scholarly journals Evaluation of transcript assembly in multiple porcine tissues suggests optimal sequencing depth for RNA-Seq using total RNA library

Animal Gene ◽  
2020 ◽  
Vol 17-18 ◽  
pp. 200105
Author(s):  
Brittney N. Keel ◽  
William T. Oliver ◽  
John W. Keele ◽  
Amanda K. Lindholm-Perry
Keyword(s):  
Sequencing Depth ◽  
Rna Seq ◽  
Total Rna ◽  
Optimal Sequencing ◽  
Transcript Assembly ◽  
Porcine Tissues

Application of RtcB ligase to monitor self-cleaving ribozyme activity by RNA-seq

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
V. Janett Olzog ◽  
Lena I. Freist ◽  
Robin Goldmann ◽  
Jörg Fallmann ◽  
Christina E. Weinberg
Keyword(s):  
Rna Seq ◽  
Site Specific ◽  
Pyrococcus Horikoshii ◽  
Total Rna ◽  
E Coli ◽  
Catalytic Rnas ◽  
Adapter Ligation ◽  
Domains Of Life ◽  
Cyclic Phosphate ◽  
A Site

Abstract Self-cleaving ribozymes are catalytic RNAs and can be found in all domains of life. They catalyze a site-specific cleavage that results in a 5′ fragment with a 2′,3′ cyclic phosphate (2′,3′ cP) and a 3′ fragment with a 5′ hydroxyl (5′ OH) end. Recently, several strategies to enrich self-cleaving ribozymes by targeted biochemical methods have been introduced by us and others. Here, we develop an alternative strategy in which 5ʹ OH RNAs are specifically ligated by RtcB ligase, which first guanylates the 3′ phosphate of the adapter and then ligates it directly to RNAs with 5′ OH ends. Our results demonstrate that adapter ligation to highly structured ribozyme fragments is much more efficient using the thermostable RtcB ligase from Pyrococcus horikoshii than the broadly applied Escherichia coli enzyme. Moreover, we investigated DNA, RNA and modified RNA adapters for their suitability in RtcB ligation reactions. We used the optimized RtcB-mediated ligation to produce RNA-seq libraries and captured a spiked 3ʹ twister ribozyme fragment from E. coli total RNA. This RNA-seq-based method is applicable to detect ribozyme fragments as well as other cellular RNAs with 5ʹ OH termini from total RNA.

scholarly journals Optimized IGH Chain Sequencing for Detection of Very Low Frequency B Cell Malignancies from RNA or DNA Input

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5777-5777
Author(s):  
Timothy Looney ◽  
Michelle Toro ◽  
Geoffrey Lowman ◽  
Jayde Chang ◽  
Loni Pickle ◽  
...  
Keyword(s):  
Bone Marrow ◽  
B Cell ◽  
High Throughput Sequencing ◽  
Limit Of Detection ◽  
Lymphocytic Leukemia ◽  
Sequencing Depth ◽  
Sequencing Analysis ◽  
Rarefaction Analysis ◽  
Total Rna ◽  
Fisher Scientific

Background High throughput sequencing (HTS) of rearranged TCRB and IGH chains has been demonstrated as a means to detect malignant T and B cells at a frequency as low as 10E-6. However, onerous input requirements (typically 20-30ug gDNA input over multiple library preparations) have impeded widespread adoption of 10E-6 as a threshold for minimal residual disease (MRD) translational research studies. Here we demonstrate an optimized highly multiplex PCR approach for amplifying IGH chains from fresh or FFPE-preserved RNA or DNA input material. Coupled with automated clonotyping and clonal lineage detection, we demonstrate detection of malignant B cell clones at a frequency of 10E-6 from a single library preparation. Methods Rearranged IGH chains were amplified using multiplex framework 3 and joining gene primers targeting all human IGH variable and joining gene alleles in the IMGT database (Oncomine IGH-SR assay). Libraries were generated from 25 or 100ng total RNA or 2ug gDNA derived from (1) peripheral blood leukocyte (PBL) or bone marrow (BM) spiked with Ramos B-cell cell line and (2) PBL or BM spiked with synthesized chronic lymphocytic leukemia (CLL) rearrangements from literature. Sequencing analysis was performed using the Gene Studio S5 and Ion Reporter to identify clonotypes, track clones across samples, and identify B cell clonal lineages. Clonal lineages were defined such that lineage members have a shared variable and joining gene identity, identical CDR3 lengths, and CDR3NT sequences within 85% similarity of other lineage members. Automated rarefaction analysis in Ion Reporter was used to determine optimal sequencing depth. Results Ramos and synthesized spike in controls were detected at a frequency of 10E-5 using 25ng of PBL total RNA and sequencing to 3M reads depth, and 10E-6 using 100ng input and 10M reads depth. gDNA-based libraries required 2ug and 3M reads depth to detect spike-in rearrangements at a frequency of 10E-5, while 10E-6 was achieved by combining the results from four 2ug gDNA libraries, each sequenced to 3M reads depth. Input and sequencing depth requirements were consistent across PBL or bone marrow derived libraries. Rarefaction analysis confirmed that the sequencing depth was appropriate for the targeted limit of detection. Conclusions These results demonstrate routine detection of B cell malignancy IGH chains at a frequency of 10E-6 using a limited amount of RNA or DNA material, comparing favorably to existing HTS-based approaches. We anticipate this approach to become a routine component of rare clone tracking applications including those involving B-ALL and CLL, particularly where sample material is limited or a low limit of detection is of paramount importance. Disclosures Looney: Thermo Fisher Scientific: Employment. Toro:Thermo Fisher Scientific: Employment. Lowman:Thermo Fisher Scientific: Employment. Chang:Thermo Fisher Scientific: Employment. Pickle:Thermo Fisher Scientific: Employment. Topacio-Hall:Thermo Fisher Scientific: Employment. Hyland:Thermo Fisher Scientific: Employment.

