lines as well as naturally degraded RNA from tissue samples to quantify the effect RNA degradation has on fusion detection when using poly-A selected RNA libraries We found that both the RNA degradation level and the distance from the 3’ end of a gene, negatively impact the read coverage profile in RNA-seq. Furthermore, the median transcript coverage decreases exponentially as a function of the distance from the 3’ end and there is a linear relationship between the coverage decay rate and the RNA integrity number (RIN). Conclusions: we found that when using poly-A pulldown techniques for library preparation in RNA-seq, the fusion sensitivity is negatively impacted by both sample degradation and distance of the fusion breakpoint from the 3’ end and developed graphs that show such effect. Such graphs can be useful in assessing the fusion sensitivity of RNA-seq in both research and clinical settings

Project description:Chimeric transcripts are commonly defined as transcripts linking two or more different genes in the genome, and can be explained by various biological mechanisms such as genomic rearrangement, read-through or trans-splicing, but also by technical or biological artefacts. Several studies have shown their importance in cancer, cell pluripotency and motility. Many programs have recently been developed to identify chimeras from Illumina RNA-seq data (mostly fusion genes in cancer). However outputs of different programs on the same dataset can be widely inconsistent, and tend to include many false positives. Other issues relate to simulated datasets restricted to fusion genes, real datasets with limited numbers of validated cases, result inconsistencies between simulated and real datasets, and gene rather than junction level assessment.Here we present ChimPipe, a modular and easy-to-use method to reliably identify fusion genes and transcription-induced chimeras from paired-end Illumina RNA-seq data. We have also produced realistic simulated datasets for three different read lengths, and enhanced two gold-standard cancer datasets by associating exact junction points to validated gene fusions. Benchmarking ChimPipe together with four other state-of-the-art tools on this data showed ChimPipe to be the top program at identifying exact junction coordinates for both kinds of datasets, and the one showing the best trade-off between sensitivity and precision. Applied to 106 ENCODE human RNA-seq datasets, ChimPipe identified 137 high confidence chimeras connecting the protein coding sequence of their parent genes. In subsequent experiments, three out of four predicted chimeras, two of which recurrently expressed in a large majority of the samples, could be validated. Cloning and sequencing of the three cases revealed several new chimeric transcript structures, 3 of which with the potential to encode a chimeric protein for which we hypothesized a new role. Applying ChimPipe to human and mouse ENCODE RNA-seq data led to the identification of 131 recurrent chimeras common to both species, and therefore potentially conserved.ChimPipe combines discordant paired-end reads and split-reads to detect any kind of chimeras, including those originating from polymerase read-through, and shows an excellent trade-off between sensitivity and precision. The chimeras found by ChimPipe can be validated in-vitro with high accuracy.

Dataset Information

Impact of RNA degradation on fusion detection by RNA-seq.

Publications

Impact of RNA degradation on fusion detection by RNA-seq.

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