We give special thanks to Ezra Lyon, Eliot Zhu, Michele Wing, Esko Kautto and Eric Samorodnitsky for technical support. We would also like to thank Jenny Badillo for her administrative support for our team. We acknowledge the Ohio Supercomputer Center (OSC) for providing disk space, processing capacity, and support to run our analyses. We thank the Comprehensive Cancer Center (CCC) at The Ohio State University Wexner Medical Center for their administrative support of this work. S.R. and Team are supported by the American Cancer Society (MRSG-12-194-01-TBG), a Prostate Cancer Foundation Young Investigator Award, NHGRI (UM1HG006508-01A1), Fore Cancer Research Foundation, American Lung Association, and Pelotonia.

<ol>
	<li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq:+a+revolutionary+tool+for+transcriptomics.">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nat Rev Genet. 10, 57-63 (2009).</li><li>Mercer, T. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeted+sequencing+for+gene+discovery+and+quantification+using+RNA+CaptureSeq.">Targeted sequencing for gene discovery and quantification using RNA CaptureSeq.</a> Nat Protoc. 9, 989-1009 (2014).</li><li>Maher, C. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chimeric+transcript+discovery+by+paired-end+transcriptome+sequencing.">Chimeric transcript discovery by paired-end transcriptome sequencing.</a> Proc Natl Acad Sci U S A. 106, 12353-12358 (2009).</li><li>Piskol, R., Ramaswami, G., Li, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reliable+identification+of+genomic+variants+from+RNA-seq+data.">Reliable identification of genomic variants from RNA-seq data.</a> Am J Hum Genet. 93, 641-651 (2013).</li><li>Quinn, E. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+strategies+for+SNP+detection+in+RNA-seq+data:+application+to+lymphoblastoid+cell+lines+and+evaluation+using+1000+Genomes+data.">Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data.</a> PLoS One. 8, e58815 (2013).</li><li>Tang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+eSNV-detect:+a+computational+system+to+identify+expressed+single+nucleotide+variants+from+transcriptome+sequencing+data.">The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data.</a> Nucleic Acids Res. 42, e172 (2014).</li><li>. Preparing Libraries for Sequencing on the MiSeq&#174; Available from: <a target="_blank" href="https://support.illumina.com/content/dam/illumina-support/documents/documentation/system_documentation/miseq/preparing-libraries-for-sequencing-on-miseq-15039740-d.pdf">https://support.illumina.com/content/dam/illumina-support/documents/documentation/system_documentation/miseq/preparing-libraries-for-sequencing-on-miseq-15039740-d.pdf</a> (2013)</li><li>. MiSeq&#174; Reagent Kit v2 Reagen Preparation Guide Available from: <a target="_blank" href="https://support.illumina.com/downloads/miseq_reagent_kit_reagent_preparation_guide.html">https://support.illumina.com/downloads/miseq_reagent_kit_reagent_preparation_guide.html</a> (2012)</li><li>Kim, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TopHat2:+accurate+alignment+of+transcriptomes+in+the+presence+of+insertions,+deletions+and+gene+fusions.">TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.</a> Genome Biol. 14, R36 (2013).</li><li>Li, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Sequence+Alignment/Map+format+and+SAMtools.">The Sequence Alignment/Map format and SAMtools.</a> Bioinformatics. 25, 2078-2079 (2009).</li><li>DeLuca, D. