scholarly journals Optimized Splitting of RNA Sequencing Data by Species

2021 ◽  
Author(s):  
Xuan Song ◽  
Hai Yun Gao ◽  
Karl Herrup ◽  
Ronald P Hart
Keyword(s):  
Rna Sequencing ◽  
Optimal Strategies ◽  
Sequencing Data ◽  
Transcript Quantification ◽  
Effective Strategies ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Reference Index ◽  
Optimal Alignments ◽  
Sequence Similarities

Gene expression studies using chimeric xenograft transplants or co-culture systems have proven to be valuable to uncover cellular dynamics and interactions during development or in disease models. However, the mRNA sequence similarities among species presents a challenge for accurate transcript quantification. To identify optimal strategies for analyzing mixed-species RNA sequencing data, we evaluate both alignment-dependent and alignment-independent methods. Alignment of reads to a pooled reference index is effective, particularly if optimal alignments are used to classify sequencing reads by species, which are re-aligned with individual genomes, generating >97% accuracy across a range of species ratios. Alignment-independent methods, such as Convolutional Neural Networks, which extract the conserved patterns of sequences from two species, classify RNA sequencing reads with over 85% accuracy. Importantly, both methods perform well with different ratios of human and mouse reads. Our evaluation identifies valuable and effective strategies to dissect species composition of RNA sequencing data from mixed populations.

Optimized splitting of mixed-species RNA sequencing data

2022 ◽  
Author(s):  
Xuan Song ◽  
Hai Yun Gao ◽  
Karl Herrup ◽  
Ronald P. Hart
Keyword(s):  
Rna Sequencing ◽  
Traditional Approach ◽  
Optimal Strategies ◽  
Error Rates ◽  
Sequencing Data ◽  
Mixed Species ◽  
Transcript Quantification ◽  
Gene Expression Studies ◽  
Reference Index ◽  
Human And Mouse

Gene expression studies using xenograft transplants or co-culture systems, usually with mixed human and mouse cells, have proven to be valuable to uncover cellular dynamics during development or in disease models. However, the mRNA sequence similarities among species presents a challenge for accurate transcript quantification. To identify optimal strategies for analyzing mixed-species RNA sequencing data, we evaluate both alignment-dependent and alignment-independent methods. Alignment of reads to a pooled reference index is effective, particularly if optimal alignments are used to classify sequencing reads by species, which are re-aligned with individual genomes, generating [Formula: see text] accuracy across a range of species ratios. Alignment-independent methods, such as convolutional neural networks, which extract the conserved patterns of sequences from two species, classify RNA sequencing reads with over 85% accuracy. Importantly, both methods perform well with different ratios of human and mouse reads. While non-alignment strategies successfully partitioned reads by species, a more traditional approach of mixed-genome alignment followed by optimized separation of reads proved to be the more successful with lower error rates.

scholarly journals A cost-effective RNA sequencing protocol for large-scale gene expression studies

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Zhonggang Hou ◽  
Peng Jiang ◽  
Scott A. Swanson ◽  
Angela L. Elwell ◽  
Bao Kim S. Nguyen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cost Effective ◽  
Expression Studies ◽  
Gene Expression Studies

scholarly journals Analysis of Small RNA Sequencing Data in Plants

2022 ◽  
pp. 497-509
Author(s):  
Vanika Garg ◽  
Rajeev K. Varshney
Keyword(s):  
Next Generation Sequencing ◽  
Rna Sequencing ◽  
Small Rna ◽  
Regulatory Mechanisms ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Meaningful Information ◽  
Expression Studies ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

AbstractOver the past decades, next-generation sequencing (NGS) has been employed extensively for investigating the regulatory mechanisms of small RNAs. Several bioinformatics tools are available for aiding biologists to extract meaningful information from enormous amounts of data generated by NGS platforms. This chapter describes a detailed methodology for analyzing small RNA sequencing data using different open source tools. We elaborate on various steps involved in analysis, from processing the raw sequencing reads to identifying miRNAs, their targets, and differential expression studies.

scholarly journals The optimal discovery procedure for significance analysis of general gene expression studies

2020 ◽  
Author(s):  
Andrew J Bass ◽  
John D Storey
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Biological Data ◽  
Supplementary Information ◽  
Testing Procedures ◽  
Significance Analysis ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Study Designs ◽  
Optimal Discovery Procedure

Abstract Motivation Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the optimal discovery procedure (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here, we extend this work to the analysis of complex study designs and RNA-sequencing studies. Results We apply our extended framework to a static RNA-sequencing study, a longitudinal study, an independent sampling time-series study,and an independent sampling dose–response study. Our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus, the extended ODP enables a favorable significance analysis of genome-wide gene expression studies. Availability and implementation The algorithm is implemented in our freely available R package called edge and can be downloaded at https://www.bioconductor.org/packages/release/bioc/html/edge.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Leveraging transcript quantification for fast computation of alternative splicing profiles

2014 ◽  
Author(s):  
Gael P Alamancos ◽  
Amadís Pagès ◽  
Juan L Trincado ◽  
Nicolás Bellora ◽  
Eduardo Eyras
Keyword(s):  
Alternative Splicing ◽  
Rna Sequencing ◽  
De Novo ◽  
Computation Time ◽  
Sequencing Data ◽  
Standard Methods ◽  
Transcript Quantification ◽  
Cellular Processes ◽  
Reconstruction Methods ◽  
Transcript Reconstruction

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available datasets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA sequencing data compared to experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large datasets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa

scholarly journals HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 120
Author(s):  
Yiyun Sun ◽  
Dandan Xu ◽  
Chundong Zhang ◽  
Yitao Wang ◽  
Lian Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Squamous Cell ◽  
Gene Pair ◽  
Gene List ◽  
Ectopic Expression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

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