SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data
- Wenlong Jia†1, 2,
- Kunlong Qiu†1, 2,
- Minghui He†1, 2,
- Pengfei Song†2,
- Quan Zhou1, 2, 3,
- Feng Zhou2, 4,
- Yuan Yu2,
- Dandan Zhu2,
- Michael L Nickerson5,
- Shengqing Wan1, 2,
- Xiangke Liao6,
- Xiaoqian Zhu6, 7,
- Shaoliang Peng6, 7,
- Yingrui Li1, 2,
- Jun Wang1, 2, 8, 9 and
- Guangwu Guo1, 2Email author
© Jia et al.; licensee BioMed Central Ltd. 2013
Received: 16 August 2012
Accepted: 14 February 2013
Published: 14 February 2013
We have developed a new method, SOAPfuse, to identify fusion transcripts from paired-end RNA-Seq data. SOAPfuse applies an improved partial exhaustion algorithm to construct a library of fusion junction sequences, which can be used to efficiently identify fusion events, and employs a series of filters to nominate high-confidence fusion transcripts. Compared with other released tools, SOAPfuse achieves higher detection efficiency and consumed less computing resources. We applied SOAPfuse to RNA-Seq data from two bladder cancer cell lines, and confirmed 15 fusion transcripts, including several novel events common to both cell lines. SOAPfuse is available at http://soap.genomics.org.cn/soapfuse.html.
Gene fusions, arising from the juxtaposition of two distinct regions in chromosomes, play important roles in carcinogenesis and can serve as valuable diagnostic and therapeutic targets in cancer. Aberrant gene fusions have been widely described in malignant hematological disorders and sarcomas [1–3], with the recurrent BCR-ABL fusion gene in chronic myeloid leukemia as the classic example . In contrast, the biological and clinical impact of gene fusions in more common solid tumor types has been less appreciated . However, recent discoveries of recurrent gene fusions, such as TMPRSS2-ERG in a majority of prostate cancers [5, 6], EML4-ALK in non-small-cell lung cancer  and VTI1A-TCF7L2 in colorectal cancer , point to their functionally important role in solid tumors. These fusion events were not detected until recently due to technical and analytic problems encountered in the identification of balanced chromosomal aberrations in complex karyotypic profiles of solid tumors.
Massively parallel RNA sequencing (RNA-Seq) using a next-generation sequencing (NGS) platform provides a revolutionary, new tool for precise measurement of levels of transcript abundance and structure in a large variety of species [9–16]. In addition, RNA-Seq has been proven to be a sensitive and efficient approach to gene fusion discovery in many types of cancers [17–20]. Compared with whole genome sequencing, which is also able to detect gene-fusion-creating rearrangements, RNA-Seq identifies fusion events that generate aberrant transcripts that are more likely to be functional or causal in biological or disease settings.
Recently, several computational methods, including FusionSeq , deFuse , TopHat-Fusion , FusionHunter , SnowShoes-FTD , chimerascan  and FusionMap , have been developed to identify fusion transcript candidates by analyzing RNA-Seq data. Although these methods were capable of detecting genuine fusion transcripts, many challenges and limitations remain. For example, to determine the junction sites in a given fusion transcript, FusionSeq selected all exons that were potentially involved in the junction from both of the gene pairs, and then covered the exons with a set of 'tiles' that were spaced one nucleotide apart . A fusion junction library was constructed by creating all pairwise junctions between these tiles, and the junctions were identified by mapping the RNA-Seq reads to the junction library. This module makes FusionSeq time consuming, especially for genes with more and larger exons. In addition, FusionHunter identified only fusion transcripts with junction sites at the exon edge (splicing junction), but could not detect a fusion transcript with junction sites in the middle of an exon . Many homologous genes and repetitive sequences often masquerade as fusion events due to ambiguous alignments of short NGS sequencing reads. The lack of effective filtering mechanisms promoted frequent detection of spurious fusion transcripts. Furthermore, several software consumed large amounts of computational resources (CPU time and memory usage), which was a serious problem when analyzing hundreds of samples in parallel.
