Impact of RNA degradation on fusion detection by RNA-seq

Background. Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way.Results. We tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that we were able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions.Conclusions. The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.

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qSVA framework for RNA quality correction in differential expression analysis

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1617384114 ◽

2017 ◽

Vol 114 (27) ◽

pp. 7130-7135 ◽

Cited By ~ 41

Author(s):

Andrew E. Jaffe ◽

Ran Tao ◽

Alexis L. Norris ◽

Marc Kealhofer ◽

Abhinav Nellore ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Brain Regions ◽

Rna Degradation ◽

Rna Seq ◽

Postmortem Human Brain ◽

Surrogate Variable Analysis ◽

Expression Levels ◽

Rna Quality

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. We demonstrate here that statistical adjustment using existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from molecular degradation experiments of human primary tissues, we introduce a method—quality surrogate variable analysis (qSVA)—as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.

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SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data

BioMed Research International ◽

10.1155/2015/780519 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Yuxiang Tan ◽

Yann Tambouret ◽

Stefano Monti

Keyword(s):

Sample Size ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Simulated Data ◽

Real Data ◽

Rna Seq ◽

Sequencing Data ◽

Detection Algorithms ◽

Fusion Detection

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

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Using pan RNA-seq analysis to reveal the ubiquitous existence of 5’ end and 3’ end small RNAs

10.1101/444117 ◽

2018 ◽

Author(s):

Xiaofeng Xu ◽

Haishuo Ji ◽

Zhi Cheng ◽

Xiufeng Jin ◽

Xue Yao ◽

...

Keyword(s):

Gene Expression ◽

Steady State ◽

Small Rnas ◽

Gene Expression Regulation ◽

Regulation Of Gene Expression ◽

Cost Effective ◽

Rna Degradation ◽

Point Of View ◽

Rna Seq ◽

Dsrna Cleavage

AbstractIn this study, we used pan RNA-seq analysis to reveal the ubiquitous existence of 5’ end and 3’ end small RNAs. 5’ and 3’ sRNAs alone can be used to annotate mitochondrial with 1-bp resolution and nuclear non-coding genes and identify new steady-state RNAs, which are usually from functional genes. Using 5’, 3’ and intronic sRNAs, we revealed that the enzymatic dsRNA cleavage and RNAi could involve in the RNA degradation and gene expression regulation of U1 snRNA in human. The further study of 5’, 3’ and intronic sRNAs help rediscover double-stranded RNA (dsRNA) cleavage, RNA interference (RNAi) and the regulation of gene expression, which challenges the classical theories. In this study, we provided a simple and cost effective way for the annotation of mitochondrial and nuclear non-coding genes and the identification of new steady-state RNAs, particularly long non-coding RNAs (lncRNAs). We also provided a different point of view for cancer and virus, based on the new discoveries of dsRNA cleavage, RNAi and the regulation of gene expression.

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Gene expression estimates: Influence of sequencing library construction, fish sampling methods, and tissue harvesting time

10.22541/au.161142130.04599849/v1 ◽

2021 ◽

Author(s):

Nickolas Moreno ◽

Leif Howard ◽

Scott Relyea ◽

James Dunnigan ◽

Matthew Boyer ◽

...

Keyword(s):

Gene Expression ◽

Sampling Methods ◽

Cutthroat Trout ◽

Rna Degradation ◽

Model Organisms ◽

Rna Seq ◽

Gene Expression Variation ◽

Oncorhynchus Clarkii ◽

Expression Variation ◽

Study Gene Expression

RNA sequencing (RNA-Seq) is becoming a popular method for measuring gene expression in non-model organisms, including wild populations sampled in the field. While RNA-Seq can be used to measure gene expression variation among wild-caught individuals and can yield important biological insights into organismal function, technical variables may also influence gene expression estimates. We examined the influence of multiple technical variables on estimated gene expression in a non-model fish species, the westslope cutthroat trout (Oncorhynchus clarkii lewisi), using two RNA-Seq methods: 3’ RNA-Seq and whole mRNA-Seq. We evaluated the effects of dip netting versus electrofishing, and of harvesting tissue immediately versus 5 minutes after euthanasia on estimated gene expression in blood, gill, muscle, and liver. We found higher RNA degradation in the liver compared to the other tissues. There were fewer expressed genes in blood compared to gill and muscle. We found no difference in gene expression among sampling methods or due to a delay in tissue collection. However, we detected fewer genes with 3’ RNA-Seq than with whole mRNA-Seq and found statistically significant differences in gene expression between 3’ RNA-Seq and whole mRNA-Seq. The magnitude and direction of these differences does not appear to be dependent on gene type or length. Our findings indicate that RNA-Seq is robust to the technical variables related to the field sampling techniques tested here but varies based on the tissue sampled and the RNA-Seq library used. This study advances understanding of usefulness of RNA-Seq to study gene expression variation in evolution, ecology, and conservation.

