登录查看更多内容

How can you identify alternative splicing events with RNA-seq data?

由人工智能和领英社区提供技术支持

Alternative splicing is a process that allows a single gene to produce multiple variants of messenger RNA (mRNA), which can encode different proteins or regulate gene expression. It is a key mechanism for increasing the diversity and complexity of the proteome and the transcriptome, and it is involved in many biological functions and diseases. However, identifying and quantifying alternative splicing events from RNA-seq data, which is a high-throughput technique that sequences and measures the abundance of RNA molecules in a sample, can be challenging and requires specialized tools and methods. In this article, you will learn how to use some of the common tools and methods to detect and analyze alternative splicing events with RNA-seq data.

此文章中的业界达人

由社区从 5 条内容中精选。了解更多

Omer Agca

Co-Founder at CureCraft

1 Preprocessing and alignment

Before you can identify alternative splicing events, you need to preprocess and align your RNA-seq data to a reference genome or transcriptome. Preprocessing involves quality control, trimming, and filtering of the raw reads to remove low-quality, adapter-contaminated, or duplicated sequences. Alignment involves mapping the processed reads to the reference sequences using an aligner that can handle spliced alignments, such as STAR, HISAT2, or TopHat2. The output of the alignment step is usually a BAM or SAM file that contains the coordinates and scores of the mapped reads.

添加您的观点

Omer Agca

Co-Founder at CureCraft
举报内容
Collect and preprocess high-quality RNA-seq data from the desired tissue or cell type. Ensure adequate sequencing depth. Align the RNA-seq reads to a reference genome using spliced aligners like STAR, HISAT2, or TopHat2, which can identify splice junctions.

已翻译

赞
Frances Turner

Bioinformatician at The University of Edinburgh
举报内容
Our ability to accurately define an quantify similar transcripts is inherently limited when using the short reads traditionally favored for RNA-Seq. At Edinburgh Genomics we are excited about the possibilities of PacBio's new Kinnex kit, which allows the sequencing of multiple complete transcripts in a single HiFi read. Combined with the higher throughput of the new Revio sequencer, this makes the long read RNA-Seq analysis far more practical and affordable.

已翻译

赞

2 Splicing event annotation

The next step is to annotate the splicing events that are present in your RNA-seq data. A splicing event is a specific way of joining exons or skipping introns in a gene. There are different types of splicing events, such as exon skipping, alternative 5' or 3' splice sites, intron retention, or mutually exclusive exons. To annotate the splicing events, you need to use a tool that can compare your alignment file to a reference annotation file, such as GTF or GFF3, that contains the gene and transcript structures. Some of the tools that can do this are rMATS, SUPPA2, or SpliceR.

添加您的观点

Omer Agca

Co-Founder at CureCraft
举报内容
After alignment, assemble the reads into transcript models using tools such as Cufflinks, StringTie, or Trinity to provide potential isoforms produced by alternative splicing. Use PCR (especially RT-PCR) with primers flanking potential splice junctions to validate predicted splicing events. Sequencing of PCR products, like Sanger sequencing, can further validate the alternative splice variants. Use tools like IGV (Integrative Genomics Viewer), Sashimi plots (available in MISO), or JBrowse for visualization of splicing patterns and events.

已翻译

赞

3 Splicing event quantification

The next step is to quantify the splicing events that are annotated in your RNA-seq data. This means estimating the relative abundance or frequency of each splicing event in each sample or condition. To do this, you need to use a tool that can calculate the number of reads that support each splicing event or each splice junction, and normalize them by the total number of reads or the gene expression level. Some of the tools that can do this are DEXSeq, MISO, or LeafCutter.

添加您的观点

Omer Agca

Co-Founder at CureCraft
举报内容
Use tools like MISO (Mixture of Isoforms), rMATS, and SpliceTrap to detect and quantify specific alternative splicing events, comparing splice junction reads and exon-exon junctions.

已翻译

赞

4 Splicing event differential analysis

The final step is to perform differential analysis of the splicing events that are quantified in your RNA-seq data. This means testing whether the abundance or frequency of each splicing event is significantly different between two or more samples or conditions, such as different tissues, developmental stages, or treatments. To do this, you need to use a tool that can apply statistical methods and models to account for the variability and noise in the RNA-seq data, and adjust for multiple testing and confounding factors. Some of the tools that can do this are DEXSeq, MISO, or MAJIQ.

添加您的观点

Omer Agca

Co-Founder at CureCraft
举报内容
For studies comparing splicing between conditions or groups, use tools like DEXSeq, rMATS, and SUPPA to identify differentially spliced events between conditions. Determine the potential functional implications of splicing variants using tools like ASpedia, SpliceAid, or Human Splicing Finder. Ensure quality control at each step and, if possible, replicate findings with independent datasets or other experimental methods.

已翻译

赞

5 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

Biotechnology

+ 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 identify alternative splicing events with RNA-seq data?

1

2

3

4

5

1 Preprocessing and alignment

2 Splicing event annotation

3 Splicing event quantification

4 Splicing event differential analysis

5 Here’s what else to consider

Biotechnology

给文章评分

感谢您的反馈

更多Biotechnology相关文章

更多相关阅读内容