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rMETL - realignment-based Mobile Element insertion detection Tool for Long read

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rMETL - realignment-based Mobile Element insertion detection Tool for Long read

PyPI version Anaconda-Server Badge Anaconda-Server Badge Anaconda-Server Badge Anaconda-Server Badge


Introduction

Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL has the ability to discover MEIs sensitively as well as prevent false positives. It is suited to produce high-quality MEI callsets in many genomics studies.


Simulated datasets

The simulated datasets use for benchmarking are available at: Google drive


Memory usage

The memory usage of rMETL can fit the configurations of most modern servers and workstations. Its peak memory footprint is about 7.05 Gigabytes (default setting), on a server with Intel Xeon CPU at 2.00 GHz, 1 Terabytes RAM running Linux Ubuntu 14.04. These reads were aligned to human reference genome hs37d5.


Dependences

1. pysam
2. Biopython
3. ngmlr
4. samtools
5. cigar

Python version 2.7

Installation

#install via pip
$ pip install rMETL

#install via conda
$ conda install -c bioconda rmetl

#install from github
$ git clone https://github.com/tjiangHIT/rMETL.git (git clone https://github.com/hitbc/rMETL.git)
$ cd rMETL/
$ pip install .

Current version of rMETL has been tested on 64bit Linux operating system.


Synopsis

Inference of putative MEI loci.

rMETL.py detection <alignments> <reference> <temp_dir> <output>

Realignment of chimeric read parts.

rMETL.py realignment <FASTA> <MEREF> <output>

Mobile Element Insertion calling.

rMETL.py calling <SAM> <reference> <out_type> <output>

Strongly recommend making output directory manually at first.:blush:


Optional Parameters

Detection

Parameters Descriptions Defaults
MIN_SUPPORT Mininum number of reads that support a ME. 5
MIN_LENGTH Mininum length of ME to be reported. 50
MIN_DISTANCE Mininum distance of two ME clusters. 20
THREADS Number of threads to use. 1
PRESETS The sequencing type <pacbio,ont> of the reads. pacbio

Realignment

Parameters Descriptions Defaults
THREADS Number of threads to use. 1
PRESETS The sequencing type <pacbio,ont> of the reads. pacbio
SUBREAD_LENGTH Length of fragments reads are split into. 128
SUBREAD_CORRIDOR Length of corridor sub-reads are aligned with. 20

Calling

Parameters Descriptions Defaults
HOMOZYGOUS The mininum score of a genotyping reported as a homozygous. 0.8
HETEROZYGOUS The mininum score of a genotyping reported as a heterozygous. 0.3
MIN_MAPQ Mininum mapping quality. 20
CLIPPING_THRESHOLD Mininum threshold of realignment clipping. 0.5
SAMPLE The name of the sample which be noted. None
MEI Enables rMETL to display MEI/MED only. False

Citation

If you use rMETL, please cite:

Tao Jiang et al; rMETL: sensitive mobile element insertion detection with long read realignment, Bioinformatics, Volume 35, Issue 18, 15 September 2019, Pages 3484–3486, https://doi.org/10.1093/bioinformatics/btz106


Contact

For advising, bug reporting and requiring help, please post on Github Issue or contact [email protected].

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