Small RNA processing

Small RNA sequencing is a type of RNA-sequencing (RNA-seq) that specifically targets and sequences small RNA molecules in a sample.

RNA-seq is a technique that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, capturing a snapshot of the transcriptome.

Small RNAs, including microRNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs), play crucial roles in gene regulation. They typically range from 20 to 30 nucleotides in length.

1. prepare raw data

   #mv Data_Ute_smallRNA_3/bundle_v1 Data_Ute_smallRNA_5
   ln -s ../Data_Ute_smallRNA_3/bundle_v1 .
   
   #OLD    MKL_1_wt_1_221216.fastq.gz -> ../221216_NB501882_0404_AHLVNMBGXM/ute/nf796/MKL_1_wt_1_S16_R1_001.fastq.gz
   #OLD    MKL_1_wt_2_221216.fastq.gz -> ../221216_NB501882_0404_AHLVNMBGXM/ute/nf797/MKL_1_wt_2_S17_R1_001.fastq.gz
   
   ln -s ./230623_newDemulti_smallRNAs/210817_NB501882_0294_AHW5Y2BGXJ_smallRNA_Ute_newDemulti/2021_nf_ute_smallRNA/nf655/MKL_1_derived_EV_miRNA_S1_R1_001.fastq.gz 2021_August_nf655_MKL-1_EV-miRNA.fastq.gz
   ln -s ./230623_newDemulti_smallRNAs/210817_NB501882_0294_AHW5Y2BGXJ_smallRNA_Ute_newDemulti/2021_nf_ute_smallRNA/nf657/WaGa_derived_EV_miRNA_S2_R1_001.fastq.gz 2021_August_nf657_WaGa_EV-miRNA.fastq.gz
   
   ln -s ./230623_newDemulti_smallRNAs/220617_NB501882_0371_AH7572BGXM_smallRNA_Ute_newDemulti/2022_nf_ute_smallRNA/nf774/0403_WaGa_wt_S1_R1_001.fastq.gz 2022_August_nf774_0403_WaGa_wt.fastq.gz
   ln -s ./230623_newDemulti_smallRNAs/220617_NB501882_0371_AH7572BGXM_smallRNA_Ute_newDemulti/2022_nf_ute_smallRNA/nf780/0505_MKL1_wt_S2_R1_001.fastq.gz 2022_August_nf780_0505_MKL-1_wt.fastq.gz
   
   ln -s ./230623_newDemulti_smallRNAs/221216_NB501882_0404_AHLVNMBGXM_smallRNA_Ute_newDemulti/2022_nf_ute_smallRNA/nf796/MKL-1_wt_1_S1_R1_001.fastq.gz 2022_November_nf796_MKL-1_wt_1.fastq.gz
   ln -s ./230623_newDemulti_smallRNAs/221216_NB501882_0404_AHLVNMBGXM_smallRNA_Ute_newDemulti/2022_nf_ute_smallRNA/nf797/MKL-1_wt_2_S2_R1_001.fastq.gz 2022_November_nf797_MKL-1_wt_2.fastq.gz
   
   ln -s ./230602_NB501882_0428_AHKG53BGXT/demulti_new/nf876/1002_WaGa_sT_Dox_S1_R1_001.fastq.gz    2023_June_nf876_1002_WaGa_sT_Dox.fastq.gz
   ln -s ./230602_NB501882_0428_AHKG53BGXT/demulti_new/nf887/2312_MKL_1_scr_DMSO_S2_R1_001.fastq.gz 2023_June_nf887_2312_MKL-1_scr_DMSO.fastq.gz
   

2. main run

   mkdir our_out
   # -qc -ra TGGAATTCTCGGGTGCCAAGGAACTCCAGTCAC -rb 4 #NOT_USED
   # -mic -mtool Blast -mdb viruses #IGNORING Microbe Module since it is very time-consuming!
   