Evaluation of performance and sensitivity of SOAPfuse
For dataset A, which contains approximately 111 million paired-end reads, SOAPfuse consumed the least CPU time (approximately 5.2 hours) and the second least memory (approximately 7.1 Gigabytes) to complete the data analysis (including the alignment of reads against reference), and was able to detect all 15 fusion events. DeFuse and FusionHunter detected comparable numbers of known fusion events (12 to 13 of the 15 fusions), but took 82.1 and 21.3 CUP hours, respectively, at least four times as much as SOAPfuse (Additional file 5). The computational resource cost of SnowShoes-FTD was comparable with SOAPfuse, but SnowShoes-FTD identified only 8 of 15 events. The remaining two tools, chimerascan and TopHat-Fusion, detected four confirmed fusion events but used significantly more CPU hours or memory usage. For dataset B containing approximately 55 million paired-end reads, SOAPfuse detected 26 of the 27 reported fusion events with 4.1 CPU hours and 6.3 Gigabytes memory. The other five tools were able to identify comparable numbers of reported fusions (15 to 21) and cost at least 6.4 hours CPU time. One fusion event, NFS1-PREX1, was missed by all methods, including SOAPfuse (Additional file 3).
The process of data analysis for all six tools included two stages: read alignment then detection of fusion events. For both datasets, SOAPfuse, SnowShoes-FTD, and chimerascan consumed less memory than the other three tools. Chimerascan used less memory than SOAPfuse because it used Bowtie , which required less memory than SOAP2  in SOAPfuse, to align reads. The memory usage of the other tools (deFuse, FusionHunter, and TopHat-Fusion) were almost two to three times that of SOAPfuse. They reached maximum memory usage at the fusion detection stage, but not at the read alignment stage, which suggests there may still be room for algorithm improvement for fusion detection. SOAPfuse uses several optimized algorithms to reduce memory consumption with low cost to computation speed. For the two datasets, SOAPfuse expended less CPU time and memory than most of the other five tools, and reached the highest detection sensitivity, with almost all reported fusion events rediscovered (41 of 42), showing its superior performance and high sensitivity.
Estimate of the false negative and false positive rates by simulated datasets
As expected, FN rates decreased with increasing expression levels of fusion transcripts (Figure 3a). SOAPfuse and deFuse achieved the lowest FN rates at 5% with fusion transcript expression levels of 30-fold or greater. TopHat-Fusion had higher FN rates, especially at low fusion transcript expression levels (5- to 20-fold). For the FP rate (Figure 3b), only SOAPfuse achieved <5% at different fusion transcript expression levels, while deFuse and TopHat-Fusion had higher FP rates at lower fusion transcript expression levels.
Generally, lower FN rates and lower FP rates are contradictory for detection of fusions; however, SOAPfuse and deFuse are good at reducing FN and FP rates during fusion transcript identification. SOAPfuse missed three simulated fusions, which are detected by both deFuse and TopHat-Fusion (Figure 3c), revealing a weakness in analysis of homologous gene sequences and short fusion transcripts of long genes (Additional file 3). In summary, SOAPfuse showed optimal performance with low FN and FP rates at different expression levels of fusion transcripts.
Application to bladder cancer cell lines
RNA-Seq data from two bladder cancer cell lines
Number of paired-end reads
Confirmed fusion events from two bladder cancer cell lines
Fusion genes (5'-3')
Fusion reads (span/junc)
Detected in both cell lines
Potential chromosomal rearrangement
We have developed a new method called SOAPfuse to aid in fusion transcript discovery from paired-end RNA-Seq data. Comparing SOAPfuse with other tools on two previously published datasets, one simulated dataset and two bladder cancer cell line datasets, we authenticated superior performance and high sensitivity of SOAPfuse. By evaluating the program on a simulated dataset, SOAPfuse showed a low FP rate (5%) at different expression levels of fusion transcripts and it also achieved a low FN rate of 5% when the expression levels of fusion transcripts were greater than 30-fold. Using the bladder cancer cell line datasets, we demonstrated with RT-PCR-validated fusions that SOAPfuse has substantially high accuracy (15 of 16, 94%) and we also identified several novel fusion transcripts that may be derived from chromosomal rearrangements.
