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Streets, A. M., X. Zhang, C. Cao, et al. "Microfluidic single-cell whole-transcriptome sequencing." Proceedings of the National Academy of Sciences 111, no. 19 (2014): 7048–53. http://dx.doi.org/10.1073/pnas.1402030111.
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Hosokawa, Kohei, Sachiko Kajigaya, Keyvan Keyvanfar, et al. "Whole transcriptome sequencing identifies increasedCXCR2expression in PNH granulocytes." British Journal of Haematology 177, no. 1 (2017): 136–41. http://dx.doi.org/10.1111/bjh.14502.
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Yang, In Seok, and Sangwoo Kim. "Analysis of Whole Transcriptome Sequencing Data: Workflow and Software." Genomics & Informatics 13, no. 4 (2015): 119. http://dx.doi.org/10.5808/gi.2015.13.4.119.
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Petrini, Iacopo, Arun Rajan, Trung Pham, et al. "Whole Genome and Transcriptome Sequencing of a B3 Thymoma." PLoS ONE 8, no. 4 (2013): e60572. http://dx.doi.org/10.1371/journal.pone.0060572.
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Siezen, Roland J., Greer Wilson, and Tilman Todt. "Prokaryotic whole-transcriptome analysis: deep sequencing and tiling arrays." Microbial Biotechnology 3, no. 2 (2010): 125–30. http://dx.doi.org/10.1111/j.1751-7915.2010.00166.x.
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Ruan, Miaomiao, Jiying Liu, Xueyang Ren, et al. "Whole transcriptome sequencing analyses of DHA treated glioblastoma cells." Journal of the Neurological Sciences 396 (January 2019): 247–53. http://dx.doi.org/10.1016/j.jns.2018.11.027.
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Seliger, Sonja, Verena Geirhos, Torsten Haferlach, et al. "Comprehensive Analysis of MYC Translocations in Multiple Myeloma By Whole Genome Sequencing and Whole Transcriptome Sequencing." Blood 134, Supplement_1 (2019): 1774. http://dx.doi.org/10.1182/blood-2019-124704.
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Background 8q24 translocations leading to overexpression of MYC are an established prognostic marker in multiple myeloma (MM). Currently FISH (fluorescence in situ hybridization) on CD138+ enriched cell population is the standard diagnostic approach to evaluate the presence of 8q24 translocations. Due to the heterogeneity of breakpoints and technical issues the design of FISH probes is challenging and so far no single FISH assay is capable of detecting each translocation. Aims (1) Evaluation of the frequency of 8q24 translocations in MM by whole genome sequencing (WGS). (2) Determination of the breakpoints on 8q24 and partners. (3) Correlation of WGS data with FISH and MYC expression determined by whole transcriptome sequencing (WTS). Patient cohort and methods CD138+ cell fractions were selected by MACS from bone marrow aspirate samples of 264 patients diagnosed with MM. FISH, WGS and WTS were performed in all cases. For WGS, 151bp paired-end sequences where generated on NovaSeq 6000 machines (Illumina, San Diego, CA). All reported p-values are two-sided and were considered significant at p<0.05. For gene expression (GE) analysis by WTS, estimated gene counts were normalized and the resulting log2 counts per million were used as a proxy of gene expression in each sample. For artefact exclusion, structural variants were checked against 4386 cases covering the spectrum of hematological malignancies. Results In 91/264 (34%) of cases, at least one rearrangement involving the MYC locus (MYCr) was detected by WGS. In 18 of these samples (20%), >1 MYCr was present (114 MYCr in total). Out of these 91 patients, in 32 (35%) the MYCr had been identified by FISH, in 46 cases (51%) it was not detected due to the heterogeneity of breakpoints, while in 13 (14%) patients FISH could not be evaluated (e.g. due to insufficient patient material). Of the 114 MYCr encountered in WGS, 42 involved one of the immunoglobulin loci (IGH n=25, IGK n=9, IGL n=8). The remaining 72 MYCr involved other rare partners. In 29 of these rearrangements, as well as in four complex rearrangements involving IGH or IGK, recurrent rare partners were identified, comprising 1p12/FAM46C (n=6), 6p24.3/BMP6 (n=10), 6q21/FOXO3 (n=4), 7p21.3 (n=3), 11q13/CCND1 (n=5), 20q11.22 (n=5). 43 MYCr involved non-recurrent (single) rare partners, for 4 of these a MYCr was also detected by FISH. The MYCr detected were rather complex: only 34 (30%) showed a simple reciprocal translocation (IGH n=7, IGL n=2, IGK n=4, rare partners n=21), 60 (53%) showed more complex rearrangements (IGH n=12, IGL n=4, IGK n=2, rare partners n=42) and in 20 cases (18%) at least one additional chromosome was involved (IGH n=6, IGL n=3, IGK n=2, rare partners n=9). In 80% of MYCr, breakpoints were located between genomic positions 128.203.605 and 129.375.490 encompassing the pre-described MYC surrounding locus PVT1. IGH-MYC rearrangements showed a tendency to cluster towards the centromere. MYCr involving rare partners showed the broadest breakpoint spectrum and clustered in both directions of the hotspot (Fig 1A). Regarding expression of MYC, all cases showed an overexpression (median GE: 6.9 vs 4.5 in normal controls). Median GE was similar in cases with Ig partners (IGH: 7.1, IGL: 6.7, IGK: 6.6) and non Ig partners (6.8) and also in cases with MYCr detected by FISH (7.0) and cases in which it was not detected by FISH (6.5). Analysis of additional chromosomal aberrations revealed that hyperdiploidy was significantly more frequent in MYCr (n=68/91, 75% vs n=76/173, 44%; p=0.001), while t(11;14) was found significantly less frequent (n=11/91, 12% vs n=49/173, 28%; p=0.003) (Fig 1B). No associations were found between MYCr and other frequent chromosomal abnormalities. Furthermore, molecular mutations frequently occurring in MM (ATM, BRAF, KRAS, NRAS, TP53, IRF4) were analyzed, revealing that patients with MYCr were significantly less frequently associated with mutations in the IRF4 gene (MYCr patients n=1/91; non-MYCr patients n=13/173; p =0.028) (Fig 1C). Conclusions (1) WGS detects ~3x more MYCr compared to FISH. (2) The complexity on the genomic level of MYCr is high, therefore the detection with targeted assays is limited while WGS allows a more comprehensive analysis. (3) MYC expression in cases with MYCr with non Ig partners is comparably high as for Ig-MYC translocations. (4) MYCr are associated with hyperdiploidy, whereas t(11;14) and IRF4 mutations were detected at a lower frequency. Disclosures Seliger: MLL Munich Leukemia Laboratory: Employment. Geirhos:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Walter:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Stengel:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.
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Cirulli, Elizabeth T., Abanish Singh, Kevin V. Shianna, et al. "Screening the human exome: a comparison of whole genome and whole transcriptome sequencing." Genome Biology 11, no. 5 (2010): R57. http://dx.doi.org/10.1186/gb-2010-11-5-r57.
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Martin, Jeffrey, Wenhan Zhu, Karla D. Passalacqua, Nicholas Bergman, and Mark Borodovsky. "Bacillus anthracis genome organization in light of whole transcriptome sequencing." BMC Bioinformatics 11, Suppl 3 (2010): S10. http://dx.doi.org/10.1186/1471-2105-11-s3-s10.
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Tang, Wei, and Ludmila Prokunina-Olsson. "Whole transcriptome sequencing of normal and tumor bladder tissue samples." Genome Biology 12, Suppl 1 (2011): P23. http://dx.doi.org/10.1186/gb-2011-12-s1-p23.
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Li, H., H. H. Yang, Z. G. Sun, H. B. Tang, and J. K. Min. "Whole-transcriptome sequencing of knee joint cartilage from osteoarthritis patients." Bone & Joint Research 8, no. 7 (2019): 290–303. http://dx.doi.org/10.1302/2046-3758.87.bjr-2018-0297.r1.
