To find drought-resistant genes in rose RNA-Seq data as a beginner, follow these steps:

1. Data Preprocessing

• Quality Control: Use FastQC to check the raw reads' quality.
• Trimming: Use Trimmomatic or Cutadapt to remove adapters and low-quality bases.

2. Read Mapping

• Align reads to the rose reference genome using HISAT2 or STAR.

3. Quantification

• Use featureCounts or HTSeq to count mapped reads per gene.
• Alternatively, use Salmon or Kallisto for pseudo-alignment.

4. Differential Gene Expression (DGE) Analysis

• Use DESeq2 or edgeR in R to find differentially expressed genes (DEGs) between drought-stressed vs. control samples.dds
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Thank you for sharing this comprehensive workflow!May I ask which reference genome you recommend for Rosa species? Also, do you have any tips for optimizing the parameters for HISAT2 alignment specifically for rose transcripts?

For Rosa species, the choice of reference genome depends on the species and the quality of the available genome assemblies. Some commonly used reference genomes for roses include:

For HISAT2 alignment optimization for rose transcripts, follow these tips:

1. Indexing the Genome

• Use the genome with transcript annotations (.gtf file) if available:hisat2-build -p 8 Rosa_chinensis_genome.fa Rosa_index
• If your species lacks a well-annotated genome, consider de novo transcriptome assembly (e.g., using Trinity).

2. Optimal Parameters for RNA-Seq Alignment

• HISAT2 is splice-aware, so use --dta for transcript assembly and --rna-strandness for strand-specific libraries.
• Example command for paired-end reads:hisat2 -p 8 --dta --rna-strandness RF -x Rosa_index \ -1 sample_R1.fastq.gz -2 sample_R2.fastq.gz \ -S output.sam
• If you have single-end reads, use:hisat2 -p 8 --dta --rna-strandness R -x Rosa_index \ -U sample.fastq.gz -S output.sam

3. Handling Large Genomes

• Increase memory efficiency for large rose genomes:hisat2 -p 8 --dta --max-intronlen 500000 -x Rosa_index \ -1 sample_R1.fastq.gz -2 sample_R2.fastq.gz \ -S output.sam
• --max-intronlen 500000 is useful if you expect large introns in the genome.

4. Post-Alignment Processing

• Convert SAM to BAM:samtools view -bS output.sam > output.bam samtools sort output.bam -o sorted_output.bam
• Quality check:samtools flagstat sorted_output.bam

5. Alternative Approach: Direct Transcriptome Alignment

• If a reference genome is unavailable or low-quality, you can align directly to a transcriptome index:hisat2-build transcriptome.fa transcriptome_index hisat2 -p 8 -x transcriptome_index -U sample.fastq.gz -S output.sam may be it will be helpful for you Leila Khosravi

Your help was incredibly kind. Many thanks!