In the simulated dataset, SOAPfuse missed three fusion transcripts. The program had some difficulties detecting fusion transcripts from gene pairs having highly similar sequences, and fusion transcripts involving short transcripts of long genes. However, preliminary solutions have been applied to remedy these shortcomings successfully (Additional file 3), and will be included in future versions of SOAPfuse. After analyzing the characteristics of the fusion events, we found that several novel fusion transcripts detected in the bladder cancer cell lines were more likely to be derived from chromosomal rearrangements of the DNA. Whole genome sequencing will be helpful for determining whether the fusion transcripts are from genomic DNA variations and if the breakpoints can be detected. We have started to develop a new algorithm to detect chromosomal rearrangements that can generate predicted fusion transcripts from whole genome sequencing data based on the results from SOAPfuse. It will be complementary to SOAPfuse for performing genome analysis of fusions with tools like CREST . We will continuously refine SOAPfuse and update it on our official website.
Here we present an optimized publicly available methodology for identifying novel fusion transcripts from RNA-Seq data. Our results suggest that SOAPfuse achieves better performance than other published tools and it produces a highly accurate list of fusion events in a time-efficient manner. Furthermore, it provides predicted junction sequences and schematic diagrams of fusion events, which are helpful to analyze detected fusions. Overall, SOAPfuse is a useful method that will enable other research groups to make discoveries from their own RNA-Seq data collections.
Materials and methods
Outline of the general approach
SOAPfuse initially aligns paired-end reads against the human reference genome sequence (hg19) using SOAP2  (SOAP-2.21; step S01 in Additional file 10). We divided the reads into three types according to the read alignment results: PE-S01, SE-S01 and UM-S01, where PE stands for paired-end mapped result, SE for single-end mapped result, and UM for unmapped read. PE-S01 reads indicate the paired-end reads mapping to the genome with the proper insert sizes (<10,000 bp). SE-S01 contains paired-end reads in which only one of two ends mapped to the reference genome, and paired-end reads indicating a fragment with an abnormal insert size or mapped orientation. All unmapped reads are saved in UM-S01 with a FASTA format. PE-S01 is used to evaluate insert size (Additional file 3). SOAPfuse then aligns UM-S01 reads against annotated transcripts (Ensembl release; step S02 in Additional file 10) and generates SE-S02 and UM-S02. To filter unmapped reads caused by small indels, UM-S02 reads are realigned to annotated transcripts using BWA  (BWA-0.5.9; maximum number of gap extensions is 5), and the remaining unmapped reads are called filtered-unmapped (FUM).
Iteratively trimming and realigning reads
Identifying candidate gene pairs
From all discordantly aligned reads, SOAPfuse seeks span-reads to support candidate gene pairs (step S05 in Additional file 10). Both the span-reads that mapped uniquely to the reference (human genome and annotated transcripts) and the trimmed reads that have multiple hits were used to detect the candidate gene pairs. The maximum hits for each span-read is a parameter in the configuration file. To ensure accurate detection of the fusion gene pairs, SOAPfuse imposes several filters on the predicted candidate gene pair list (Additional file 3), such as excluding gene pairs from the same gene families and pairs with overlapped or homogenous exon regions (Figure 6b).
Determining the upstream and downstream genes in the fusion events
A span-read (paired-end reads 'a' and 'b') supports a candidate gene pair (Gene A and Gene B). According to the serial number ('1' or '2') and mapped orientation ('+' or '-') of paired-end reads (read 'a' and 'b'), there are 16 combinations, but only 4 are rational. These four combinations support two types of fusions in which the upstream and downstream genes are different (Additional file 11. Table S12). The judgment rule is: the gene aligned by reads in the plus orientation must be the upstream gene. Here, we presume that read 'a' maps to Gene A and read 'b' maps to Gene B (Figure 8b,c). In Figure 8b, read 'a' aligns to Gene A (annotated transcripts) in the plus orientation, so Gene A must be the upstream gene; while in Figure 8c, read 'b' aligns to Gene B in the plus orientation, so Gene B must be the upstream gene. According to this rule, SOAPfuse defines the upstream and downstream genes in fusion events.
Obtaining the fused regions
SOAPfuse also uses span-reads to detect the fused regions in candidate gene pairs (step S07-a in Additional file 10). Span-reads, the paired-end reads supporting the candidate fusion gene pairs, are derived from the fused transcripts and the junction sites are often located in regions of the fused transcripts between both ends of span-reads. For upstream and downstream genes, we can extend one region with length equal to insert size (evaluated in step S01) from the mapped position of each 3' end span-read to estimate the fused region covering the junction site (Figure 9b). Every gene pair is always supported by at least two span-reads, corresponding to several fused regions that may have overlaps with each other. We presumed that end 1 of a span-read mapped to position MP1 in Gene A, and end 2 of the span-read mapped to position MP2 in Gene B. The lengths of ends 1 and 2 of the span-reads are RL1 and RL2, respectively. The average of insert sizes (INS) and their standard deviation (SD) are evaluated in step S01. The fused regions were estimated by the following intervals:
In the above formula, a flanking region with length of FLB was considered because sometimes a few bases from the 3' end of a span-read cover the junction sites in the mismatch-allowed alignment.
SOAPfuse combined the fused regions determined by the above two methods to detect the junction sites using the partial exhaustion algorithm as described below.
Construction of fusion junction sequence library with partial exhaustion algorithm
To simplify the explanation of the algorithm, we call the fused regions determined by the above two methods as fused regions 1 and fused regions 2, respectively. Fused region 1, defined by the mapped HUM reads, is a small region covering the junction sites with length smaller than one NGS read. Fused region 2 is a large region defined by the NGS library insert sizes, which are always much longer than HUM reads. Generally, fused region 1 is more useful than fused region 2 to define the junction sites.
In order to build the fusion junction sequence library, we covered fused region 2 from each gene pair with 'tiles' that are spaced one nucleotide apart and we finally generated the candidate fusion junction library by creating all pair-wise connections between these tiles (Figure 10b). To eliminate the false positives in the junction sequence library, only the junction sequences in which at least one of two junction sites in a gene pair is located in the credible-region were selected for further analysis. SOAPfuse carried out this partial exhaustion algorithm to reduce the size of the putative junction library and retain genuine junction sequences as much as possible.
Detection of junction sites in fusion transcripts
To identify the junction sites of fusion transcripts, we mapped the useful-unmapped-reads (UUM reads; see the 'Obtaining the fused regions' section) to the putative fusion junction sequence library to seek the junction reads (step S07-b in Additional file 10). We required that a candidate fusion should be supported by multiple span-reads, junction reads, and other criteria (step S08 in Additional file 10; Additional file 3). To exclude FP fusion events, we removed the initial candidate fusion gene pairs that closed with each other and that had homogenous/overlapping regions around the junction sites (Figure 6c; step S09 in Additional file 10). SOAPfuse not only reports high-confident fusions but also provides the predicted junction sequences for further RT-PCR experimental validations. SVG figures are also created, showing the alignments of supporting reads on junction sequences and expression level of gene pairs (for example, Additional file 12).
Preparation of simulated datasets
Simulated RNA-Seq data were generated to evaluate the FN and FP rate of SOAPfuse. We generated 150 simulated fusion transcripts in two steps based on human annotated genes. The first step involved randomly selecting candidate gene pairs with several criteria, such as controlling the distance between paired genes and avoiding gene pairs from gene families. The second step involved randomly selecting transcripts and junction sites at the exon edges or in the middle of exons. Using the short-read simulator provided by MAQ , we generated paired-end reads at nine sequencing depth (5- to 200-fold) to simulate different expression levels of fusion transcripts. Paired-end reads from H1 human embryonic stem cells were used as background data. Details of the simulation work can be found in Additional file 3.