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-SeQC:+RNA-seq+metrics+for+quality+control+and+process+optimization.">RNA-SeQC: RNA-seq metrics for quality control and process optimization.</a> Bioinformatics. 28, 1530-1532 (2012).</li><li>Dobin, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=STAR:+ultrafast+universal+RNA-seq+aligner.">STAR: ultrafast universal RNA-seq aligner.</a> Bioinformatics. 29, 15-21 (2013).</li><li>Van der Auwera, G. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+FastQ+data+to+high+confidence+variant+calls:+the+Genome+Analysis+Toolkit+best+practices+pipeline.">From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.</a> Curr Protoc Bioinformatics. 11, 11.10.1-11.10.33 (2013).</li><li>Trapnell, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcript+assembly+and+quantification+by+RNA-Seq+reveals+unannotated+transcripts+and+isoform+switching+during+cell+differentiation.">Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.</a> Nat Biotechnol. 28, 511-515 (2010).</li><li>Iyer, M. K., Chinnaiyan, A. M., Maher, C. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChimeraScan:+a+tool+for+identifying+chimeric+transcription+in+sequencing+data.">ChimeraScan: a tool for identifying chimeric transcription in sequencing data.</a> Bioinformatics. 27, 2903-2904 (2011).</li><li>Kim, D., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TopHat-Fusion:+an+algorithm+for+discovery+of+novel+fusion+transcripts.">TopHat-Fusion: an algorithm for discovery of novel fusion transcripts.</a> Genome Biol. 12, R72 (2011).</li><li>Fernandez-Cuesta, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+novel+fusion+genes+in+lung+cancer+using+breakpoint+assembly+of+transcriptome+sequencing+data.">Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data.</a> Genome Biol. 16, 7 (2015).</li><li>Shugay, M., Ortiz de Mendibil, ., Vizmanos, I., L, J., Novo, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oncofuse:+a+computational+framework+for+the+prediction+of+the+oncogenic+potential+of+gene+fusions.">Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions.</a> Bioinformatics. 29, 2539-2546 (2013).</li><li>Clark, M. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+gene+profiling+of+long+noncoding+RNAs+with+targeted+RNA+sequencing.">Quantitative gene profiling of long noncoding RNAs with targeted RNA sequencing.</a> Nat Methods. 12, 339-342 (2015).</li><li>Cieslik, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+exome+capture+RNA-seq+for+highly+degraded+RNA+with+application+to+clinical+cancer+sequencing.">The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing.</a> Genome Res. , (2015).</li><li>Cabanski, C. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=cDNA+hybrid+capture+improves+transcriptome+analysis+on+low-input+and+archived+samples.">cDNA hybrid capture improves transcriptome analysis on low-input and archived samples.</a> J Mol Diagn. 16, 440-451 (2014).</li><li>Costa, C., Gimenez-Capitan, A., Karachaliou, N., Rosell, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+molecular+screening:+from+the+RT-PCR+to+the+RNA-seq.">Comprehensive molecular screening: from the RT-PCR to the RNA-seq.</a> Transl Lung Cancer Res. 2, 87-91 (2013).</li><li>Zhao, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+RNA-Seq+by+poly+(A)+capture,+ribosomal+RNA+depletion,+and+DNA+microarray+for+expression+profiling.">Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling.</a> BMC Genomics. 15, 419 (2014).</li></ol>

