Find Transposon Element insertions using long reads (nanopore), by alignment directly. (minimap2)

Overview

find_te_ins

find_te_ins is designed to find Transposon Element (TE) insertions using long reads (nanopore), by alignment directly. (minimap2)

Install

$ git clone https://github.com/bakerwm/find_te_ins.git
$ cd find_te_ins

Change the following variables upon your condition: genome_fa and te_fa in line-10 and line-11;

$ bash run_pipe.sh
run_pipe.sh 
    
    

    
   

Prerequisite

  • minimap2 - 2.17-r974-dirty, align long reads to reference genome
  • featureCounts - v2.0.0, quantification
  • samtools - v1.12, working with BAM files
  • python 3.8+
  • pysam 0.16.0.1, python module, working with BAM files

Getting Started

1 Prepare input files

  • genome_fa - reference genome in fasta format, in script run_pipe.sh, line-10
  • te_fa - TE consensus sequence in fasta format, in script run_pipe.sh, line-11
  • long reads - Long reads from NanoPore or Pacbio, in fasta or fastq format

2 Run pipe

$ cd ~/work/te_ins
# specify the path of long reads data: 
   
    /
   
$ git clone https://github.com/bakerwm/find_te_ins.git 
$ bash find_te_ins/run_pipe.sh <path-to-long-reads>/ results

[1/9] align to reference genome
[2/9] extract raw insertions from BAM, by CIGAR
[3/9] convert raw insertions to fasta format
[4/9] align raw_insertion to transposon
[5/9] extract transposon name for insertions
[6/9] merge raw_insertions by window=100
[7/9] count reads for each insertion
[8/9] save final insertions to file
[9/9] Done!

3 Output

The following files listed below are the output of the pipeline, the TE insertions saved in file *.te_ins.final.bed

$ tree -L 2 results/ONT_sample-1
.
├── ONT_sample-1
│   ├── ONT_sample-1.bam
│   ├── ONT_sample-1.bam.bai
│   ├── ONT_sample-1.raw_ins.bed
│   ├── ONT_sample-1.raw_ins.fa
│   ├── ONT_sample-1.raw_ins.fa.bam
│   ├── ONT_sample-1.raw_ins.fa.bam.bai
│   ├── ONT_sample-1.te_ins.bed
│   ├── ONT_sample-1.te_ins.final.bed
│   ├── ONT_sample-1.te_ins.final.bed6
│   ├── ONT_sample-1.te_ins.gtf
│   ├── ONT_sample-1.te_ins.quant.stderr
│   ├── ONT_sample-1.te_ins.quant.stdout
│   ├── ONT_sample-1.te_ins.quant.txt
│   ├── ONT_sample-1.te_ins.quant.txt.summary
│   ├── ONT_sample-1.te_ins.raw.txt
│   ├── run_minimap2.dm6.stderr
│   └── run_minimap2.dm6_transposon.stderr
...

{sample_name}.te_ins.final.bed

column 1. chr name of reference 
column 2. start pos of Insertion 
column 3. end pos of Insertion 
column 4. insertion name 
column 5. a fixed integer [255]  
column 6. strand # in current version, not consider the dirction of TE insertions !!!
column 7. name of TE consensus 
column 8. length of TE consensus  
column 9. proportion of the TE consensus identified  
column 10. number of supported reads for the insertion 
column 11. number of all reads cover the insertion 
column 12. proportion TE supported reads 
column 13. type of the TE insertions [full, p3, p5]

{sample_name}.te_ins.raw.txt

column 16 (last column), is the type of TE insertions: [full, p3, p5]

  • full, more then cutoff [60%] of the TE consensus were detected
  • p3, only the 3' end of the TE consensus were detected
  • p5, only the 5' end of the TE consensus were detected

In the .final.bed file, ONLY full TE insertions were saved for further analysis

Change criteria

TE types were defined in run_pipe.sh by anno_te.py, the criteria -c 0.6 could be changed to [0-1] float number based on your condition. see line-100 in file run_pipe.sh

# line-100 of run_pipe.sh
[[ ! -f ${te_ins_txt} ]] && python ${src_dir}/anno_te.py -x ${te_fa_fai} ${te_bam} | sort -k4,4 -k5,5n > ${te_ins_txt}

# change criteria to 0.7
[[ ! -f ${te_ins_txt} ]] && python ${src_dir}/anno_te.py -x ${te_fa_fai} -c 0.7 ${te_bam} | sort -k4,4 -k5,5n > ${te_ins_txt}

# remove te_ins files, and run the command again
$ rm results/ONT_sample-1.te_ins*
$ bash find_te_ins/run_pipe.sh 
   
    / results

   

How it works?

  1. extract INSERTIONS
Owner
Ming Wang
Ming Wang
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