   #jhuang@hamburg:~/DATA/Data_Ute/Data_Ute_smallRNA_5$ java -jar COMPSRA.jar -ref hg38 -qc -ra TGGAATTCTCGGGTGCCAAGGAACTCCAGTCAC -rb 4 -rh 20 -rt 20 -rr 20 -rlh 8,17 -aln -mt star -ann -ac 1,2,3,4,5,6 -mic -mtool Blast -mdb viruses -in 2021_August_nf655_MKL-1_EV-miRNA.fastq.gz -out ./our_out/
   for sample in 2021_August_nf655_MKL-1_EV-miRNA 2021_August_nf657_WaGa_EV-miRNA    2022_August_nf774_0403_WaGa_wt 2022_August_nf780_0505_MKL-1_wt    2022_November_nf796_MKL-1_wt_1 2022_November_nf797_MKL-1_wt_2    2023_June_nf876_1002_WaGa_sT_Dox 2023_June_nf887_2312_MKL-1_scr_DMSO; do
     mkdir our_out/${sample}/
     java -jar COMPSRA.jar -ref hg38   -rh 20 -rt 20 -rr 20 -rlh 8,17 -aln -mt star -ann -ac 1,2,3,4,5,6  -in ${sample}.fastq.gz -out ./our_out/
   done
   

3. prepare Data_Ute/Data_Ute_smallRNA_4/sample.list

   2021_August_nf655_MKL-1_EV-miRNA
   2021_August_nf657_WaGa_EV-miRNA
   2022_August_nf774_0403_WaGa_wt
   2022_August_nf780_0505_MKL-1_wt
   2022_November_nf796_MKL-1_wt_1
   2022_November_nf797_MKL-1_wt_2
   2023_June_nf876_1002_WaGa_sT_Dox
   2023_June_nf887_2312_MKL-1_scr_DMSO
   

4. extract the raw counts and perform statistical test on pre-defined groups.

   #The following results calculate raw counts (Note: If you only want to merge the count files, you can use -fm -fms.)
   java -jar COMPSRA.jar -ref hg38 -fun -fm -fms 1-8 -fdclass 1,2,3,4,5 -fdann -pro COMPSRA_MERGE -inf ./sample.list -out ./our_out/
   #The following command calculate statistical test after defining case and control.
   java -jar COMPSRA.jar -ref hg38 -fun -fd -fdclass 1,2,3,4,5,6 -fdcase 1-2 -fdctrl 3-6 -fdnorm cpm -fdtest mwu -fdann -pro COMPSRA_DEG -inf ./sample.list -out ./our_out/
   
   sed -i -e 's/_August/-08/g' COMPSRA_MERGE_0_miRNA.txt
   sed -i -e 's/_November/-11/g' COMPSRA_MERGE_0_miRNA.txt
   sed -i -e 's/_June/-06/g' COMPSRA_MERGE_0_miRNA.txt
   sed -i -e 's/_STAR_Aligned_miRNA.txt//g' COMPSRA_MERGE_0_miRNA.txt
   #sed -i -e 's/_piRNA.txt//g' COMPSRA_MERGE_0_piRNA.txt
   #sed -i -e 's/_tRNA.txt//g' COMPSRA_MERGE_0_tRNA.txt
   #sed -i -e 's/_snoRNA.txt//g' COMPSRA_MERGE_0_snoRNA.txt
   #sed -i -e 's/_snRNA.txt//g' COMPSRA_DEG_0_snRNA.txt
   sed -i -e 's/_August/-08/g' COMPSRA_DEG_0_miRNA.txt
   sed -i -e 's/_November/-11/g' COMPSRA_DEG_0_miRNA.txt
   sed -i -e 's/_June/-06/g' COMPSRA_DEG_0_miRNA.txt
   sed -i -e 's/_STAR_Aligned_miRNA.txt//g' COMPSRA_DEG_0_miRNA.txt
   