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Objectives The aim of this study was to provide a comprehensive understanding of alterations in messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) in cartilage affected by osteoarthritis (OA). Methods The expression profiles of mRNAs, lncRNAs, and circRNAs in OA cartilage were assessed using whole-transcriptome sequencing. Bioinformatics analyses included prediction and reannotation of novel lncRNAs and circRNAs, their classification, and their placement into subgroups. Gene ontology and pathway analysis were performed to identify differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed circRNAs (DECs). We focused on the overlap of DEGs and targets of DELs previously identified in seven high-throughput studies. The top ten DELs were verified by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in articular chondrocytes, both in vitro and in vivo. Results In total, 739 mRNAs, 1152 lncRNAs, and 42 circRNAs were found to be differentially expressed in OA cartilage tissue. Among these, we identified 18 overlapping DEGs and targets of DELs, and the top ten DELs were screened by expression profile analysis as candidate OA-related genes. WISP2, ATF3, and CHI3L1 were significantly increased in both normal versus OA tissues and normal versus interleukin (IL)-1β-induced OA-like cell models, while ADAM12, PRELP, and ASPN were shown to be significantly decreased. Among the identified DELs, we observed higher expression of ENST00000453554 and MSTRG.99593.3, and lower expression of MSTRG.44186.2 and NONHSAT186094.1 in normal versus OA cells and tissues. Conclusion This study revealed expression patterns of coding and noncoding RNAs in OA cartilage, which added sets of genes and noncoding RNAs to the list of candidate diagnostic biomarkers and therapeutic agents for OA patients. Cite this article: H. Li, H. H. Yang, Z. G. Sun, H. B. Tang, J. K. Min. Whole-transcriptome sequencing of knee joint cartilage from osteoarthritis patients. Bone Joint Res 2019;8:290–303. DOI: 10.1302/2046-3758.87.BJR-2018-0297.R1.
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Basu, Gargi D., Kevin Drenner, Audrey Ozols, et al. "Whole exome and transcriptome sequencing of colorectal and pancreatic cancer." Journal of Clinical Oncology 38, no. 15_suppl (2020): e15666-e15666. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e15666.
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e15666 Background: Integration of Whole Exome Sequencing (WES) into clinical cancer therapeutics has revolutionized medicine in recent years. DNA sequencing alone may miss clinically actionable variants or identify aberrations that are not being transcribed. In this study we investigated the utility of integrating DNA and RNA sequencing in clinical samples. Methods: A cohort of 32 patient samples were analyzed by WES and RNA sequencing. Differential expression analysis was performed using a cohort of controls. Pathway analysis was performed using Ingenuity Pathway Analysis. WES and RNA analysis detected alterations including SNVs, indels, copy number events, fusions, alternate transcripts, TMB, MSI status and differential expression. Results: A total of 25 CRC (39-78yrs) and 7 pancreatic cancers (PCs) (51-91 yrs) were profiled by WES and RNA seq. A 66 yr old pt with neoplasm of rectosigmoid junction tumor was found to be KRAS wildtype and was treated with cetuximab plus FOLFIRI. Patient failed therapy after 2 yrs and sequencing revealed MET amplification which is a known mechanism of resistance to cetuximab treatment. Further, RNA expression analysis showed 44-fold increase in MET expression along with overexpression of AREG and EREG. Out of the 7 PCs, 3 cases that did not harbor KRAS mutation were found to harbor VTCN1/NRG1 fusion, FGFR1/G3BP2 fusion and BRAF V600E mutation respectively. A 50 year-old stage IV metastatic (met) PC pt was treated with standard of care regimens. Upon relapse the sample was found to harbor VTCN1/NRG1 fusion along with a TERT promoter mutation. RNA expression analysis revealed 54-fold increased expression of NRG1 which may lead to clinical trial enrollment. A 52 year-old male met Stage IV PC, was treated with rucaparib and irinotecan based on prior sequencing data. Upon relapse, the pt went on ATR inhibitor (BAY1895344) and progressed very quickly. Sequencing of the met lesion was found to harbor FGFR1/G3BP2 fusion which was also confirmed by RNA expression. A 55 year-old met pt with PC was treated with standard chemotherapy. Upon disease progression pt was sequenced and based on presence of BRAF V600E, pt was treated with trametinib and dabrafenib. Following disease progression on BRAF/MEKi, met sample was resequenced and RNA expression analysis found increased expression of MET, MACC1 and SMAD7 and 4-fold decrease in PTEN which could potentially cause resistance to BRAF/MEKi therapy. Conclusions: Our study highlights the utility of comprehensive testing integrating genomic and transcriptomic data, in identifying targeted therapy options for cancer patients.
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Jiang, Zhihua, Xiang Zhou, Rui Li, et al. "Whole transcriptome analysis with sequencing: methods, challenges and potential solutions." Cellular and Molecular Life Sciences 72, no. 18 (2015): 3425–39. http://dx.doi.org/10.1007/s00018-015-1934-y.
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Hong, M., G. Kang, and K. Kim. "Genetic Alterations in Gists Using Whole Exome and Transcriptome Sequencing." Annals of Oncology 25 (September 2014): iv498. http://dx.doi.org/10.1093/annonc/mdu354.13.
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Gianfelici, Valentina, Sabina Chiaretti, Zeynep Kalender Atak, et al. "Whole Transcriptome Sequencing In Refractory T-Cell Acute Lymphoblastic Leukemia." Blood 122, no. 21 (2013): 350. http://dx.doi.org/10.1182/blood.v122.21.350.350.
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Abstract T-cell acute lymphoblastic leukemia (T-ALL) is a malignancy of the lymphoblasts committed to the T-cell lineage. Despite the therapeutic improvements witnessed over the years, ∼25% of children and ∼50% of adults still show a poor long-term outcome. While many recurrent oncogenic lesions have been identified through the characterization of chromosomal aberrations and candidate gene sequencing, several observations indicate that additional genetic alterations, not evident by conventional cytogenetics, might influence leukemogenesis and treatment outcome. Improvement of our knowledge in the identification and characterization of new oncogenic genome variations is expected to lead to a better prognostic classification and should also allow the design of tailored therapeutic strategies. To get further insights into the molecular pathogenesis of T-ALL and to identify novel markers for risk stratification and treatment improvement, we performed whole transcriptome sequencing (RNA-seq) on 18 refractory T-ALL cases sampled at diagnosis (median age 37.5 years, range 11-55). A pool of normal thymus cells was used as negative control. Next generation sequencing libraries were constructed from the mRNA fraction, followed by paired-end sequencing on a HiSeq2000 (Illumina). Sequence reads were aligned to the reference genome and were processed to identify gene expression levels, gene fusion transcripts and single nucleotide variations (SNVs). We first determined accurate gene expression levels from the RNA-seq data and used them to classify patients into T-ALL subtypes. Next, we applied the deFuse algorithm to detect fusion transcripts. Fusion transcripts detected also in normal thymus cells were filtered out, as well as fusions involving ribosomal genes. After applying these filters, we obtained 407 fusion transcripts (average: 22.6/sample, range: 0-84) predominantly involving genes localized on the same chromosome and mostly generated by deletion (306/407). Novel candidate fusion transcripts were confirmed by RT-PCR and Sanger sequencing. The SET-NUP214 fusion was identified in 2 cases, as well as 2 novel fusion transcripts involving the T-cell receptor (TCR) genes and not detected by conventional cytogenetics: the first fusion resulted in a chromosomal rearrangement between HOXA-AS4 and TRBC2 (also accompanied by overexpression of the HOXA genes) and the second between TRAC and SOX8 (associated with SOX8 overexpression). Interestingly, we also found out-of-frame fusion transcripts leading to the potential inactivation of tumor suppressor genes, such as PTEN-FAS and MAST3-C19orf10. Finally, we performed SNV calling on our dataset. After removing the most common polymorphisms, we obtained 1,483 protein-altering SNVs (missense, nonsense mutations and mutations affecting splicing), ranging between 30 and 131 per sample, with 85 genes that contain a protein-altering mutation in at least 3 of the 18 samples (i.e. 16% of cohort). Members or modulators of NOTCH and JAK/STAT pathways were the most recurrently mutated, each accounting for ∼38% of cases. In particular, 7/18 samples showed previously reported lesions in the NOTCH1 (n=5) and FBXW7 (n=1) genes but also in novel candidates as NOTCH2 (n=1), NOTCH3 (n=1) and SPEN (n=1). Interestingly, 1 patient showed 2 different mutations in the exon 26 of NOTCH1, while in 2 samples NOTCH1 mutation was associated with mutations in NOTCH2 or NOTCH3. Similarly, the JAK/STAT pathway was affected in 7/18 samples, including JAK1 (n=2), JAK3 (n=5), TYK2 (n=1) but also the novel candidates STAT5A (n=2) and STAT6 (n=1). Four of the 5 JAK3-positive patients showed also a mutation in another gene of the same pathway, such as JAK1 (n=1), STAT5A (n=2) and STAT6 (n=1). Thus, mutational screening of both the NOTCH and JAK/STAT pathway shows that mutations can occur simultaneously and suggests that more than one lesion is required for leukemic transformation. In conclusion, RNA-seq appears as a promising tool to dissect the heterogeneity of T-ALL and to identify targets that might be useful for tailored therapeutic interventions. Further investigations are ongoing to determine the recurrence and specificity of these lesions, and their potential in inducing a refractory phenotype. Finally, in vitro experiments will be carried out to investigate the transforming capability of specific lesions and the targettability of the recurrently impaired pathways. Disclosures: No relevant conflicts of interest to declare.
