scholarly journals A Framework for the RNA-Seq Based Classification and Prediction of Disease

2019 ◽  
Author(s):  
Naiyar Iqbal ◽  
Pradeep Kumar
Keyword(s):  
Microarray Gene Expression Data ◽  
Biological Data ◽  
Disease Classification ◽  
Rna Seq ◽  
Microarray Gene Expression ◽  
Early Detection Of Disease ◽  
Medical Practitioners ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

High concordance for MammaPrint 70 genes by RNA next generation sequencing.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 3065-3065
Author(s):  
Lorenza Mittempergher ◽  
Iris de Rink ◽  
Marja Nieuwland ◽  
Ron M Kerkhoven ◽  
Annuska Glas ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Rna Sequencing ◽  
Pearson Correlation ◽  
Microarray Gene Expression Data ◽  
Good Prognosis ◽  
Rna Seq ◽  
Next Generation ◽  
Illumina Hiseq ◽  
Microarray Gene Expression ◽  
Generation Sequencing

3065 Background: The development of new biomarkers often requires fresh frozen (FF) samples. Recently we showed that microarray gene expression data generated from FFPE material are comparable to data extracted from the FF counterpart, including known signatures such as the 70-gene prognosis signature (Mittempergher L et al., 2011). As described by Luo et al (2010) RNA profiling using next generation sequencing (RNA-Seq) is now applicable to archival FFPE specimens. Methods: Technical performance and the comparison between the RNA-Seq 70-gene read-out and the MammaPrint test (Glas et al., 2006) is evaluated in a series of 15 patients (11/15 with matched FFPE/FF material). RNA-Seq was carried out using minor adjustments of the Illumina TruSeq RNA preparation method. RNA sequencing libraries were prepared starting from 100ng of total RNA. Next, the DSN (Duplex-Specific Nuclease) normalization process was used to remove ribosomal RNA and other abundant transcripts (Luo et al, 2010). The libraries were paired-end sequenced on the Illumina HiSeq 2000 instrument with multiplexing of 4 libraries per lane. The resulting sequences were mapped to the human reference genome (build 37) using TopHat 1.3.1(Trapnell et al., 2009). The HTSeq-count tool was used to generate the total number of uniquely mapped reads for each gene. Results: Between 14% and 45% of the total number of reads were assigned to protein-coding genes. The minimum coverage per 1000bp of CDS was 38 reads. The 70 MammaPrint genes were successfully mapped to the RNA-Seq transcripts. We calculated the Pearson correlation coefficient between the centroids of the original good prognosis template (van’t Veer et al., 2002) and the 70-gene read count determined by RNA-Seq of each sample. Predictions based on the 70-gene RNA-Seq data showed a high agreement with the actual MammaPrint test predictions (>90%), irrespective of whether the RNA-seq was performed on FF or FFPE tissue. Conclusions: New generation RNA-sequencing is a feasible technology to assess diagnostic signatures.

scholarly journals Next-Generation Sequencing Mutational Landscape and Clinical Features of Chinese Adults with Myeloproliferative Neoplasms

2021 ◽  
Author(s):  
Shuna Luo ◽  
Zanzan Wang ◽  
Xiaofei Xu ◽  
Lan Zhang ◽  
Shengjie Wang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Comprehensive Evaluation ◽  
Myeloproliferative Neoplasms ◽  
Primary Myelofibrosis ◽  
Next Generation ◽  
Chinese Adults ◽  
Leukocyte Counts ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract Background: Myeloproliferative neoplasms (MPNs) include three classical subtypes: polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). Since prefibrotic primary myelofibrosis (pre-PMF) was recognized as a separate entity in the 2016 revised classification of MPN, it has been a subject of debate among experts due to its indefinite diagnosis. However, pre-PMF usually has a distinct outcome compared with either ET or overt PMF. In this study, we examined the clinical, haematologic, genetic, and prognostic differences among pre-PMF, ET, and overt PMF.Methods: We retrospectively reviewed the clinical parameters, haematologic information, and genetic mutations of patients who were diagnosed with pre-PMF, ET, and overt PMF according to the WHO 2016 criteria using next-generation sequencing (NGS).Results: Pre-PMF patients exhibited higher leukocyte counts, higher LDH values, a higher frequency of splenomegaly, and a higher incidence of hypertension than ET patients. On the other hand, pre-PMF patients had higher platelet counts and haemoglobin levels than overt PMF patients. Molecular analysis revealed that the frequency of EP300 mutations was significantly increased in pre-PMF patients compared with ET and overt PMF patients. In terms of outcome, male sex, along with symptoms including MPN-10, anaemia, thrombocytopenia, and KMT2A and CUX1 mutations, indicated a poor prognosis for PMF patients.Conclusion: The results of this study indicated that comprehensive evaluation of BM features, clinical phenotypes, haematologic parameters, and molecular profiles is needed for the accurate diagnosis and treatment of ET, pre-PMF, and overt PMF patients.

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