polyGembler is for construction of genetic linakge maps or chromosomal-scale pseudomolecules combining de novo genome assembly and genetic mapping. The method assumes availability of genome-wide genotyping data such as GBS, RAD-seq and SNP array data, collected on a F1 outbred mapping population, as well as high coverage (i.e. greater than 30X) whole genome sequence data on a reference sample, or alternatively the availability of a set of reference contigs or scaffolds. By mapping marker set to contigs, polyGembler infers contig haplotypes for each sample. Contig haplotypes are then used to infer linkage groups corresponding to chromosomes as well as the optimal ordering of contigs within these chromosomes.
polyGembler provides a standalone haplotype phasing tool for outbred mapping populations (haplotyper, see quick-start). It accepts either genotype data or allele depth data and support polyploids. It is very robust to the presence of substantial amounts of missing genotype data and genotyping errors and therefore can handle low-coverage, error-prone genotyping data generated with the NGS-based high-throughput genetic marker discovery methods such as GBS, RAD-seq and RRL.
polyGembler also provides a tool for simulating outbred F1 mapping population GBS data. It uses the software PedigreeSim V2.0 to simulate the full-sib family genomes and imitates the GBS protocol to generate GBS reads.
polyGembler is written in Java. It has a user friendly design. All the tasks could be done with an one-liner command require no further manual intervention. It has been multithreaded where possible.
The release includes an executable jar file with all dependencies, which could be run directly.
Java build tool either Apache Ant or Apache Maven is required.
Quick installation guide:
$ git clone https://github.com/c-zhou/polyGembler.git
$ cd polyGembler
$ ant
or
$ mvn clean package
Both of them will generate an executable jar file polyGembler-${version}-jar-with-dependencies.jar. For Apache Ant, the jar file is under polyGembler/dist directory. For Apache Maven, the jar file is under polyGembler/target directory.
The code has been tested with Java 7 and 8. The default version is Java 8. The Apache Maven will fail if the system default Java version is not 8. This could be solved by either changing the system Java version or modifying the pom.xml file at line 22-23. You can change the Java version for Apache Ant by modifying the build.xml file at line 10-11.
polyGembler is a java program. You need JRE installed in your system to run polyGembler. The code has been tested with Java 7 and 8. It should to safe to run the program with Java 7 or a newer version.
For genetic linkage map construction (polyGembler program map), R need to be available in your system as well. R packages argparse, TSP, MDSMap, igraph, doParallel, and foreach need to be installed.
A one-liner command can be used to run polyGembler for construction of whole-chromosome pseudomolecules.
Input a VCF file, a FASTA file of contigs/scaffolds
Output several files related to the pseudomolecule construction
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar gembler -i ${in_vcf_file} -a ${seq_fasta_file} -p ${ploidy} -parent ${parent_samples} -t 32
The program takes input a VCF file and a FASTA file containing contig/scaffold sequences and outputs several files related to the pseudomolecule construction (see detailed outputs). Some important parameters include the ploidy (-p option) and the parent samples (-parent option) and see detailed options for other options. This pipeline could be time-consuming, so it is important run it with as many threads as possible (-t option).
The pipeline comprises more than ten steps calling different polyGembler subprograms (see quick-start). The figure above depicts the flowchart of the pipeline. polyGembler first converts the input VCF file to a zip file (datapreparation) that can be read by polyGembler programs. It then runs haplotype phasing for each contig/scaffold (haplotyper) and detects assembly errors (asmerr). The misaseembled contigs/scaffolds are split to generate new contigs/scaffolds which subject to another round of haplotype phasing (haplotyper). With the haplotype phasing results, polyGembler calculates recombination frequencies (RFs) for each pair of contigs/scaffolds (twopoint) which are used to build super scaffolds (superscaffold). polyGembler performs one more round of haplotype phasing with super scaffolds to improve the accuracy of haplotypes for contigs/scaffolds. The pairwise RFs between contigs/scaffolds are refined with the haplotypes of superscaffolds (twopoint). The RFs for SNP pairs within each contig/scaffold are also calculated to estimate the genetic length of the contig/scaffold (singlepoint). Next, with the RF estimations, polyGembler constructs genetic linkage maps for contigs/scaffolds (map). The genetic linkage maps are then subject to several rounds of polishing before finalisation. Precisely, for each linkage map, polyGembler run haplotype phasing for it on the whole-linkage-map scale followed by RF estimations and reordering of contigs/scaffolds. This process repeats for several rounds. Finally, pseudomolecules are constructed based on the polished genetic linkage maps (chromosomer). The superscaffold and polishing steps are optional for refinement and can be turned off by specific parameter settings (see detailed options).
