MicroRNAs (miRNAs) are short, non‐coding RNA molecules that function to regulate gene expression post‐transcriptionally. Due to the potential of one miRNA to target multiple gene transcripts, miRNAs are recognized as a primary mechanism to regulate gene expression. MicroRNA targets can be identified by pairing between the miRNA seed region and complementary sites within target mRNAs. Several miRNA target prediction tools, based on different computational approaches (e.g., modeling of physical interactions, machine learning techniques, etc.) are available. These tools include TargetScan, miRanda, and so on.
A common set of rules for miRNA target prediction includes complementarity, free energy calculations and evolutionary arguments and cooperativity of binding.
This chapter aims at predicting mRNA targets of the given microRNAs using various miRNA prediction tools.
Select the species “Human” and enter the human gene symbol (i.e., HMGA2).
Select the required conditions from the drop‐down menu of given options.
Click on the “submit” button to obtain the result, or the “reset” button to perform a new prediction (Figure 46.1).
46.2.3 Results
For the gene “HMGA2”, TargetScan gives multiple transcripts. Click on the transcript to see the details.
The resulting page gives the conserved sites for miRNA families corresponding to the gene HMGA2 (in all the Mammalia in this case).
The table has compiled detailed information (position, length, score, etc.) about the conserved sites for miRNA families corresponding to the gene HMGA2).
46.3 miRNA TARGET PREDICTION BY TARGETSCAN IN HUMAN
46.3.1 Objective
To search for all the predicted miRNA targets for the miRNA “miR‐1‐3p” in the TargetScan tool.
46.3.2 Procedure
Select the species “Human” and enter the microRNA name (i.e., miR‐1‐3p).
Click on the “submit” button to obtain the result or the “reset” button to perform a new prediction.
46.3.3 Results
The resulting page gives a list of all the predicted targets for the human miRNA “miR‐1‐3p”. The table has the information about the target gene symbol, transcripts with sites, gene name, score, and so on.
psRNATarget is a plant small RNA target analysis server, which provides reverse complementary matching between small RNA and target transcript, and target‐site accessibility evaluation by calculating the unpaired energy essential to “open” secondary structure around the target site on mRNA of the small RNA (Dai and Zhao, 2011).
46.4.1 Objective
To search for the targets of Arabidopsis thaliana (denoted as ath) miRNA sequences as inputted by the user, against a genomic library of Arabidopsis thaliana.
Paste the following Arabidopsis thaliana miRNA sequences in the box provided:
>ath‐miR156a UGACAGAAGAGAGUGAGCAC
>ath‐miR157a UUGACAGAAGAUAGAGAGCAC
>ath‐miR158a UCCCAAAUGUAGACAAAGCA
>ath‐miR398a UGUGUUCUCAGGUCACCCCUU
>ath‐miR398b UGUGUUCUCAGGUCACCCCUG
>ath‐miR398c UGUGUUCUCAGGUCACCCCUG
>ath‐miR834 UGGUAGCAGUAGCGGUGGUAA
>ath‐miR390a AAGCUCAGGAGGGAUAGCGCC
>ath‐miR390b AAGCUCAGGAGGGAUAGCGCC
An existing library of transcripts or a genomic library for target search is to be selected from the list – namely, Arabidopsis thaliana, genomic DNA, 3.4 K segments from a strand with 0.4 K overlapped region, TAIR.
Add the required parameters given options, or use default values.
Click on the “submit” button to obtain the result or the “reset” button to perform a new prediction.
46.4.3 Results
The result page gives a list of predicted miRNA/Target pairs for the input miRNA sequences against the selected genomic library. The result tabulates miRNA accession, target accession, expectation score, target accessibility, alignment, and also the target description. The server also has a facility for batch‐downloading of the complete result.
This is command‐line software that runs on Linux and uses a weighted dynamic programming algorithm to obtain the candidate sequences. This algorithm uses a score to rank the predictions that consist of a weighted sum based on matches, mismatches, and G:U wobbles (Enright et al., 2004).
46.5.1 Objective
To search for the targets of user submitted miRNA sequences against the RNA sequence.
46.5.2 Procedure
Here, sheep miRNA will be used to predict the target by search against the set of sheep RNA, using miRanda:
Paste the following sequence, create an input file named mature.fa. and keep it in the src directory of miRandatool:
>oar‐let‐7b MIMAT0014963 UGAGGUAGUAGGUUGUGUGGU
Download a coding set of sheep from NCBI from the link ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF_000298735.2_Oar_v4.0/GCF_000298735.2_Oar_v4.0_rna.fna.gz, and keep in the src directory of miRanda software.
Sheep.fa is the coding sequence for sheep, as downloaded from NCBI.
Type the command./mirandamature.fasheep.fa > sheep_target.out
46.5.3 Result
miRanda provides details on predicted miRNA/Target pairs for the input miRNA sequences against the coding sequence of sheep. Gene symbol, gene names, RefSeq id, alignment, score, and so on are stored in the output file.
46.6 QUESTIONS
1. Predict biological targets of the conserved miRNA family in mouse using the TargetScan web server with broadly conserved microRNA family “miR‐21‐5p/590‐5p”. How many target genes are predicted from this miRNA family?
2. From the above search made, how many are conserved sites, and how many poorly conserved sites are there?
3. Find the predicted target with conserved sites only in the mouse, for conserved miRNA family miR‐21‐5p/590‐5p.
4. Find the predicted conserved miRNA target sites for gene Khc‐73 in D. melanogaster using the TargetScanFly server. Discuss the output in detail.