TruSight RNA Fusion targets 507 genes involved in fusions in cancer. Download the gene list from TruSight RNA Fusion Product Files.
Download the target regions files from the Product Files page.
Yes. This kit targets 160 bp of the 5ʹ and 3ʹ UTR of every targeted gene, which ensures that the full gene of every targeted gene is covered.
Use NanoDrop to quantify the RNA concentration. If NanoDrop is not available, use a fluorometric method.
The protocol is optimized for 10–100ng of human total RNA. Lower input amounts may result in low yield and reduced sensitivity. Use 10ng for high-quality universal human reference total RNA as input.
For FFPE RNA, the sample input amount is based on sample quality. Use the percentage of RNA fragments > 200 nt fragment distribution value (DV200) as a reliable determinant of FFPE RNA quality.
Input Requirement Per Reaction
For more information, see the Evaluating RNA Quality from FFPE Samples tech note.
For successful library preparation, use an RNA isolation method that includes a reverse-crosslinking step and DNase1 treatment, such as the QIAGEN RNeasy FFPE kit or the QIAGEN AllPrep DNA/RNA FFPE kit.
For samples that border quality classifications, use the higher end of the input recommendation.
Success is not guaranteed with poor quality libraries. For libraries with DV200 < 30%, input of 100 ng or greater is recommended.
The kit provides sufficent reagent to prepare six batches of eight samples.
Use 200 ng of each RNA library. All enrichments reactions are single-plex only. If you achieve lower than 200 ng library yield, input the entire amount into the enrichment. However, expect the quality of sequencing results to vary.
Due to the enrichment step in the workflow, the ribosomal RNA is washed away during the hybridization and capture steps. The hybridization times have been optimized to allow for less rRNA to be captured during the enrichment pull-down steps. The residual amount of ribosomal RNA contamination can be determined from the “% Aligned to ribosomal RNA” field in the sample analysis report.
RNA with DNA contamination results in an underestimation of the amount of RNA used, which can impact data quality.
Use Agilent Technologies Human UHR total RNA (catalog # 740000) as a control sample for this protocol. UHR contains the following known fusions: BCR-ABL1, BCAS3-BCAS4, and NUP214-XKR3 (at low levels).
Quantify the library using a Fragment Analyzer or Bioanalyzer. Alternatively, use PicoGreen.
When used together, TruSeq RNA Single Index Sets A and B allow for pooling up to 24 samples using the 12 different indexes in each kit.
The probes are specifically designed to target 507 fusion-associated genes, covering all coding exons of each targeted gene.
This kit is available in a Set A and a Set B, each containing 12 indexes. When used together, sets A and B provide a total of 24 unique indexes.
There are seven safe stopping points in this protocol. The safe stopping points are after the following steps:
For storage details, see the reference guide.
It takes 2½ days for 8–24 samples from total RNA input until libraries are ready to load on the flow cell. This protocol includes approximately 11 hours of hands-on time.
Use TruSight RNA Pan-Cancer for fusion detection and gene expression profiles. TruSight RNA Fusion contains only the fusion-associated genes, which might improve fusion detection sensitivity.
TruSight RNA Fusion is a large gene panel covering 507 cancer-related genes. TruSeq Targeted RNA Expression typically provides smaller panels and fusion detection is more difficult.
Additionally, TruSight RNA Fusion has discovery power for fusion detection because only one gene fusion partner is required. With TruSeq Targeted RNA Expression, oligos must be designed either side of a known breakpoint. TruSight RNA Fusion is more amenable to low input FFPE.
Assess the final library quality with either an Advanced Analytical Technologies Fragment Analyzer using a NGS Fragment Analysis Kit or Agilent Technologies 2100 Bioanalyzer using a DNA 1000 chip.
The size of the final product is ~250–300 bp. A larger fragment size is expected for good FFPE RNA (> 350 nt), while a smaller fragment size is expected for poor FFPE RNA.
Use qPCR to quantify libraries. For more information, see the Sequencing Library qPCR Quantification Guide.
The recommended read length is 2 x 76 bp. Fusion detection requires paired-end reads.
To create a MiSeq-compatible sample sheet, select the RNA Sequencing category and the RNA-Seq application or the Other category and the FASTQ Only application. For the library prep kit, select TruSeq RNA Access. Then select 1 Index Read, Paired End Read, and 76 bp Read Length.
The standard sequencing primers included in the cluster generation kits are required.
See the Denature and Dilute instructions for your instrument.
Performing 2 x 76 bp runs is recommended for optimal performance with fusion callers. If the paired reads are overlapping, fusion calling may be less efficient.
Minimum 3 M reads per sample.
MiniSeq System—The Local Run Manager RNA Fusion Analysis Module performs alignment with STAR and analyzes for gene fusions with Manta. The Local Run Manager RNA Fusion Module generates a summary report of fusions.
MiSeq System—The off-instrument version of Local Run Manager and the Local Run Manager RNA Fusion Analysis Module perform alignment with STAR and analyze for gene fusions with Manta. The RNA Fusion Analysis Module generates a summary report of fusions. Install the off-instrument Local Run Manager on a compatible PC first, and then install the RNA Fusion Module.
BaseSpace Sequence Hub—The BaseSpace RNA-Seq Alignment App performs alignment and analyzes for gene fusions with STAR/Manta or TopHat.
Third-party analysis tools are also available, including open source Manta and STAR.
The disease associations are defined by data collected from the Mitelman Database on August 26, 2015. The RNA Fusion analysis module does not retrieve updates to the database and users cannot update the database. The module reports only the most frequently observed disease association of the fusion from scientific literature recorded in the Mitelman database as of August 26, 2015. Disease associations for fusions with undefined disease associations are reported as “NA” (not available). The disease association that the RNA Fusion analysis module provides, via the Mitelman Database, is for research use only and must not be used for any clinical decisions. The TruSight RNA Fusion System is classified as Research Use Only.
Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (2015/08/26). Mitelman F, Johansson B and Mertens F (Eds.), http://cgap.nci.nih.gov/Chromosomes/Mitelman
Yes, you can change three settings:
Minimum Breakpoint Distance—Excludes reads that are in close proximity and may represent read through events rather than fusions.
Confidence Score Filter—Set by Illumina based on internal testing. The scores are calculated as a weighted average of individual features (for example, split reads, fusion contig alignment length, etc). If the fusion does not meet the confidence score, it is not shown, which helps to eliminate false positives. For detailed information about scoring calculations and what is being evaluated as part of the confidence score, see the Local Run Manager RNA Fusion Analysis Module Workflow Guide (document # 1000000010786).
Confidence Score Threshold—Value established by Illumina. Fusion calls with a confidence level above this threshold are considered "High Confidence."
A high confidence fusion call means that a fusion meets the threshold filters, which are based on scores from calculated values of split read scores, paired read scores, break-end homology, and several other features.
For more information on the calculations, see the Local Run Manager RNA Fusion Analysis Module Workflow Guide.
Low confidence fusion calls are fusion calls listed as recurrent in the Mitelman database, but do not pass or meet the minimum threshold score. These calls might be true positive fusions expressed at lower levels. Assessing low confidence fusion calls might require Orthogonal approaches.
Recurrent fusions are fusions identified in the Mitelman database as present in 2 or more cases with the same morphology and topography.
Recurrent Fusions Not Called is a table that displays recurrent fusions, per the Mitelman database, that were not detected. The table provides information to assess whether genes are involved in recurrent fusions, where the fusion was not called, and whethere it had sufficient read coverage.
The BaseSpace RNA-Seq Alignment App with the STAR aligner may call a fusion if there are at least three unique reads that meet all the quality metrics, including the following threshold filters.
However, a high number of nonfusion supporting reads (ie, wild type transcript) in that region would be expected to cause noise that can affect fusion calling.
Reference reads refer to reads/read-pairs that support structurally normal genes at that fusion breakpoint. The 0 means that no evidence of structurally normal genes is found the RNA-Seq data; this is distinct from whole gene read counts, which are shown in the last table of the report.
Additional rearrangements and duplications that occur in concert with the fusion could also affect this number.
To confirm fusion calls, perform additional investigation or independent validation. Fusion transcripts between nearby genes on the same chromosome and strand can be a result of read-through transcription rather than genomic translocation. To reduce these calls, the fusion software is optimized to filter read-through transcripts, but can result in filtering of biologically relevant fusions between adjacent genes (eg, STIL-TAL1). Also, fusions called between genes of high homology, such as a gene and its pseudogene, can be artifacts of multiple alignment instead of genomic rearrangements. Illumina reccommends that the quality score and chromosomal location be assessed and confirmed using independent molecular biology approaches.
Several factors can cause a fusion to not get called:
- Low expression levels of the fusion gene. More sequencing read depth or lower fusion score threshold may be required.
- Low quality of the sample. More sample input RNA may be required.
- Close proximity of 2 genes in the same orientation, on the same chromosome (eg, STIL-TAL1). Reducing the default breakpoint distance thresholds in the Local Run Manager module can help identify these fusions, but may also display more false positive calls.
- Differences in bioinformatics algorithms used.
- Differences in reference genomes. GENCODE has higher genomic coverage than RefSeq and fusions between exons that are not annotated are not called in RefSeq. For example, an EML4 transcript has an exon that is annotated in GENCODE but not RefSeq, meaning fusions at that exon would not be called when using the RefSeq reference.
Chromosomal translocations resulting in overexpression or deletion of a transcript can be reflected in gene expression levels but would not create a fusion gene. The Local Run Manager RNA Fusion module is not designed for detection of gene expression changes. To detect these changes, the RNA-Seq Alignment App in BaseSpace Sequence Hub is recommended. In addition, it is recommended to confirm these findings in DNA.
Follow the directory path: RNA_Fusion_Analysis/samples/[SampleName]/.
The sample name folder contains three additional folders: Align, GeneCounts, and MantaFusion. These folders contain the number of read counts with passing values and other detailed information.
No, only one of the gene fusion partners needs to be detected. The enrichment approach allows you to pull down the target and the partner fusion gene with it.
Fusions are reported as High Confidence fusions when they meet the minimum threshold scores defined by Illumina. Fusions that meet these criteria are reported regardless of whether they are listed in the Mitelman database.
Fusions are reported as Low Confidence when they do not pass all quality filters or meet the minimum threshold score but are listed in the Mitelman database. Potential low quality fusions that are not listed in the Mitelman database can be found in the fusions.csv file.
The Local Run Manager RNA Fusion module was designed to be a discovery tool. As such, it does not search for specific fusions, but performs an unbiased, whole genome alignment. To perform this whole genome alignment on a desktop sequencer or on a PC, it was necessary to create a compacted human genome reference – the Minimal Genome Reference. Although this provides the advantage of a local analysis solution, the Minimal Genome Reference does not retain the context that a contiguous whole genome reference has. Therefore in some cases, read-through transcripts may be called as fusions. In addition, an unbiased, whole genome alignment approach may inherently identify unexpected transcripts. It is always recommended to confirm findings with an orthogonal approach, such as RT-PCR.
The minimal reference genome is a compact human reference genome that allows novel fusion calling. Per RefSeq, the minimal reference genome contains all exons in the human genome with the introns removed. It is based on the hg19 genome.