scholarly journals Identifying new COVID-19 receptor Neuropilin-1 in severe Alzheimer’s diseases patients group brain using genome-wide association study approach

2021 ◽  
Author(s):  
Key-Hwan Lim ◽  
Sumin Yang ◽  
Sung-Hyun Kim ◽  
Jae-Yeol Joo
Keyword(s):  
In Silico ◽  
Genome Wide Association Study ◽  
Transmembrane Protein ◽  
In Silico Analysis ◽  
Therapeutic Drug ◽  
Rna Seq ◽  
Total Rna ◽  
Neuropilin 1 ◽  
Silico Analysis ◽  
Brain Tissues

Abstract Background Numerous studies have been conducted on different aspects of the COVID-19 (coronavirus disease 2019) pandemic, which is caused by SARS-CoV-2, since its emergence in late 2019. Mutual relations among SARS-CoV-2 and neuro-pathophysiological phenomena are continuously being demonstrated, and several underlying diseases, such as those in the elderly, are positively correlated with susceptibility to SARS-CoV-2 infection. The expression of angiotensin converting enzyme 2 (ACE2), which is required for SARS-CoV-2 infection, was recently demonstrated to be increased in Alzheimer’s disease (AD) patients. Methods Recent preclinical studies have shown that Neuropilin-1 (NRP1), which is a transmembrane protein with roles in neuronal development, axonal outgrowth, and angiogenesis, also plays a role in the infectivity of SARS-CoV-2. Thus, we hypothesized that NRP1 may be upregulated in AD patients and that a correlation between AD and SARS-CoV-2 NRP1-mediated infectivity may exist. We used an AD mouse model that mimics AD and performed high throughput total RNA-seq with brain tissue and whole blood. For quantification of NPR1 in AD, brain tissues and blood were subjected to western blotting and RT-qPCR analysis. In silico analysis for NRP1 expression in AD patients has been performed on the human hippocampus data sets (GSE4226, GSE1297). Results Many cases of severe symptom of COVID-19 are concentrated in elderly group who have complications such as diabetes, degenerative disease, and brain disorders. Total RNA-seq analysis showed that Nrp1 gene was commonly overexpressed in AD model. Similar to ACE2, NRP1 protein also strongly expressed in the AD brain tissues. Interestingly, in silico analysis revealed that the level of expression for NRP1 was distinct at age and AD progression. Conclusions Given that the NRP1 is highly expressed in AD, it will be important to understand and predict that NRP1 may a risk factor for SARS-CoV-2 infection in AD patients. This will support to development of potential therapeutic drug to reduce SARS-CoV-2 transmission.

scholarly journals Determining sequencing depth in a single-cell RNA-seq experiment

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Jinye Zhang ◽  
Vasilis Ntranos ◽  
David Tse
Keyword(s):  
Single Cell ◽  
Sequencing Depth ◽  
Rna Seq

scholarly journals Hybridization-based capture of pathogen mRNA enables paired host-pathogen transcriptional analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Viktoria Betin ◽  
Cristina Penaranda ◽  
Nirmalya Bandyopadhyay ◽  
Rui Yang ◽  
Angela Abitua ◽  
...  
Keyword(s):  
Transcriptional Profiling ◽  
Transcript Abundance ◽  
Abundant Species ◽  
Single Step ◽  
Sequencing Depth ◽  
Transcriptional Analysis ◽  
Rna Seq ◽  
Low Input ◽  
Temporal Gene Expression ◽  
Host Pathogen

