A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery

Abstract Motivation Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. Results In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementation Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary information Supplementary data are available at Bioinformatics online.

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State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity

BioMed Research International ◽

10.1155/2013/340620 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 57

Author(s):

Matteo Carrara ◽

Marco Beccuti ◽

Fulvio Lazzarato ◽

Federica Cavallo ◽

Francesca Cordero ◽

...

Keyword(s):

Synthetic Data ◽

Rna Seq ◽

Comparison Analysis ◽

Generating Functional ◽

Specificity And Sensitivity ◽

Sensitive Tool ◽

Fusion Junction ◽

Synthetic Datasets ◽

Very High ◽

Fusion Detection

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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Improved RNA-Seq Partitions in Linear Models for Isoform Quantification

2011 IEEE International Conference on Bioinformatics and Biomedicine ◽

10.1109/bibm.2011.102 ◽

2011 ◽

Author(s):

Brian E. Howard ◽

Paola Veronese ◽

Steffen Heber

Keyword(s):

Linear Models ◽

Rna Seq ◽

Isoform Quantification

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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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WemIQ: an accurate and robust isoform quantification method for RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btu757 ◽

2014 ◽

Vol 31 (6) ◽

pp. 878-885 ◽

Cited By ~ 13

Author(s):

Jing Zhang ◽

C.-C. Jay Kuo ◽

Liang Chen

Keyword(s):

Rna Seq ◽

Quantification Method ◽

Isoform 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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LIQA: long-read isoform quantification and analysis

Genome Biology ◽

10.1186/s13059-021-02399-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yu Hu ◽

Li Fang ◽

Xuelian Chen ◽

Jiang F. Zhong ◽

Mingyao Li ◽

...

Keyword(s):

Alternative Splicing ◽

Myeloid Leukemia ◽

Expectation Maximization Algorithm ◽

Read Length ◽

Specific Gene ◽

Rna Seq ◽

Sequencing Data ◽

Long Read ◽

Isoform Quantification ◽

Universal Human Reference

AbstractLong-read RNA sequencing (RNA-seq) technologies can sequence full-length transcripts, facilitating the exploration of isoform-specific gene expression over short-read RNA-seq. We present LIQA to quantify isoform expression and detect differential alternative splicing (DAS) events using long-read direct mRNA sequencing or cDNA sequencing data. LIQA incorporates base pair quality score and isoform-specific read length information in a survival model to assign different weights across reads, and uses an expectation-maximization algorithm for parameter estimation. We apply LIQA to long-read RNA-seq data from the Universal Human Reference, acute myeloid leukemia, and esophageal squamous epithelial cells and demonstrate its high accuracy in profiling alternative splicing events.

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FDJD: RNA-Seq Based Fusion Transcript Detection Using Jaccard Distance

10.1101/2021.11.17.469019 ◽

2021 ◽

Author(s):

Hamid Reza Mohebbi ◽

Nurit Haspel

Keyword(s):

False Positive Rate ◽

Cancer Cell Line ◽

Fusion Transcript ◽

High Volume ◽

Detection Methods ◽

Data Sets ◽

Gene Fusions ◽

Rna Seq ◽

Jaccard Distance ◽

Fusion Detection

Gene fusions events, which are the result of two genes fused together to create a hybrid gene, were first described in cancer cells in the early 1980s. These events are relatively common in many cancers including prostate, lymphoid, soft tissue, and breast. Recent advances in next-generation sequencing (NGS) provide a high volume of genomic data, including cancer genomes. The detection of possible gene fusions requires fast and accurate methods. However, current methods suffer from inefficiency, lack of sufficient accuracy, and a high false-positive rate. We present an RNA-Seq fusion detection method that uses dimensionality reduction and parallel computing to speed up the computation. We convert the RNA categorical space into a compact binary array called binary fingerprints, which enables us to reduce the memory usage and increase efficiency. The search and detection of fusion candidates are done using the Jaccard distance. The detection of candidates is followed by refinement. We benchmarked our fusion prediction accuracy using both simulated and genuine RNA-Seq datasets. Paired-end Illumina RNA-Seq genuine data were obtained from 60 publicly available cancer cell line data sets. The results are compared against the state-of-the-art-methods such as STAR-Fusion, InFusion, and TopHat-Fusion. Our results show that FDJD exhibits superior accuracy compared to popular alternative fusion detection methods. We achieved 90% accuracy on simulated fusion transcript inputs, which is the highest among the compared methods while maintaining comparable run time.

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