登录查看更多内容

How can you use RNA-seq data to analyze gene expression?

由人工智能和领英社区提供技术支持

RNA-seq is a powerful technique to measure gene expression levels across different samples, conditions, or time points. It can reveal how genes are regulated, how they interact, and how they contribute to biological functions and phenotypes. However, RNA-seq data also pose many challenges, such as quality control, normalization, alignment, quantification, differential expression analysis, and interpretation. In this article, you will learn how to use RNA-seq data to analyze gene expression in six steps.

此文章中的业界达人

由社区从 6 条内容中精选。了解更多

Jingbo Zhang

Gene therapy | Viral vector | Gene delivery
Dennis Odoi

Biomedical Scientist || Public Health Enthusiast || Maternal and Child Health Advocate || Reproductive Health Advocate…
Frances Turner

Bioinformatician at The University of Edinburgh

1 Step 1: Prepare your samples and sequencing libraries

Before you can use RNA-seq data to analyze gene expression, you need to prepare your samples and sequencing libraries. This involves extracting RNA from your biological samples, such as cells, tissues, or organisms, and converting it into cDNA. Then, you need to fragment, size-select, and ligate adapters to the cDNA to generate sequencing libraries. The quality and quantity of your RNA and libraries will affect the accuracy and reliability of your gene expression analysis, so you should check them using methods such as gel electrophoresis, qPCR, or bioanalyzer.

添加您的观点

Dennis Odoi

Biomedical Scientist || Public Health Enthusiast || Maternal and Child Health Advocate || Reproductive Health Advocate || Entrepreneur || Certified Google Digital Marketer
举报内容
The preparation of samples and sequencing libraries is a critical step in utilizing RNA-seq data for gene expression analysis. This process involves converting RNA molecules into sequencer-ready libraries, ensuring accurate representation of gene expression levels. Proper sample preparation enhances the reliability and reproducibility of RNA-seq results, allowing researchers to draw meaningful insights into gene expression patterns. This meticulous step is foundational for unlocking the full potential of RNA-seq data and gaining a comprehensive understanding of the dynamic landscape of gene expression in biological systems.

已翻译

赞
Iftekhar Rafiqullah

PhD candidate (expected to graduate in Fall 2024) | Laboratory Manager (NGS) in Ezbiome Inc. | Bioinformatician | Laboratory Scientist | Next Generation Sequencing | Linux Python R Bash
举报内容
I agree. Sample preparation is the first critical step. Extraction of RNA from biological samples needs to be done carefully under the hood so that it doesn’t get contaminated. Kits and reagents need to be separated. Once cDNA is ready, keep continuing the protocol for fragmentation, adapter ligation, etc. Make sure QC is done to check concentrations, size , etc. appropriate for sequencing breadth and depth.

已翻译

赞

2 Step 2: Perform sequencing and quality control

The next step is to perform sequencing and quality control of your RNA-seq data. You can use different sequencing platforms, such as Illumina, PacBio, or Oxford Nanopore, depending on your budget, throughput, and research questions. You should also choose the appropriate sequencing depth, read length, and paired-end or single-end mode for your experiment. After sequencing, you should perform quality control of your raw data using tools such as FastQC, MultiQC, or Trim Galore. These tools can help you identify and remove low-quality reads, adapter contamination, or other artifacts that can affect your gene expression analysis.

添加您的观点

3 Step 3: Align reads to a reference genome or transcriptome

The third step is to align reads to a reference genome or transcriptome using tools such as STAR, HISAT2, or Salmon. This step will allow you to map your reads to the genomic or transcriptomic regions that they originate from, and quantify the expression levels of genes or transcripts. Depending on the tool you use, you may need to provide a reference genome or transcriptome file, an annotation file, or an index file as inputs. You should also adjust the parameters of the alignment tool to optimize the speed, accuracy, and sensitivity of the alignment process.

添加您的观点

Jingbo Zhang

Gene therapy | Viral vector | Gene delivery
举报内容
If the whole genome date is not available for the organism you studied, you can also do the De novo transcriptome assembly from the RNA-seq data you received. The reads can then align to the De novo transcriptome.

已翻译

赞
Frances Turner

Bioinformatician at The University of Edinburgh
(已编辑)
举报内容
Sometimes more than one species is present in a sample (for instance a host and a pathogen). Even if you are only interested in one species, it is best practice to concatenate the the genomes/transcriptomes of all the species in your samples, then align all data to the concatenated genome. Once you have performed the alignments, further QC can be carried out. We find the % of reads mapping to the genome/transcriptome, median PCR duplicate rate per gene, and % of rRNA are all useful metrics to assess library quality and ensure appropriate downstream analysis. Picard tools is useful at this point.

已翻译

赞

4 Step 4: Perform differential expression analysis

The fourth step is to perform differential expression analysis using tools such as DESeq2, edgeR, or limma. This step will allow you to compare the expression levels of genes or transcripts across different samples, conditions, or time points, and identify the ones that are significantly upregulated or downregulated. Depending on the tool you use, you may need to provide a count matrix, a design matrix, or a contrast matrix as inputs. You should also consider the factors that may affect your differential expression analysis, such as normalization, dispersion estimation, statistical testing, multiple testing correction, and fold change threshold.

添加您的观点

Frances Turner

Bioinformatician at The University of Edinburgh
举报内容
Before testing for differential expression, it is important to remove lowly expressed genes. Some tools (such as DESeq2) will do this for you, other tools (such as edgeR) required you to do this yourself. Failure to perform this step will result in much less significant adjusted p-values (or false discovery rates) after multiple correction testing. It is also important to consider any factors in your experiment (such a sample batch) that may influence gene expression, and ensure you control for these by including them in your design matrix

已翻译

赞

5 Step 5: Interpret and visualize the results

The fifth step is to interpret and visualize the results of your differential expression analysis using tools such as R, Bioconductor, or Python. This step will allow you to explore the biological meaning and significance of your differentially expressed genes or transcripts, and communicate your findings to others. You can use various methods to interpret and visualize the results, such as gene ontology analysis, pathway analysis, gene set enrichment analysis, volcano plots, heatmaps, or clustering.

添加您的观点

6 Step 6: Validate and extend the results

The final step is to validate and extend the results of your gene expression analysis using other methods or data sources. This step will allow you to confirm the reliability and reproducibility of your results, and discover new insights or hypotheses. You can use various methods to validate and extend the results, such as RT-qPCR, western blot, RNA interference, CRISPR-Cas9, or single-cell RNA-seq.

添加您的观点

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Salma B.

Innovation Ecosystem & Programming ? Community Building
举报内容
Analyzing RNA-seq data is a common method for estimating gene and transcript expression. The process typically involves several steps: Trimming, aligning, counting, and normalizing the sequenced reads, followed by differential expression (DE) analysis across different conditions. Statistical analysis is then performed to determine p-values and establish the false discovery rate (FDR) cutoff. Subsequent analyses depend on the specific hypothesis being investigated. Additional approaches may include utilizing Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape, EnrichmentMap, and Metascape for pathway enrichment analysis. These methods help provide mechanistic insights into the gene lists generated from RNA-Seq data.

已翻译

赞

Laboratory Management

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can you use RNA-seq data to analyze gene expression?

1

2

3

4

5

6

7

1 Step 1: Prepare your samples and sequencing libraries

2 Step 2: Perform sequencing and quality control

3 Step 3: Align reads to a reference genome or transcriptome

4 Step 4: Perform differential expression analysis

5 Step 5: Interpret and visualize the results

6 Step 6: Validate and extend the results

7 Here’s what else to consider

Laboratory Management

给文章评分

感谢您的反馈

更多Laboratory Management相关文章

更多相关阅读内容