RNA-seq Differential Expression, Alternative Splicing, Transcript Assembly and Gene Fusion

最新推荐文章于 2024-10-24 22:13:35 发布

原创最新推荐文章于 2024-10-24 22:13:35 发布 · 1k 阅读

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#RNA-seq Differential #Alternative Splicing #Transcript Assembly #Gene Fusion

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本文介绍了RNA-seq数据分析的关键方面，包括如何评估差异表达，如通过估计变异性来计算p值；探讨了读取分布的泊松分布和负二项分布模型；并提到了一些常用的差异表达分析工具，如Cufflinks、LIMMA-VOOM和DESeq2。此外，还讨论了RPKM、FPKM和TPM等表达指数的优缺点。RNA-seq的剪接变异分析中，MATS工具用于多变量分析，而Tophat则用于分配reads到剪接异构体。在转录组装部分，介绍了参考依赖性和de novo组装方法。最后，简述了在癌症样本中常见的基因融合现象及其检测的挑战。

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RNA-seq Differential Expression:

Gene X’s expression in condition A doubles expression in condition B. But how reliable is this? What’s the chance of observing it by rendom? All comes to variation estimation! How to meassure the variance between different biological replicates. Once you have the variation estimation, you’re able to assign a p-value for expression changes. Variation can be estimated if you have many biological replicates.But in practice, we only have 2-3 replicates. What we can do next is proper statistical models.

Sequencing Read Distribution:

1. Poisson distribution:λ=E(X)=Var(X)

The easiest model for RNA-seq reads count is Poisson distribution.

Assumption : Mean = Variance

But: sequencing data is over-dispersed,not only RNA-seq (Mean<Variance)

2. Negative binomial: X ~ NB(r;p)( 2 parameters : r,p)

Definition : number of successes before r failures occur, if Pb(each success) is p .

Every gene has 2 parameters : mean and variance.

Negative binomial for RNA-seq : K_ij~NB(μ_ij,θ_ij²)

Variance estimated by borrowing information from all the genes - hierarchical models

Test whether μ_iis the same for gene i between samples j.

Tools used in differential expression:

Cufflinks: versatile(RPKM/FPKM)

LIMMA-VOOM and DESeq: better variance estimates(TPM)

EdgeR; DESeq/DESeq2

Expression Index:

1. RPKM (Reads per kilobase of transcript per million reads of library)

Corrects for coverage, gene length

1 RPKM ~ 0.3 -1 transcript / cell

Comparable between different genes within the same dataset

TopHat / Cufflinks

2. FPKM (Fragments), PE libraries, RPKM/2

3. TPM (transcripts per million)

Normalizes to transcript copies instead of reads

Longer transcripts have more reads

RSEM, HTSeq

Note: TPM is not able to compare expression of different genes across different samples. We should do differential expression on RPKM or FPKM

RNA-seq Alternative Splicing:

Different AS events:

TopHat: Assign reads to splice isoforms

MATS: Multivariate Analysis of Transcript Splicing

Transcript Assembly

Reference-based assembly: Cufflinks

1. Read mapping using Tophat

2. Construct a graph of reads

“Incompatible”fragments (reads) means they are definitely NOT from the same transcript

3. Identify the minimum # paths that cover all reads (each path is one possible transcript)

4. Transcript abundance estimation

De novo assembly: Trinity

De bruijn graph (1946):

Used in the earliest human genome assemblies

Standard algorithm for genome assembly

A sequence of length k can be represented as an edge between two sequences (length k-1)

Gene Fusion:

More seen in cancer samples

Still a bit hard to call

TopHatFusion in TopHat2