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Introduction to RNA sequencing
Bryan Quach edited this page Mar 28, 2023
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The knowledge required to work with RNA sequencing data can vary widely depending on the type of analysis you are doing and the state of the data. This page includes resources to get a better conceptual understanding of RNA sequencing and analysis.
- RNA sequencing and analysis (2015)
- Comparing bioinformatic gene expression profiling methods: microarray and RNA-Seq (2014)
- RNA sequencing: the teenage years (2019)
- Methodologies for transcript profiling using long-read technologies (2020)
- The RIN: an RNA integrity number for assigning integrity values to RNA measurements (2006)
- RNA‐seq data: challenges in and recommendations for experimental design and analysis (2014)
- Determining sufficient sequencing depth in RNA-Seq differential expression studies (2019)
- RNA-seq differential expression studies: more sequence or more replication? (2013)
- A survey of best practices for RNA-seq data analysis (2016)
- Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis (2014)
- How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives (2019)
- Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015)
- A comprehensive assessment of RNA-seq protocols for degraded and low-quantity samples (2017)
- Influence of RNA extraction methods and library selection schemes on RNA-seq data (2014)
- Comparison of RNA isolation methods on RNA-Seq: implications for differential expression and meta-analyses (2020)
- Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing (2021)
- Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap (2015)
- Transcript length bias in RNA-seq data confounds systems biology (2009)
- RNA-seq: impact of RNA degradation on transcript quantification (2014)
- Transcriptome diversity is a systematic source of variation in RNA-sequencing data (2022)
- Alignment and mapping methodology influence transcript abundance estimation (2020)
- On the optimal trimming of high-throughput mRNA sequence data (2014)
- Trimming of sequence reads alters RNA-Seq gene expression estimates (2016)
- Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols (2020)
- Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples (2012)
- Inflated false discovery rate due to volcano plots: problem and solutions (2021)
- Predictability of human differential gene expression (2019)
- Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences (2016)
- Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation (2016)
- Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions (2018)
- Detecting and correcting systematic variation in large-scale RNA sequencing data (2014)
- DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis (2019)
- Gene ontology analysis for RNA-seq: accounting for selection bias (2010)
- Gene set analysis controlling for length bias in RNA-seq experiments (2017)
- Capturing heterogeneity in gene expression studies by surrogate variable analysis (2007)
- Normalization of RNA-seq data using factor analysis of control genes or samples (2014)
- Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage (2016)
- Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data (2022)
- Comparison of confound adjustment methods in the construction of gene co-expression networks (2022)