Skip to content

Latest commit

 

History

History
749 lines (572 loc) · 61.1 KB

README.md

File metadata and controls

749 lines (572 loc) · 61.1 KB
Raw

status: support status: paper nextflow

docker apptainer

🗞️ The GraffiTE paper is now out!

Description

GraffiTE is a pipeline that finds polymorphic transposable elements in genome assemblies or long read datasets and genotypes the discovered polymorphisms in read sets using a pangenomic approach. GraffiTE is developed by Cristian Groza and Clément Goubert in Guillaume Bourque's group at the Genome Centre of McGill University (Montréal, Canada). GraffiTE is based on the concept developped in Groza et al., 2022.

  1. First, each genome assembly or long read dataset is aligned to the reference genome with minimap2, alternatively, winnowmap is available. For each sample considered, structural variants (SVs) are called with svim-asm if using assemblies or sniffles2 if using long reads and only insertions and deletions relative to the reference genome are kept.

  2. Candidate SVs (INS and DEL) are scanned with RepeatMasker, using a user-provided library of repeats of interest (.fasta). SVs covered ≥80% by repeats are kept. At this step, target site duplications (TSDs) are searched for SVs representing a single TE family.

  3. Each candidate repeat polymorphism is induced in a graph-genome where TEs and repeats are represented as bubbles, allowing reads to be mapped on either presence of absence alleles with Pangenie, Giraffe or GraphAligner.

⚠️ Bug/issues as well as comments and suggestions are welcomed in the Issue section of this Github.

Changelog

Last update: 10/22/24 | commit: 47ad044

Thank you @Han-Cao for submitting a pull request:

  • Improve speedup for large VCF annotation
  • 🪲 bug fix: change 1-based to 0-based coordinates system for SVA-VNTR module

Previous update: 10/21/24

  • 🪲 bug fix: transform RepeatMasker coordinates from 1-based to 0-based in order to meet the bed format standard and measure accurate hit length. This fixes issue #43
06/24/24 update:

  • New option --break_scaffolds (see additional parameters) that automatically split contigs at runs of N > 4. With some scaffolded genomes, minimap2 can indeed return an error related to some CIGAR string being too long, typically [E::parse_cigar] CIGAR length too long at position .... Breaking scaffolds at N stretches typicaly solve this problem, caused by limitations of the htslib/SAM specification.

06/17/24 update:

  • Added new/alternative compatible classes names: MITE, TIR and IS. e.g.: >TEnameX#MITE >TEnameY#TIR/Mariner or >TEnameX#IS. In previous versions, TE named with these classes were discarded by OneCodeToFindThemAll
    • The compatible classes in the fasta header includes (i.e. Class in >TEname#Class/Superfamily): LINE, LTR, SINE, RC/Helitron (will be treated as DNA/RC), DNA, TIR, MITE, Retroposon, IS, Unknown, Unspecified
    • TE for which a classification is absent will be treated as Unknown (e.g. >TEnameZ)
    • All >TEnames and Superfamily will be accepted as long as the Class name is among those supported.

02/13/24 update:

  • Since > beta 0.2.5 we switched versioning to commit id. Please refer to the commit ID of the version of GraffiTE you are using if you need support.
  • 🪲 bug fix: recently, the L1 inversion flag was not working (--mammal). It has now been fixed.
  • Winnowmap is now available as an alternative mapper instead of Minimap2. To enable Winnowmap, use the flag --aligner winnowmap; default remains minimap2.

beta 0.2.5 (09-11-23):

  • 🪲 bug fix: fix a VCF annotation issue that was happening when two distinct variants shared the same VCF POS field. Annotations are now distinct depending on the variant sequence.
  • cleanup GraphAligner VCF outputs for clarity.

beta 0.2.4 (06-27-23):

  • Refactored GraffiTE to use the DSL2 Nextflow syntax.

beta 0.2.3 (02-21-22):

  • 🆕 feature: You can now perform the initial SV search from both assemblies and long-read together. The variants discovered with each method will be merged together for the filtering and genotyping.
  • 🆕 parameters with defaults added to control time, cpu and memory for each process. This is useful to manage cluster requests when -profile cluster is used.
  • 🪲 bug fix: merging of variant now only occurs for the same SVTYPE flag (INS or DEL).

beta 0.2.2 (02-01-22):

  • 🆕 feature: adds sniffles2 as an alternative to svim-asm in order to start SV search from long reads (instead of a genomic assembly).
    • Using the parameter --longreads instead of --assembly (see inputs) will prompt GraffiTE to use sniffles2
    • For now, svim-asm and sniffles2 pipeline are separated (either --longreads or --assembly. We will soon allow to merge the findings of both callers before filtering for repeats.
  • 🆕 feature: adds a divergence preset option to minimap2 ahead of svim-asm. Use the flag --asm_divergence <asm5/asm10/asm20>. Defaults is asm5 (< 5% expected divergence between assembly and reference genome). See minimap2 documentation.
  • 🆕 time, cpu and memory directives options added to control the resources needed for each GraffiTE process. Useful to optimize scheduler requests while using the cluster profile of GraffiTE. See details here.

beta 0.2.1 (11-30-22 - click to drop-down details):

  • 🆕 feature: adds --RM_vcf and --RM_dir input options. Allows to start a run directly at the TSD search step by providing the VCF and repeatmasker_dir produced by the processes repeatmasker or repeatmasker_fromVCF (found in the output folder 2_Repeat_Filtering). This is useful if a run crashed during any of the TSD search processes and the job is not recoverable by Nextflow. Providing --RM_vcf and --RM_dir will bypass SV calling with minimap2/svim_asm (svim_asm process) and repeatmasker/repeatmasker_fromVCF processes.
  • 🪲 bug fix: TSD search is now performed by batches of 100 variants, which will reduce by a factor 100 the number of temporary working directories (which can cause storage to run over inodes' quota). If more than 100 variants are present, TSDs will be searched in parallel batches (up to the number of available CPUs).

beta 0.2 (11-11-22 - click to drop-down details):

  • 🆕 feature: adds two new read aligners: giraffe (short read optimized, works also with long-reads) and graphAligner (long-read, error-prone compliant).
    • usage: --graph_method [pangenie/giraffe/graphaligner] default: pangenie (short accurate reads)
  • 🆕 feature: adds --vcf input option: requires a sequence resolved (REF and ALT allele sequences in VCF). Will bypass genome alignments and proceed with repeat annotations, TSD search, and reads mapping (optional).
  • 🆕 feature: adds --graffite_vcf input option: requires a VCF created by GraffiTE (in the outputs 3_TSD_search/pangemome.vcf). Will skip all steps but read mapping.
  • 🪲 bug fix: remove the dependency to biomartr

beta 0.1 (11-02-22 - click to drop-down details):

  • first release

It is required to update both the repository (git pull) and image to see changes

Workflow

Installation

Prerequisites

GraffiTE is a Nextflow pipeline, with all the dependencies wrapped in an Apptainer image. It is thus compatible with any Linux system including HPCs.

