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.
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.
Use NanoDrop to quantify the RNA concentration. If NanoDrop is not available, use a fluorometric method.
The kit provides sufficent reagent to prepare six batches of eight samples.
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 designed to target 1385 cancer genes and detect fusions by spanning all exons of every 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.
TruSight RNA Pan-Cancer is a large gene panel covering 1385 cancer-related genes. TruSeq Targeted RNA Expression typically provides smaller panels and fusion detection is more difficult.
Additionally, TruSight RNA Pan-Cancer 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 Pan-Cancer is more amenable to low input FFPE.
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.
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.
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.
RNA with DNA contamination results in an underestimation of the amount of RNA used, which can impact data quality.
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.
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.
See the Denature and Dilute instructions for your instrument.
The BaseSpace RNA-Seq Alignment App performs alignment and analyzes for gene fusions with STAR or TopHat. STAR is recommended for optimal fusion calling.
The BaseSpace TopHat Alignment App can also be used for alignment and fusion calling. The BaseSpace Cufflinks Assembly & DE App can be used to run differential expression analysis.
Third-party analysis tools are also available.
The main difference between the apps is that the RNA-Seq Alignment App is specifically designed for optimal fusion calling using STAR.
You can try both analysis methods and compare results.
Run the RNA-Seq workflow (FASTQ only) on the MiSeq and stream the data to BaseSpace. The BaseSpace RNA-Seq Alignment App analyzes data from the TruSight RNA Pan-Cancer Panel, providing a simple results summary that includes a fusion table, variant table, and gene expression table.
You can also use your own pipeline for analysis.
The STAR aligner in the RNA-Seq Alignment App as been optimized for fusion calling and is recommended.
You can also use the TopHat Alignment App. Running both analysis options and comparing the data can be useful.
Use the Homo sapiens (PAR-masked)/hg19 (RefSeq) or Homo sapiens (PAR-masked)/hg 19 (Gencode) reference genome. Because of different annotations within these genomes, results can vary by reference.
In BaseSpace, click your Projects folder. Select Analysis and select the app session name that you saved your analysis under.
Yes, there are example data sets in BaseSpace Public Data.
The expected range of percent duplicates is ≤ 25% for controls (UHR) at 0.5M subsampled reads.
The percent of reads passing filter (PF) aligned to human ribosomal RNA is typically ≤ 8%.
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.
A paired read is a fusion where one read aligns to the left gene and the other read aligns to the right gene.
A split read is a fusion where one of the reads spans the fusion junction.
The best way to confirm that an identified fusion is 'real' is to use an orthogonal approach. Additionally, assess the quality score and the chromosomal locations of the fusions to help indicate confidence.
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.
Yes, use the RNA-Seq Differential Expression BaseSpace Sequence Hub app.
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.
Sensitivity of fusion detection varies depending on the expression level of the fusion transcript in the sample. Illumina performed titration experiments with synthetic fusion constructs. Spike-in fusions were detected as low as concentrations equivalent to ~13 fusion copies/cell (1 E-7 pM) (based on 500 cells/10ng input).
The RNA-Seq Alignment App requires at least three unique fusion-supporting reads to call a fusion positively (with some additional quality and read metrics). However, a high number of nonfusion supporting reads in that region is expected to cause noise that can affect fusion calling.