Total RNA preparation from bladder cancer cell lines
Two bladder cancer cell lines (5637 and T24) were purchased from the American Type Culture Collection (Manassas, VA, USA). They were cultured in RPMI 1640 medium (Invitrogen, Grand Island, NY, USA) containing 10% fetal bovine serum (Sigma, Saint Louis, MO, USA). Total RNAs were prepared using Trizol (Invitrogen) according to the manufacturer's instructions. They were treated with RNase-free DNase I to remove residual DNA. The quality of total RNAs was evaluated using an Agilent 2100 Bioanalyser.
cDNA library construction for RNA-Seq
The cDNA libraries were constructed as described in previous studies [36, 37]. Briefly, beads (Invitrogen) with oligo (dT) were used to isolate poly (A) mRNA from total RNAs. To avoid priming bias in the process of synthesizing cDNA, mRNA was fragmented before the cDNA synthesis. Purified mRNA was then fragmented in fragmentation buffer at an elevated temperature. Using these short fragments as templates, random hexamer-primers were used to synthesize the first-strand cDNA. The second-strand cDNA was synthesized using buffer, dNTPs, RNase H and DNA polymerase I. Short double-stranded cDNA fragments were purified with a QIAquick PCR extraction kit (Qiagen, Hilden, Germany) and then subjected to an end repair process and the addition of a single 'adenine' base. Next, the short fragments were ligated to Illumina sequencing adaptors. cDNA fragments of a selected size were gel-purified and amplified by PCR. In total, we constructed one paired-end transcriptome library for each cell line, and sequenced them on the Illumina HiSeq2000 platform. Both paired-end libraries were sequenced to a 90-bp read length with insert sizes ranging from 150 to 200 bp. RNA-Seq data from the two bladder cancer cell lines has been submitted to the NCBI Sequence Read Archive (SRA) and are available under accession number [SRA052960].
Fusion validation by RT-PCR
The digested total RNAs from the bladder cancer cell lines were reverse-transcribed to cDNA for validation using reverse transcriptase (Invitrogen) and oligo-d(t) primers (TaKaRa, Dalian, China). Then, fusion transcripts were validated using RT-PCR amplification followed by Sanger sequencing. For the RT-PCR amplification, the primers were designed using Primer (version 5.0) and all primer sequences can be found in Table S11 in Additional file 8. We carried out the RT-PCR amplifications using TaKaRa Taq™ Hot Start Version and performed reactions in 20 μl volumes with 2 μl of 10× PCR buffer (Mg2+ Plus), 2 μl of dNTP mixture (each 2.5 mM), 2 μl of primers (each 10 μM), 0.5 μl of TaKaRa Taq HS (5 U/μl), 20 ng of cDNA and up to 20 μl using ddH2O. The thermocycler program used was the following: (i) 95°C for 4 minutes, (ii) 95°C for 40 seconds, (iii) 55°C to 62°C for 30 seconds, (iv) 72°C for 45 seconds, (v) steps 2 through 4 repeated 35 times, and (vi) 72°C for 10 minutes. The products of RT-PCR amplification were analyzed on a 2% agarose gel to make sure that no unexpected bands were amplified. The purified RT-PCR products were sequenced in forward and reverse directions with the ABI PRISM Big Dye Terminator Cycle Sequencing Ready Reaction kit (version 3) and ABI PRISM 3730 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). Chromatograms were generated by Chromas (version 2.22), and then were analyzed by BLAT (online genome alignment on the UCSC Genome Browser .
central processing unit
filtered unmapped reads
half unmapped read bisected from FUM reads
paired-end mapped result
reverse transcription polymerase chain reaction
single-end mapped result
Scalable Vector Graphics
This work was supported by the National Basic Research Program of China (973 program 2011CB809200), National High Technology Research and Development Program of China (863 Program, 2012AA02A201). This project was also funded by the Shenzhen municipal government of China and the local government of the Yantian District of Shenzhen. Thanks to Xueda Hu (BGI-shenzhen) for giving advice and supporting this project. Thanks to TianHe research and development team of National University of Defense Technology for testing, optimizing and deploying the software on TianHe series supercomputers. We thank D Kim (TopHat-Fusion group), SL Salzberg (TopHat-Fusion group), and A McPherson (deFuse group) for help on operating software. We also thank the FusionMap group for helpful comments on simulation data.
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