Note that we have received report that Apptainer installation with Conda can cause issues. We recommend to install Apptainer directly.

GraffiTE install

  • If an internet connection is accessible from the compute nodes, the general command shown in the next section will download and cache the GraffiTE pipeline and Apptainer image for local use. Later runs will skip the slow download step. It is however required to add the repository to the apptainer list, by typing:
apptainer remote add --no-login SylabsCloud cloud.sycloud.io
apptainer remote use SylabsCloud

  • Alternatively, this repository can be cloned and the apptainer image downloaded at a specific location:

      1. Clone the Github repository
    git clone https://github.com/cgroza/GraffiTE.git
      1. Pull the apptainer image (this is long but only required once)
    apptainer remote add --no-login SylabsCloud cloud.sycloud.io
apptainer remote use SylabsCloud
apptainer pull --arch amd64 graffite_latest.sif library://cgroza/collection/graffite:latest
      1. Override the default image path in the file nextflow.config from library://cgroza/collection/graffite:latest to <your-path>/graffite_latest.sif. Alternatively, the Nextflow command -with-singularity <your-path>/graffite_latest.sif can be used when running GraffiTE (it will override the presets in nextflow.config).

  • We now provide a Docker image too. Apptainer can obtain it with:

apptainer pull graffite_latest.sif docker://cgroza/graffite

Of course, you can also reconfigure nextflow to use the Docker image without Apptainer if your system supports Docker.

Important note

We are aware of a common issue araising when the pipeline call a temporary directory (/tmp). The most common symptom is that though the program may complete without error, it skips over "tsd_search" and "tsd_report". The program will not produce a vcf file (3_TSD_search/pangenome.vcf) and the vcf in 2_Repeat_Filter has no variants. While we will try to fix this in a next update, an easy fix is to ammend the nextflow.config file as follow.

  1. Locate the file:

    • Either in ~/.nextflow/assets/cgroza/GraffiTE/nextflow.config
    • or in the cloned GitHub repository.

  2. Ammend the file:

replace:

singularity.runOptions = '--contain'

with

singularity.runOptions = '--contain -B <path-to-writable-dir>/:/tmp'

replace <path-to-writable-dir> with any writable path on your host machine

Running GraffiTE

  • The general command to run GraffiTE is as follow:
nextflow run cgroza/GraffiTE \
   --assemblies assemblies.csv \
   --TE_library library.fa \
   --reference reference.fa \
   --graph_method pangenie \
   --reads reads.csv
  • If using from a local apptainer image and with a clone of the Github repository:
nextflow run <path-to-install>/GraffiTE/main.nf \
   --assemblies assemblies.csv \
   --TE_library library.fa \
   --reference reference.fa \
   --reads reads.csv [-with-singularity <your-path>/graffite_latest.sif]

As a Nextflow pipeline, commad line arguments for GraffiTE can be distinguished between pipeline-related commands, prefixed with -- such as --reference and Nextflow-specific commands, prefixed with - such as -resume (see Nextflow documentation).

A small test set is included in the test/human_test_set.tar.gz file. Download and decompress the file and run:

nextflow run https://github.com/cgroza/GraffiTE --reference hs37d5.chr22.fa --assemblies assemblies.csv --reads reads.csv --TE_library human_DFAM3.6.fasta

This will show a complete run of the GraffiTE pipeline, with the output stored in out.

Software Update

To make sure that you are running the latest version of GraffiTE, you can update the pipeline using the following command:

nextflow pull -r main https://github.com/cgroza/GraffiTE

In case nextflow returns the following error:

Checking https://github.com/cgroza/GraffiTE ...
cgroza/GraffiTE contains uncommitted changes -- cannot pull from repository

You'll need to first delete the cached, older verison like so:

rm -rf ~/.nextflow/assests/cgroza/GraffiTE/
nextflow pull -r main https://github.com/cgroza/GraffiTE

Parameters

Input files

  • --assemblies: a CSV file that lists the genome assemblies and sample names from which polymorphisms are to be discovered. One assembly per sample and sample names must be unique. The header is required.

    Example assemblies.csv:

    path,sample
/path/to/assembly/sampleA.fa,sampleA_name
/path/to/assembly/sampleB.fa,sampleB_name
/path/to/assembly/sampleZ.fa,sampleZ_name

With some scaffolded genomes, minimap2 can return an error related to some CIGAR string being too long, typically [E::parse_cigar] CIGAR length too long at position ... and would crash. We now provide the option --break_scaffolds (false by default) that automatically split contigs at runs of N > 4.

AND/OR

  • --longreads: a CSV file that lists the longreads FASTQ, sample names, and type of longreads (hifi/pb/ont/lr:hq) from which polymorphisms are to be discovered. One FASTQ per sample and sample names must be unique. The header is required.

    Example longreads.csv:

    path,sample,type
/path/to/reads/sampleA.fq.gz,sampleA_name,pb
/path/to/reads/sampleB.fq.gz,sampleB_name,hifi
/path/to/reads/sampleC.fq.gz,sampleC_name,lr:hq
/path/to/reads/sampleZ.fq.gz,sampleZ_name,ont

AND (always required)

  • --TE_library: a FASTA file that lists the consensus sequences (models) of the transposable elements to be discovered. Must be compatible with RepeatMasker, i.e. with header in the format: >TEname#class/superfamily for example AluY#SINE/Alu. The library can include a single repeat model or all the known repeat models of your species of interest.

