scholarly journals RNA Sequencing in Schizophrenia

2015 ◽  
Vol 9s1 ◽  
pp. BBI.S28992
Author(s):  
Xin Li ◽  
Shaolei Teng
Keyword(s):  
Rna Sequencing ◽  
Global Gene Expression ◽  
The Novel ◽  
Rna Seq ◽  
Gene Expressions ◽  
Splice Isoforms ◽  
Heavy Burden ◽  
Induced Pluripotent ◽  
Genome Scale ◽  
Generation Sequencing

Schizophrenia (SCZ) is a serious psychiatric disorder that affects 1% of general population and places a heavy burden worldwide. The underlying genetic mechanism of SCZ remains unknown, but studies indicate that the disease is associated with a global gene expression disturbance across many genes. Next-generation sequencing, particularly of RNA sequencing (RNA-Seq), provides a powerful genome-scale technology to investigate the pathological processes of SCZ. RNA-Seq has been used to analyze the gene expressions and identify the novel splice isoforms and rare transcripts associated with SCZ. This paper provides an overview on the genetics of SCZ, the advantages of RNA-Seq for transcriptome analysis, the accomplishments of RNA-Seq in SCZ cohorts, and the applications of induced pluripotent stem cells and RNA-Seq in SCZ research.

Genomic landscape of adult mixed phenotype acute leukemia (MPAL).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 7023-7023 ◽  
Author(s):  
Kiyomi Morita ◽  
Feng Wang ◽  
Keyur Patel ◽  
Carlos E. Bueso-Ramos ◽  
Abdallah Abou Zahr ◽  
...  
Keyword(s):  
Acute Leukemia ◽  
Rna Sequencing ◽  
Promoter Methylation ◽  
Cell Receptor ◽  
Methylation Pattern ◽  
The Novel ◽  
Gene Expressions ◽  
Genomic Landscape ◽  
Genetic Profiles ◽  
Mixed Phenotype

7023 Background: MPAL is a rare subgroup of acute leukemia characterized by both myeloid and lymphoid phenotypes. Genetic basis of MPAL is not well understood. Methods: We studied 31 patients (pts) with MPAL (median age 53) that met 2008 WHO criteria. Bone marrow samples were studied by targeted capture sequencing of 295 genes (median 393x), RNA sequencing, and Infinium methylation EPIC array (Illumina). Mutational landscape was compared to 194 AML, 71 B-ALL, and 6 T-ALL cases. Promoter methylation pattern was compared to the data from 194 AML (TCGA), 505 B-ALL and 101 T-ALL cases (Nordlund et al. Genome Biology. 2013). Results: Eighteen (58%) pts had myeloid-T and 13 (42%) had myeloid-B phenotype. Four pts had t(9;22), 1 had 11q23 rarrangement, and 8 had complex karyotype. MPAL had similar number of mutations with AML but had higher number of mutations than B-ALL or T-ALL. Both AML-type and ALL-type mutations were detected in MPAL, supporting the mixed phenotypic features. However, NPM1, CEBPA and GATA2 mutations were specific to AML and were not found in MPAL. Myeloid-T and myeloid-B showed distinct patterns of mutations, in which DNMT3A, IDH2, NOTCH1, IL7R, and FBXW7 mutations were enriched in myeloid-T whereas RUNX1 mutations were enriched in myeloid-B. Myeloid-T and myeloid-B also showed distinct patterns of promoter methylation. Overall, myeloid-T had more hypermethylated CpG loci than myeloid-B. Genes that have essential role in T-cell receptor (TCR) pathway ( CD3D, CD7, CD247, LCK, PRKCQ, CCR9, and TCL1A) were differentially methylated and differentially expressed between myeloid-T and myeloid-B. RNA sequencing revealed several known translocations such as, NSD1-NUP98, and KMT2A-MLLT4, in addition to the novel fusion proteins such as FOXP1-DNAJC15, RUNX1-NAP1L1, and BCL2-TM9SF3. Unbiased hierarchical clustering of MPAL, AML, B-ALL and T-ALL by promoter methylation revealed that myeloid-T had consistent similarity with T-ALL, while myeloid-B showed random similarity with either B-ALL or AML. Conclusions: MPAL is genetically heterogeneous and myeloid-T and myeloid-B shows distinct patterns of mutations, methylation and gene expressions. Therapy for MPAL may need to be tailored based on the genetic profiles.