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Ryu, D., H. J. Kim, J. G. Joung, et al. "Whole-exome sequencing and transcriptome analysis for IgM multiple myeloma." Clinical Lymphoma Myeloma and Leukemia 15 (September 2015): e106. http://dx.doi.org/10.1016/j.clml.2015.07.279.
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Ahmed, R., M. S. Hossain, S. M. T. Kabir, et al. "Whole transcriptome sequencing and analysis of jute (Corchorus olitorius) fiber cell." Journal of Bioscience and Agriculture Research 26, no. 02 (2020): 2204–010. http://dx.doi.org/10.18801/jbar.260220.269.
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The demand for products made by jute fiber is increasing day-by-day for its biodegradable nature regarding environmental concerns. To gain this opportunity correctly, the development of high yielding and improved fiber quality jute variety is essential for ensuring diversified use of jute fiber. The major developed jute varieties, so far, are the outcome of conventional breeding which is a very time consuming process. Improvement of fiber quality and yield through genetic modification approach is highly desired. However, very little is known about the molecular mechanism behind fiber cell formation in jute. Here, we attempted to do the whole transcriptome sequencing of fiber cell RNA to reveal the molecular mechanisms were happening in the premises of jute fiber cells. We performed RNA isolation from jute fiber cells followed by whole transcriptome sequencing. De novo assembly of sequencing reads resulted in 21,294 contigs representing the transcriptome size of 4.07 Mbp. Gene ontology analysis assigned 14144 genes (52.21%) for biological process, 8399 genes (31%) involved in molecular function and 4549 genes (16.79%) for cellular component. Total 66 fiber related genes were found from reference based annotation where 9 genes involved in fiber cell initiation and elongation and the rest 57 for secondary cell wall development. We presented the overall view of the jute fiber cell transcriptome in this study. These findings help for understanding the molecular basis of fiber formation in jute plant.
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Nadarajah, Niroshan, Erika Pelaez Coyotl, James Golden, et al. "Automated Disease Classification Using Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) Data with Transparent Artificial Intelligence (AI)." Blood 138, Supplement 1 (2021): 275. http://dx.doi.org/10.1182/blood-2021-152970.
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Abstract Background: Currently, hematologic neoplasms are diagnosed using a combination of methods, which require complex equipment and highly skilled clinical laboratory scientists and technicians - scarce resources. WGS and WTS could streamline this process and become a singular method. Interpretation of WGS and WTS data in a diagnostic setting is extremely challenging due to the breadth of data and its high-dimensional data types. AI will be mandatory to identify clinically meaningful genetic patterns and produce unbiased diagnosis. Aim: Compute leukemia diagnosis using AI methods with WGS and WTS data only, depicting relevant features for a decision and thus making its results comprehensible and transparent to humans. Methods: To train the model we used our cohort of 4,689 samples both with WGS (100x coverage, 2x151bp) and WTS (50 mio reads/sample, 2x101bp), along with our independent final routine diagnosis based on gold standard techniques (GST) and following WHO guidelines. Single nucleotide variants (SNV), structural variants (SV) and copy number alterations (CNA) from WGS data using a tumor w/o normal pipeline and gene fusions (GF) and gene expression (GE) from WTS were extracted. The cohort comprised of 30 different neoplasms and was severely imbalanced (n: 20 - 773). To test its performance another independent cohort which was not used during model creation (n=202, 22 entities) was selected. Results: We trained an ensemble of multi-class classifier using SageMaker (AWS, Seattle, WA) based on LGBM implementation of gradient boosted decision trees (Ke et al, 2017) in a one vs. rest architecture (1vRA). The model accuracy reached 85% overall on a 5-fold cross-validation (Fig 1a). Since neighboring disease types such as MGUS/MM, MDS-EB-2/AML/CMML are in some cases difficult to classify correctly using GST, we trained entity-specific classifiers operating independently. Rather than forcing a single predicted class to be predicted as overwhelmingly likely, this architecture accounts for ambiguous entities. In addition to reflecting biological similarity, the 1vRA resulted in improved probability calibration, so that cases with ambiguous leukemias are more easily identifiable by the distribution of predicted class probabilities and flagged for a human. Expected calibration error was only 3.8% for the 1vRA with entity-specific components, compared to 8.7% for a single LGBM model. Typically, AI methods are black boxes, making it hard for a medical professional to understand predictions, which results in low confidence and acceptance of such systems. Thus, we particularly focused on the transparency aspect of the model. We employed the SHAP library (Lundberg et al, 2017) to retrace the models output and gain insight into the features (i.e. which SNV, SV, GE etc.) predominantly driving classification results (e.g. LPL case Fig 1b). Fig. 1c illustrates the application of SHAP at the global cohort level for two individuals wrongly diagnosed with CML compared to CML correctly predicted cohort. By using a decision plot, we can observe which features are the most important contributors to the model's prediction. Fig 1c shows that predictions for CML are primarily driven by the BCR-ABL1 features, as expected. In our independent test cohort the following entities reached a very high concordance, such as AML (16/21) AUL (11/12), BCP-ALL (10/10), CML (13/13), HZL (8/8), MGUS (7/7), Multiple Myeloma (9/11), PNH (10/10), T-ALL (6/7). Other clear cut entities with correct high level predictions include BPDCN, FL, LPL, PPBL, NK-cell, HCL-variant and HGBL. In other entities such as T-NHL results were more heterogeneous, but this was also expressed in the probability scores given by the model. The first choice had a probability score of ~50%, exposing the correct diagnosis as the second likeliest one with ~40%. Test cohort included cases with mixed diagnostic characteristics, e.g. MDS/MPN-RS-T (4/11 correct, 4 predicted as MDS, and 3 as MPN). Conclusion: We present an AI tool to interpret WGS and WTS data aiming to predict the final diagnosis without any human input and high concordance to today's WHO classification. Due to the high data dimensionality of WGS and WTS data, an impossible feat for a human. The tool is exposed via a web application and visualizations make the automated decisions transparent and verifiable through humans paving a way for better adoption of WGS and WTS into a clinical routine setting. Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.
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Zheng, Juyun, Zeliang Zhang, Yajun Liang, et al. "Whole Transcriptome Sequencing Reveals Drought Resistance-Related Genes in Upland Cotton." Genes 13, no. 7 (2022): 1159. http://dx.doi.org/10.3390/genes13071159.