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DeLTa-Seq: direct-lysate targeted RNA-Seq from crude tissue lysate

10.1101/2020.09.15.299180 ◽

2020 ◽

Author(s):

Makoto Kashima ◽

Mari Kamitani ◽

Yasuyuki Nomura ◽

Hiromi Hirata ◽

Atsushi J. Nagano

Keyword(s):

Reverse Transcription ◽

Large Scale ◽

Field Experiments ◽

Rna Extraction ◽

Rna Degradation ◽

Individual Variability ◽

Rna Seq ◽

Tissue Samples ◽

Comparable Amount ◽

Chemical Screening

AbstractUsing current mRNA quantification methods such as RT-qPCR and RNA-Seq, it is very difficult to examine thousands of tissue samples due to cost and labor of RNA extraction and quantification steps. Here, we developed Direct-RT buffer in which homogenization of tissue samples and direct-lysate reverse transcription can be conducted without RNA purification. We showed that appreciate concentration of DTT prevented RNA degradation but not RT in the lysates of several plants’ tissues, yeast, and zebrafish larvae. Using the buffer, direct reverse transcription on the lysates could produce comparable amount of cDNA with that synthesized from purified RNA. Furthermore, we established DeLTa-Seq (Direct-Lysate reverse transcription and Targeted RNA-Seq) method. DeLTa-Seq is a cost-effective, high-throughput and highly-precise quantification method for the expressions of hundreds of genes. It enables us to conduct large-scale studies using thousands of samples such as chemical screening, field experiments and studies focusing on individual variability.

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Abstract 20232: Haploinsufficiency of MYBPC3 in the Development of Hypertrophic Cardiomyopathy

Circulation ◽

10.1161/circ.130.suppl_2.20232 ◽

2014 ◽

Vol 130 (suppl_2) ◽

Author(s):

David Barefield ◽

Mohit Kumar ◽

Joshua Gorham ◽

Jonathan Seidman ◽

Christine Seidman ◽

...

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Rna Degradation ◽

Rna Seq ◽

Reduced Ejection Fraction ◽

Force Development ◽

Protein Levels ◽

Maximal Force ◽

Myosin Binding Protein ◽

Myosin Binding Protein C ◽

Truncation Mutant

Introduction: Mutations in MYBPC3, encoding cardiac myosin binding protein-C (cMyBP-C), account for ~40% of hypertrophic cardiomyopathy (HCM) cases. MYBPC3 mutations are usually encode truncated proteins and are not found in tissue and are typically heterozygous (Het) in humans. Reduced protein levels occur in human HCM patients with these mutations, suggesting haploinsufficiency. However, it is unknown if cMyBP-C reduction causes or results from hypertrophy. Hypothesis: To test whether haploinsufficiency occurs following cardiac stress and if heterozygous MYBPC3 mice had worsened disease progression. Methods & Results: Transverse aortic constriction (TAC) was performed on 3 month old wild type (WT) and Het MYBPC3 truncation mutant mice which were allowed to hypertrophy for 4 or 12 weeks. Het TAC mice showed increased hypertrophy 12 weeks post-TAC compared to WT TAC controls. Het TAC hearts showed reduced ejection fraction compared to WT TAC at 4 and 12 weeks. MYBPC3 transcript levels were significantly reduced in sham and TAC Het hearts. cMyBP-C levels decreased in Het sham and TAC at 4 weeks but returned to baseline levels at 12 weeks. Het TAC myocytes showed higher Ca2+ sensitivity at 4 weeks, and impaired maximal force development. Het sham and TAC skinned cardiomyocytes showed reduced length dependent increases in Ca2+ sensitivity and maximal force development. RNA-Seq shows no alterations in proteasome of RNA-degradation pathways which have been suggested to play a role in the pathology of these mutations. Overexpression of WT cMyBP-C in the presence of truncated MYBPC3 rescued the decline in force observed in Het myocytes in the absence of stress. Conclusions: Heterozygous MYBPC3 truncation mutant carriers develop more profound hypertrophy and dysfunction following stress. Also, increased MYBPC3 expression reverses myocyte deficits in force generation in the presence of truncated alleles.

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RNA-seq: impact of RNA degradation on transcript quantification

BMC Biology ◽

10.1186/1741-7007-12-42 ◽

2014 ◽

Vol 12 (1) ◽

pp. 42 ◽

Cited By ~ 199

Author(s):

Irene Gallego Romero ◽

Athma A Pai ◽

Jenny Tung ◽

Yoav Gilad

Keyword(s):

Rna Degradation ◽

Rna Seq ◽

Transcript Quantification

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Alignment-free filtering for cfNA fusion fragments

Bioinformatics ◽

10.1093/bioinformatics/btz346 ◽

2019 ◽

Vol 35 (14) ◽

pp. i225-i232 ◽

Cited By ~ 2

Author(s):

Xiao Yang ◽

Yasushi Saito ◽

Arjun Rao ◽

Hyunsung John Kim ◽

Pranav Singh ◽

...

Keyword(s):

Nucleic Acid ◽

Cell Line ◽

De Novo ◽

High Sensitivity ◽

Detection Methods ◽

Rna Seq ◽

Sequencing Data ◽

Alignment Free ◽

Fusion Detection ◽

High Depth

Abstract Motivation Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers. Results AF4 was developed to address these challenges. It uses a novel alignment-free kmer-based method to detect candidate fusion fragments with high sensitivity and orders of magnitude faster than existing tools. Candidate fragments are then filtered using a max-cover criterion that significantly reduces spurious matches while retaining authentic fusion fragments. This efficient first stage reduces the data sufficiently that commonly used criteria can process the remaining information, or sophisticated filtering policies that may not scale to the raw reads can be used. AF4 provides both targeted and de novo fusion detection modes. We demonstrate both modes in benchmark simulated and real RNA-seq data as well as clinical and cell-line cfNA data. Availability and implementation AF4 is open sourced, licensed under Apache License 2.0, and is available at: https://github.com/grailbio/bio/tree/master/fusion.

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