RNA Analysis Methods
Downsampling
Each sample is downsampled to 30 million RNA reads. This number represents the total number of single reads (eg, R1 + R2, from all lanes). When using the recommended sequencing configurations or plexity, the samples can have fewer reads than the downsampling limit. In these cases, the FASTQ files are left as-is.
Read Trimming
Reads are trimmed to 76 base pairs for further processing.
RNA Alignment and Fusion Detection
RNA alignment and fusion detection uses trimmed reads in FASTQ format as input. The outputs include a BAM file that contains duplicate-marked read alignments, an SJ.out.tab file that contains unannotated splice junctions, and a CSV file that contains fusion candidates.
DRAGEN aligns RNA reads in a transcript-aware mode using the human hg19 genome containing unplaced contigs (ie, chrUn_gl regions) and uses GENCODEv19 transcript annotations to identify splice sites. DRAGEN identifies and marks duplicate read alignments using start and end coordinates of alignments, which are adjusted for soft clipped reads.
Fusion and splice variant calling only use deduped fragments to score variants. DRAGEN identifies fusion candidates using chimeric split read alignments (pairs of primary and supplementary alignments) against multiple genes. DRAGEN scores and filters reads based on the various features of each candidate such as the number of supporting reads, mapping quality of supporting reads, and sequence homology between parent genes.
The DRAGEN RNA Fusion caller identifies gene fusions by searching for chimeric reads spanning two distinct parent genes. Based on the chimeric reads, DRAGEN first creates a list of fusion candidates, then scores the candidates to report the list of high confidence fusion calls from the candidate pool.
DRAGEN RNA Fusion caller performs the following steps:
Generates fusion candidate generation based on split read alignment.
Recruits additional evidence from fusion supporting discordant read pairs and soft-clipped reads.
Computes fusion candidate features such as gene coverage, read mapping quality, alternate allele frequency, gene homology, alignment anchor length, and breakpoint distance from exon boundary.
Scores and ranks the fusion candidates using a logistic regression model.
Selects a final list of fusion calls based on score and other filters including number of supporting reads, unique read alignment count, read through transcripts, and fusions matching the enriched regions.
Splice Variant Calling
RNA splice variant calling is performed for RNA sample libraries. Candidate splice variants (junctions) from RNA Alignment are compared against a database of known transcripts and a splice variant baseline of non-tumor junctions generated from a set of normal FFPE samples from different tissue types. Any splice variants that match the database or baseline are filtered out unless they are in a set of junctions with known oncological function. If there is sufficient read support, the candidate splice variant is kept. This process also identifies candidate RNA fusions.
RNA Fusion Merging
Fusions identified during RNA fusion calling are merged with fusions from proximal genes identified during RNA splice variant calling. These are then annotated with gene symbols or names with respect to a static database of transcripts (GENCODE Release 19). The result of this process is a set of fusion calls that are eligible for reporting
RNA Splice Variant Annotation
The Illumina Annotation Engine annotates detected RNA splice variant calls with transcript-level changes (eg, affected exons in the transcript of a gene) with respect to RefSeq. This RefSeq database is the same RefSeq database used by the small variant annotation process.
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