S.R. receives funding from Novartis and Ariad Pharmaceuticals for conduct of clinical trials. S.R. immediate family members own stock in Johnson and Johnson.

RNAseq Capture is an intermediate strategy between RNAseq and microarray approaches for evaluating a selected part of the transcriptome. The advantages of Capture include reduced cost, rapid turnaround time on a desktop sequencer, high throughput, and detection of genomic alterations. The method can be adapted to characterize non-coding RNAs23, detect single nucleotide variants4-6, examine RNA splicing, and to identify gene fusions or structural rearrangements24. Further, this approach can be applied to clinical or processed samples that have undergone fixation with formalin and embedded in paraffin blocks24,25.
There are several significant benefits of RNAseq capture as compared to microarray, real-time quantitative PCR, Sanger sequencing and DNA sequencing. Microarray is limited by high background due to cross-hybridization and non-specific binding of probes. Quantification of genes with low expression is restricted due to background noise, while highly expressed gene measurements are affected by signal saturation1. Compared to RNAseq capture, real-time PCR proves difficult to reproduce. Additionally, RNAseq allows for detection of novel transcripts, requires less starting input material and can detect alternative splicing26.In contrast to Sanger sequencing, RNAseq allows for higher throughput and analysis of low expressed miRNA. Sanger sequencing has proved to be a valuable tool for verification of fusions with known exon-exon junctions and somatic DNA mutations, however identification of novel fusions is hindered by requirements of a priori candidate breakpoint. DNA sequencing is not cost efficient, requires larger storage space for data, and is incapable of detection of post-transcriptional modifications.
There are several critical steps involved in RNAseq Capture. First, to improve yield of library products from the RNA/cDNA specific paramagnetic beads and paramagnetic beads during washes, be cautious not to over dry the beads, which will lead to loss of yield. Also, do not under-dry the beads, ensure all ethanol is removed from the sample tubes, as ethanol can reduce cDNA yield. Second, the hybridization of cDNA libraries with complementary probes is dependent on consistent temperature, we recommend warming Wash Buffer I and Stringent Buffer to 65 &#176;C for at least two hr in advance. Further, after hybridization it is essential to maintain 65 &#176;C during the binding and wash steps. The probes used here were designed for the exons of genes of interest for drug development including kinases, genes involved in common rearrangements such as transcription factors, and house keeping genes. Moreover, gene content is customizable and capture panel sizes can vary. Further, as new information on genomic regions arises, additional probes can be designed and added to the existing capture panel.
Evaluation of alignment metrics, specifically on-target rate, provides information on how well the targeted region was enriched. A low on-target rate may be due to a failed hybridization and capture, whereby the desired target region was not captured and enriched. In this case, a re-hybridization and capture of the library set must be performed. A low on-target rate may also be due to failure to deplete rRNA, which can be confirmed by calculating the percentage of rRNA in the samples. High rRNA percentage within the sample will require re-preparation of the sample beginning with rRNA depletion. Additionally, if library concentration falls below the requirements for hybridization and capture, it would be advisable to optimize the amount of starting input for the sample type and quality (range: 50-1,000 ng).
While there are several advantages for targeted RNAseq applications, there are also limitations to consider. Samples with poor RNA quality based on RIN or degree of fragmentation may not yield quality libraries for sequencing. Several groups have demonstrated success with formalin fixed paraffin-embedded samples, however there are samples that will not pass for sequencing24,25,27. Further, since RNAseq Capture focuses on known transcripts, it loses the benefits of unbiased RNAseq for novel or unannotated transcripts. In addition, for SNP detection, RNAseq methods can only detect mutations in expressed transcripts.
Future opportunities of RNAseq Capture include research and clinical applications. Recent discovery of thousands of long non-coding RNA and their role in biology will require focused characterization. In the clinic, RNAseq Capture may extend beyond research testing and translate into clinical assays to characterize human disease such as cancer, infectious disease, and non-invasive testing. In conjunction with genomic sequencing approaches, RNAseq Capture can be integrated to study and characterize the expressed genome.

Whole transcriptome or RNA sequencing (RNAseq) is an unbiased sequencing method to assess all RNA products. The goal of targeted RNAseq (Capture) is a focused evaluation of selected transcripts with increased sensitivity, dynamic range, reduced cost or scale, and increased throughput compared to standard RNAseq. Similar to standard RNAseq, targeted enrichment approaches can be used to evaluate gene expression, multiple RNA species such as mRNA, microRNA (miRNA), lncRNA1, other noncoding RNAs2, gene fusions3, and mutations4-6.
Capture involves hybridization of complementary oligonucleotides to enrich cDNA libraries for sequencing. The rationale for RNAseq Capture is similar to microarray approaches where complementary oligonucleotides or probes are hybridized to samples and then measured for relative abundance. For microarray technologies, expression is based on relative signal measured for transcripts binding to these probes. Microarrays are thus limited by range, potential background noise from non-specific binding, and cross-hybridization of probes. Furthermore, arrays have limited dynamic range for low and highly expressed transcripts compared to RNAseq1. Microarrays are widely utilized due to their reduced cost and high throughput capacity compared to RNAseq. 
Here, we demonstrate a method for RNAseq Capture that offers a middle ground between RNAseq and microarray approaches for evaluating the transcriptome. RNAseq Capture has intermediate throughput, greater dynamic range and sensitivity, and is scaled for fast turnaround on desktop sequencers. RNAseq Capture also requires reduced computational resources in terms of storage space and data processing.

