#CLC RNA SEQ DRIVER#
#CLC RNA SEQ MANUAL#
#CLC RNA SEQ SOFTWARE#

A characteristic feature of NSCLCs is the addiction to single oncogenic driver events, which render these cancer types vulnerable to drugs targeting corresponding kinases. Unfortunately, around 75% of patients with lung cancer present with non-resectable, advanced stages of disease and thus most patients are not eligible for curative therapy. Lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC) are the two most commonly occurring NSCLC subgroups. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers. Subsequently, many fusion genes were identified in a variety of cancer types since, including lung cancer and many other solid tumors. The first fusion gene, BCR- ABL was discovered in chronic myeloid leukemia patients. In summary, especially RNA-based parallel sequencing approaches are potent tools for reliable detection of targetable gene fusions in clinical diagnostics.Ĭhromosomal rearrangements leading to the fusion of coding regions of two genes can result in expression of oncogenic hybrid proteins driving tumor progression. Additionally, many false positive fusions were observed.

#CLC RNA SEQ SOFTWARE#

The DNA-based SureSelect XT HS Custom Panel (Agilent) missed three fusions and nine fusions were only called by one software version. The Oncomine Focus Assay (Thermo Fisher Scientific) was the least adequate assay for our purposes, seven fusions were not covered by the assay and two fusions were classified as uncertain. Among the RNA-based assays, the Qiagen panel had the highest number of false positive events. Both, the ArcherDX and Qiagen panels missed only one fusion event. The Illumina assay detected all tested fusions and showed the smallest number of false positive results. Four RNA-based assays and one DNA-based assay were compared two were hybrid capture-based, TruSight Tumor 170 Assay (Illumina) and SureSelect XT HS Custom Panel (Agilent), and three were amplicon-based, Archer FusionPlex Lung Panel (ArcherDX), QIAseq RNAscan Custom Panel (Qiagen) and Oncomine Focus Assay (Thermo Fisher Scientific). Reliable detection of multiple gene fusions is therefore essential.įive commercially available parallel sequencing assays were evaluated for their ability to detect gene fusions in eight cell lines and 18 FFPE tissue samples carrying a variety of known gene fusions. Without knowing more about your experiment I can't recommend specific starting parameters, but it's worth varying the parameters of interest by large amounts and comparing the results side-by-side in IGV or IGB.Gene fusions represent promising targets for cancer therapy in lung cancer. You can also try using -b2-very-sensitive for a quick setup segment-mismatches How many mismatches a given segment is allowed to have segment-length You can reduce this from the default of 25 to cut the reads up into smaller chunks, which would make the mapping more generous g The maximum total alignments that will be reported for a read report-secondary-alignments Don't report only the best alignments m How many mismatches are allowed in an anchor region of a spliced alignment a How many bases must be mapped to each side of a junction N maximum number of mismatches a read can have without being discarded

#CLC RNA SEQ MANUAL#

From the manual (which is well worth your time to read through to get an idea of the knobs you can manipulate): You'll probably want to experiment with different parameters, but Tophat is extremely flexible with regards to its parameters.

Clc rna seq