5-prime-mNET-seq-data-analysis

Alignment

Aligner bowtie is used for alignment.

Bowtie alignment command:

./bowtie-1.1.2/bowtie-build -f NC_000913_3.fna bowtie_nc000913_3
./bowtie-1.1.2/bowtie --phred33-quals -5 6 -3 15 -n 0 -l 30 -m 1 \
bowtie_nc000913_3 \
sampleID_5_prime_mNET_seq_data.fastq \
sampleID_5_prime_mNET_seq_Tf6t15N0L30M1_ec3_bowtieOut.txt \
2> sampleID_5_prime_mNET_seq_Tf6t15N0L30M1_ec3_bowtieStats.txt

Parameters:

• -5 6: trim off first 6bp
• -3 15: trim off last 15bp (30bp left for alignment)
• -n 0: maximum number of mismatch in seed is 0
• -l 30: seed length is 30, which is equal to the trimmed read length
• -m 1: Suppress all alignments for a particular read or pair if more than 1 reportable alignments exist for it. Only report uniquely aligned reads. Single reads aligned uniquely to both + and - strands will also be discarded.

Count Aligned Reads

Parse bowtie output:

 ./count_bowtie_out.py 10 bowtie_output.txt count_stats.txt count_table.txt

Output format: Tab delimited. 3 fileds from left to right: TSS position (0-based), + strand read count, - strand read count.

Filter tRNA Reads

Filter out reads started within the annotated tRNA regions. Python intervaltree package used in the script.

./filter_tRNA.py NC_000913_3.gff count_table.txt count_table_tRNAf_stats.txt count_table_tRNAf.txt NC_000913_3_tRNA.gff

Identify TSS

Python script command:

./identify_TSS.py count_table_tRNAf.txt NC_000913_3.fna call_tss_stats.txt tss_list.txt

Output fields are:

1. TSS coordinate (first base is 0)
2. Strand
3. Sequence from upstream 50bp to downstream 50bp of TSS
4. 4 - 14: Read counts from upstream 5bp to downstream 5bp.

Notes

1. Type ./somePythonScript.py and RETURN to get usage help of the script.
2. Genome coordinates are kept 0-based in all scripts.