<table><tbody><tr><td>Thermomixer R</td><td>Eppendorf</td><td>21516-166</td><td></td></tr><tr><td>Centrifuge 5417R</td><td>Eppendorf</td><td>5417R</td><td></td></tr><tr><td>miRNeasy Mini Kit</td><td>Qiagen</td><td>217004</td><td></td></tr><tr><td>Molecular Biology Grade Ethanol</td><td>Sigma Aldrich</td><td>E7023-6X500ML</td><td></td></tr><tr><td>Thermoblock 24 x 1.5 ml</td><td>Eppendorf</td><td>21516-166</td><td></td></tr><tr><td>MiSeq Reagent Kit v2 (300-cycles)</td><td>Illumina</td><td>MS-102-2002</td><td></td></tr><tr><td>MiSeq Desktop Sequencer</td><td>Illumina</td><td></td><td></td></tr><tr><td>PhiX Control v3</td><td>Illumina</td><td>FC-110-3001</td><td></td></tr><tr><td>TruSeq Stranded Total RNA Kit with RiboZero Gold SetA</td><td>Illumina</td><td>RS-122-2301</td><td></td></tr><tr><td>25 rxn xGen&amp;reg; Universal Blocking Oligo - TS-p5</td><td>IDT</td><td>127040822</td><td></td></tr><tr><td>25 rxn xGen&amp;reg; Universal Blocking Oligo - TS-p7(6nt)</td><td>IDT</td><td>127040823</td><td></td></tr><tr><td>25 rxn xGen&amp;reg; Universal Blocking Oligo - TS-p7(8nt)</td><td>IDT</td><td>127040824</td><td></td></tr><tr><td>Agencourt&amp;reg; AMPure&amp;reg; XP - PCR Purification beads&amp;nbsp;</td><td>Beckman-Coulter</td><td>A63880</td><td></td></tr><tr><td>Dynabeads&amp;reg; M-270 Streptavidin</td><td>Life Technologies</td><td>65305</td><td></td></tr><tr><td>COT Human DNA, Fluorometric Grade, 1 mg</td><td>Roche Applied Science</td><td>05480647001</td><td></td></tr><tr><td>Qubit&amp;reg; Assay Tubes&amp;nbsp;</td><td>Life Technologies</td><td>Q32856</td><td></td></tr><tr><td>Qubit&amp;reg; dsDNA HS Assay Kit</td><td>Life Technologies</td><td>Q32851</td><td></td></tr><tr><td>SeqCap&amp;reg; EZ Hybridization and Wash Kits&amp;nbsp; (24 or 96 reactions)</td><td>Roche NimbleGen&amp;nbsp;</td><td>05634261001 or 05634253001&amp;nbsp;</td><td></td></tr><tr><td>Qubit&amp;reg; 2.0 Fluorometer&amp;nbsp;</td><td>Life Technologies</td><td>Q32866</td><td></td></tr><tr><td>10 x 2 ml IDTE pH 8.0 (1x TE Solution)</td><td>IDT</td><td></td><td></td></tr><tr><td>Tween20 BioXtra</td><td>Sigma</td><td>P7949-500ML</td><td></td></tr><tr><td>Nuclease Free Water</td><td>Life Technologies</td><td>AM9937</td><td></td></tr><tr><td>C1000 Touch&amp;trade; Thermal Cycler with 96&amp;ndash;Well Fast Rection Module</td><td>Biorad</td><td>185-1196</td><td></td></tr><tr><td>SeqCap EZ Hybridization and Wash Kits</td><td>Roche Applied Science</td><td>05634253001</td><td></td></tr><tr><td>SuperScript II Reverse Transcription 200 U/&amp;#956;l</td><td>Life Technologies</td><td>18064-014</td><td></td></tr><tr><td>D1000 ScreenTape</td><td>Agilent Technol. Inc.</td><td>5067-5582</td><td></td></tr><tr><td>Agencourt RNAClean XP - 40 ml</td><td>Beckman Coulter Inc</td><td>A63987</td><td></td></tr><tr><td>RNA ScreenTape</td><td>Agilent Technol. Inc.</td><td>5067-5576</td><td></td></tr><tr><td>RNA ScreenTape Ladder</td><td>Agilent Technol. Inc.</td><td>5067-5578</td><td></td></tr><tr><td>RNA ScreenTape Sample Buffer</td><td>Agilent Technol. Inc.</td><td>5067-5577</td><td></td></tr><tr><td>Sodium Hydroxide</td><td>Sigma</td><td>72068-100ML</td><td></td></tr><tr><td>DynaBeads MyOne Streptavidin T1</td><td>Life Technologies</td><td>65602</td><td></td></tr><tr><td>DYNAMAG -96 SIDE EACH</td><td>Life Technologies</td><td>12331D</td><td></td></tr><tr><td>Chloroform</td><td>Sigma</td><td>C2432-1L</td><td></td></tr><tr><td>KAPA HotStart ReadyMix</td><td>KAPA Biosystems</td><td>KK2602</td><td></td></tr><tr><td>NanoDrop 2000 Spectrophotometer</td><td>Thermo Scientific</td><td></td><td></td></tr><tr><td>My Block Mini Dry Bath</td><td>Benchmark</td><td>BSH200</td><td></td></tr><tr><td>D1000 Reagents</td><td>Agilent Technol. Inc.</td><td>5067- 5583</td><td></td></tr><tr><td>Vacufuge Plus</td><td>Eppendorf</td><td>022829861&amp;nbsp;</td><td></td></tr></tbody></table>