    • Following this formatting, the compatible classes in the fasta header includes (i.e. class in >TEname#class/superfamily): LINE, LTR, SINE, RC/Helitron (will be treated as DNA/RC), DNA, TIR, MITE, Retroposon, IS, Unknown, Unspecified
      • TE for which a classification is absent will be treated as Unknown (e.g. >TEnameZ)
      • All >TEnames and Superfamily will be accepted as long as the Class name is among those supported.
    • To create a compatible library from popular TE databses:
      • From DFAM (open access): download the latest DFAM release (Dfam.h5 or Dfam_curatedonly.h5 files) and use the tool FamDB to extract the consensus for your model: famdb.py -i <Dfam.h5> families -f fasta_name -a <taxa> --include-class-in-name > TE_library.fasta
      • From Repbase (paid subscription): use the "RepeatMasker Edition" libraries > If the Repbase (or other library) format at hand is of the type>TEname<tab/space>Classification<tab/space>Anything, you can use this awkcommand to easily convert your library in a format compatible with GraffiTE:awk '/>/ {print $1"#"$2; next} !/>/ {print $0}' library.fasta > library_OK.fasta`

  • --reference: a reference genome of the species being studied. All assemblies or long-reads in input are compared to this reference genome.

  • --graph_method: can be pangenie, giraffe or graphaligner, select which graph method will be used to genotyped TEs. Default is pangenie and it is optimized for short-reads. giraffe can handle both short and long reads, and graphaligner is optimized for long reads.

Note that both giraffe and graphaligner will spawn a process called graphAlignReads, while pangenie will spawn a process called pangenie.

  • --reads: a CSV file that lists the read sets (FASTQ/FASTQ.GZ) and sample names from which polymorphisms are to be genotyped. These samples may be different than the genome assemblies. The header is required. Only one FASTQ/FASTQ.GZ per sample, and sample names must be unique. Paired-end reads must be concatenated into a single file (Pangenie). In case --longreads is used as input, the same table can be used for --longreads and --reads (but not the opposite: type column is needed in --longreads, optional for --reads).

    Example reads.csv:

    path,sample
/path/to/reads/sample1.fastq,sample1_name
/path/to/reads/sample2.fastq,sample2_name
/path/to/reads/sampleN.fastq,sampleN_name

    or

    path,sample,type
/path/to/reads/sampleA.fq.gz,sampleA_name,pb
/path/to/reads/sampleB.fq.gz,sampleB_name,hifi
/path/to/reads/sampleZ.fq.gz,sampleZ_name,ont

Additional parameters

  • --out: if you would like to change the default output directory (out/).
  • --genotype: true or false. Use this if you would like to discover polymorphisms in assemblies but you would like to skip genotyping polymorphisms from reads.
  • --tsd_win: the length (in bp) of flanking region (5' and 3' ends) for Target Site Duplication (TSD) search. Default 30bp. By default, 30bp upstream and downstream each variant will be added to search for TSD. (see also TSD section)
  • --cores: global CPU parameter. Will apply the chosen integer to all multi-threaded processes. See here for more customization.
  • --mammal: Apply mammal-specific annotation filters (see Mammal filter section for more details).
    • (i) will search for LINE1 5' inversion (due to Twin Priming or similar mechanisms). Will call 5' inversion if (and only if) the variant has two RepeatMasker hits on the same L1 model (for example L1HS, L1HS) with the same hit ID, and a C,+ strand pattern.
    • (ii) will search for VNTR polymorphism between orthologous SVA elements.
  • --break_scaffolds: true or false. Break input assemblies at runs of Ns. Use this if the assemblies passed with --assemblies are scaffolded to avoid [E::parse_cigar] CIGAR length too long error.

Pipeline Shortcuts

These parameters can be used to bypass different steps of the pipeline.

  • --vcf: a sequence resolved VCF containing both REF and ALT variants sequences. This option will bypass the SV discovery and will proceed to annotate and filter the input VCF for repeats and TSD, as well as genoyping (unless --genotype false is set). The VCF must be biallelic (can be done with bcftools norm -m-) and each variant must have a unique variant ID that is also a valid FASTA contig name (no > character in variant ID). For those who wish to start directly from a graph genome in GFA format, we currently recommend to convert the graph to VCF using vg deconstruct, then modify the VCF as described above.
  • --RM_vcf+--RM_dir: bypasses SV discovery and filtering (RepeatMasker) and starts at the TSD search process. --RM_vcf can be found in the outputs: 2_Repeat_Filtering/genotypes_repmasked_filtered.vcf and --RM_dir in 2_Repeat_Filtering/repeatmasker_dir
  • --graffite_vcf: Use this if you already have a VCF file that was produced by GraffiTE (see output: 3_TSD_Search/pangenome.vcf), or from a difference source and would like to use the graph genotyping step. The file must be a fully-phased VCF. Note that TE annotation won't be performed on this file (see --vcf instead), and only genotyping will be performed.

Process-specific parameters

SV detection with svim-asm (from assemblies)

  • --map_asm_threads: number of cores allocated to minimap2 or winnowmap in the map_asm process. Overrides --cores.

  • --map_asm_memory: RAM limit for the minimap2 or winnowmap in the map_asm process. Default is unset.

  • --map_asm_time: for cluster profile, max time for the scheduler for the map_asm process. Default is 1h.

  • --svim_asm_threads: number of cores allocated to svim_asm process. Overrides --cores.

  • --svim_asm_memory: RAM limit for the SV search in svim_asm process. Default is unset.

  • --svim_asm_time: for cluster profile, max time for the scheduler for this process. Default is 1h.

  • --asm_divergence: divergence preset option for minimap2 ahead of svim-asm. Use the flag . asm5/asm10/asm20 Defaults is asm5 (< 5% expected divergence between assembly and reference genome). See minimap2 documentation.

  • --mini_K: minimap2 parameter -K. Number of bases loaded into memory to process in a mini-batch. Similar to option -I, K/M/G/k/m/g suffix is accepted. A large NUM helps load balancing in the multi-threading mode, at the cost of increased memory. Default 500M.

  • --stSort_m: samtools sort parameter -m (for each alternative assembly, post-minimap2): Approximately the maximum required memory per thread, specified either in bytes or with a K, M, or G suffix. Default in GraffiTE is 4G.

  • --stSort_t: samtools sort parameter @ (for each alternative assembly, post-minimap2): Set number of sorting and compression threads. Default in GraffiTE is 4 threads.

