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Genome or transcriptome? The key difference in choosing the right assembly method!
With the development of emerging sequencing technologies, transcriptome research has entered a new era. Especially between 2008 and 2012, the significant decline in sequencing costs has made it possible to assemble and analyze transcriptomes of many non-model organisms. This change goes beyond finding phenotypic variation in specific organisms, allowing us to more fully understand the diversity and biological mechanisms of life on Earth.
"The greatest benefit of transcriptome assembly is its potential to reveal new proteins and their isoforms that may play key roles in specific biological phenomena."
There are two main methods for transcriptome assembly: de novo assembly and reference-based assembly. For non-model organisms for which a complete genome has not yet been established, de novo transcriptome assembly is obviously a more appropriate choice. This approach does not rely on previous genome sequences, allowing researchers to explore unknown gene transcription information.
De novo vs. reference-based assembly
In the past, analysis of transcriptome data has relied primarily on comparison to existing reference genomes. However, this approach may not cover all mRNA structural variations, especially when alternative splicing is involved, and many transcript variants may be missed because they cannot be mapped discontinuously to the genome. Therefore, even with a reference genome, it is still necessary to perform a de novo assembly, as the new assembly can recover transcripts that are missing from the reference genome.
Transcriptome and genome assembly
The coverage depth of the transcriptome can directly reflect the expression level of the gene, while the coverage depth of the genome is usually affected by repetitive sequences. In addition, one of the biggest challenges facing transcriptome assembly is that different transcript variants in the same gene may share exons, which makes their identification more complicated.
Methods for transcriptome assembly
RNA-seqAfter RNA extraction and purification, the samples will be sent to a high-throughput sequencing facility for reverse transcription to obtain a cDNA library. Depending on the platform, these cDNAs will be cut into specific lengths and then sequenced using different technologies, including 454 sequencing, Illumina, and SOLiD.
Assembly Algorithm
The sequence data of the transcripts will be assembled into transcripts using a short-read transcript assembly program. Because transcripts can be similar but have amino acid variations, these variations can reflect different protein isoforms. A number of assembly programs can be used to perform this process, but transcriptome assembly presents many unique challenges.
"Most short-read assemblers follow two basic algorithms: overlap graph and de Bruijn graph, with de Bruijn graph being preferred due to its relatively low computational requirements."
Functional Notes
Functional annotation of assembled transcripts can provide in-depth understanding of their potential biological functions. Using tools such as Blast2GO, unannotated sequence data can be mined based on gene ontology. This process can help identify the biological processes in which the transcripts are involved and their molecular functions.
Validation and Quality Control
Since it is rare to have a good reference genome available, the quality of the assembled sequence needs to be verified by comparing it to the raw reads. Filtering of short sequences is also necessary because these short sequences usually cannot effectively fold into functional proteins.
Choose an Assembler
There are many assembly software available in the market that can be used to generate transcriptomes. For example, tools such as SOAPdenovo-Trans and Trinity have their own unique features. These programs can not only efficiently assemble transcripts, but also account for different splicing events and gene expression levels.
In this rapidly evolving field, the choice of genome or transcriptome assembly method ultimately depends on the researcher's needs and the characteristics of the organism being studied. Each method has its advantages and disadvantages. Have researchers chosen a research path that best suits their needs?