targeted rna sequencing assay to characterize gene expression and genomic alterations

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding RNAs. Standard RNAseq requires 30 - 100 million sequencing reads and can include multiple RNA products such as mRNA and noncoding RNAs. We demonstrate how targeted RNAseq (capture) permits a focused study on selected RNA products using a desktop sequencer. RNAseq capture can characterize unannotated, low, or transiently expressed transcripts that may otherwise be missed using traditional RNAseq methods. Here we describe the extraction of RNA from cell lines, ribosomal RNA depletion, cDNA synthesis, preparation of barcoded libraries, hybridization and capture of targeted transcripts and multiplex sequencing on a desktop sequencer. We also outline the computational analysis pipeline, which includes quality control assessment, alignment, fusion detection, gene expression quantification and identification of single nucleotide variants. This assay allows for targeted transcript sequencing to characterize gene expression, gene fusions, and mutations.

A schematic highlighting key steps in RNAseq Capture is shown in Figure 1. Four cancer cell lines with known mutations were used to demonstrate the effectiveness of the RNAseq Capture technique (K562 with ABL1 fusion, LC2 with RET fusion, EOL1 with PDGFRalpha fusion and RT-4 with FGFR3 fusion). The four samples were pooled together and sequenced with 2x 100 bp reads on a desktop sequencer, which generates FASTQ files. FASTQ files were run through an RNAseq analysis pipeline, which includes five main components: 1) quality control assessment, 2) alignment to human transcriptome, 3) gene expression quantification, 4) fusion calling, and 5) variant calling. The alignment file (BAM) is used to call single nucleotide variants and calculate gene expression. Fusions are called using fusion callers, such as TopHat Fusion (performing their own alignment) and the output is annotated using fusion detection software.
Comparison of gene expression from RNAseq and capture demonstrates enrichment of targeted transcripts by 10 to 1,000-fold using the capture method (Figure 2A). Additionally, Figure 2B shows an increase in the percent of reads mapping to the targeted transcript regions using capture compared to RNAseq. Assessment of quality control measures is represented in Figure 3. Capture and RNAseq perform equally in terms of alignment to the transcriptome (3A, 94% vs. 93%) and mean insert size (3B, 174 bp vs. 162 bp). Using the capture method, a higher percentage of exonic regions are sequenced (3C, 77% vs. 60%), and conversely a lower percentage of intronic regions are sequenced (3D, 4% vs. 20%). Total read counts per sample are depicted in 3E, and as expected, RNAseq generated over 50-fold more reads than capture. Finally, the percentage of rRNA sequences present in each sample was lower using the capture method when compared to RNAseq (3F, 4% vs. 15%).
Fusion detection output shown in Table 1 is generated with normalized fusion supporting reads. Capture RNAseq was successful in detecting fusions for all four cell lines. Comparison of single nucleotide variants called in overlapping regions of capture and RNAseq is displayed in Figure 4. This demonstrates a high concordance of variants between Capture and RNAseq within the target region.
<img alt="Figure 1" src="/files/ftp_upload/54090/54090fig1.jpg" /> 
Figure 1. Schematic of RNAseq Capture Steps. In this experimental demonstration, RNA is first depleted of ribosomal RNA, followed by chemical fragmentation and synthesis of complementary DNA (cDNA) using reverse transcriptase. Next, the cDNA is polyadenylated and ligated on both ends to platform-specific adaptors to generate a library. Only cDNA libraries with proper adaptors are then amplified by PCR. Libraries are then hybridized to custom oligonucleotide probes and captured using magnetic beads. This small amount of captured library must be amplified a second time to have enough for next generation sequencing. Multiple libraries can then be sequenced in parallel. Sequencing data is analyzed for RNA events of interest such as gene fusions, expression or mutations. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54090/54090fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54090/54090fig2.jpg" /> 
