Data quantity correlates with the genome size of the studied species.

For standard species, it's usually recommended around 6G.

If detecting genes with low expression levels is necessary, increasing the data amount to enhance sequencing depth is advisable.

Microarray employs probes designed for known gene sequences.

Generally available arrays are for human and mouse species, and they can't detect variations occurring at the probe binding sites.

RNA-seq directly sequences cDNA obtained from RNA reverse transcription. It offers higher sensitivity and resolution, can sequence species without a reference genome, and detects low-expression genes, generating qualitative and quantitative data.

Current publishing standards generally require at least biological duplicates for the data.

More stringent journals might even require triplicates in the experiment itself.

Small RNA-seq involves the isolation and sequencing of small RNA molecules. Common steps include library preparation, fragment size selection, and high-throughput sequencing using platforms like Illumina.

Subsequently, bioinformatics tools are used to analyze the sequencing data, identify types of miRNA, discover target genes associated with differentially expressed miRNA, and evaluate their biological significance through functional enrichment analysis.

The ideal scenario for Small RNA-Seq involves using isolated small RNA; however, isolation of small RNA is not a routine and straightforward experiment.

Currently, most sequencing is conducted directly on total RNA, leading to limitations in only being able to select potential small RNA based on insert size during library construction.

Therefore, there is a higher requirement for RNA sample quality (RIN value needs to be above 7) as degradation or the presence of degraded RNA fragments might cause contamination and reduce the detectability of miRNA during sequencing.