Shulou(Shulou.com)06/01 Report--

What this article shares with you is about what the quantitative principle of miRNA is. The editor thinks it is very practical, so I share it with you to learn. I hope you can get something after reading this article.

In the data analysis of transcriptome, quantitative and difference analysis are the basic analysis contents. For the quantification of mRNA, we can directly compare reads to the reference genome, but for miRNA data, this mode of operation is not appropriate.

The length of the miRNA is between 18bp-36b and is very short. If you compare the whole genome with such a short reads, there will certainly be multiple alignment positions. When quantifying, it is impossible to effectively distinguish which miRNA the reads comes from. Moreover, the gtf file will only record the genome location of the miRNA gene, but does not provide the 5 'and 3' end two mature miRNA genome locations.

In order to quantify miRNA accurately, the developers of mirdeep software put forward such an idea to compare reads with the precursor sequence of miRNA, so that we can effectively determine which miRNA; reads belongs to. Considering that one miRNA precursor can produce two mature miRNA, two mature miRNA can be distinguished according to the alignment position of reads, as shown below.

At the beginning of the study of miRNA, it was found that a precursor of miRNA could produce two small RNA, the one with high expression was called mature miRNA, the one with relatively low expression was called star miRNA, and later both RNA were called mature miRNA directly. The naming method of star sequence is still used in mirdeep software, which is actually miRNA sequence.

In practice, through bowtie1 alignment, bowtie1 is more sensitive to short sequence alignment tasks, so it is suitable for short sequence alignment work such as miRNA. First, index the miRNA precursor. The code is as follows

Bowtie-build precursor.converted miRNA_precursor

Next, compare the mature miRNA to the miRNA precursor, as follows

Bowtie-p $threads-f-v 0-a-- best-- strata-- norc miRNA_precursor mature.converted ${name1} _ mapped.bwt

Finally, the reads of miRNA sequencing is compared to the miRNA precursor, as follows

Bowtie-p $threads-f-v $mismatches-a-- best-- strata-- norc miRNA_precursor $name2.converted ${name2} _ mapped.bwt

According to the result file produced by the two comparisons, the miRNA can be quantified. First, the position of the mature miRNA on the miRNA precursor is determined from the comparison results of the mature miRNA. The comparison results are as follows.

Hsa-miR-550b-2-5p + hsa-mir-550b-1 15 ATGTGCCTGAGGGAGTAAGACA IIIIIIIIIIIIIIIIIIIIII 1 hsa-miR-550b-3p + hsa-mir-550b-1 57 TCTTACTCCCTCAGGCACTG IIIIIIIIIIIIIIIIIIII 1

For the precursor hsa-mir-550b-1, there are two mature miRNA,hsa-miR-550b-2-5p, hsa-miR-550b-2-5p and hsa-miR-550b-3p, which are aligned to the 15-36bp position of the precursor and hsa-miR-550b-3p to the 57-76bp position of the precursor, which is obtained according to the value of the fourth column plus the sequence length of the fifth column.

Next, the expression amount of each miRNA is determined from the comparison results of the sequencing reads, and the comparison results are as follows

SW1_224845_x3 + hsa-mir-550b-1 17 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII 4 18 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII C > GSW1_287750_x2 + hsa-mir-550b-1 18 TGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIII 4 17 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII C > GSW2_152540_x1 + hsa-mir-550b-1 17 GTGCCTGAGGGAGTAAGACT IIIIIIIIIIIIIIIIIIII 2 19V A > TSW2_152541_x7 + hsa-mir-550b-1 17 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII 4 18 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII C > GSW2_199319_x1 + hsa-mir-550b-1 18 TGCCTGAGGGAGTAAGAA IIIIIIIIIIIIIIIIII 4 17 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII C > ASW3_404761_x1 + hsa-mir-550b-1 17 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII 4 18 hsa-mir-550b-1 C > GSW3_516112_x1 + hsa-mir-550b-1 18 TGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIII 4 17 hsa-mir-550b-1 C > GSW4_451132_x4 + hsa-mir-550b-1 17 GTGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIIII 4 18 hsa-mir-550b-1 C > GSW4_589021_x1 + hsa-mir-550b-1 18 TGCCTGAGGGAGTAAGAGA IIIIIIIIIIIIIIIIIII 4 17 hsa-mir-550b-1 C > G

We can see that in the actual sequencing samples, there are so many reads comparisons to the precursor hsa-mir-550b-1; the next task is to distinguish which mature miRNA these reads belong to.

From the comparison results, it is obvious that the position of the actual sequenced reads on the precursor is between 17-36bp. For this interval, it can only be the mature miRNA of hsa-miR-550b-2-5p.

In order to speed up the alignment process, mirdeep processes the sequencing reads and merges the redundant reads, using the number after x in the sequence identifier to represent the number of times the reads appears in the original sequencing result. When finally counting, the corresponding numbers after x can be added directly.

Mirdeep software is a very classical software in miRNA analysis. Understanding its quantitative principle will help us to better understand the process of miRNA data analysis.

This is what the quantitative principle of miRNA is. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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