We developed a pipeline to identify population-scale TIPs based on a pan-genome analysis and large-scale resequencing of accessions in B. rapa. The pipeline for identifying population-scale TIPs employed three sequential steps: identification of insertions and deletions, construction of the TE insertion dataset, and determination of TIPs at a population scale.
In this step, we used each of the 20 B. rapa genomes as the reference and identified insertions and deletions in the B. rapa pan-genome using the smartie-sv piepeline.
After obtaining insertions and deletions, we mapped each insertion or deletion onto the B. rapa TE library. If the similarity and coverage of one deletion (or insertion) were greater than 80% (also called ‘the 80-80 rule’), then the deletion (or insertion) was defined as a TE insertion. Furthermore, we proposed the concepts of ‘aligned regions’ and ‘unaligned regions’ to describe TIPs in the pan-genome. The concepts were based on Chiifu genomic sequences. If genomic sequences from the other 19 accessions could be covered by the Chiifu sequences, we denoted such regions as being ‘aligned regions’; if the genomic sequences in the other genomes could not be covered by the Chiifu sequences, we defined them as ‘unaligned regions’.
We implemented the strategy by mapping the short reads onto the TE insertion and their flanking sequences. If one or two boundaries for a TE insertion were covered by the short reads, we defined this accession as harboring the same TE insertion. The detailed process included three steps: we first extracted the flanking sequences of each TE insertion (including 1 kb upstream and downstream of the TE insertion); then, the upstream flanking sequence, the TE insertion sequence, and the downstream flanking sequence were linked together in order. After that, we mapped the population-scale resequencing short reads onto our constructed target sequences. If a read in one accession was directly aligned to the upstream and downstream flanking sequences, we considered that there was no TE insertion in this accession; if a read in one accession was directly aligned to the TE insertion sequence and at least one flanking sequence (upstream or downstream flanking sequence), then the accession was considered to harbor the same TE insertion.
The pipeline ITIPs is installation-free but requires dependencies: smartie-sv and bwa (Version: 0.7.17-r1188). The binary file of bwa have been provided in the /ITIPs/scripts/ folder.
git clone https://github.com/caixu0518/ITIPs.git
cd ITIPs
chmod u+x *pl
cd scripts
chmod u+x *
Two types of inputs are required for ITIPs
- Genome fasta. i.e. reference genome, query 1 genome, query 2 genome ......
- population-scale resequencing reads. i.e. Sam1_1.fq.gz, Sam1_2.fq.gz ......
Phase 1: the pipeline will generated reference TE insertion and non-reference TE insertion. i.e. reference_TE.insertions.xls, non-reference_TE.insertions.xls
Chr Start End Type SVlen Upstream Downstream Gene CDS
A10 90093 90797 deletion 704 query1;query2 BraA10g000190.3.1C BraA10g000200.3.1C - -
A10 158873 161879 deletion 3006 query1 BraA10g000350.3.1C BraA10g000340.3.1C - -
A10 161994 162256 deletion 262 query1 BraA10g000350.3.1C - - -
A10 248968 253788 deletion 4820 query2 - BraA10g000500.3.1C - -
A10 252712 253372 deletion 660 query1 - BraA10g000500.3.1C - -
A10 253389 254595 deletion 1206 query1 - BraA10g000500.3.1C - -
A10 253794 254442 deletion 648 query2 - BraA10g000500.3.1C - -
A10 325403 326892 deletion 1489 query1 BraA10g000690.3.1C - - -
A10 325405 325631 deletion 226 query2 BraA10g000680.3.1C - - -
A10 325635 326898 deletion 1263 query2 BraA10g000690.3.1C - - -
A10 329657 332373 deletion 2716 query2 - BraA10g000690.3.1C - -
Phase 2: Genotypes of TE insertions in each resequencing genome. i.e. Sam1.refereceTEinsertion and Sam1.Non-refereceTEinsertion.
TEindex AB BC AC L R Genotype
Dref100 0 0 0 5 0 GG
Dref103 0 0 0 0 0 NA
Dref104 0 0 0 5 0 GG
Dref113 0 0 0 1 0 GG
Dref116 0 0 0 0 0 NA
Dref12 0 0 0 1 0 GG
Dref122 0 0 0 0 0 NA
Dref123 0 0 0 1 0 GG
Dref125 0 0 0 1 0 GG
CC indicates that the genotype in the corresponding accession was consistent with the reference genome, and GG indicates that the genotype in the accession was different from the reference genome, NA represents missing loci.