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China, particularly the cotton-growing province of Xinjiang, is experiencing acute agricultural water shortages, stifling the expansion of the cotton sector. Discovering drought resistance genes in cotton and generating high-quality, drought-resistant cotton varieties through molecular breeding procedures are therefore critical to the cotton industry’s success. The drought-resistant cotton variety Xinluzhong No. 82 and the drought-sensitive cotton variety Kexin No. 1 were utilised in this study to uncover a batch of drought-resistant candidate genes using whole transcriptome sequencing. The following are the key research findings: A competing endogenous RNA network (ceRNA) was built using complete transcriptional sequencing to screen the core genes in the core pathway, and two drought-related candidate genes were discovered. It was found that γ-aminobutyric acid aminotransferase (GhGABA-T, Gohir.A11G156000) was upregulated at 0 h vs. 12 h and downregulated at 12 h vs. 24 h. L-Aspartate oxidase (GhAO, Gohir.A07G220600) was downregulated at 0 h vs. 12 h and upregulated at 12 h vs. 24 h. GABA-T is analogous to a pyridoxal phosphate-dependent transferase superfamily protein (POP2) in Arabidopsis thaliana and influences plant drought resistance by controlling γ-aminobutyric acid (GABA) concentration. The analogue of GhAO in A. thaliana is involved in the early steps of nicotinamide adenine dinucleotide (NAD) production as well as in plant antioxidant responses. This study revealed that gene expression regulatory networks can be used for rapid screening of reliable drought resistance genes and then utilised to validate gene function.
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Arindrarto, Wibowo, Daniel M. Borràs, Ruben A. L. de Groen, et al. "Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing." Leukemia 35, no. 1 (2020): 47–61. http://dx.doi.org/10.1038/s41375-020-0762-8.
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AbstractAcute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform.
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Ghukasyan, L., G. Krasnov, L. Baidun, and T. Nasedkina. "PS1005 WHOLE-TRANSCRIPTOME SEQUENCING OF CYTOGENETICALLY NORMAL PEDIATRIC ACUTE MYELOID LEUKEMIA." HemaSphere 3, S1 (2019): 452. http://dx.doi.org/10.1097/01.hs9.0000562316.04098.aa.
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Li, Tao-Tao, Xiao-Yan Li, Li-Xin Jia, et al. "Whole Transcriptome Analysis of Hypertension Induced Cardiac Injury Using Deep Sequencing." Cellular Physiology and Biochemistry 38, no. 2 (2016): 670–82. http://dx.doi.org/10.1159/000438659.
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Background/Aims: Hypertension plays a critical role in the cardiac inflammation and injury. However, the mechanism of how hypertension causes the cardiac injury at a molecular level remains to be elucidated. Methods: RNA-Seq has been demonstrated to be an effective approach for transcriptome analysis, which is essential to reveal the molecular constituents of cells and tissues. In this study, we investigated the global molecular events associated with the mechanism of hypertension induced cardiac injury using RNA-Seq analysis. Results: Our results showed that totally 1,801 genes with different expression variations were identified after Ang II infusion at 1, 3 and 7 days. Go analysis showed that the top 5 high enrichment Go terms were response to stress, response to wounding, cellular component organization, cell activation and defense response. KEGG pathway analysis revealed the top 5 significantly overrepresented pathways were associated with ECM-receptor interaction, focal adhesion, protein digestion and absorption, phagosome and asthma. Moreover, protein-protein interaction network analysis indicated that ubiquitin C may play a key role in the processes of hypertension-induced cardiac injury. Conclusion: Our study provides a comprehensive understanding of the transcriptome events in hypertension-induced cardiac pathology.
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Macchini, Marina, Annalisa Astolfi, Valentina Indio, et al. "Whole-transcriptome paired-end sequencing and the pancreatic cancer genetic landscape." Journal of Clinical Oncology 31, no. 15_suppl (2013): 4048. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.4048.
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4048 Background: A deeper knowledge of the pancreatic cancer (PDAC) biology is needed to improve the prognosis of the disease. Methods: 17 PDAC samples were collected by ultrasound-guided biopsy used for DNA and RNA extraction. 14 samples were analyzed by high resolution copy number analysis (CNA) on Affymetrix SNP array 6.0 and with segmentation algorithm against a reference of 270 Ceu HapMap individuals (Partek Genomic Suite). 17 samples were analyzed by whole transcriptome massively parallel sequencing, performed at 75x2 bp on a HiScanSQ Illumina platform. An average of 7, 3x107 reads per sample were generated, with a mean read depth of 50X. Single nucleotide variants (SNVs) were detected with SNVMix2 and compared with genetic variation databases (dbSNP, 1000genomes, Cosmic). Non-synonimous SNVs were analyzed with the predictors SNPs and GO and PROVEAN. Results: CNA results in 9/14 samples exhibited both macroscopic and cryptic cytogenetic alterations, with a mean of 10 CNA per patient. Most frequent gains were observed in 18q11.2 involving GATA6 (3/14) and 19q13 targeting AKT2 (3/14) while hotspot deletions were found on 18q21 (7/14), 17p13 (6/14), 9p21.3 (6/14), 15q (5/14) and 1q35 (4/14). RNAseq showed that samples exhibited a mean of 145 (range: 61-240) non-synonimous SNVs, of which 16 on average are potentially disease-related. Merging copy number and RNAseq data we highlighted the major oncogenic hits of PDAC, confirming the prevalence (14/17) of KRAS mutations, in one case also NRAS (G13D), and the three oncosuppressor CDKN2A (mutated in 3 cases and deleted in 6 cases, in hetero- or homozygosity), SMAD4 (altered by point mutation or gene deletion in 7/14), and TP53 (lost in 6/14 and mutated in 5/17). The signaling pathways affected were: KRAS/MAPK, TGFbeta and integrin signaling, proliferation and apoptosis, DNA damage response, and epithelial to mesenchymal transition. Moreover we found new oncogenic alterations, such as HMGCR, that displayed mutations in 17% of the analyzed patients (3/17). Conclusions: NGS combined with high resolution cytogenetic analysis can improve the understanding of pancreatic carcinogenesis.
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Kridel, Robert, Barbara Meissner, Sanja Rogic, et al. "Whole Transcriptome Sequencing Reveals Recurrent NOTCH1 Mutations in Mantle Cell Lymphoma." Blood 118, no. 21 (2011): 436. http://dx.doi.org/10.1182/blood.v118.21.436.436.
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Abstract Abstract 436 Background: Mantle cell lymphoma (MCL) is an aggressive subtype of non-Hodgkin's lymphoma that is characterized by the hallmark t(11;14)(q13;q32) translocation, as well as a high number of secondary chromosomal alterations. Further, a small number of genes such as TP53, ATM and CCND1 have been reported to be recurrently mutated in MCL, but do not fully explain the biology and do not adequately account for the wide spectrum of clinical manifestations, response to treatment and prognosis. The aim of this study was to discover new somatic mutations that could contribute to our understanding of the pathogenesis of MCL. Methods: In our discovery cohort, we sequenced the transcriptomes of 18 clinical samples (11 diagnostic and 7 progression biopsies) and 2 mantle cell lymphoma-derived cell lines (Mino and Jeko-1). For this purpose, whole transcriptome shotgun sequencing was performed on RNA extracted from fresh frozen tissue. We assembled an extension cohort of 103 diagnostic patient samples and 4 additional cell lines (Rec-1, Z-138, Maver-1, JVM-2), and performed Sanger sequencing of NOTCH1 exons 26, 27 and 34 on genomic DNA. We further exposed the 6 cell lines to 1 μM of the γ-secretase inhibitor XXI (compound E) for 7 days and measured cellular proliferation with an EdU incorporation assay. Survival analysis was carried out in the 113 patients with diagnostic biopsies and available outcome data. Results: NOTCH1 mutations were found in 14 out of 121 patient samples (11.6%) and in 2 out of 6 cell lines, Mino and Rec-1 (33.3%). The majority of these mutations (12 out of 14) lie in exon 34 that encodes the PEST domain of NOTCH1 and consist of either small frameshift-causing indels (10 cases) or nonsense mutations (2 cases). These mutations are predicted to cause truncations of the C-terminal PEST domain. To gain further insight into functional relevance, we treated 6 cell lines with compound E, an inhibitor of the γ-secretase complex that plays a critical role in the release of the intracellular domain of NOTCH1 after ligand-induced activation. In Rec-1, that harbours a NOTCH1 mutation, we observed a significant decrease in proliferation (mean percentage of cells in culture incorporating EdU decreasing from 47.5% to 1.4%, p<.001). No effect of compound E was observed in Mino, the other cell line with a NOTCH1 mutation, nor in the 4 cell lines that are wild type for NOTCH1. Outcome correlation analysis showed that NOTCH1 mutations are associated with poor overall survival (1.56 versus 3.86 years respectively, p=.001), but not with significantly shortened progression-free survival (0.88 versus 1.73 years respectively, p=.07). Discussion: We have identified recurrent mutations in NOTCH1 in a subset of patients with MCL (11.6%). The frequency and the pattern of mutations are strikingly similar to what has recently been reported in chronic lymphocytic leukemia, the other major CD5 positive B-cell malignancy (Nature, 2011 Jun 5, 475:101–105 and J Exp Med, 2011 Jul 4, 208:1389–1401). NOTCH1 mutations are associated with adverse prognosis as evidenced by shortened overall survival. This latter finding, however, should ideally be validated in a larger and uniformly treated cohort. Finally, the sensitivity of the Rec-1 cell line to compound E suggests that NOTCH1 mutations could serve as the target for tailored therapy in mantle cell lymphoma. Disclosures: Sehn: Roche/Genentech: Consultancy, Honoraria, Research Funding. Connors:Roche: Research Funding.