scholarly journals A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1947
Author(s):  
Samarendra Das ◽  
Anil Rai ◽  
Michael L. Merchant ◽  
Matthew C. Cave ◽  
Shesh N. Rai
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Differential Expression Analysis ◽  
Individual Performance ◽  
Rna Seq ◽  
Gene Expressions ◽  
Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

scholarly journals Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs

Life ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
Davide Vacca ◽  
Antonino Fiannaca ◽  
Fabio Tramuto ◽  
Valeria Cancila ◽  
Laura La Paglia ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Systematic Errors ◽  
N Gene ◽  
Nanopore Sequencing ◽  
Rna Seq ◽  
Bioinformatics Pipeline ◽  
Critical Material ◽  
Oxford Nanopore ◽  
Oropharyngeal Swab ◽  
Generation Sequencing

In consideration of the increasing prevalence of COVID-19 cases in several countries and the resulting demand for unbiased sequencing approaches, we performed a direct RNA sequencing (direct RNA seq.) experiment using critical oropharyngeal swab samples collected from Italian patients infected with SARS-CoV-2 from the Palermo region in Sicily. Here, we identified the sequences SARS-CoV-2 directly in RNA extracted from critical samples using the Oxford Nanopore MinION technology without prior cDNA retrotranscription. Using an appropriate bioinformatics pipeline, we could identify mutations in the nucleocapsid (N) gene, which have been reported previously in studies conducted in other countries. In conclusion, to the best of our knowledge, the technique used in this study has not been used for SARS-CoV-2 detection previously owing to the difficulties in the extraction of RNA of sufficient quantity and quality from routine oropharyngeal swabs. Despite these limitations, this approach provides the advantages of true native RNA sequencing and does not include amplification steps that could introduce systematic errors. This study can provide novel information relevant to the current strategies adopted in SARS-CoV-2 next-generation sequencing.

Profiling novel alternative splicing within multiple tissues puts insight into the porcine genome annotation

2019 ◽  
Author(s):  
Jian-Feng Liu ◽  
Wen Feng ◽  
Pengju Zhao ◽  
Xianrui Zheng
Keyword(s):  
Alternative Splicing ◽  
Rna Sequencing ◽  
Genome Annotation ◽  
Donor Site ◽  
The Novel ◽  
Rna Seq ◽  
Novel Proteins ◽  
Genome Wide ◽  
Alternative Donor ◽  
Mrna Precursor

Abstract Background Alternative splicing (AS) is a process that mRNA precursor splices intron to form the mature mRNA. AS plays important roles in contributing to transcriptome and proteome divert. However, to date there is no research about pig AS in genome-wide level by RNA sequencing. Results To characterize the AS in pigs, herein we detected genome-wide transcripts and events by RNA sequencing technology (RNA-seq) 34 different tissues in Duroc pigs. In total, we identified 138, 403 AS events and 29, 270 expressed genes. We found alternative donor site was the most common AS form, which is accounted for 44% of the total AS events. The percentage of the other 3 AS forms are all around 19%. The results showed that the most common AS events (alternative donor site) can produce different transcripts or different proteins which affect the biological process. Among these AS events, 109, 483 were novel AS events, and the number of alternative donor splice site has increased the most (Accounting for 44% of the novel AS events).Conclusions The expression of gene with tissue specific AS events showed that the functions of these genes were consistent with the tissue function. AS increased proteome diversity and resulted in novel proteins that gained and lost important functional domains. In summary, these findings extend genome annotation and highlight roles that AS acts in tissue identity in pig.Key words: Alternative splicing; transcript; protein; SNP

The Next Generation Sequencing Techniques and Application in Drug Discovery and Development