The pseudomolecule construction pipeline can be time-consuming and resource-intensive, which is impractical to run gembler for extremely large datasets. It is possible, however, to run the pipeline step by step with a high performance computing (HPC) system. In the example folder, we provide a script (gembler_daemon.sh) to run the pipeline with HPC using Slurm Workload Manager. The scripts support other workload managers such as PBS and LSF are yet to come.
polyGembler consists of twelve programs. To get a full list of these programs, run
$ java -jar polyGembler-${version}-jar-with-dependencies.jar
The basic command to invoke a polyGembler progam is,
$ java -jar polyGembler-${version}-jar-with-dependencies.jar ${program_name}
Here we give some examples for a quick start for running polyGembler programs.
Input a reference FASTA file with all chromosomes
Output gzipped FASTQ files containing GBS reads for per each sample
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar popsimulation -r ${ref_fasta_file} -n 192 -p 4 -c 200 -t 32 -o ${pop_out_dir}
$ java -jar polyGembler-${version}-jar-with-dependencies.jar gbssimulation -f ${pop_out_dir}/Sc1 -m 10 -s 5 -t 32 -o ${gbs_out_dir}
The first step simulates a tetraploid (-p option) F1 mapping population with 192 samples (-n option) from the reference genome ${ref_fasta_file} (-r option). The total genetic length of the chromosomes is 200cm (-c option). It uses 32 CPUs (-t option). The output sample genomes (FASTA files) will be in ${pop_out_dir}/Sc1 (-o option). The second step takes the simulated mapping population genomes in the first step as input (-f option). The average sequencing depth of coverage for each copy of the chromosome around the restriction enzyme is 10 (-m option) and the standard deviation is 5 (-s option). It uses 32 CPUs (-t option). The output GBS data (FASTQ file) will be in ${gbs_out_dir} (-o option). The program write GBS reads for each sample separately as a gzipped file. If you need to put all GBS reads together, simply use the cat command.
$ cat ${gbs_out_dir}/*.gz > ${merged_gbs_file}.gz
The GBS data could be used for variant calling with TASSEL-GBS pipeline or Stacks. A example script to run TASSEL-GBS is provided in example ([runGBSv2.sh][example/runGBSv2.sh]).
Input a VCF file
Output a zip file accept by polyGembler programs
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar datapreparation -i ${in_vcf_file} -s ${run_id} -u 3000 -q 30 -f 0.05 -m 0.5 -o ${out_dir}
The VCF file is provided with -i option. The program will filter the variants according to the parameters specified by user. Here the upper bound of total allele depth (DP field in the VCF file) for a position is set to 3000 (-u option). If the DP field exceeds 3000, the variant will be filtered out. This is used to remove ambiguous variants caused by copy number variation. The minimum quality score of a SNP is set 30 (-q option). SNPs with quality score lower than 30 will be discarded. The lower bound of minor allele frequency is set to 0.05. The rare variants called from a F1 full-sib family is very likely caused by sequencing errors. The maximum missing data rate across a locus is set to 0.5. Loci with more than 50% missing genotypes will be filtered out. It should be noted here that the program only deals with biallelic SNPs currently. The output is a zip file named ${run_id}.zip located in directory ${out_dir}.
Input preprocessed zip file
Output inferred haplotypes for a contig or scaffold
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar haplotyper -i ${in_zip_file} -ex ${expriment_id} -c ${contig_str_id} -p 2 -f ${parent_sample_1}:${parent_sample_2} -D -o ${haps_out_dir}
The program takes the zip file generated in the data preparation step and a contig/scaffold string id as input. User need to specify the ploidy and optionally the ids of parental samples if available. -D option tells the program to use the allele depth information, which is default. Otherwise user could run it with -G option which utilises genotype information. The output file directory is specified with -o option and the output is a zip file starts with ${expriment_id}. See detailed outputs for the details about the output file.