SV detection with sniffles2 (from long reads)
  • --map_longreads_threads: number of cores allocated to minimap2 or winnowmap in the map_longreads process. Overrides --cores.
  • --map_longreads_memory: RAM limit for the minimap2 or winnowmap in the map_longreads process. Default is unset.
  • --map_longreads_time: for cluster profile, max time for the scheduler for the map_longreads process. Default is 1h. ---sniffles_threads: number of minimap2 threads (parameter -t in minimap2). Overrides --cores. ---sniffles_memory: RAM limit for the SV search (minimap2+sniffles2) process. Default is unset. ---sniffles_time: for cluster profile, max time for the scheduler for this process. Default is 2h.
  • --stSort_m: samtools sort parameter -m (for each long-read alignment, post-minimap2): Approximately the maximum required memory per thread, specified either in bytes or with a K, M, or G suffix. Default in GraffiTE is 4G.
  • --stSort_t: samtools sort parameter @ (for each long-read alignment, post-minimap2): Set number of sorting and compression threads. Default in GraffiTE is 4 threads.
SV Annotation (RepeatMasker)
  • --repeatmasker_threads: number of RepeatMasker threads. Overrides --cores.
  • --repeatmasker_memory: RAM limit for the RepeatMasker (annotation) process. Default is unset.
  • --repeatmasker_time: for cluster profile, max time for the scheduler for this process. Default is 2h.
Genotyping with Pangenie
  • --pangenie_threads: number of Pangenie threads. Overrides --cores.
  • --pangenie_memory: RAM limit for the Pangenie (genotyping) process. Default is unset.
  • --pangenie_time: for cluster profile, max time for the scheduler for this process. Default is 2h.
Genotyping with Giraffe, GraphAligner and vg call

  • --make_graph_threads: threads for creating the graph with vg autoindex (Giraffe) or vg construct (GraphAligner). Default is 1.

  • --make_graph_memory: RAM limit for creating the graph with vg autoindex (Giraffe) or vg construct (GraphAligner). Default is unset.

  • --graph_align_theads: threads for aligning reads to the graph with vg giraffe or GraphAligner. Default is 1.

  • --graph_align_memory: RAM limit for aligning reads to the graph with vg giraffe or GraphAligner. Default is unset.

  • --graph_align_time: for cluster profile, max time for the scheduler for this process. Default is 12h.

  • --vg_call_threads: threads for calling genotypes with vg call on graph alignments. Default is 1.

  • --vg_call_memory: RAM limit for calling genotypes with vg call on graph alignments. Default is unset.

  • --min_mapq: Minimum mapping quality to consider when counting read depth on nodes. Default is 0.

  • --min_support: Minimum required read depth on allele,bubble to consider for genotyping. The first number is the minimum read depth on allele, and the second is the minimum depth on the entire bubble/locus. Default is 2,4.

GraffiTE modes

In the main publication of GraffiTE, we refer to different "modes" relative to the different combination of assembly, long-reads (for discovery) and reads (for genotyping). The following table recapitulate the arguments to use in order to repliate these modes. Please refer the the reads file description above for proper formating.

Mode Arguments Description
GT-sv --assemblies <assemblies.csv> --genotype false pME discovery from assemblies
GT-sn --longreads <long_reads.csv> --genotype false pME discovery from long reads
GT-svsn --assemblies <assemblies.csv> --longreads <long_reads.csv> --genotype false pME discovery from both assemblies and long reads
GT-sv-PG --assemblies <assemblies.csv> --reads <short_reads.csv> pME discovery from assemblies and genotyping from short reads with Pangenie
GT-sn-PG --longreads <long_reads.csv> --reads <short_reads.csv> pME discovery from long reads and genotyping from short reads with Pangenie
GT-svsn-PG --assemblies <assemblies.csv> --longreads <longreads.csv> --reads <short_reads.csv> pME discovery from both assemblies and short reads and genotyping from short reads with Pangenie
GT-sv-GA --assemblies <assemblies.csv> --reads <long_reads.csv> --graph_method graphaligner pME discovery from assemblies and genotyping from long reads with GraphAligner
GT-sn-GA --longreads <long_reads.csv> --reads <long_reads.csv> --graph_method graphaligner pME discovery from long reads and genotyping from long reads with GraphAligner
GT-svsn-GA --assemblies <assemblies.csv> --longreads <long_reads.csv> --reads <long_reads.csv> --graph_method graphaligner pME discovery from both assemblies and short reads and genotyping from long reads with GraphAligner

Nextflow parameters

Nextflow-specific parameters can be passed in addition to those presented above. These parameters can be distinguished by the use of a single -, such as -resume. See Nextflow documentation for more details.

  • -resume: if nothing is changed in the command line and the /work folder created by Nextflow, the pipeline will resume after the last chached process.
  • -with-singularity: if a local apptainer image is used, this parameter will override the default image path given in nextflow.config.
  • -with-report report.html: for a Nextflow report on resource usage to help tune the CPU and memory parameters for your genome/species.

Outputs

The results of GraffiTE will be produced in a designated folder with the option --out. The output folder contains up to 4 sub-folders (3 if --genotype false is set). Below is an example of the output folder using two alternative assemblies of the human chromosome 1 (maternal and paternal haplotypes of HG002) and two read-sets from HG002 for genotyping.

OUTPUT_FOLDER/
├── 1_SV_search
│   ├── HG002_mat.vcf
│   └── HG002_pat.vcf
├── 2_Repeat_Filtering
│   ├── genotypes_repmasked_filtered.vcf
│   └── repeatmasker_dir
│       ├── ALL.onecode.elem_sorted.bak
│       ├── indels.fa.cat.gz
│       ├── indels.fa.masked
│       ├── indels.fa.onecode.out
│       ├── indels.fa.out
│       ├── indels.fa.out.length
│       ├── indels.fa.out.log.txt
│       ├── indels.fa.tbl
│       ├── onecode.log
│       └── OneCode_LTR.dic
├── 3_TSD_search
│   ├── pangenome.vcf
│   ├── TSD_full_log.txt
│   └── TSD_summary.txt
└── 4_Genotyping
    ├── GraffiTE.merged.genotypes.vcf
    ├── HG002_s1_10X_genotyping.vcf.gz
    ├── HG002_s1_10X_genotyping.vcf.gz.tbi
    ├── HG002_s2_10X_genotyping.vcf.gz
    └── HG002_s2_10X_genotyping.vcf.gz.tbi
  • 1_SV_search/
    • This folder will contain 1 VCF file per alternative assembly. The format is [assembly_name].vcf with [assembly_name] as set in the file assemblies.csv
  • 2_Repeat_Filtering/
    • genotypes_repmasked_filtered.vcf a vcf file with the merged variants detected in each alternative assembly. The merge is made with SURVIVOR with the parameters SURVIVOR merge vcfs.txt 0.1 0 0 0 0 100. Details about the vcf annotation can be found in the VCF section of the manual. This VCF contains only variants for witch repeats in the --TE_library file span more than 80% of the sequence (from 1 or more repeat models).
    • repeatmasker_dir/:
      • indels.fa.*: RepeatMasker output files. indels.fa represents all SV sequences queried to RepeatMasker. See the RepeatMasker documentation for more information.
      • ALL.onecode.elem_sorted.bak: original OneCodeToFindThemAll outputs. see here fore more details.
      • OneCode_LTR.dic: OneCodeToFindThemAll LTR dictionary automatically produced from --TE_library see here fore more details.
      • onecode.log: log file for OneCodeToFindThemAll process.
  • 3_TSD_Search/ (see TSD section)
    • pangenome.vcf final VCF containing all retained repeat variants and annotations (with TSD if passing the TSD filters). This file is used later by Pangenie,Giraffe or graphAligner to create the genome-graph onto which reads are mapped for genotyping. (example here). Can be re-used for genotyping only with --graffite_vcf pangenome.vcf
    • TSD_summary.txt: tab delimited output of the TSD search module. 1 line per variant. See TSD section for more information. "PASS" entries are reported in the pangenie.vcf and final (with genotypes) VCF.
    • TSD_full_log.txt:detailed (verbose rich) report of TSD search for each SV (see TSD section).
  • 4_Genotyping/
    • GraffiTE.merged.genotypes.vcf: final mutli-sample VCF with the genotypes for each sample present in the --reads file. See VCF section for more details.
    • *.vcf.gz individual genotypes (do not contain TE annotation)
    • *.vcf.gz.tbi index for individual VCFs.