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Ikonnikova, A. Yu, Yu I. Ammour, A. V. Snezhkina, G. S. Krasnov, A. V. Kudryavtseva та T. V. Nasedkina. "Identification of Fusion Transcripts in Leukеmic Cells by Whole-Transcriptome Sequencing". Molecular Biology 52, № 2 (2018): 200–205. http://dx.doi.org/10.1134/s0026893318020048.
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Kridel, Robert, Barbara Meissner, Sanja Rogic, et al. "Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma." Blood 119, no. 9 (2012): 1963–71. http://dx.doi.org/10.1182/blood-2011-11-391474.
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Abstract Mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma, is characterized by the hallmark translocation t(11;14)(q13;q32) and the resulting overexpression of cyclin D1 (CCND1). Our current knowledge of this disease encompasses frequent secondary cytogenetic aberrations and the recurrent mutation of a handful of genes, such as TP53, ATM, and CCND1. However, these findings insufficiently explain the biologic underpinnings of MCL. Here, we performed whole transcriptome sequencing on a discovery cohort of 18 primary tissue MCL samples and 2 cell lines. We found recurrent mutations in NOTCH1, a finding that we confirmed in an extension cohort of 108 clinical samples and 8 cell lines. In total, 12% of clinical samples and 20% of cell lines harbored somatic NOTCH1 coding sequence mutations that clustered in the PEST domain and predominantly consisted of truncating mutations or small frame-shifting indels. NOTCH1 mutations were associated with poor overall survival (P = .003). Furthermore, we showed that inhibition of the NOTCH pathway reduced proliferation and induced apoptosis in 2 MCL cell lines. In summary, we have identified recurrent NOTCH1 mutations that provide the preclinical rationale for therapeutic inhibition of the NOTCH pathway in a subset of patients with MCL.
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Lamb, Carla, Lahey Hospital, Medical Center, et al. "LUNG CANCER DETECTION VIA WHOLE-TRANSCRIPTOME RNA SEQUENCING OF NASAL EPITHELIUM." Chest 156, no. 4 (2019): A1091—A1092. http://dx.doi.org/10.1016/j.chest.2019.08.1005.
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Wrzeszczynski, Kazimierz O., Heather Geiger, Sowmya T. Srinivasa, et al. "Abstract 757: Clinical interpretation and utility of whole genome and whole transcriptome sequencing for precision oncology." Cancer Research 82, no. 12_Supplement (2022): 757. http://dx.doi.org/10.1158/1538-7445.am2022-757.
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Abstract The New York Genome Center CLIA laboratory has been providing New York State approved molecular diagnostic whole genome and whole transcriptome sequencing (WGTS) since October 2018. Indications for testing are cancers (solid tumors or hematological malignancies) where a mutational profile from multiple genes would be informative for disease stratification, prognosis, treatment options or alternative treatments or clinical trials. Germline analysis for ACMG designated cancer predisposition variants also is performed for consented patients. To date we have provided clinical next generation sequencing (NGS) genomic profile reports for 139 oncological cases from 31 different cancer types including GBM (41 cases), Pancreas (17), CRC (15), Lung (8) and others. The clinical interpretation of WGTS data of molecular tumor markers from NGS encompasses automated variant calling tools with human interpretation. The final mostly manual review of data is intensive, involving highly trained scientists engaged in substantial literature review and interpretation for each case alongside pathologists, molecular geneticists, and treating oncologists. We first present the technical challenges of validating a WGTS oncological diagnostic assay for appropriate clinical grade accuracy and sensitivity acceptable for patient care. We then present case studies illustrating the varying degree of tumor profiling and analysis outlining the current clinical utility and challenges of precision oncology medicine and therapeutic associations from sequencing of cancer patient tumors. We conclude with a previously unreported summary of therapeutic actionability derived from WGTS for all cancer cases sequenced at NYGC. We show that 28% percent of all samples in our cohort contain a tier 1 variant but additional second line therapies (or off-label drugs) can be considered for over 75% of WGTS sequenced cancer patients. Our case studies being in both solid tumor and hematological cancers illustrate the variability in sequencing data and the individual patient specificities in each interpretation of clinical findings. We show how NGS sequencing can offer multiple treatment outcomes when combining all genomic aberrations (copy number, structural variants and SNV/indels) found in a subtype of prostate cancer. We present hematological malignancies that show how certain DNA mutations point to RNA aberrations leading to therapeutic associations. In pancreatic cancer we present how a unique alteration in BRAF can lead to second line treatment with clinical benefit. We therefore demonstrate that more complete NGS assays those examining both the whole genome and transcriptome have added value in precision oncology practice enabling to find second-line treatment options or alternative therapeutic options when primary approaches fail or are not identified following a targeted sequencing approach. Citation Format: Kazimierz O. Wrzeszczynski, Heather Geiger, Sowmya T. Srinivasa, Marilena Melas, Valisha Shah, Vanessa Felice, Luisa Suarez, Endre Hegedus, Shruti Phadke, Saurav Guha, Dina Manaa, Dino Robinson, Lena Fielding, Vaidehi Jobanputra. Clinical interpretation and utility of whole genome and whole transcriptome sequencing for precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 757.
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Oeck, Sebastian, Alicia I. Tüns, Sebastian Hurst, and Alexander Schramm. "Streamlining Quantitative Analysis of Long RNA Sequencing Reads." International Journal of Molecular Sciences 21, no. 19 (2020): 7259. http://dx.doi.org/10.3390/ijms21197259.
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Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses.
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Li, Na, Mukaram Amatjan, Pengke He, Meiwei Wu, Hengxiu Yan, and Xiaoni Shao. "Whole transcriptome expression profiles in kidney samples from rats with hyperuricaemic nephropathy." PLOS ONE 17, no. 12 (2022): e0276591. http://dx.doi.org/10.1371/journal.pone.0276591.
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Hyperuricaemic nephropathy (HN) is a common clinical complication of hyperuricaemia (HUA) and poses a huge threat to human health. Hence, we aimed to prospectively investigate the dysregulated genes, pathways and networks involved in HN by performing whole transcriptome sequencing using RNA sequencing. Six kidney samples from HN group (n = 3) and a control group (n = 3) were obtained to conduct RNA sequencing. To disclose the relevant signalling pathways, we conducted the analysis of differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A competitive endogenous RNA (ceRNA) network was established to reveal the interactions between lncRNAs, circRNAs, mRNAs and miRNAs and investigate the potential mechanisms of HN. Ultimately, 2250 mRNAs, 306 lncRNAs, 5 circRNAs, and 70 miRNAs were determined to be significantly differentially expressed in the HN group relative to the control group. We further authenticated 8 differentially expressed (DE)-ncRNAs by quantitative real-time polymerase chain reaction, and these findings were in accordance with the sequencing results. The analysis results evidently showed that these DE-ncRNAs were significantly enriched in pathways related to inflammatory reaction. In conclusion, HUA may generate abnormal gene expression changes and regulate signalling pathways in kidney samples. Potentially related genes and pathways involved in HN were identified.
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Tanner, Elizabeth A., Tracy A. Kim, Melody A. Gary, et al. "Evaluating the Effects of Systemic, Exercise-Induced Skeletal Muscle Injury using Whole Transcriptome Sequencing." Journal of Immunology 200, no. 1_Supplement (2018): 42.14. http://dx.doi.org/10.4049/jimmunol.200.supp.42.14.
