1. Gather known miRNAs and pre-miRNAs from miRBase
cd-hit -i hairpin.fa -o hairpin_nr.fa -c 0.9 -n 5
cd-hit -i mature.fa -o mature_nr.fa -c 0.9 -n 5
2. Align pre-miRNA sequences to the bitter gourd genome using BLASTn
blastn -query hairpin_nr.fa -db bitter_gourd_genome.fa -out pre_miRNA_alignments.txt -outfmt 6 -num_threads 4
3. Filter alignments
python filter_alignments.py pre_miRNA_alignments.txt
4. Fragment sequences into 200-nucleotide segments using SeqKit
seqkit sliding -s 25 -W 200 -o fragmented_sequences.fa filtered_sequences.fa
1. Align high-quality clean reads to the bitter gourd genome using BWA
bwa mem -T 20 bitter_gourd_genome.fa reads.fq > aligned_reads.sam
2. Identify circRNAs using CIRI2
CIRI2.pl -T 4 -I aligned_reads.sam -O circRNAs.txt -F bitter_gourd_genome.fa -A annotation.gtf
1. Map RNA-seq reads using HISAT2
hisat2 -x bitter_gourd_genome -1 reads_1.fq -2 reads_2.fq -S aligned_reads.sam
2. Assemble transcripts using StringTie
stringtie aligned_reads.sam -o assembled_transcripts.gtf
1. Quality check of raw reads
fastqc *.fastq.gz
2. Reads quality filtering
AdapterRemoval --file1 filename.fastq.gz --basename filename --minlength 30 --trimns --trimqualities --gzip
3. Alignment of pre-processed reads to the reference genome with BWA aln
bwa aln reference.fasta filename.fastq.gz > filename.sai
4. Variant calling
samtools mpileup -B -ugf reference.fasta filename.final.sort.rescaled.bam | bcftools call -vmO z - > filename.vcf.gz
1. SSR Identification using MISA
perl misa.pl genome1.fa
Output: genome1.fa.misa (file containing all detected SSRs)
2. Extract Flanking regions using perl script
perl ssr.pl genome1.fa.misa
3. Prepare Primer3 Input Files
perl p3in.pl genome1
4. Design Primers using Primer3
primer3-2.3.6/src/primer3_core < reference.p3in > reference.p3out
5. Process Primer3 Output
perl p3out.pl reference.p3out reference.out primers
Output files:
- reference.out (formatted primer details)
- primers (final primer sequences in FASTA format)
