使用MEANGS组装线粒体

第一次体会到文献鸟的好处，给我推送了一篇bioinformatics的文章
https://doi.org/10.1093/bib/bbab538](https://doi.org/10.1093/bib/bbab538
https://github.com/YanCCscu/meangs

功能是“The MEANGS is a seed-free software that applies trie-search to extend contigs from self-discovery seeds and assemble mitogenome, from NGS data.”即组装线粒体。

目前MEANGS (v1.0)只支持paired-end data

1.安装和使用

git clone https://github.com/YanCCscu/MEANGS.git
cd MEANGS
./meangs.py --silence -1 1.fq.gz -2 2.fq.gz -o Out -t 16 -i 300 --species_class Arthropoda

-i 是library insert length
-o 就是指定输出文件夹，${prefix}_deep_detected_mito.fas就是组装好的线粒体
--species_class是指定组装的物种，具体有哪些见下文参数说明

这个软件自带注释，注释用的是mitos2，mitos2有在线网页版直接百度就搜得到，但个人亲测mitos1似乎有些时候更好用，，具体用法就是把fasta文件上传上去即可。
亲测，这样注释出来的CDS会有丢失起始密码子和终止密码子的情况，NCBI上找几个近缘物种手动改一下就行

mitos1/2网址
http://mitos.bioinf.uni-leipzig.de/index.py
http://mitos2.bioinf.uni-leipzig.de/index.py

image.png

好像是把测序文件比对到这些数据库上，基于此来组装，这样的话估计只能组装这些物种

我的命令行

./meangs.py -1 1.fq.gz -2 2.fq.gz -o out_deep -t 16 -i 300 --species_class Arthropoda --deepin

3.参数说明

usage: meangs.py [-h] [-1 FQ1] [-2 FQ2] [-o OUTBASE] [-t THREADS] [-i INSERT]
                 [-q QUALITY] [-n NSAMPLE] [-s SEQSCAF]
                 [--species_class {A-worms,Arthropoda,Bryozoa,Chordata,Echinodermata,Mollusca,Nematoda,N-worms,Porifera-sponges}]
                 [--deepin] [--clip] [--keepIntMed] [--keepMinLen KEEPMINLEN]
                 [--skipassem] [--skipqc] [--skiphmm] [--skipextend]
                 [--silence]

optional arguments:
  -h, --help            show this help message and exit
  -1 FQ1, --fq1 FQ1     Input paired end _1.fq[.gz] files,seprated by ','
  -2 FQ2, --fq2 FQ2     Input paired end _2.fq[.gz] files,seprated by ','
  -o OUTBASE, --outBase OUTBASE
                        Output prefix of dir and files
  -t THREADS, --threads THREADS
                        Analysis threads
  -i INSERT, --insert INSERT
                        library insert length
  -q QUALITY, --quality QUALITY
                        Threshold value for low base quality
  -n NSAMPLE, --nsample NSAMPLE
                        Number of reads sampled from input reads, default 0
                        (keep all reads)
  -s SEQSCAF, --seqscaf SEQSCAF
                        specific a sequences files(fasta) just for annotation
  --species_class {A-worms,Arthropoda,Bryozoa,Chordata,Echinodermata,Mollusca,Nematoda,N-worms,Porifera-sponges}
                        taxon of species belong to
  --deepin              run deeper mode to assembly mitogenome
  --clip                detect circle clip point for mitogenome
  --keepIntMed          keep the intermediate files
  --keepMinLen KEEPMINLEN
                        Threshold of reads length to keep after remove low
                        quality bases
  --skipassem           skip the process of assembly
  --skipqc              skip the process of QC
  --skiphmm             skip the process of hmmer
  --skipextend          skip the process of extend in deepin mode
  --silence             run the program in silence mode, the standard output
                        will redirect to specific log file

Example:
#run meangs in a quick mode with paird-end library of insert size 350bp, 16 threads are called.
    meangs.py --silence -1 1.fq.gz -2 2.fq.gz -o OutBase -t 16 -i 350
#run meangs in a 'deepin mode' the first 2000000 reads in both input fastq files will be used the construct mito-genome
    meangs.py -1 R1.fastq.gz -2 R2.fastq.gz -o A3 -t 16 -n 2000000 -i 300 --deepin

关于输出
All output files were stored in one directory assigned by the -o option.
The prefix_deep_detected_mito.fas is the finally assembled mitochondrial genome, Genes in mitochondrail genome is annotated automatically and stored in the file prefix_hmmout_tbl_sorted.gff