LIQA: a revolutionary method of quantifying isoform expression

Saakshi Bangera, DY Patil School of Biotechnology and Bioinformatics

RNA splicing is one of the most important processes of generating discrepancy in the transcriptome and is of utmost importance to humans. Errors in the regulation of RNA splicing lead to a wide range of genetic diseases, specifically caused by genetic mutations. RNA sequencing techniques have accelerated our understanding of transcriptomic differences to better grasp the medical conditions associated with genetic disorders.

Using RNA sequencing techniques, we can:

• Quantitate isoform-specific gene expression;
• Detect events of the differential alternative splicing (DAS) in the RNA;
• Discover unique transcript isoforms.

Up until now, short-red RNA-seq were used for transcriptomic studies, but due to its read-length limit, analysis becomes difficult. Therefore, it became crucial to detect and estimate isoform expression. 

Long-read RNA-seq has become popular over short-read sequencing techniques. The Nanopore sequencing method has been able to generate reads long enough to sequence an entire mRNA or cDNA. This advantage facilitates isoform expression quantification, novel isoform discovery, and DAS detection.

Challenges faced during the analysis of long-read RNA-seq data

However, short-read RNA seq do not perform in an optimum manner when used on long-read RNA-seq directly. This is mainly because high read coverage is required for this kind of bias correction. Another problem that this technique faces – high sequencing error rates, which can cause misalignment of sequencing reads. An additional difficulty is – underestimation of potential read coverage biases. Biased coverage caused due to fragmentation and pore-block can result in shortened ends and thus affect the accuracy of quantification of isoform expression.

The solution

LIQA -long-read isoform quantification and analysis- is a revolutionary method of statistically quantifying isoform expression by allowing each read to have its own weight. This method provides each read with a different weight to spot read-specific error and biases at the gene itself. 

Results that demonstrate the accuracy of LIQA

1. Outline of LIQA
   • LIQA requires an isoform annotation file as input and aligned long-read RNA-seq files in BAM or SAM format.
   • Quality score and read coverage bias are accounted for to read the information and correct biases.
   • An expectation maximization (EM) algorithm is used to estimate the isoform expression.
   • Using estimates of isoform expression, differential alternative splicing events can also be detected.

The performance of LIQA was evaluated by comparing it with other long-read RNA-seq algorithms namely – The Oxford Nanopore Pipeline (ONP), Mandalorion, TALON, and FLAIR. All the methods were valued on real as well as simulated data.

1. Isoform expression quantification accuracy
   • RMSE and Spearman’s correlation was calculated. 
     • LIQ showed a higher Spearman’s correlation than others for simulated data. 
     • LIQA also outperformed the rest for relative abundance estimation. When analyzing simulated data with real data, the quantification accuracy of LIQA was almost unchanged. 
     • LIQA also demonstrated an advantage over other approaches in handling read-length bias and 3’ bias correction.
2. Differential alternative splicing (DAS) detection
   • Three summary statistics were considered-For recall value,
     • LIQA was outperformed by FLAIR.
     • For precision value, LIQA outperformed FLAIR.
     • For F1 score (harmonic mean between recall and precision values that is used to rate performance), LIQA, FLAIR, and TALON performed similarly.
3. Application to Universal Human Reference (UHR) RNA-seq data
   • Quantitative real-time PCR (qRT-PCR) was considered to measure accurate isoform abundance. The accuracy between qRT-PCR values and estimates was assessed by Spearman’s correlation logarithmically.
     • LIQA was observed to have a strong linear correlation between logarithmic estimates and qRT-PCR.

LIQA in the future of transcriptomic profiling

The rise of long-read RNA-seq technologies has made it possible to accurately estimate the isoform-specific expression of genes. It has made it possible to discover rare isoforms and quantify them without any amplification bias. Results of a simulation study have proved that LIQA is far more effective in bias correction than other approaches. Long-read RNA-seq offers us a lead to help us better understand transcriptomic variations. LIQA is a strong computational tool and can be of immense use in biomedical research as well as biomedical applications.

Also read: Novel brain cells named “Gorditas” and “OPC” discovered

Source:

Hu, Y., Fang, L., Chen, X. et al. LIQA: long-read isoform quantification and analysis. Genome Biol 22, 182 (2021). https://doi.org/10.1186/s13059-021-02399-8

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