How do you read SC RNA Seq data?
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| Therefore, the choice of RNA-Seq library preparation kit can profoundly affect data outcomes. | |
| It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. | |
37 Citations | With this tool, biologists can easily perform most of the analyses on their computer for their RNA-Seq data, from the mapped data to the discovery of important loci. |
1.8K Citations | Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. |
61 Citations | It is a useful method designed for comparing two groups of RNA-seq samples. |
69 Citations | Applying the protocol described here and using the provided data sets represent a useful first step toward building RNA-Seq data analysis expertise in a research group. |
| Different library preparations can have significant impacts on downstream analysis and interpretation of RNA-seq data. | |
| We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. | |
| Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data. | |
29 Citations | This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available. |
Related Questions
How to analyse RNA sequencing data?5 answersRNA sequencing data analysis involves several steps. First, data pre-processing is performed, which includes quality control, adapter removal, trimming, and filtering. After pre-processing, the data are analyzed using various tools such as differential gene expression analysis, alternative splicing analysis, and assessment of active RNA synthesis. For differential gene expression analysis, several methods have been proposed, including edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, and Voom. To analyze alternative splicing, tools for detecting novel exons or whole transcripts, assessing gene expression, and studying alternative splicing structure are used. Single-cell RNA sequencing (scRNA-seq) data analysis involves normalization, dimensionality reduction, and clustering methods to analyze the dataset. It is important to combine these methods appropriately to improve the efficiency and accuracy of scRNA-seq data analysis.
What are some new offshoots of scRNA-seq?5 answersSingle-cell RNA-sequencing (scRNA-seq) has led to the development of several new offshoots. One such offshoot is the minicore library, which enables efficient k-means clustering of scRNA-seq data. It supports various distance measures and produces lower-cost centerings more efficiently than scikit-learn. Another offshoot is the COTAN method, which analyzes the co-expression of gene pairs at the single-cell level. It provides a new correlation index and can identify cell-identity markers. Additionally, the eQTLsingle method allows for the discovery of eQTLs using only scRNA-seq data. It models gene expression and detects mutations to find associations between genotypes and phenotypes at the single-cell level. These offshoots enhance the analysis of scRNA-seq data and provide valuable insights into gene expression and cellular interactions.
What is MDSC and where can i get MDSC RNA-seq data?3 answersMyeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immune cells that play a role in immune suppression and tumor progression. MDSC RNA-seq data can be obtained from various sources. One study by Marot and Rau provides an example of using the metaRNASeq package to combine data from multiple RNA-seq experiments, including MDSC data. Another study by de Sá et al. sequenced the cDNA of Corynebacterium pseudotuberculosis under different stress conditions, which may provide insights into the adaptation mechanisms of MDSCs during infection. Additionally, the RSEQREP RNA-Seq Reports framework, developed by Git, is an open-source cloud-enabled tool that can be used for reproducible RNA-Seq data processing and analysis, including MDSC data. It is important to explore these resources and databases to access MDSC RNA-seq data for further analysis and understanding of their role in immune regulation.
Which tool is used for counting the mapped reads for RNA Seq data?5 answerstiny-count is the tool used for counting the mapped reads for RNA Seq data.
What are the steps of SCONE-SEQ?3 answersSCONE-seq is a versatile single-cell whole-genome RNA sequencing (scWGS-RNA-seq) method that amplifies single-cell DNA and RNA without separating them from each other. It is compatible with frozen biobanked samples and can be used to perform single-cell multi-omics interrogation on these samples. The steps taken to enhance the user experience of SCONE-seq are listed in the paper by Kowalski et al.. The current version of the code is compared against Serpent and shown to provide sufficient accuracy for teaching and proof-of-concept applications..
How much is RNA seq per sample?10 answers