AbstractDual transcriptional profiling of host and bacteria during infection is challenging due to the low abundance of bacterial mRNA. We report Pathogen Hybrid Capture (PatH-Cap), a method to enrich for bacterial mRNA and deplete bacterial rRNA simultaneously from dual RNA-seq libraries using transcriptome-specific probes. By addressing both the differential RNA content of the host relative to the infecting bacterium and the overwhelming abundance of uninformative structural RNAs (rRNA, tRNA) of both species in a single step, this approach enables analysis of very low-input RNA samples. By sequencing libraries before (pre-PatH-Cap) and after (post-PatH-Cap) enrichment, we achieve dual transcriptional profiling of host and bacteria, respectively, from the same sample. Importantly, enrichment preserves relative transcript abundance and increases the number of unique bacterial transcripts per gene in post-PatH-Cap libraries compared to pre-PatH-Cap libraries at the same sequencing depth, thereby decreasing the sequencing depth required to fully capture the transcriptional profile of the infecting bacteria. We demonstrate that PatH-Cap enables the study of low-input samples including single eukaryotic cells infected by 1–3 Pseudomonas aeruginosa bacteria and paired host-pathogen temporal gene expression analysis of Mycobacterium tuberculosis infecting macrophages. PatH-Cap can be applied to the study of a range of pathogens and microbial species, and more generally, to lowly-abundant species in mixed populations.

scholarly journals SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

2019 ◽  
Vol 36 (6) ◽  
pp. 1779-1784 ◽  
Author(s):  
Chuanqi Wang ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Count Data ◽  
Deep Neural Network ◽  
Sequencing Depth ◽  
Supplementary Information ◽  
Neural Network Classifier ◽  
Rna Seq ◽  
Scale Invariant ◽  
Downstream Analysis

Abstract Motivation Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly. Results We call an analysis method ‘scale-invariant’ (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. Availability and implementation This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Limitations of alignment-free tools in total RNA-seq quantification

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Douglas C. Wu ◽  
Jun Yao ◽  
Kevin S. Ho ◽  
Alan M. Lambowitz ◽  
Claus O. Wilke
Keyword(s):  
Rna Seq ◽  
Total Rna ◽  
Alignment Free

scholarly journals On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

2020 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Dynamic Range ◽  
Substantial Reduction ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Sample Pooling ◽  
Pooling Strategies ◽  
The Cost

Abstract Background: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. Results: The data generating model in pooled experiments is defined mathematically to evaluate the the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. Conclusion: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

scholarly journals Targeted enrichment outperforms other enrichment techniques and enables more multi-species RNA-Seq analyses

2018 ◽  
Author(s):  
Matthew Chung ◽  
Laura Teigen ◽  
Hong Liu ◽  
Silvia Libro ◽  
Amol Shetty ◽  
...  
Keyword(s):  
Systematic Bias ◽  
Rna Seq ◽  
Protein Coding ◽  
Total Rna ◽  
Ratio Difference ◽  
Fold Enrichment ◽  
Agilent Sureselect ◽  
Positive Linear Correlation ◽  
Enrichment Techniques ◽  
Targeted Enrichment

AbstractEnrichment methodologies enable analysis of minor members in multi-species transcriptomic analyses. We compared standard enrichment of bacterial and eukaryotic mRNA to targeted enrichment with Agilent SureSelect (AgSS) capture for Brugia malayi, Aspergillus fumigatus, and the Wolbachia endosymbiont of B. malayi (wBm). Without introducing significant systematic bias, the AgSS quantitatively enriched samples, resulting in more reads mapping to the target organism. The AgSS-enriched libraries consistently had a positive linear correlation with its unenriched counterpart (r2=0.559-0.867). Up to a 2,242-fold enrichment of RNA from the target organism was obtained following a power law (r2=0.90), with the greatest fold enrichment achieved in samples with the largest ratio difference between the major and minor members. While using a single total library for prokaryote and eukaryote in a single sample could be beneficial for samples where RNA is limiting, we observed a decrease in reads mapping to protein coding genes and an increase of multi-mapping reads to rRNAs in AgSS enrichments from eukaryotic total RNA libraries as opposed to eukaryotic poly(A)-enriched libraries. Our results support a recommendation of using Agilent SureSelect targeted enrichment on poly(A)-enriched libraries for eukaryotic captures and total RNA libraries for prokaryotic captures to increase the robustness of multi-species transcriptomic studies.

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