Figure 2. Comparison of Targeted Genes in Capture versus RNAseq. A, Gene expression comparison between Capture and RNAseq in four cancer cell lines K562, LC2, EOL1 and RT-4 measured by reads per kilobase per million mapped reads (FPKM)(Log scale). Targeted genes of interest are enriched (blue) compared to non-targeted genes (grey). B, Percentage of reads mapping to targeted region is increased in Capture versus RNAseq libraries in four cancer cell lines. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54090/54090fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54090/54090fig3.jpg" /> 
Figure 3. Sequencing Metrics of Capture versus RNAseq in Four Representative Cancer Cell Lines. A, Percentage of reads mapping to the transcriptome, B, Mean insert size of libraries. C, Percentage of reads in exonic regions. D, Percentage of reads in intronic regions. E, Total sequencing reads. F, Percentage of reads mapping to ribosomal RNA. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54090/54090fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Cell Line</td>
			<td rowspan="2">Fusion</td>
			<td rowspan="2">Library Type</td>
			<td rowspan="2">Total Reads</td>
			<td rowspan="2">On Target Reads</td>
			<td colspan="3">Normalized Fusion Supporting Reads (NFSR)</td>
		</tr>
		<tr>
			<td>TophatFusion</td>
			<td>ChimeraScan</td>
			<td>TRUP</td>
		</tr>
		<tr>
			<td>K562</td>
			<td>BCR-ABL</td>
			<td>RNAseq</td>
			<td>150,300,482</td>
			<td>279,438</td>
			<td>0</td>
			<td>438</td>
			<td>0</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>Capture</td>
			<td>9,341,148</td>
			<td>7,566,087</td>
			<td>598</td>
			<td>343</td>
			<td>0</td>
		</tr>
		<tr>
			<td>LC2</td>
			<td>CCDC6-RET</td>
			<td>RNAseq</td>
			<td>128,861,790</td>
			<td>307,566</td>
			<td>0</td>
			<td>97</td>
			<td>0</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>Capture</td>
			<td>12,320,692</td>
			<td>10,314,284</td>
			<td>71</td>
			<td>44</td>
			<td>6</td>
		</tr>
		<tr>
			<td>EOL1</td>
			<td>FIP1L1-PDGFRA&#160;</td>
			<td>RNAseq</td>
			<td>135,321,406</td>
			<td>225,222</td>
			<td>0</td>
			<td>0</td>
			<td>170</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>Capture</td>
			<td>9,317,418</td>
			<td>7,680,818</td>
			<td>143</td>
			<td>0</td>
			<td>7</td>
		</tr>
		<tr>
			<td>RT4</td>
			<td>FGFR3-TACC3&#160;</td>
			<td>RNAseq</td>
			<td>161,350,024</td>
			<td>208,741</td>
			<td>0</td>
			<td>131</td>
			<td>469</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>Capture</td>
			<td>8,305,950</td>
			<td>6,563,574</td>
			<td>358</td>
			<td>88</td>
			<td>34</td>
		</tr>
	</tbody>
</table>
Table 1. Fusion Detection for Capture versus RNAseq of K562, LC2, EOL1 and RT-4. This Table displays four cancer cell lines and three different fusion detection algorithms, TopHat2, ChimeraScan, and TRUP utilized in this demonstration. This table demonstrates the ability to detect fusions with Capture using less than 10 million total reads compared to greater than 60 million reads utilized for RNAseq. Fusion supporting reads were calculated by dividing fusion supporting reads by kinase reads, multiplied by one million.
<img alt="Figure 4" src="/files/ftp_upload/54090/54090fig4.jpg" /> 
Figure 4. SNV calling for Capture versus RNAseq. These Venn diagrams show the number of Single Nucleotide Variants (SNVs) that were detected by Capture and RNAseq for each of four cell lines (K562, LC2, EOL1 and RT-4). This illustrates high concordance of SNVs between Capture and RNAseq within targeted-region: K562 (81.3%), LC2 (78.3%), EOL1 (89.5%) and RT-4 (73.9%). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54090/54090fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations at JoVE.com

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding ...