Note that intermediate files will be written in the ./work folder created by Nextflow. Each Nextflow process is run in a separate working directory. If an error occurs, Nextflow will points to the specific working directory. Moreover, it is possible to resume interrupted jobs if the ./work folder is intact and you use the same command, plus the -resume (1 single -) tag after your command. It is recommended to delete the ./work folder regularly to avoid storage issues (more than space, it can aggregate a LOT of files through time). More info about Nextflow usage can be found here.

Output VCFs

GraffiTE outputs variants in the VCF 4.2 format. Additional fields are added in the INFO column of the VCF to annotate SVs containing TEs and other repeats (3_TSD_Search/pangenie.vcf [do not contain individual genotypes, only the list of variants] and 4_Genotyping/GraffiTE.merged.genotypes.vcf which contains a genotype column for each reads-set).

  • 3_TSD_Search/pangenie.vcf
1       8501990 HG002_mat.svim_asm.INS.94       T       TCAATACACACACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCCAGGCCGGACTGCGGACTGCAGTGGCGCAATCTCGGCTCACTGCAAGCTCCGCTTCCCGGGTTCACGCCATTCTCCTGCCTCAGCCTCCCCAGTAGCTGGGACTACAGGCGCCCGCCACCGCGCCCGGCTAATTTTTTGTATTTTTAGTAGAGACGGGGTTTCACCGTGTTAGCCAGGATGGTCTCGATCTCCTGACCTCATGATCCACCCGCCTCGGCCTCCCAAAGTGCTGGGACTACAGGCGTGAGCCACCGCGCCCGGC        .       PASS    SUPP=1;SUPP_VEC=10;SVLEN=345;SVTYPE=INS;SVMETHOD=SURVIVOR1.0.7;CHR2=1;END=8501990;CIPOS=0,0;CIEND=0,0;STRANDS=+-;n_hits=1;fragmts=1;match_lengths=316;repeat_ids=AluYb9;matching_classes=SINE/Alu;RM_hit_strands=C;RM_hit_IDs=15016;total_match_length=316;total_match_span=0.913295;mam_filter_1=None;mam_filter_2=None;TSD=AATACACACACTTTTT,AATACACACACTTTTT    GT  1|0

An example of AluYb9 insertion relative to the reference genome (hg19 was used for this example). The genotype is always heterozygous in order to create both allele in the graph used for genotyping

  • 4_Genotyping/GraffiTE.merged.genotypes.vcf
1       8501990 HG002_mat.svim_asm.INS.94       T       TCAATACACACACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCCAGGCCGGACTGCGGACTGCAGTGGCGCAATCTCGGCTCACTGCAAGCTCCGCTTCCCGGGTTCACGCCATTCTCCTGCCTCAGCCTCCCCAGTAGCTGGGACTACAGGCGCCCGCCACCGCGCCCGGCTAATTTTTTGTATTTTTAGTAGAGACGGGGTTTCACCGTGTTAGCCAGGATGGTCTCGATCTCCTGACCTCATGATCCACCCGCCTCGGCCTCCCAAAGTGCTGGGACTACAGGCGTGAGCCACCGCGCCCGGC        .       PASS    UK=51;MA=0;AF=0.5;AK=13,38;CIEND=0,0;CIPOS=0,0;CHR2=1;END=8501990;SVLEN=345;SVMETHOD=SURVIVOR1.0.7;SVTYPE=INS;SUPP_VEC=10;SUPP=1;STRANDS=+-;n_hits=1;match_lengths=316;repeat_ids=AluYb9;matching_classes=SINE/Alu;fragmts=1;RM_hit_strands=C;RM_hit_IDs=15016;total_match_length=316;total_match_span=0.913295;mam_filter_1=None;mam_filter_2=None;TSD=AATACACACACTTTTT,AATACACACACTTTTT      GT:GQ:GL:KC     0/1:10000:-81.8909,0,-64.99:7   0/1:10000:-81.8909,0,-64.99:7

An example of AluYb9 insertion relative to the reference genome (hg19 was used for this example). Genotypes are based on read mapping for each individual.

