AnnoSINE

SINE Annotation Tool for Plant Genomes

Introduction
Prerequisites
Installation
Usage
- Argument
- Inputs
- Outputs
- Testing
Citations

Introduction

AnnoSINE is a SINE annotation tool for plant genomes. The program is designed to generate high-quality non-redundant SINE libraries for genome annotation. It uses the manually curated SINE library in the Oryza sativa genome to benchmark the annotation performance.

Prerequisites

To use AnnoSINE, you need to install the tools listed below.

Python 3.7.4
HMMER 3.3.1
BLAST+ 2.10.1
TRF 4.09.1
IRF 3.08
CD-HIT 4.8.1
RepeatMasker 4.1.2 (Search Engine: RMBlast 2.14.1+)
Node 12.18.2

Installation

cd ./AnnoSINE/bin
pip3 install -r requirements.txt

Usage

python3 AnnoSINE.py [options] <mode> <input_filename> <output_filename>

Argument

positional arguments:
  mode                  [1 | 2 | 3]
                        Choose the running mode of the program.
                                1--Homology-based method;
                                2--Structure-based method;
                                3--Hybrid of homology-based and structure-based method.
  input_filename        input genome assembly path
  output_filename       output files path

optional arguments:
  -h, --help                 show this help message and exit
  -l, --length_factor        Threshold of the local alignment length relative to the the BLAST query length (default: 0.3)
  -c, --copy_number_factor   Threshold of the copy number that determines the SINE boundary (default: 0.15)
  -s, --shift                Maximum threshold of the boundary shift (default: 80)
  -g, --gap                  Maximum threshold of the trancated gap (default: 10)
  -minc, --copy_number       Minimum threshold of the copy number for each element (default: 20)
  -b, --boundary             Output SINE seed boundaries based on TSD or MSA (default: msa)
  -f, --figure          Output the SINE seed MSA figures and copy number profiles (y/n) (default: n)
  -r, --non_redundant    Annotate SINE in the whole genome based on the non—redundant library (y/n) (default: y)

Inputs

Genome sequence(fasta format).

Outputs

Redundant SINE library: $ Step7_cluster_output.fasta
Non-redundant SINE library with serial number: $Seed_SINE.fa.
Whole-genome SINE annotation: $Input_genome.fasta.out. This file contains high-similarity SINE annotations.

Intermediate Files

SINE candidates information predicted by homology search: $ ../Family_Seq/Family_Name/Family_Name.out. (m=1 or 3 required)
SINE candidate sequences predicted by structure search: $ ../Input_Files/Input_genome-matches.fasta. (m=2 or 3 required)
Extended candidate sequences for TSD search: $ Step1_extend_tsd_input.fa
TSD identification outputs: $ Step2_tsd.txt
MSA extended input sequences flanked with TSD: $ Step2_extend_blast_input.fa
MSA output: $ Step3_blast_output.out
Intermediate sequences with MSA quality examination: $ Step3_blast_process_output.fa
SINE candidate sequences after MSA quality examination: $ Step4_rna_input.fasta
SINE candidates blast against RNA database outputs $ Step4_rna_output.out
Classified SINE candidates after RNA examintation $ Step4_rna_output.fasta
TRF output $ Step4_rna_output.fasta.2.5.7.80.10.10.2000.dat
SINE candidates after removing elements consist of tandem repeats $ Step5_trf_output.fasta
SINE candidate sequences after extension: $ Step6_irf_input.fasta.
IRF output $ Step6_irf_input.fasta.2.3.5.80.10.20.500000.10000.dat
SINE candidates after removing elements flanked with inverted repeats: $ Step6_irf_output.fasta
CD-HIT output: $ Step7_cluster_output.fasta.clstr

Testing

You can test the AnnoSINE with one chromosome in Arabisopsis thaliana (it takes about 6 mins).

cd ./AnnoSINE/bin
python3 AnnoSINE.py 3 ../Testing/A.thaliana_Chr4.fasta ../Output_Files

Results of AnnoSINE tests on testing data are saved in Output_Files.

Citations

Please cite the paper if you use this code:

Yang Li, Ning Jiang, Yanni Sun, AnnoSINE: a short interspersed nuclear elements annotation tool for plant genomes, Plant Physiology, Volume 188, Issue 2, February 2022, Pages 955–970, https://doi.org/10.1093/plphys/kiab524

yangli557 / AnnoSINE