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Nanyang Technological University

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Biology

10.3K Views.  The Ohio State University. We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.

Video: Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

Cancer Research

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Genetics

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is unregulated proliferation and cell division. Inhibition of proliferation by halting cell cycle progression has been shown to be a promising approach for cancer treatment, including LC. 
miRNA therapeutics have emerged as important post-transcriptional gene regulators and are increasingly being studied for use in cancer treatment. In recent work, we utilized two miRNAs, miR-143 and miR-506, to regulate cell cycle progression. A549 non-small cell lung cancer (NSCLC) cells were transfected, gene expression alterations were analyzed, and apoptotic activity due to the treatment was finally analyzed. Downregulation of cyclin-dependent kinases (CDKs) were detected (i.e., CDK1, CDK4 and CDK6), and cell cycle halted at the G1/S and G2/M phase transitions. Pathway analysis indicated potential antiangiogenic activity of the treatment, which endows the approach with multifaceted activity. Here, described are the methodologies used to identify miRNA activity regarding cell cycle inhibition, induction of apoptosis, and effects of treatment on endothelial cells by inhibition of angiogenesis. It is hoped that the methods presented here will support future research on miRNA therapeutics and corresponding activity and that the representative data will guide other researchers during experimental analyses.

miRNA therapeutics have significant potential in regulating cancer progression. Demonstrated here are analytical approaches used for identification of the activity of a combinatorial miRNA treatment in halting cell cycle and angiogenesis.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is ...

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells

The development of new drugs that precisely target key proteins in human cancers is fundamentally altering cancer therapeutics. However, before these drugs can be used, their target proteins must be validated as therapeutic targets in specific cancer types. This validation is often performed by knocking out the gene encoding the candidate therapeutic target in a genetically engineered mouse (GEM) model of cancer and determining what effect this has on tumor growth. Unfortunately, technical issues such as embryonic lethality in conventional knockouts and mosaicism in conditional knockouts often limit this approach. To overcome these limitations, an approach to ablating a floxed embryonic lethal gene of interest in short-term cultures of malignant peripheral nerve sheath tumors (MPNSTs) generated in a GEM model was developed.
This paper describes how to establish a mouse model with the appropriate genotype, derive short-term tumor cultures from these animals, and then ablate the floxed embryonic lethal gene using an adenoviral vector that expresses Cre recombinase and enhanced green fluorescent protein (eGFP). Purification of cells transduced with adenovirus using fluorescence-activated cell sorting (FACS) and the quantification of the effects that gene ablation exerts on cellular proliferation, viability, the transcriptome, and orthotopic allograft growth is then detailed. These methodologies provide an effective and generalizable approach to identifying and validating therapeutic targets in vitro and in vivo. These approaches also provide a renewable source of low-passage tumor-derived cells with reduced in vitro growth artifacts. This allows the biological role of the targeted gene to be studied in diverse biologic processes such as migration, invasion, metastasis, and intercellular communication mediated by the secretome.

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells

Here, we present a protocol for performing gene knockouts that are embryonic lethal in vivo in genetically engineered mouse model-derived tumors and then assessing the effect that the knockout has on tumor growth, proliferation, survival, migration, invasion, and the transcriptome in vitro and in vivo.

The development of new drugs that precisely target key proteins in human cancers is fundamentally altering cancer therapeutics. However, before these ...

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using bioinformatics pipelines (&#34;dry bench&#34;). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible &#34;tag&#34; PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.

An integrated system for targeted next-generation sequencing of oncology specimens is described. This cross-platform system is optimized for low-quality and low-quantity tumor biopsies, accommodates low DNA inputs, includes well-characterized multi-variant controls, and features a novel variant caller that is informed by quantitative pre-analytical quality control measures.

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using ...