There are three main sequential steps to identify and genotype TE insertions, corresponding to 01.Reference_Nonreference_TEinsertion.pl, 02.get_TE_insertions_and_flankingSequences.pl, and 03.TE_insertions_genotype.pl.
Step 1: Identification of reference and non-reference TE insertions between different genomes.
perl 01.Reference_Nonreference_TEinsertion.pl -h
Usage: perl 01.Reference_Nonreference_TEinsertion.pl -query <query.info.lst> -ref <reference.info.lst> -TElib <EDTA.TElib.fa> -bin <the path to smartie-sv> -script <the path to scripts>
-query [required] the query id and query genome files. Two columns (queryName queryGenomeFile).
-ref [required] the reference information. Three columns (referenceName ReferenceGenomeFile ReferenceGff3)
-TElib [required] the species TE library
-bin [required] the path to smartie-sv. i.e. /10t/caix/src/smartie-sv/bin
-script [required] the path to perl scripts
Step 2: extract flanking seuqences of each TE insertion
perl 02.get_TE_insertions_and_flankingSequences.pl -h
Usage: perl 02.get_TE_insertions_and_flankingSequences.pl -refGenome <ref.fa> -refName <ref> -script <the path to scripts> reference_TE.insertions.xls non-reference_TE.insertions.xls
-refGenome [required] the reference genome in fasta foramt.
-refName [required] the reference genome name, same as provided in the first step.
-script [required] the path to perl scripts
Step 3: Genotype TE insertions using short reads
perl 03.TE_insertions_genotype.pl -h
perl 03.TE_insertions_genotype.pl -Fasta <ref.referenceTEinsertions_and_flanking1kb.fasta> -leftRead <Sam1_1.fq.gz> -rightRead <Sam1_2.fq.gz> -samId <Sam1> -output <Sam1.refereceTEinsertion> -script <the path to scripts> -threads <threads>
-Fasta [required] the TE insertion and flanking sequences in fasta format
-leftRead [required] left read
-rightRead [required] right read
-samId [required] sample name i.e. Sam1
-output [required] TE genotype results
-script [required] the path to scripts
-threads [optional] threads default: 6 cores
we recommend that users modified their file format with those we provided in the testData.
cd testData
sh runMe.sh
cat runMe.sh
#!/bin/bash
path=`pwd`
script="${path}/../scripts/"
perl ../01.Reference_Nonreference_TEinsertion.pl -query query.info.lst \
-ref reference.info.lst \
-TElib EDTA.TElib.fa \
-bin /10t/caix/src/smartie-sv/bin \
-script ${script}
perl ../02.get_TE_insertions_and_flankingSequences.pl -refGenome ref.fa \
-refName ref \
-script ${script}
perl ../03.TE_insertions_genotype.pl -Fasta ref.Non-referenceTEinsertions_and_flanking1kb.fasta \
-leftRead Sam1_1.fq.gz \
-rightRead Sam1_2.fq.gz \
-samId Sam1 \
-output Sam1.Non-refereceTEinsertion \
-script ${script} \
-threads 10
perl ../03.TE_insertions_genotype.pl -Fasta ref.referenceTEinsertions_and_flanking1kb.fasta \
-leftRead Sam1_1.fq.gz \
-rightRead Sam1_2.fq.gz \
-samId Sam1 \
-output Sam1.refereceTEinsertion \
-script ${script} \
-threads 10
Final results:
Phase 1: reference_TE.insertions.xls and non-reference_TE.insertions.xls
Pahse 2: Sam1.refereceTEinsertion and Sam1.Non-refereceTEinsertion
Xu Cai, Runmao Lin, Jianli Liang, Graham J. King, Jian Wu, Xiaowu Wang. (2022). Transposable element insertion: a hidden major source of domesticated phenotypic variation in Brassica rapa. Plant Biotechnology Journal. https://doi.org/10.1111/pbi.13807
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent