April 11, 2025
Identifying gene mRNA from RNA-Seq data involves isolating and analyzing the messenger RNA sequences from RNA molecules. Messenger RNA (mRNA) carries the coding information necessary for protein synthesis, making it essential for understanding cellular function.
However, extracting these sequences is very challenging because the RNA-Seq dataset contains a mix of different RNA types, such as ribosomal RNA (rRNA) and non-coding RNA (ncRNA), which need to be filtered out to focus on mRNA. Therefore, accurate mapping and assembly methods are critical for aligning RNA fragments to a reference genome and reconstructing complete mRNA sequences.
These precise techniques enable researchers to gain accurate insights into gene expression and regulation. Let's explore this concept briefly and understand how to find an RNA sequence, the full procedure, techniques, methods, and more.
Source: Pixabay
Before we dive into how to find a gene's mRNA from RNA seq data, let's explore why RNA sequencing was developed. RNA sequencing was designed to determine which genomic regions are active in a cell population at a specific time.
Compared to traditional methods, RNA-seq can even detect lowly expressed transcripts while reducing false positives. Moreover, RNA-seq isn't just about measuring mRNA levels between conditions. It can also uncover non-coding RNAs, splice isoforms, novel transcripts, and protein-RNA interaction sites.
As you understand the goal of this technology development, it is also crucial to know how to find an RNA sequence from RNA sequence data.
To find a gene's mRNA from RNA-Seq data, the process typically follows these steps:
The above gives a general idea of how to find a gene's mRNA from RNA-seq data. Now, let's explore the topic in more detail. This comprehensive guide is divided into clear sections for easy understanding.
Source: NIH
The first step in finding a gene's mRNA sequence from RNA-Seq data is data preparation, including file management and preprocessing. The raw sequencing data—FASTQ, VCF, and BAM files—must be properly organized before analysis.
At this stage, the RNA-Seq data is prepared, enabling further analysis. The next step is utilizing K-mer matching for quality control.
K-mer matching is a fundamental step in RNA-Seq data analysis because it helps filter, classify, and assemble sequencing reads with high accuracy. It is essential for preprocessing the data and improving the quality of RNA-Seq reads. K-mer matching improves mapping accuracy.
BBDuk (BBMap's Deduper and K-mer Remover) is widely used for preprocessing sequencing reads. BBDuk is mainly used to clean up and remove contaminants and low-quality sequences.
Although K-mer matching does not directly identify mRNA sequences, it is essential in generating high-quality sequencing reads that improve the accuracy of RNA-Seq analysis.
Mapping RNA-Seq is an essential step in RNA-Seq analysis. This process uses specialized mapping algorithms capable of handling splicing events. Outputs are often converted to formats like BAM (Binary Alignment Map) and BED (Browser Extensible Data) for further analysis.
Once the reads are aligned, they undergo post-processing, which includes filtering low-quality or ambiguous reads and converting data formats for analysis.
After mapping, the next steps include detecting splice junctions, quantifying gene expression, and performing differential expression analysis:
After mapping, the next step is de novo assembly. This approach builds transcripts directly from your RNA-Seq reads without needing a reference genome. Let's explore it below.
You might wonder why this "de novo transcriptome assembly" is included in this procedure. It is an essential step when studying organisms without a reference genome. De novo transcriptome assembly does not rely on a preexisting reference genome, essentially building the transcript sequences "from scratch" using only the information present in the RNA reads themselves.
Challenges
These challenges are highlighted to help you anticipate and mitigate potential issues in de novo transcriptome assembly.
As you have uncovered the importance of de nova transcript assembly, now you’ll explore gene sequence quantification, which is also a critical step in finding a gene’s mRNA.
Gene sequence quantification is critical in RNA-Seq analysis. It helps determine the abundance of mRNA transcripts in a given sample, which is necessary to identify and analyze a gene’s mRNA sequence accurately.
Gene sequence quantification involves aligning reads to a reference genome and counting the number of reads that map to a gene or transcript. This can be done using alignment-based or alignment-free tools.
Tools like HISAT2 and STAR align reads to a reference genome, after which the number of reads mapping to each gene is counted.
Alignment-free methods, such as Salmon and Kallisto, directly estimate transcript abundance without the need for aligning reads to a genome.
As you have explored the gene sequence quantification below, you’ll explore the last step, advanced techniques used in mRNA identification.
Do you know? A real-world contribution of RNA-Seq and mRNA sequence analysis is the finding of HER2-positive breast cancer, which is a well-known oncogene located on chromosome 17 (17q12-21). It encodes a transmembrane receptor tyrosine kinase involved in cell growth and survival.
You have explored almost all the major steps that need to be followed on how to find a gene RNA sequence from RNA sequencing data. Now, you have landed on the last step, which is an advanced technique that should be used in mRNA identification. This involves identifying which genes are upregulated or downregulated under different conditions. Several methods are used for this:
As mentioned above, you explored the full procedure of finding mRNA sequences from RNA-Seq data. This offered you an understanding of how you can seamlessly incorporate this procedure to accomplish your research goals. Below, you’ll find the recap of the whole content.
Identifying mRNA sequences from RNA-Seq data involves critical steps, from data preparation and quality control to alignment and variant detection. Researchers can ensure trustworthy results by following effective strategies such as assessing raw sequence quality, using reliable alignment tools, and employing techniques like K-mer matching for accuracy.
As RNA-Seq technologies evolve, it is essential to stay updated on the latest advancements in tools and techniques. The field is constantly growing, offering more efficient ways to handle complex datasets, improve alignment accuracy, and uncover new insights into gene regulation.
This is why new emerging projects like Biostate.ai are great options if you want complete RNA sequencing done for any sample at an affordable cost. The team handles everything from sample collection to final insights. Get Your Quote Today!