1  33108378 HG002_pat.svim_asm.INS.206 T  TTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCACCAGACTGGAGTACAATGGCACAATCTCGGCTTACTGCAACTTCCGCCTCCTGGGTTCAAGCAATTCCCCTGCCTCAGCCTCCTGAGTAGCTGGGATTACAGACGTGTGCCACCATGCCTGGCTAATTTTTTGTATTTTA
GCAGAGACGGAGTTTCACCATGTTGGCCAGGATGCTCTCAATCTCCTTACCTCATGATCCGCCAGCCTCGGCCTCCCAAAGTGCTGGGATTATTACAGGCATGAGCCACAGTCCCAGGTCTTTAGACAAACTCAACCCATTATCAATCAAAAAATGTTTAAATTCACTTATAGCATGGAAGCTACCCCACCCCTCCCCCCTCCCCCCTCCCGCCCCCCCCAGCTTTGAGTTGTCCCACCTTTCTGGACCAAAGCA ATGTATTTCTTAAACTTAATTGATTAATGTCTCATGCCTCTCTGAAATGTATAAAACCAAACTGTGCCCTGACCACCTTGGGCACACTGAGCACATGTTCTCAGGATCTCCAGAGGGCTGTGTCAGGGGCCATGGTCACATTTGGCTCAGAATACATCTCTTCAAATATTTTATAGAGTTCGACTATTTTGTCAACAATTAAAAAGGCACCTATTCAGAAT
ATTAAAAGTTAAGATTTAATAACATCAACAGTTCTTACTGATTCATCAAATATTTTTTTTTTTGAGACCGAGTCTCGCTCTATCGCCCAGGCTGGAGGGCAGTGGCACAATCTCTGTTCACTGCAACCTCCGCCTCCCGGGTTCAAGCGATTCTCCTGCCTCAGCCTCCCGAATAGCTGGGACTACATGCGCGTGCCACCACGCCTGGCTAATTTTTGTATTTTTAGTAGAGACGGAGTTTCACAACGTTGGCCAGGATGGTCTCGATCCCTTGACCTCATGATCCGCCTGCCTCGGCCTCCCAAAGTGCTGGGATTACAGGTGTGAGCCACCGGCGCCTGGCCAAAACAAAA  .PASS K=301;MA=0;AF=0.5;AK=2,299;CIEND=0,1;CIPOS=0,0;CHR2=1;END=33108378;SVLEN=1002;SVMETHOD=SURVIVOR1.0.7;SVTYPE=INS;SUPP_VEC=11;SUPP=2;STRANDS=+-;n_hits=4;match_lengths=293,331,80,291;repeat_ids=AluSc8,MER4E1,Charlie1a,AluSc;
matching_classes=SINE/Alu,LTR/ERV1,DNA/hAT-Charlie,SINE/Alu;fragmts=1,1,1,1;RM_hit_strands=C,+,C,C;RM_hit_IDs=28269,28270,28271,28272;total_match_length=991;total_match_span=0.988036;mam_filter_1=None;mam_filter_2=None   GT:GQ:GL:KC 1/1:10000:-450.343,-147.4,0:4 1/1:10000:-450.343,-147.4,0:4

A more complex example with n_hit=4

VCF column:

  • (1) CHROM: chromosome/scaffold/contig
  • (2) POS: position (in bp) of the SV start, relative to the reference genome
  • (3) ID: variant name
  • (4) REF: reference allele
  • (5) ALT: alternative allele
  • (6) QUAL: not used
  • (7) FILTER: currently not used. "PASS" is used by default but does not inform about variant quality (for now!)
  • (8) INFO:
    • UK (4_Genotyping/GraffiTE.merged.genotypes.vcf only): [Pangenie] Total number of unique kmers
    • MA (4_Genotyping/GraffiTE.merged.genotypes.vcf only): [Pangenie] Number of alleles missing in panel haplotypes
    • AF (4_Genotyping/GraffiTE.merged.genotypes.vcf only): [Pangenie] Allele Frequency
    • AK (4_Genotyping/GraffiTE.merged.genotypes.vcf only): [Pangenie] Number of unique kmers per allele. Will be -1 for alleles not covered by any input haplotype path
    • CIEND (ignore)
    • CIPOS (ignore)
    • CHR2 (ignore)
    • END: End position of the SV on the reference genome
    • SVLEN: Length of the SV (bp), can be negative
    • SVMETHOD=SURVIVOR1.0.7; (ignore)
    • SVTYPE: Type of SV (can be INS or DEL)
    • SUPP_VEC: Support Vector from SURVIVOR (merge of individual loci). SUPP_VEC=01 means two alternative assemblies were used, the SV is absent from the first one and present in the second one.
    • SUPP: Number of assemblies with the variant
    • STRANDS=+-; (ignore)
    • n_hits: number of distinct RepeatMasker hits on the SV
    • match_lengths: length of each RepeatMasker hit. If n_hits > 1, lengths of each hit are comma separated
    • repeat_ids: target name of each RepeatMasker hit. If n_hits > 1, names for each hit are comma separated
    • matching_classes: classification of each RepeatMasker hit. If n_hits > 1, classification for each hit are comma separated
    • fragmts: number of fragments stitched together for each RepeatMasker hit. If n_hits > 1, the number of stitched fragments for each hit are comma separated
    • RM_hit_strands: strands for each RepeatMasker hit. If n_hits > 1, the strands of each hit are comma separated. Can be + or C (complement)
    • RM_hit_IDs: unique RepeatMasker hit ID (last column of the .out file of repeatmasker). If n_hits > 1, hit IDs are comma separated. Fragments stitched with OneCodeToFindThemAll are shown separated with /.
    • total_match_length: total number of bp covered by repeats in the SV
    • total_match_span: proportion of the SV covered by repeats (minimum is 0.8)
    • mam_filter_1: 5P_INV will be shown if the SV is a LINE1 with a 5' inversion; Null otherwise; (only present if --mammal is set)
    • mam_filter_2: SVA_VNTR if the SV is a length polymorphism of the VNTR region of an SVA element; Null otherwise; (only present if --mammal is set)
    • TSD: Target Site Duplication (left_TSD,right_TSD); only present if TSD passes filters (see TSD section)
  • (9) FORMAT and (10) GENOTYPE
    • GT: Genotype (0=reference allele, 1=alternative allele, .=missing)
    • GQ: (4_Genotyping/GraffiTE.merged.genotypes.vcf only): [Pangenie] Genotype quality: phred scaled probability that the genotype is wrong.
    • GL: (4_Genotyping/GraffiTE.merged.genotypes.vcf only): [Pangenie] Comma-separated log10-scaled genotype likelihoods for absent, heterozygous, homozygous.
    • KC: (4_Genotyping/GraffiTE.merged.genotypes.vcf only): [Pangenie] Local kmer coverage.