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Abstract Recent advances in next generation sequencing have dramatically reduced the cost of whole transcriptome sequencing to measure differential gene expression. Extreme physical performances represent a unique model for investigating the combined effects of oxidative stress and eccentric muscle contraction on systemic skeletal muscle injury outcomes. Studying changes in whole transcriptome RNA expression may allow identification of specific potential treatment targets for a variety of disease states associated with chronic inflammation and oxidative stress. The purpose of this study was to investigate changes in whole transcriptome RNA expression in response to prolonged endurance running. The protocols were approved by the University IRB committee (in accordance with the latest Declaration of Helsinki) and subjects gave written informed consent to participate. Blood samples were collected in PAXgene RNA tubes at baseline, 4-h, and 24-h after performing a half-marathon race. After collection, tubes were frozen and total RNA was extracted and verified using accepted methods. RNAseq analysis was conducted using an Illumina NextSeq 500 sequencing platform. RNAseq analysis revealed distinct changes in injury, inflammation, anti-oxidant defense, and stress-associated RNA expression with responses appearing more pronounced at 24-h compared to 4-h post-race. These results confirm the systemic changes that occur following whole-body exercise-induced muscle injury in a human model. The next step in this research is to test potential therapeutic strategies to determine their effectiveness on altering the changes in the transcriptome.
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Zhang, Haijin, Xue Song, Zongyan Teng, et al. "Key circular RNAs identified in male osteoporosis patients by whole transcriptome sequencing." PeerJ 9 (May 26, 2021): e11420. http://dx.doi.org/10.7717/peerj.11420.
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Background Osteoporosis (OP) is a systemic disease with bone loss and microstructural deterioration. Numerous noncoding RNAs (ncRNAs) have been proved to participate in various diseases, especially circular RNAs (circRNAs). However, the expression profile and mechanisms underlying circRNAs in male osteoporosis have not yet been explored. Methods The whole transcriptome expression profile and differences in mRNAs, circRNAs, and microRNAs (miRNAs) were investigated in peripheral blood samples of patients with osteoporosis and healthy controls consisting of males ≥ 60-years-old. Results A total of 398 circRNAs, 51 miRNAs, and 642 mRNAs were significantly and differentially expressed in osteoporosis compared to healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the host genes of significantly differentially expressed circRNAs were mainly enriched in the regulation of cell cycle process: biological process (BP), organelle part cellular components (CC), protein binding molecular function (MF), Toll-like receptor signaling pathway, tumor necrosis factor (TNF) signaling pathway, and thyroid hormone signaling pathway. circRNA-miRNA-mRNA regulatory network was constructed using the differentially expressed RNAs. Moreover, key circRNAs (hsa_circ_0042409) in osteoporosis were discovered and validated by qPCR. Conclusions The key cicrRNAs plays a major role in the pathogenesis of osteoporosis and could be used as potential biomarkers or targets in the diagnosis and treatment of osteoporosis.
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Li, J. "P358: COMPREHENSIVE DIAGNOSTICS OF ACUTE LYMPHOBLASTIC LEUKEMIA BY WHOLE TRANSCRIPTOME RNA SEQUENCING." HemaSphere 6 (June 2022): 258–59. http://dx.doi.org/10.1097/01.hs9.0000844320.70283.26.
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Park, Ha Young, Seung-Bok Lee, Hae-Yong Yoo, et al. "Whole-exome and transcriptome sequencing of refractory diffuse large B-cell lymphoma." Oncotarget 7, no. 52 (2016): 86433–45. http://dx.doi.org/10.18632/oncotarget.13239.
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Xu, Yanjie, Shan Gao, Yingjie Yang, et al. "Transcriptome sequencing and whole genome expression profiling of chrysanthemum under dehydration stress." BMC Genomics 14, no. 1 (2013): 662. http://dx.doi.org/10.1186/1471-2164-14-662.
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Huis in 't Veld, Robert Antonius Gerhardus, Antonius Marcellinus Willemsen, Antonius Hubertus Cornelis van Kampen та ін. "Deep Sequencing Whole Transcriptome Exploration of the σE Regulon in Neisseria meningitidis". PLoS ONE 6, № 12 (2011): e29002. http://dx.doi.org/10.1371/journal.pone.0029002.
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Griffioen, M., W. Arindrarto, D. M. Borràs, et al. "PF262 COMPREHENSIVE DIAGNOSTICS OF ACUTE MYELOID LEUKEMIA BY WHOLE TRANSCRIPTOME RNA SEQUENCING." HemaSphere 3, S1 (2019): 83. http://dx.doi.org/10.1097/01.hs9.0000559260.80814.15.
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Sun, Jie, Jing Wang, Na Zhang, Renjun Yang, Keyang Chen, and Derun Kong. "Whole transcriptome analysis of chemically induced hepatocellular carcinoma using RNA ‐sequencing analysis." FEBS Open Bio 9, no. 11 (2019): 1900–1908. http://dx.doi.org/10.1002/2211-5463.12724.
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Reimann, Ene, Sulev Kõks, Xuan Ho, Katre Maasalu, and Aare Märtson. "Whole exome sequencing of a single osteosarcoma case¿integrative analysis with whole transcriptome RNA-seq data." Human Genomics 8, no. 1 (2014): 20. http://dx.doi.org/10.1186/preaccept-1873296159134645.
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Chee, T. M., H. Oey, K. Fong, I. Yang, L. Krause, and R. Bowman. "MA23.09 Fusion Genes Identified from Whole Genome and Whole Transcriptome Sequencing of Malignant Pleural Mesothelioma Tumours." Journal of Thoracic Oncology 14, no. 10 (2019): S344—S345. http://dx.doi.org/10.1016/j.jtho.2019.08.697.
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Tran, Nguyen H., Pankaj Vats, Dan R. Robinson, et al. "Integrative whole exome, transcriptome, and clinical profiling of biliary tract cancers (BTCs)." Journal of Clinical Oncology 36, no. 4_suppl (2018): 279. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.279.
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279 Background: BTC is clinically and genomically heterogeneous and next generation sequencing may identify disease subsets with distinct prognostic and therapeutic implications. Methods: Patients (pts) with BTC underwent whole exome and transcriptome sequencing via the Michigan Oncology Sequencing (MI-ONCOSEQ) platform between 09/2011 and 07/2017. Results: 53 pts (47.2% female) with median age 60 (range 17-72) years had 38 intrahepatic, 6 perihilar, and 4 extrahepatic distal cholangiocarcinoma (CCA) while 3 had gallbladder and 2 mixed CCA/hepatocellular carcinoma. Forty-one pts (77.3%) had advanced BTC at diagnosis and 40 (75.5%) received platinum doublet as first line therapy. The most frequent somatic mutations were TP53 (35.8%), BAP1 (18.9%), KRAS (17.0%), IDH1 (15.1%), PBRM1 (13.2%), ARID1A (11.3%), and SMAD4 (11.3%). Median overall survival (OS) in 8 pts with IDH1 mutation was 16.8 months (none received IDH1 inhibitor). Putative pathogenic germline variants were noted in 6 (11.3%) pts of which 4 were biallelic (MSH2, BRCA1, BRCA2 and MUTYH) and 2 monoallelic (ATM and FH). Germline mutation in FH has not been reported in BTC. Biallelic DNA damage repair pathway mutations were noted in 14 (26.4%; 11 somatic, 3 germline) pts and their median OS was 16.8 months. Of these 9 pts with advanced BTC received 1st line platinum therapy and had a median PFS of 9.3 months (4 PR, 3 SD). Nine (17%) pts had FGFR2 fusion (6 partners); median OS not yet reached (6 alive; 5 received FGFR inhibitor). Potentially targetable molecular alterations (IDH1, MSH2, BRCA1/2, PALB2, ATM, FGFR2, ERBB2) were identified in 27 (50.9%) pts. Copy number profile shows frequent loss of chr1p, chr3p, chr4, chr6q, chr8p, chr9, and gain of chr1q, chr7, and chr8q. Immune profiling & pathway analysis of BTCs with clinical correlation is ongoing. Conclusions: Integrative sequencing of BTCs with clinical profiling will lead to more precise treatment of pts with BTCs.
