Is RNA seq Big Data?
Answers from top 10 papers
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| Papers (10) | Insight |
|---|---|
558 Citations | Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. |
| Stormbow is a scalable, cost effective, and open-source based tool for large-scale RNA-Seq data analysis. | |
211 Citations | RNA-Seq clearly has a bright future in bioinformatic data collection. |
| However, raw RNA-Seq data may have quality issues, which can significantly distort analytical results and lead to erroneous conclusions. | |
46 Citations | RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. |
| Unlike previously favored analysis methods, RNA-Seq is extremely high-throughput, and is not dependent on an annotated transcriptome, laying the foundation for novel genetic discovery. | |
| QuickRNASeq advances primary RNA-seq data analyses to the next level of automation, and is mature for public release and adoption. | |
| It is well suited for high-dimensional data with small sample sizes like RNA-seq data. | |
| Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data. | |
6.4K Citations | This removes a major computational bottleneck in RNA-seq analysis. |
Related Questions
How can RNA-seq data be used to identify new targets for the treatment of diseases, including cancer?5 answersRNA-seq data can be used to identify new targets for the treatment of diseases, including cancer. The Todai OncoPanel (TOP) RNA panel is a custom target RNA-seq method that can detect fusion gene transcripts and analyze the expression profiles of genes, allowing for the identification of molecular targets in gliomas. RNA-seq can also be used for biomarker discovery, characterization of tumor heterogeneity, resistance, the tumor's microenvironment, and immunotherapy in precision oncology. Additionally, RNA-seq can provide an objective measurement to confirm critical immunohistochemistry (IHC) findings, potentially changing clinical care. Machine learning (ML) techniques applied to RNA-seq data can also provide insights into the causal mechanisms of diseases like breast cancer, by identifying deeply associated genes and analyzing protein-protein interaction networks. In bladder cancer, RNA-seq can identify differentially expressed genes and potential anticancer targets, such as Livin.
What are some of the most interesting findings from the articles that analyzed public RNA seq data?4 answersSeveral interesting findings have emerged from the articles that analyzed public RNA-seq data. One study found that alternative splicing plays a promising role in regulating RNA subcellular localization. Another study compared gene expression measurements in different RNA-seq studies and concluded that published human tissue RNA-seq expression measurements appear relatively consistent, with samples clustering by tissue rather than laboratory of origin. A third study used RNAprof, a program for the detection of differential processing events, to analyze RNA-seq data and identified intron retention events and alternative transcript structures that were confirmed by qRT-PCR. Finally, a study on deep learning models for RNA-seq data analysis found that a straightforward application of deep nets was not enough to outperform simpler models like LASSO, suggesting the need for incorporating prior biological knowledge into deep learning models.
How to analyze RNA seq with tumor sample?3 answersRNA-seq analysis with tumor samples involves several steps. First, a systematic examination of single-cell RNA-seq clustering algorithms can be performed to reflect the heterogeneity of cell populations in cancer samples. This can help cluster single-cell profiles into groups that reflect the underlying cell types. Additionally, a highly customizable workflow for single-cell data analysis can be used, which includes steps such as quality control, preprocessing, clustering, and identification of gene markers. Another approach is to use RNA-seq data alone for somatic variant detection and driver mutation analysis. This can be done using a containerized pipeline called RNA-VACAY, which automates somatic variant calling from tumor RNA-seq data. Finally, for the quantification of tumor immune infiltrates, the computational pipeline quanTIseq can be used to analyze bulk RNA-seq data from tumor samples. These approaches provide valuable insights into the biology and characteristics of tumors and can aid in cancer research and treatment.
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