When using Giraffe and GraphAligner with vg call, the following fields are also present:

  • AT: Allele traversal as path in graph
  • DP: Total Depth
  • AD: Allelic depths for the ref and alt alleles in the order listed">
  • MAD: Minimum site allele depth
  • GL: Genotype Likelihood, log10-scaled likelihoods of the data given the called genotype for each possible genotype generated from the reference and alternate alleles given the sample ploidy
  • GQ: Genotype Quality, the Phred-scaled probability estimate of the called genotype
  • GP: Genotype Probability, the log-scaled posterior probability of the called genotype
  • XD: eXpected Depth, background coverage as used for the Poisson model

TSD module

For SVs with a single TE insertion detected (n_hits=1, and LINE1s with the flag mam_filter_1=5P_INV) target site duplication are searched by comparing the flanking regions following this workflow:

  • 1 extract the flanking sequences of each filtered SV:
    • 1.1 extract the bases not identified as repeat by RepeatMasker in the 5' and 3' end of the SV (these regions will often include one TSD, or a partial sequence of the TSD)
    • 1.2 extract an additional (by default 30) bp on each side of the SV from the reference genome.
  • 2 perform the TSD search:
    • Combine the extracted flanking and create the L (5') and R (3') fragments for each SV.
    • If present, trim 5' poly-A or 3' poly-T (leaves only 3 As or Ts) before alignments but keep track of the poly-A/T length.
    • Call blastn to align with a seed of 4 bp
    • Applies PASS filters and return summary files. PASS is currently given if:
      • L and R flanks match within +/- 5 bp of the TE ends (as defined by RepeatMasker, "Ns" nucleotides)
      • tolerate (TE hit divergence to consensus x alignment length) mismatches+gaps or 1 mismatch+gap if (TE hit divergence to consensus x alignment length) < 1
      • tolerate offset of +/- poly-A/T length

The script also account for the presence of poly-A/T

  • TSD_summary.txt output file (The header is not present in the real file). 
    SV_name                          RM_family_name    RM_hit_strand  RM_hit_divergence TSD_length  Mismatches  Gaps    5P_TSD_end   5P_offset      3P_TSD_start    3P_offset     5P_TSD            3P_TSD            FILTER
HG002_mat.svim_asm.DEL.1014      AluY              C              2.2               10          0           0       -1           0              1               0             ATTATTATTA        ATTATTATTA        PASS
HG002_mat.svim_asm.DEL.1013      L1HS              C              1.3               16          0           0       -15          3              1               0             AGTATTCTGGATTTTT  AGTATTCTGGATTTTT  FAIL
G002_mat.svim_asm.DEL.1015       L1HS              +              1.0738            4           0           0       -9           0              1               0             AAAG              AAAG              FAIL
HG002_mat.svim_asm.DEL.102       AluYa5            C              0.3               11          0           0       -1           0              1               0             CTGCATACTTT       CTGCATACTTT       PASS
HG002_mat.svim_asm.DEL.1011      L1P2              C              6.9               4           0           0       -21          0              1               0             CATC              CATC              FAIL
HG002_mat.svim_asm.DEL.1005      AluY              C              1.0               12          0           0       -1           0              1               0             CCAGAAGTCTTT      CCAGAAGTCTTT      PASS
HG002_mat.svim_asm.DEL.1010      AluYh3            +              2.4               12          0           0       -1           0              1               0             AATTTCTATCTC      AATTTCTATCTC      PASS
  • TSD_full_log.txt:detailed (verbose rich) report of TSD search for each SV. 
       --- TSD search for HG002_mat.svim_asm.DEL.1014 ---

>L|5P_end
ACAGGCGTGAGCCTCCACGCCTGGCCTAGATATTATTATTATTATTATTA
||||||||||||||||||||||||||||||||||||||||||||||||||
1   5    10   15   20   25   30   35   40   45   50
>R|3P_end
ATTATTATTAACCTATTTTACAGATGAGGG
||||||||||||||||||||||||||||||||||||||||||||||||||
1   5    10   15   20   25   30   35   40   45   50

3' poly_A: element is in C orientation, will not search for poly_A
5' poly_T: 0 bp, will not remove anything for alignment


Building a new DB, current time: 11/02/2022 22:27:12
New DB name:   /scratch/cgoubert/GraffiTE/work/d1/3d8805a29e13fad52ed5aa1e7a9e76/L.short.fasta
New DB title:  L.short.fasta
Sequence type: Nucleotide
Keep MBits: T
Maximum file size: 1000000000B
Adding sequences from FASTA; added 1 sequences in 0.000507116 seconds.

candidate hits from blastn:
R|3P_end        L|5P_end        100.000 10      0       0       1       10      41      50      0.001   19.6
R|3P_end        L|5P_end        100.000 4       0       0       1       4       47      50      3.1      8.5
R|3P_end        L|5P_end        100.000 10      0       0       1       10      38      47      0.001   19.6
R|3P_end        L|5P_end        100.000 10      0       0       1       10      35      44      0.001   19.6
R|3P_end        L|5P_end        100.000 10      0       0       1       10      32      41      0.001   19.6
R|3P_end        L|5P_end        100.000 8       0       0       3       10      31      38      0.018   15.9
R|3P_end        L|5P_end        100.000 4       0       0       12      15      25      28      3.1      8.5
R|3P_end        L|5P_end        87.500  8       0       1       14      20      37      44      3.1      8.5
R|3P_end        L|5P_end        87.500  8       0       1       14      20      31      38      3.1      8.5
R|3P_end        L|5P_end        100.000 4       0       0       20      23      1       4       3.1      8.5
R|3P_end        L|5P_end        100.000 4       0       0       22      25      28      31      3.1      8.5
R|3P_end        L|5P_end        100.000 4       0       0       25      28      8       11      3.1      8.5

candidate TSDs:
ACAGGCGTGAGCCTCCACGCCTGGCCTAGATATTATTATTATTATTATTA[ <<< AluY C <<< ]ATTATTATTAACCTATTTTACAGATGAGGG
                                        ‾‾‾‾‾‾‾‾‾‾                  ‾‾‾‾‾‾‾‾‾‾

PASS

3' end: nothing to extend
5' end: nothing to extend
SVname  TEname  Strand  Div     AlnLen  MM      Gaps    5P_TSD_end      5P_offset       3P_TSD_start    3P_offset       5P_TSD  3P_TSD
HG002_mat.svim_asm.DEL.1014     AluY    C       2.2     10      0       0       -1      0       1       0       ATTATTATTA      ATTATTATTA      PASS

Mammalian filters --mammal

In order to account for the particularities of several TE families, we have introduced a --mammal flag that will search for specific features associated with mammalian TEs. So far we are accounting for two particular cases: 5' Inversion of L1 elements and VNTR polymorphism between orthologous SVA insertions. We will try to add more of these filters, for example to detect solo vs full-length LTR polymorphisms. If you would like to see more of these filters, please share your suggestions on the Issue page!