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Orr, Brian, Robert P. Edwards, and Mackenzy Radolec. "Abstract 5789: Multi-omic artificial intelligence outcome modeling of ovarian cancer, phase I: Whole exome and whole transcriptome data." Cancer Research 82, no. 12_Supplement (2022): 5789. http://dx.doi.org/10.1158/1538-7445.am2022-5789.
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Abstract Background: Over a decade ago, the Cancer Genome Atlas (TCGA) provided the initial genomic characterization of ovarian cancer with targeted exome capture and sequencing. Unlike other TCGA analysis, they were unable to identify prognostic mutational profiles outside of BRCA status. There has been limited characterization of the ovarian cancer genomic profile since. Our objective presented here was to perform whole exome and whole transcriptome analysis of 241 ovarian cancer samples and compare to the TCGA dataset. This is the first phase of a muti-step ongoing analysis wherein we will input and correlate the complete genomic profile, complete patient outcome data, and immunohistochemical staining into a multi-omic artificial intelligence (AI) machine learning model with the aim of improved individualized outcome prediction. Methods: Tumor samples from 2000-2016 were obtained at time of surgery under approved University of Pittsburgh approved IRB consent. Whole exome and whole transcriptome sequencing were performed in collaboration with Helomics Corporation. Sequencing analysis was compared to TCGA data. Results: 241 patient samples underwent sequencing analysis. Similar to TCGA, most mutations were missense. I am having a difficulty interpreting the magee vs tcga comparison graphs, how to discuss the TP53 and BRCA comparison without using the lollipop charts and highlighting the rna-seq data into written form. Please add. Conclusions: We present our findings of one of the largest molecular characterizations of ovarian cancer. Similar to the TCGA, there is noted heterogeneity to the genomic profile of ovarian cancer. Multi-omic AI outcome modeling has the potential to overcome the gap defining prognostic sub-groups so that we can tailor therapies to the individual Citation Format: Brian Orr, Robert P. Edwards, Mackenzy Radolec. Multi-omic artificial intelligence outcome modeling of ovarian cancer, phase I: Whole exome and whole transcriptome data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5789.
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Gylfe, Alexandra E., Eve Shinbrot, Boyko Kakaradov, et al. "Tumor profiling from whole-genome and whole transcriptome sequencing to uncover gene fusions and structural variations in clinically relevant cancer genes." Journal of Clinical Oncology 35, no. 15_suppl (2017): e23118-e23118. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e23118.
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e23118 Background: Current targeted cancer therapies rely on the identification of clinically relevant somatic alterations in the tumor. Hotspot gene-panels and exome sequencing are designed to quickly assess somatic variations in frequently mutated regions and/or the coding regions of relevant genes, but they have limited ability to detect complex genomic rearrangements or novel structural variations. Here, we describe an integrative and comprehensive approach to fully characterize the genomic complexity of solid tumors using high throughput whole genome sequencing (WGS) and whole transcriptome sequencing (RNA Seq). Methods: We performed WGS and high-depth sequencing of known cancer genes in 14 paired tumor-normal samples of a variety of tumor types. Tumor-specific somatic alteration assessments included protein-coding mutations, copy number variations, gene fusions and structural variants. In addition, RNA Seq data was analyzed to identify expressed somatic alterations. Results: We identified 2 novel fusion genes as well as important structural alterations which could have clinical and therapeutic implications. We described a novel BRAF fusion gene in a cholangiocarcinoma devoid of other known driver mutations. BRAF fusions have not been described previously in cholangiocarcinoma; this fusion may represent an alternative mechanism for MAPK activation and could be a useful drug target. We also identified a novel NTRK3 fusion partner in a glioblastoma tumor. This fusion may imply a novel mechanism for NTRK3 activation. Finally, we identified numerous tandem duplications in an ovarian cancer. Recent advances describe tandem duplication hotspots in ovarian cancer as a potential driver mechanism characterizing a specific mutational signature. Conclusions: Comprehensive genomics assessment of paired tumor-normal samples through whole-genome and transcriptome sequencing can yield additional clinically actionable genomic characteristics that may not be detected in whole-exome or hotspot gene-panel sequencing. These findings have the potential to aid in clinical decision making.
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Shin, Myung Geun, Jun Hyung Lee, Hyun Jung Choi, et al. "RNA Sequencing Based Whole Transcriptome Analysis Detected Precisely All Fusion Transcripts in Leukemias." Blood 132, Supplement 1 (2018): 1496. http://dx.doi.org/10.1182/blood-2018-99-115785.
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Abstract Introduction: Fusion transcript is a chimeric RNA encoded by a fusion gene or by two different genes by subsequent trans-splicing. Detection of fusion transcripts is an integral part of routine diagnostics of hematological malignancies. However, most of previous analytical methods couldn't detect all fusion transcripts in leukemia. In this study, we developed accurate fusion transcript detection methood using whole transcriptome sequencing, fusion gene detection software and expression analysis. Methods: RNA sequencing (RNA-seq) for whole transcriptome was performed in 11 patients with hematological malignancies (4 AML, 2 APL, 2 ALL, and 3 CML) having fusion transcripts detected by multiplex RT-PCR (HemaVision, DNA Diagnostic, Risskov, Denmark). Library were prepared with 1 ug of total RNA for each sample by TruSeq mRNA Sample Prep kit (Illumina, San Diego, USA). The libraries were quantified using qPCR according to the qPCR Quantification Protocol Guide (KAPA Library Quantificatoin kits for Illumina Sequecing platforms) and qualified using the TapeStation D1000 ScreenTape (Agilent Technologies, Santa Clara, USA). Indexed libraries were then sequenced using the HiSeq2500 platform (Illumina). The data obtained from the sequencing was analyzed using STAR-Fusion (v1.2.0). Novel fusion transcripts were confirmed by conventional sequencing. Results: Using STAR-Fusion, average number of fusion candidates per sample was 949.8 (range, 286-1752). To exclude false positive results and obtain true positive results, we developed the following filtering algorithm. First filtering criterion is to have more than 5 junction reads, the second is to detect more than one number of spanning reads, and the third criterion is to be in-frame fusion, which type of fusion can actually synthesize intact protein. Fusion candidates remaining after applying the above three filtering criteria were 1-3 per sample. All known fusion transcripts (PML--RARA, RUNX--RUNX1T1, CBFB--MYH11, KMT2A--MLLT3, BCR--ABL1, DEK--NUP214, ETV6--RUNX1) by multiplex RT-PCR were also detected in RNA-seq. In addition, 10 novel fusion transcripts (IGKV4-1--IGKC, IGLV1-47--IGLC2, HBA2--HBB, DEFA3--MBNL1, HBB--HBA2, MPO--HBA2, HBS1L--AHI1, HBB--HBA2, IGKV4-1--IGKC, SS18L1--ADRM1) were detected and among them, 6 fusions were confirmed by conventional sequencing. Conclusions: Whole transcriptome sequencing and optimized filtering algorithms successfully detected all known fusion transcripts and various novel fusions. Disclosures No relevant conflicts of interest to declare.
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Seki, Masafumi, Kenichi Yoshida, Shiraishi Yuichi, et al. "Whole Exome and Transcriptome Analyses in Pediatric T-Cell Acute Lymphoblastic Leukemia." Blood 124, no. 21 (2014): 3527. http://dx.doi.org/10.1182/blood.v124.21.3527.3527.