   import pandas as pd
   df = pd.read_csv('COMPSRA_MERGE_0_miRNA.txt', sep='\t', index_col=0)
   df = df.drop(columns=['Unnamed: 9'])
   # Assuming df is your DataFrame
   df.loc['Sum'] = df.sum(numeric_only=True)
   
   df.to_csv('COMPSRA_MERGE_0_miRNA_.txt', sep='\t')
   df = pd.read_csv('COMPSRA_MERGE_0_piRNA.txt', sep='\t', index_col=0)
   df = df.drop(columns=['Unnamed: 9'])
   df.loc['Sum'] = df.sum(numeric_only=True)
   df.to_csv('COMPSRA_MERGE_0_piRNA_.txt', sep='\t')
   df = pd.read_csv('COMPSRA_MERGE_0_snoRNA.txt', sep='\t', index_col=0)
   df = df.drop(columns=['Unnamed: 9'])
   df.loc['Sum'] = df.sum(numeric_only=True)
   df.to_csv('COMPSRA_MERGE_0_snoRNA_.txt', sep='\t')
   df = pd.read_csv('COMPSRA_MERGE_0_snRNA.txt', sep='\t', index_col=0)
   df = df.drop(columns=['Unnamed: 9'])
   df.loc['Sum'] = df.sum(numeric_only=True)
   df.to_csv('COMPSRA_MERGE_0_snRNA_.txt', sep='\t')
   df = pd.read_csv('COMPSRA_MERGE_0_tRNA.txt', sep='\t', index_col=0)
   df = df.drop(columns=['Unnamed: 9'])
   df.loc['Sum'] = df.sum(numeric_only=True)
   df.to_csv('COMPSRA_MERGE_0_tRNA_.txt', sep='\t')
   
   #samtools flagstat **.bam
   #47217410 + 0 in total (QC-passed reads + QC-failed reads)
   #45166321 + 0 mapped (95.66% : N/A)
   #2051089 + 0 in total (QC-passed reads + QC-failed reads)
   
   #TODO: check the microRNA in the paper mentioned in which position?
   #Single publications on EVs as transport vehicles for specific miRNAs in the pathogenesis of Merkel cell carcinoma have also been reported, such as miR-375 and its function in proliferation
   
   ~/Tools/csv2xls-0.4/csv_to_xls.py COMPSRA_MERGE_0_miRNA_.txt \
   COMPSRA_MERGE_0_piRNA_.txt \
   COMPSRA_MERGE_0_tRNA_.txt \
   COMPSRA_MERGE_0_snoRNA_.txt \
   COMPSRA_MERGE_0_snRNA_.txt \
   -d$'\t' -o raw_counts.xls;
   
   # sorting the DEG table, change the sheet labels to 'miRNA_between_columns_B-C_and_columns_D-G'
   ~/Tools/csv2xls-0.4/csv_to_xls.py COMPSRA_DEG_0_miRNA.txt -d$'\t' -o normalized_and_significance_test_miRNA.xls;
   
   ##merging the row counts and statical values
   #cut -f1-1 COMPSRA_MERGE_0_snoRNA.txt > f1_MERGE
   #cut -f1-1 COMPSRA_DEG_0_snoRNA.txt > f1_DEG
   #cut -f1-1 COMPSRA_MERGE_0_miRNA.txt > f1_MERGE
   #cut -f1-1 COMPSRA_DEG_0_miRNA.txt > f1_DEG
   #diff f1_MERGE f1_DEG
   

5. calculate the number of total reads, total human reads and total non-human reads.

   for sample in 2021_August_nf655_MKL-1_EV-miRNA 2021_August_nf657_WaGa_EV-miRNA    2022_August_nf774_0403_WaGa_wt 2022_August_nf780_0505_MKL-1_wt    2022_November_nf796_MKL-1_wt_1 2022_November_nf797_MKL-1_wt_2    2023_June_nf876_1002_WaGa_sT_Dox 2023_June_nf887_2312_MKL-1_scr_DMSO; do
     echo "--------------- ${sample} ---------------"
     samtools flagstat ./${sample}/${sample}_STAR_Aligned.out.bam
     samtools flagstat ./${sample}/${sample}_STAR_Aligned_UnMapped.bam
   done
   

COMPSRA was composed of five functional modules: Quality Control, Alignment, Annotation, Microbe and Function. They are integrated into a pipeline and each module can also process independently.