L1 5' inversion

SV detected by GraffiTE and corresponding to non-canonical TPRT (Target Primed Reverse Transcription), such as Twin Priming (see here and here) may be skipped by the TSD script because it artificially creates 2 hits instead of one for a single TE insert.

Whether or not the L1 is inserted on the + or - strand, at Twin-Primed L1 will have the same pattern with RepeatMasker:

  • hit 1 = C
  • hit2 = +

This is because an inversion on the - strand feature will look like + on the consensus ((-)*(-) = (+) or a "reverted reverse")

However, we can differentiate the two based on the coordinates of the hit on the TE consensus (cartoon not to scale to compare two L1 insertions with the same consensus):

For each pair (C,+) of hits, we look at the target hit coordinates:

  • if hit 1 ( C ) coordinates are < hit 2 (+), the TE inserted on the + strand (top, blue example)
  • if hit 1 ( C ) coordinates are > hit 2 (+), the TE inserted on the - strand (bottom, orange example)

L1 inversions will be reported with the flag mam_filter_1=5P_INV in the INFO field of the VCFs.

VNTR polymorphisms in SVA elements

drawing

If GraffiTE detects:

  • SV annotated as SVA and,
  • RepeatMasker hit corresponding only to the VNTR region of these elements and,
  • If the flanking is an SVA in the same orientation

The variant will be flagged with mam_filter_2=VNTR_ONLY:SVA_F:544:855 with SVA_F:544:855 varying according to the element family and VNTR region:

SVA model VNTR period size Repeat # start end
SVA_A 37 10.5 436 855
SVA_B 37 10.8 431 867
SVA_C 37 10.5 432 851
SVA_D 37 6.4 432 689
SVA_E 37 10.8 428 864
SVA_F 37 10.5 435 857

GraffiTE execution profiles

By default, the pipeline will inherit the nextflow configuration and run accordingly. To execute locally, on SLURM, or AWS, pass one of the -profile provided with the GraffiTE:

  • standard
  • cluster
  • cloud

For example,

nextflow run cgroza/GraffiTE -profile cluster ...

will run on SLURM.

Specifying memory and CPU allocation at each step

You may alter the following parameters on the command line or in your own nextflow configuration file to change how many CPUs and how much memory will be required by each step.

  • Step 1, polymorphisms discovery. The memory requirement depends on the genome size of the species. More cores is faster.
params.svim_asm_memory
params.svim_asm_threads
  • Step 2, merging polymorphisms. The requirements depends on the number of assemblies.
params.make_vcf_memory
params.make_vcf_threads
  • Step 3, genotyping polymorphisms from reads. The memory requirements depend on the genome size and size of the read sets. More cores is faster.
params.pangenie_memory
params.pangenie_threads

The requirements are numbers or strings accepted by nextflow. For example, 40 for number of CPUs and '100G' for memory.

Resource usage examples:

Model species Ref genome size / TE content (%) Input sample (if applicable) process # of processes measured CPUs (available) RAM (peak) Process run time
human 3 Gbp / 50% haploid assemblies minimap2 2 40 46-47Gb 38-49 mn
5X HiFi long reads minimap2 1 40 65Gb 20 mn
10X HiFi long reads minimap2 1 40 78Gb 39 mn
20X HiFi long reads minimap2 1 40 99Gb 1h 14 mn
30X HiFi long reads minimap2 1 40 109Gb 1h 48 mn
VCF RepeatMasker 1 40 2Gb 37 mn
VCF make_graph (vg) 2 1 4-8Gb 6 mn
30X HiFi long reads GraphAligner 2 40 124-125Gb 4h 1 mn
30X Illumina Pangenie 2 40 85-87Gb 46 mn
C. sativa 740 Mbp / 70% haploid assemblies minimap2 9 40 56-118Gb 12 mn-1h 31 mn
Long reads (pb, hifi or ONT) minimap2 5 40 58-93Gb 3h 18 mn-9h 24 mn
VCF RepeatMasker 1 40 5Gb 7h 24 mn
Z. mays 2.4 Gbp / 85% assembly minimap2 1 40 143G 74.4 mn
70X PacBio longreads minimap2 1 40 57G 13h 42 mn
VCF RepeatMasker 1 40 31G 203 mn
VCF make_graph (vg) 1 40 11G 4 mn
70X PacBio longreads GraphAligner 1 40 66 G 11h 25 mn
Algined GAM vg call 1 40 12 G 8 mn

Large, complex and highly repetitive genomes

The default parameters, in particular for request of RAM and execution time, may be inssuficient for large, complex and repeat rich genomes such as Maize and other models. Nextflow's error message may be hard to interpret and sometimes misleading with regards to the actual cause of the error. We advise users that suspect their model to be challenging for GraffiTE to initially use as much ressource are necessary. So far, a higher bound of 120h and 400Gb per process (these being requested resources, not actual usage -- for actual usage, see above) have been reported to allow successful run with Maize models, the most ressource intensive step being long-read alignments.

Known Issues / Notes / FAQ

Please use and abuse the Issue section of this Github page. With the userbase growing, it becomes more likely that someone else has already encountered a similar issue. If not, other users will benefits from your experience! We will try to respond swiftly to any help request, and the Issue page is the only place we actively monitor for user support. Thank you!

  • The "stitching" method to identify unique TE insertion from fragmented hits has some degree of limitation. This can be flagrant for full-length LTR insertion, which can show n_hits > 1, and thus wont be recognized as a "single" element insertion, nor run through the TSD module. For now, names between LTR and I(nternal) sequences much match in the header name (e.g. TIRANT_LTR and TIRANT_I) to be automatically recognized as a single hit. We will make use of the RepeatMasker hit ID in order to improve this stitching procedure. In the meantime, we recommend to check/rename your LTR of interest in the --TE_library file.

  • As mentioned above, in order to improve runtime, the TSD module is only run for SVs with a single TE hit. We will improve this feature in order to be able to run the module on all SVs.

  • There are currently several bottlenecks in the pipeline: samtools sort can be tricky to parallelize properly (piped from minimap2 alignments, which are often fast) and the performance will depends on the genomes size, complexity and the parameter used. RepeatMasker can be slow with a large number of SVs and a large library, hang-on! If you find satisfactory combinations of parameters for your model, please share them in the issues section! Thanks!

Cite

Groza, C., Chen, X., Wheeler, T.J. et al. A unified framework to analyze transposable element insertion polymorphisms using graph genomes. Nat Commun 15, 8915 (2024). https://doi.org/10.1038/s41467-024-53294-2