MEGAnE, Mobile Element Genotyping Analysis Environment, identifies and genotypes polymorphic mobile elements from short-read whole genome shotgun sequencing data (WGS). The current version does not support whole exome sequencing data nor is it tuned to detect somatic polymorphisms. The initial release of MEGAnE officially supports human and mouse datasets. However, we designed MEGAnE to allow analysis of other species, if the end user provides a repeat library (e.g. consensus sequences from RepBase or Dfam).
MEGAnE (眼鏡 in Japanese) is pronounced like "mega" + "ne" as in "net." In Japanese, megane refers to a glass/lens that fine-tunes our vision enabling us to see something more clearly or understand truth.
Currently MEGAnE is beta version. Please use this version at your own risk.
Mobile elements in human population-specific genome and phenotype divergence
Shohei Kojima et al, bioRxiv 2022.03.25.485726; doi: https://doi.org/10.1101/2022.03.25.485726
Please see our Wiki page for further instruction and details.
MEGAnE can be available as docker and Singularity containers from dockerhub.
We highly recommend using such containers rather than preparing the required environment by yourself.
# build for Singlarity
sudo singularity build MEGAnE.sif docker://shoheikojima/megane:v1.0.0.beta
# or
singularity build --fakeroot MEGAnE.sif docker://shoheikojima/megane:v1.0.0.beta
# build for Docker
docker pull docker://shoheikojima/megane:v1.0.0.beta
- MEGAnE can take a position-sorted BAM and CRAM file aligned by BWA-MEM and DRAGEN (in the case of DRAGEN,
-skip_unmappedshould be specified). - MEGAnE only supports paired-end WGS. Single-end WGS is not compatible. MEGAnE does not support WES.
- We recommend to analyze WGS of 25x or higher depth, but it can also analyze 15x depth WGS by using the
-lowdepoption. - MEGAnE has a best performance with WGS of 150-bp or longer read length. We do not recommend to use WGS of less than 100-bp.
- For more details, please see our Wiki page.
Please see our Wiki page.
- Before analyzing your BAM/CRAM files, you need to make MEGAnE k-mer files from your human reference genome (e.g. GRCh38DH, hs37d5, etc).
- Usually, this takes ~10 min and requires ~50GB RAM.
sif=/path/to/MEGAnE.sif
singularity exec ${sif} build_kmerset \
-fa /path/to/reference_human_genome.fa \
-prefix reference_human_genome \
-outdir megane_kmer_set
- MEGAnE can analyze both BAM and CRAM format.
- MEGAnE supports BAM/CRAM mapping to both GRCh37- and GRCh38-related genomes.
- One 30x human WGS takes ~1 hour using 4 threads.
sif=/path/to/MEGAnE.sif
# In the case of BAM file mapping to GRCh37-related genome (e.g. hs37d5, human_g1k_v37, hg19)
singularity exec ${sif} call_genotype_37 \
-i /path/to/input.bam \
-fa /path/to/reference_human_genome.fa \
-mk /path/to/megane_kmer_set/reference_human_genome.mk \
-outdir MEGAnE_result_test \
-sample_name test_sample \
-p 4
# In the case of CRAM file mapping to GRCh38-related genome (e.g. GRCh38DH, hg38)
singularity exec ${sif} call_genotype_38 \
-i /path/to/input.cram \
-fa /path/to/reference_human_genome.fa \
-mk /path/to/megane_kmer_set/reference_human_genome.mk \
-outdir MEGAnE_result_test \
-sample_name test_sample \
-p 4
- After analyzing multiple BAM/CRAM files, you can make a joint call.
- This will take several hours when merging 1000s samples.
- MEGAnE supports joint calling from massive WGS (e.g. 10s of thousands). For more details, please see the Wiki page.
sif=/path/to/MEGAnE.sif
# first, list up samples (output directories from Step 1) you are going to merge
ls -d /path/to/[all_output_directories] > dirlist.txt
# merge non-reference ME insertions
singularity exec ${sif} joint_calling_hs \
-merge_mei \
-f dirlist.txt \
-fa /path/to/reference_human_genome.fa \
-cohort_name test
# merge reference ME polymorphisms
singularity exec ${sif} joint_calling_hs \
-merge_absent_me \
-f dirlist.txt \
-fa /path/to/reference_human_genome.fa \
-cohort_name test
- The step 2 above will generate two VCF files. This step merges the two files generated in the step 2.
- When merging the two VCF files, MEGAnE removes multi-allelic variants.
- This is particularly useful when you do haplotype phasing using MEGAnE's results.
sif=/path/to/MEGAnE.sif
singularity exec ${sif} reshape_vcf \
-i /path/to/jointcall_out/[cohort_name]_MEI_jointcall.vcf \
-a /path/to/jointcall_out/[cohort_name]_MEA_jointcall.vcf \
-cohort_name test
- Here is one example of how to phase MEGAnE's result.
- This step requires external softwares.
threads=6
memory=30000
# first, generate a SNP VCF
snp_vcf=/path/to/SNP.vcf chr=1
exclude=/path/to/vcf_for_phasing/cohort_name_biallelic.bed.gz
chr=1
plink2 \
--threads ${threads} \
--memory ${memory} \
--vcf ${snp_vcf} \
--make-pgen \
--max-alleles 2 \
--mac 2 \
--hwe 1e-6 \
--chr ${chr} \
--indiv-sort natural \
--exclude bed0 ${exclude} \
--export vcf bgz \
--out SNP
# next, generate a ME VCF
me_vcf=/path/to/vcf_for_phasing/[cohort_name]_biallelic.vcf.gz
plink2 \
--threads ${threads} \
--memory ${memory} \
--vcf ${me_vcf} \
--make-pgen \
--max-alleles 2 \
--mac 2 \
--hwe 1e-6 \
--chr ${chr} \
--indiv-sort natural \
--vcf-half-call missing \
--export vcf bgz \
--out ME
# merge SNP and ME VCFs
cat SNP.vcf.gz > _SNP_ME.vcf.gz
zcat ME.vcf.gz | grep -v '#' | pigz -c >> _SNP_ME.vcf.gz
zcat _SNP_ME.vcf.gz | pigz -c > SNP_ME.vcf.gz
rm _SNP_ME.vcf.gz
plink2 \
--threads ${threads} \
--memory ${memory} \
--vcf SNP_ME.vcf.gz \
--make-pgen \
--sort-vars \
--vcf-half-call missing \
--out SNP_ME
plink2 \
--threads ${threads} \
--memory ${memory} \
--pfile SNP_ME \
--export vcf bgz \
--out SNP_ME_sorted
tabix SNP_ME_sorted.vcf.gz
# haplotype-phasing
map=/path/to/genetic_map
shapeit4 \
--input SNP_ME_sorted.vcf.gz \
--map ${map} \
--region ${chr} \
--thread ${threads} \
--output SNP_ME.phased.vcf.gz
- MEGAnE uses thread-pool in this page: https://github.com/progschj/ThreadPool
- MEGAnE uses htslib: https://github.com/samtools/htslib
- MEGAnE wraps BLAST, samtools, bedtools.
- MEGAnE was developed by Shohei Kojima and Nicholas F. Parrish at RIKEN with great input from Atsushi Takeda and Michiaki Hamada at Waseda University.