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Abstract T-cell acute lymphoblastic leukemia (T-ALL) accounts for 10% to 15% of newly diagnosed cases of childhood acute lymphoblastic leukemia (ALL). Recent genome-wide approach revealed frequent NOTCH1 and FBXW7 oncogenic mutations in T-ALL. In addition, previous whole-exome sequencing disclosed novel CNOT3 mutations in approximately 10% of adult T-ALL cases, and thus, CNOT3 is thought to be one of the novel tumor suppressor gene for adult T-ALL. However, somatic mutations in these genes have been found in a fraction of childhood T-ALL, suggesting that the existence of other genetic pathogenesis. Although chromosomal translocations are the most frequent genetic abnormalities detected in other types of leukemia, recurrent translocations except for SIL-TAL1 rearrangement have been poorly defined in T-ALL. To discover driver mutations or fusion genes which involved in the pathogenesis of pediatric T-ALL and to identify novel prognostic markers of childhood T-ALL, we performed whole-exome sequencing (WES) and transcriptome sequencing (WTS) in 25 cases with T-ALL. Diagnostic total DNA from 25 cases and RNA from 15 cases were analyzed for both WES and WTS, and 8 relapsed samples were also analyzed for WES. Median age at diagnosis was 9 years old (1–15), and male to female ratio was 20 to 5. Libraries for WES and WTS were generated using the SureSelect (Agilent) or TruSeq RNA Sample Preparation kit (Illumina), respectively. High throughput sequencing was performed using the Illumina HiSeq 2000 platform. To detect somatic mutations or fusion transcripts, we used our pipeline “Genomon-exome” and “Genomon-fusion” algorithm. Subsequently, somatic mutations were validated using deep amplicon sequencing. Candidate fusion transcripts were validated by reverse - transcription polymerase-chain-reaction (RT-PCR) and Sanger sequencing. Most frequent mutation was NOTCH1, which was detected in 52% (13/25) by WES. FBXW7 mutations were also frequently found in 28% (7/25), and 43 % (3/7) were compound heterozygous mutations. In those 6 cases, only one case with FBWX7 mutation had a NOTCH1 mutation. CNOT3 mutations were reported to be frequent in adult T-ALL; however we found only 2 cases with CNOT3 mutations (8.0%). In addition, PHF6 mutation, which is known as X-linked tumor suppressor gene in T-ALL, was recurrently detected in 4 cases (16%). Other recurrent mutations were shared between 2 cases, respectively. We identified previously known fusion genes, such as MLL-ENL and FGFROP1-FGFR1 in 2 cases. MLL-ENL is one of the frequent translocation for infant multilineage leukemia (MLL), but also reported in non-infant B cell precursor ALL or T-ALL. FGFR1OP is ubiquitously expressed, and the predicted chimeric FGFR1OP-FGFR1 protein contains the catalytic domain of FGFR1. It is thought to be promote hematopoietic stem cell proliferation and leukemogenesis through a constitutive phosphorylation and activation of the downstream pathway of FGFR1. In conclusion, although NOTCH1 and FBXW7 mutations were relatively frequently detected in our series, we could not detect frequent additional mutations in this study. Consistent with other reports, frequent translocations were not observed in T-ALL, suggesting the genetic differences between T-ALL and other hematological malignancies. Further studies will be necessary to unravel oncogenic mechanisms that implicated in new therapeutic strategy for pediatric T-ALL. Disclosures No relevant conflicts of interest to declare.
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Koh, Youngil, Inho Park, Chung-Hyun Sun, et al. "Detection of a Distinctive Genomic Signature in Rhabdoid Glioblastoma, A Rare Disease Entity Identified by Whole Exome Sequencing and Whole Transcriptome Sequencing." Translational Oncology 8, no. 4 (2015): 279–87. http://dx.doi.org/10.1016/j.tranon.2015.05.003.
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Chooback, Negar, Cheryl Ho, Yaoqing Shen, et al. "Whole genome and transcriptome sequencing of lung cancer: Options for personalized cancer treatment." Journal of Clinical Oncology 35, no. 15_suppl (2017): e20567-e20567. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e20567.
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e20567 Background: Targeted therapy against driver mutations has revolutionized lung cancer management. The Personalized OncoGenomics (POG) program uses whole genome and transcriptome derived information to build pathways and identify potential therapeutic targets. We examined the lung adenocarcinoma (LUAD) patients enrolled in POG in order to identify novel cancer drivers and correlate the findings with clinical characteristics. Methods: Patients with advanced LUAD and survival > 6 months were eligible. Blood, archival and fresh tumour specimens were subjected to comprehensive DNA and RNA sequencing. SNV data were compared to the TCGA-LUAD cohort using the cBioPortal platform. Whole tumor transcriptome data were compared to matched normal blood specimens. Clinical characteristics were collected by chart review. Results: 30 POG LUAD cases were analyzed. Baseline characteristics; 47% female, median age 60, 57% never/light smokers, biopsy site - 50% lung, 50% metastatic lesion. High mutations rates in TP53, KRAS, NF1 were comparable to the TCGA-LUAD cohort. Four genes ( GOLGA6L2, FAM186A, ARMCX4 and RBMXL3), were mutated 17-27% of the time in POG patients, while the rate in TCGA-LUAD was < 1%. Driver mutations ( KRAS and EGFR) and known fusions (ROS1 and RET) were present in 63%.Other potential drivers included ERBB3, ERBB2, SDC:NRG1 fusion were identified. Copy number alterations and expression data revealed variations in cell cycle, mTOR, androgen receptor,HSP90, MET and Wee1 proteins, all potential targets for therapy. PD-L1 over-expression and a strong smoking signature were not mutually exclusive to EGFR copy gain and FGFR3 overexpression. Conclusions: The molecular signature of NSCLC is complex and involves multiple key oncogenic drivers. Whole genome sequencing and transcriptome data should be used together to map out the pathways of carcinogenesis and reliably identify targets for therapy.
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Hu, Jiaqing, Dandan Yang, Wei Chen, et al. "Whole Blood Transcriptome Sequencing Reveals Gene Expression Differences between Dapulian and Landrace Piglets." BioMed Research International 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7907980.
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There is little genomic information regarding gene expression differences at the whole blood transcriptome level of different pig breeds at the neonatal stage. To solve this, we characterized differentially expressed genes (DEGs) in the whole blood of Dapulian (DPL) and Landrace piglets using RNA-seq (RNA-sequencing) technology. In this study, 83 DEGs were identified between the two breeds. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses identified immune response and metabolism as the most commonly enriched terms and pathways in the DEGs. Genes related to immunity and lipid metabolism were more highly expressed in the DPL piglets, while genes related to body growth were more highly expressed in the Landrace piglets. Additionally, the DPL piglets had twofold more single nucleotide polymorphisms (SNPs) and alternative splicing (AS) than the Landrace piglets. These results expand our knowledge of the genes transcribed in the piglet whole blood of two breeds and provide a basis for future research of the molecular mechanisms underlying the piglet differences.
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Kutikhin, Anton, Maxim Sinitsky, Arseniy Yuzhalin, and Elena Velikanova. "Whole-Transcriptome Sequencing: A Powerful Tool for Vascular Tissue Engineering and Endothelial Mechanobiology." High-Throughput 7, no. 1 (2018): 5. http://dx.doi.org/10.3390/ht7010005.
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Hilmi, Marc, Lucile Armenoult, Mira Ayadi, and Rémy Nicolle. "Whole-Transcriptome Profiling on Small FFPE Samples: Which Sequencing Kit Should Be Used?" Current Issues in Molecular Biology 44, no. 5 (2022): 2186–93. http://dx.doi.org/10.3390/cimb44050148.
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RNA sequencing (RNA-Seq) appears as a great tool with huge clinical potential, particularly in oncology. However, sufficient sample size is often a limiting factor and the vast majority of samples from patients with cancer are formalin-fixed paraffin-embedded (FFPE). To date, several sequencing kits are proposed for FFPE samples yet no comparison on low quantities were performed. To select the most reliable, cost-effective, and relevant RNA-Seq approach, we applied five FFPE-compatible kits (based on 3′ capture, exome-capture and ribodepletion approaches) using 8 ng to 400 ng of FFPE-derived RNA and compared them to Nanostring on FFPE samples and to a reference PolyA (Truseq) approach on flash-frozen samples of the same tumors. We compared gene expression correlations and reproducibility. The Smarter Pico V3 ribodepletion approach appeared systematically the most comparable to Nanostring and Truseq (p < 0.001) and was a highly reproducible technique. In comparison with exome-capture and 3′ kits, the Smarter appeared more comparable to Truseq (p < 0.001). Overall, our results suggest that the Smarter is the most robust RNA-Seq technique to study small FFPE samples and 3′ Lexogen presents an interesting quality–price ratio for samples with less limiting quantities.