Medicine

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Efforts to detect and investigate key oncogenic mutations have proven valuable to facilitate the appropriate treatment for cancer patients. The establishment of high-throughput, massively parallel "next-generation" sequencing has aided the discovery of many such mutations. To enhance the clinical and translational utility of this technology, platforms must be high-throughput, cost-effective, and compatible with formalin-fixed paraffin embedded (FFPE) tissue samples that may yield small amounts of degraded or damaged DNA. Here, we describe the preparation of barcoded and multiplexed DNA libraries followed by hybridization-based capture of targeted exons for the detection of cancer-associated mutations in fresh frozen and FFPE tumors by massively parallel sequencing. This method enables the identification of sequence mutations, copy number alterations, and select structural rearrangements involving all targeted genes. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus conferring high sensitivity for detecting low frequency mutations.

We describe the preparation of barcoded DNA libraries and subsequent hybridization-based exon capture for detection of key cancer-associated mutations in clinical tumor specimens by massively parallel "next generation" sequencing. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus yielding high sensitivity for detecting low frequency mutations.

Efforts  to detect and investigate key oncogenic mutations have proven valuable to  facilitate the appropriate treatment for cancer patients. The ...

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during development and in disease states. Single-cell RNA sequencing allows us to comprehensively profile gene expression changes observed in retinal development and disease at a cellular level, while single-cell ATAC-Seq allows analysis of chromatin accessibility and transcription factor binding to be profiled at similar resolution. Here the use of these techniques in the developing retina is described, and MULTI-Seq is demonstrated, where individual samples are labeled with a modified oligonucleotide-lipid complex, enabling researchers to both increase the scope of individual experiments and substantially reduce costs.

Here, the authors showcase the utility of MULTI-seq for phenotyping and subsequent paired scRNA-seq and scATAC-seq in characterizing the transcriptomic and chromatin accessibility profiles in retina.

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during ...

Neuroscience

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study.
RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment.
In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.

RNA-sequencing and bioinformatics analyses were used to identify significantly and differentially expressed transcription factors in Lin-CD34+ and Lin-CD34- subpopulations of mouse EMLcells. These transcription factors might play important roles in determining the switch between self-renewing Lin-CD34+ and partially differentiated Lin-CD34- cells.

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as ...

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Because precision medicine is highly dependent on the accurate detection of biomarkers, there is an increasing need for standardized and robust technologies that measure RNA biomarkers in situ in clinical specimens. While grind-and-bind assays like RNAseq and quantitative RT-PCR enable highly sensitive gene expression measurements, they also require RNA extraction and thus prevent valuable expression analysis within the morphological tissue context. The in situ hybridization (ISH) assay described here can detect RNA target sequences as short as 50 nucleotides at single-nucleotide resolution and at the single-cell level. This assay is complementary to the previously developed commercial assay and enables sensitive and specific in situ detection of splice variants, short targets, and point mutations within the tissue. In this protocol, probes were designed to target unique exon junctions for two clinically important splice variants, EGFRvIII and MET&#916;14. The detection of short target sequences was demonstrated by the specific detection of CDR3 sequences of T-cell receptors &#945; and &#946; in the Jurkat T-cell line. Also shown is the utility of this ISH assay for the distinction of RNA target sequences at single-nucleotide resolution (point mutations) through the visualization of EGFR L858R and KRAS G12A single-nucleotide variations in cell lines using automated staining platforms. In summary, the protocol shows a specialized RNA ISH assay that enables the detection of splice variants, short sequences, and mutations in situ for manual performance and on automated stainers.

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Here, we describe an in situ hybridization assay which enables sensitive and specific detection of sequences as short as 50 nucleotides with single-nucleotide resolution at the single-cell level. The assay, which can be performed manually or automatically, can enable visualization of splice variants, short sequences, and mutations within the tissue context.

Because precision medicine is highly dependent on the accurate detection of biomarkers, there is an increasing need for standardized and robust ...

RNA-Seq

Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.Here, JoVE presents the basic principles behind RNA-seq. We then discuss the experimental and analytical steps of a general RNA-seq protocol. Finally, we examine how researchers are currently using RNA-seq, for example, to compare gene expression between different biological samples, or to characterize protein-RNA interactions.

Next-Gen Transcriptomics Using RNA-Seq

Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed ...

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Summary

Explore More Videos

Chapters in this video

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

RNA-Seq