• Quality Control: To deal with fastq files and filter out the adapter sequences and reads with low quality.

  • FASTQ files from the small RNA sequencing of biological samples are the default input.
  • First, the adapter portions of a read are trimmed along with any randomized bases at ligation junctions that are produced by some small RNA-seq kits (e.g., NEXTflexT M Small RNA-Seq kit). The adapter sequences, typically situated at the 3' (3-prime) end, vary across different kits. Some commonly employed adapter sequences are listed below:
    • TruSeq Small RNA (Illumina) TGGAATTCTCGGGTGCCAAGG
    • Small RNA Kits V1 (Illumina) TCGTATGCCGTCTTCTGCTTGT
    • Small RNA Kits V1.5 (Illumina) ATCTCGTATGCCGTCTTCTGCTTG
    • NEXTflex Small RNA Sequencing Kit v3 for Illumina Platforms (Bioo Scientific) TGGAATTCTCGGGTGCCAAGG
    • LEXOGEN Small RNA-Seq Library Prep Kit (Illumina) TGGAATTCTCGGGTGCCAAGGAACTCCAGTCAC
  • The read quality of the remaining sequence is evaluated using its corresponding Phred score.
  • Poor quality reads are removed according to quality control parameters set in the command line (-rh 20 –rt 20 –rr 20).
  • Users can specify qualified reads of specific length intervals for input into subse-quent modules.

• Alignment:

  • To align the clean reads to the reference genome. COMPSRA uses STAR as its default RNA sequence aligner with default parameters which are customizable on the command line.
  • Qualified reads from the QC module output are first mapped to the human genome hg19/hg38, and then aligned reads are quantified and annotated in the Annotation Module.
  • Reads that could not be mapped to the human genome are saved into a FASTA file for input into the Microbe Module.

• Annotation:

  • To annotate different kinds of circulating RNAs based on the alignment result.
  • COMPSRA currently uses several different small RNA databases for annotating human genome mapped reads and provides all the possible annotations:
  • miRBase (Kozomara and Griffiths-Jones, 2011) for miRNA;
  • piRNABank (Sai Lakshmi and Agrawal, 2008);
  • piRBase (Zhang, et al., 2014) and piRNACluster (Rosenkranz, 2016) for piRNA;
  • gtRNAdb (Chan and Lowe, 2016) for tRNA;
  • GENCODE release 27 (Harrow, et al., 2012) for snRNA and snoRNA;
  • circBase (Glazar, et al., 2014) for circular RNA.
  • To conform the different reference human genome versions in these databases, we use an automatic LiftOver created by the UCSC Genome Browser Group.
  • All the databases used are already pre-built, enabling speedy annotation.

• Microbe:

  • To predict the possible species of microbes existed in the samples.
  • The qualified reads that could not be mapped to the human genome in the Alignment Module are aligned to the nucleotide (nt) database (Coordinators, 2013) from UCSC using BLAST.
  • The four major microbial taxons archaea, bacteria, fungi and viruses are supported.

• Function:

  • To perform differential expression analysis and other functional studies to be extended.
  • The read count of each RNA molecule that is identified in the Annotation Module is outputted as a tab-delimited text file according to RNA type.
  • With more than one sample FASTQ file inputs, the output are further aggregated into a data matrix of RNA molecules as rows and samples as columns showing the read counts of an RNA molecule across different samples.
  • The user can mark each sample FASTQ file column as either a case or a control in the command line, and perform a case versus control differential expression analysis for each RNA molecule using the Mann-Whitney rank sum test (Wilcoxon Rank Sum Test) as the default statistical test.

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