2019 ◽  
pp. 240-259
Author(s):  
Afzal Hussain
Keyword(s):  
Next Generation Sequencing ◽  
Rna Sequencing ◽  
Expression Profiling ◽  
Massively Parallel Sequencing ◽  
Life Sciences ◽  
Rna Seq ◽  
Next Generation ◽  
Parallel Sequencing ◽  
Short Period ◽  
Generation Sequencing

Next-generation sequencing or massively parallel sequencing describe DNA sequencing, RNA sequencing, or methylation sequencing, which shows its great impact on the life sciences. The recent advances of these parallel sequencing for the generation of huge amounts of data in a very short period of time as well as reducing the computing cost for the same. It plays a major role in the gene expression profiling, chromosome counting, finding out the epigenetic changes, and enabling the future of personalized medicine. Here the authors describe the NGS technologies and its application as well as applying different tools such as TopHat, Bowtie, Cufflinks, Cuffmerge, Cuffdiff for analyzing the high throughput RNA sequencing (RNA-Seq) data.

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 Single-Cell Heterogeneity of Cutaneous T-Cell Lymphomas Revealed Using RNA-Seq Technologies

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2129
Author(s):  
Karolina Rassek ◽  
Katarzyna Iżykowska
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Patient Specific ◽  
Molecular Heterogeneity ◽  
The Novel ◽  
Rna Seq ◽  
Diagnosis And Prognosis ◽  
T Cell Lymphomas ◽  
Novel Biomarkers

Cutaneous T-cell lymphomas (CTCLs) represent a large, heterogeneous group of non-Hodgkin lymphomas that primarily affect the skin. Among multiple CTCL variants, the most prevalent types are mycosis fungoides (MF) and Sézary syndrome (SS). In the past decade, the molecular genetics of CTCL have been the target of intense study, increasing the knowledge of CTCL genomic alterations, discovering novel biomarkers, and potential targets for patient-specific therapy. However, the detailed pathogenesis of CTCL development still needs to be discovered. This review aims to summarize the novel insights into molecular heterogeneity of malignant cells using high-throughput technologies, such as RNA sequencing and single-cell RNA sequencing, which might be useful to identify tumour-specific molecular signatures and, therefore, offer guidance for therapy, diagnosis, and prognosis of CTCL.

scholarly journals Identification of aberrant innate and adaptive immunity based on changes in global gene expression in the blood of adults with autism spectrum disorder

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fumie Horiuchi ◽  
Yuta Yoshino ◽  
Hiroshi Kumon ◽  
Rie Hosokawa ◽  
Kiwamu Nakachi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Adaptive Immunity ◽  
Global Gene Expression ◽  
Autism Spectrum ◽  
Rna Seq ◽  
Replication Cohort ◽  
Gene Expressions ◽  
Innate And Adaptive Immunity ◽  
Adults With Asd

Abstract Background Autism spectrum disorder (ASD) is characterized as a neurodevelopmental disorder, and one of the main hypotheses regarding its cause is genetic factors. A previous meta-analysis of seven microarray studies and one RNA sequencing (RNA-seq) study using the blood of children with ASD identified dysregulation of gene expressions relevant to the immune system. In this study, we explored changes in global gene expression as the phenotype of ASD in the blood of adults with ASD. Methods We recruited an RNA-seq cohort (ASD vs. control; n = 6 each) and a replication cohort (ASD vs. control; n = 19 each) and conducted RNA-seq to explore changes in global gene expression. We then subjected the significantly up- and downregulated genes to gene ontology (GO) and core analyses. Weighted gene correlation network analysis (WGCNA) was performed with all 11,617 genes detected in RNA-seq to identify the ASD-specific gene network. Results In total, 117 significantly up- and 83 significantly downregulated genes were detected in the ASD compared with the control group, respectively (p < 0.05 and q < 0.05). GO analysis revealed that the aberrant innate and adaptive immunity were more obvious in the 117 upregulated than in the 83 downregulated genes. WGCNA with core analysis revealed that one module including many immune-related genes was associated with the natural killer cell signaling pathway. In the results for the replication cohort, significant changes with same trend found in RNA-seq data were confirmed for MAFB (p = 0.046), RPSAP58 (p = 0.030), and G2MK (p = 0.004). Limitations The sample size was relatively small in both the RNA-seq and replication cohorts. This study examined the mRNA expression level, so the interaction between mRNA and protein remains unclear. The expression changes between children and adults with ASD were not compared because only adults with ASD were targeted. Conclusions The dysregulated gene expressions confirmed in the blood of adults with ASD were relevant to the dysfunction of innate and adaptive immunity. These findings may aid in understanding the pathogenesis of ASD.

scholarly journals Using Mixtures of Biological Samples as Process Controls for RNA-sequencing experiments

2015 ◽  
Author(s):  
Jerod Parsons ◽  
Sarah Munro ◽  
P. Scott Pine ◽  
Jennifer McDaniel ◽  
Michele Mehaffey ◽  
...  
Keyword(s):  
Linear Model ◽  
Rna Sequencing ◽  
Messenger Rna ◽  
Complex Mixture ◽  
Rna Seq ◽  
Total Rna ◽  
Performance Benchmarking ◽  
Mrna Content ◽  
Genome Scale ◽  
Process Controls

Genome-scale ?-omics? measurements are challenging to benchmark due to the enormous variety of unique biological molecules involved. Mixtures of previously-characterized samples can be used to benchmark repeatability and reproducibility using component proportions as truth for the measurement. We describe and evaluate experiments characterizing the performance of RNA-sequencing (RNA-Seq) measurements, and discuss cases where mixtures can serve as effective process controls. We apply a linear model to total RNA mixture samples in RNA-seq experiments. This model provides a context for performance benchmarking. The parameters of the model fit to experimental results can be evaluated to assess bias and variability of the measurement of a mixture. A linear model describes the behavior of mixture expression measures and provides a context for performance benchmarking. Residuals from fitting the model to experimental data can be used as a metric for evaluating the effect that an individual step in an experimental process has on the linear response function and precision of the underlying measurement while identifying signals affected by interference from other sources. Effective benchmarking requires well-defined mixtures, which for RNA-Seq requires knowledge of the messenger RNA (mRNA) content of the individual total RNA components. We demonstrate and evaluate an experimental method suitable for use in genome-scale process control and lay out a method utilizing spike-in controls to determine mRNA content of total RNA in samples. Genome-scale process controls can be derived from mixtures. These controls relate prior knowledge of individual components to a complex mixture, allowing assessment of measurement performance. The mRNA fraction accounts for differential enrichment of mRNA from varying total RNA samples. Spike-in controls can be utilized to measure this relationship between mRNA content and input total RNA. Our mixture analysis method also enables estimation of the proportions of an unknown mixture, even when component-specific markers are not previously known, whenever pure components are measured alongside the mixture.

scholarly journals Prime-seq, efficient and powerful bulk RNA-sequencing

2021 ◽  
Author(s):  
Aleksandar Janjic ◽  
Lucas Esteban Wange ◽  
Johannes Walter Bagnoli ◽  
Johanna Geuder ◽  
Phong Nguyen ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Statistical Power ◽  
Gene Expression Analysis ◽  
Measurement Accuracy ◽  
Rna Isolation ◽  
Rna Seq ◽  
Specialized Equipment ◽  
Library Complexity ◽  
Cost Efficient ◽  
Generation Sequencing

With the advent of Next Generation Sequencing, RNA-sequencing (RNA-seq) has become the major method for quantitative gene expression analysis. Reducing library costs by early barcoding has propelled single-cell RNA-seq, but has not yet caught on for bulk RNA-seq. Here, we optimized and validated a bulk RNA-seq method we call prime-seq. We show that with respect to library complexity, measurement accuracy, and statistical power it performs equivalent to TruSeq, a standard bulk RNA-seq method, but is four-fold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step that further improves cost and time-efficiency, show that intronic reads are derived from RNA, validate that prime-seq performs optimal with only 1,000 cells as input, and calculate that prime-seq is the most cost-efficient bulk RNA-seq method currently available. We discuss why many labs would profit from a cost-efficient early barcoding RNA-seq protocol and argue that prime-seq is well suited for setting up such a protocol as it is well validated, well documented, and requires no specialized equipment.

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