You can run multiple contigs/scaffolds simultaneously.
$ java -jar polyGembler-${version}-jar-with-dependencies.jar haplotyper -i ${in_zip_file} -ex ${expriment_id} -c ${contig_str_id}:${contig_str_id2}:${contig_str_id3} -r true:false:false -s 0.05:0.1 -p 2 -f ${parent_sample_1}:${parent_sample_2} -D -o ${haps_out_dir}
This is very similar to run it with the single contig/scaffold. Multiple contigs/scaffolds are provided with string ids separated by colons. As there could be different concatenation directions, -r option specifies if each contig/scaffold is in reverse direction or not. The default is not. The distances between the adjacent contigs/scaffolds are initialised with -s option, otherwise the program will generate them randomly. The distances could either be RFs or physical distances in base pair. The program will check these numbers. If all of them are smaller than 0.5, then they will be taken as RFs, otherwise physical distances.
Expectation-maximisation (EM) algorithm is used for optimisation in construction of haplotypes, which could be trapped in local optima. Therefore, multiple independent runs should be performed to improve the accuracy.
Input output files for haplotype phasing, VCF file
Output misassembled contigs/scaffolds, a new VCF
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar asmerr -i ${haps_out_dir} -vi ${vcf_in} -r 0.1 -ex ${experiment_id} -t 8 -o ${out_prefix}
The program takes input the phased haplotypes (-i option) and the VCF file (-vi option) and outputs a list of potentially misassembled contigs/scaffolds (including the misassembly position) in ${out_prefix}.err file as well as a new VCF file file for new contigs/scaffolds after splitting old ones in ${out_prefix}.vcf file. The program supports multithreading (-t option).
Input output files for haplotype phasing
Output a file contains the recombination information for each contig/scaffold
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar singlepoint -i ${haps_out_dir} -t 8 -ex ${experiment_id} -o ${out_prefix}
The program takes input the phased haplotypes (-i option) and outputs the recombination information for each contig/scaffold in the ${out_prefix}.map file. There could be multiple runs of haplotype phasing for each contig/scaffold in the input directory. The RFs are then averaged on multiple runs. If the -ex option is provided, only haplotype files match the experiment id are included, otherwise the program will detect experiment id automatically.
Input output files for haplotype phasing
Output a file containing the RFs between each pair of contigs/scaffolds
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar twopoint -i ${haps_out_dir} -t 8 -ex ${experiment_id} -o ${out_prefix}
The program takes input the phased haplotypes (-i option) and outputs pairwise RFs for contigs/scaffolds in the ${out_prefix}.txt file. There could be multiple runs of haplotype phasing for each contig/scaffold in the input directory. The minimum RF is recorded as the final estimation. If the -ex option is provided, only haplotype files match the experiment id are included, otherwise the program will detect experiment id automatically.
Input singlepoint RF estimation file, twopoint RF estimation file
Output a genetic linkage map file
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar map -i ${twopoint_rf}.txt -m ${singlepoint_rf}.map -rlib ${rlib_external_dir} -t 8 -o ${out_prefix}
The program consists of two steps: grouping and ordering. To run this program, Rscript need to be in the environment. The R packages argparse, TSP, MDSMap, igraph, doParallel, and foreach should be installed. If the R packages are installed in non-default library paths, the option -rlib could be used to provide the installation path to the program. The output file ${out_prefix}.mct contains the genetic map information. This program is multithreaded (-t option).
Input twopoint RF estimation file
Output a file describes the super scaffolds
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar map -i ${twopoint_rf}.txt -r 0.1 -o ${out_prefix}
The program constructs super scaffolds with the pairwise RF estimations using a nearest-neighbour-joining method. Precisely, two contigs/scaffolds with the minimum RF are joined to generated a new scaffold if the RF is smaller than a predefined threshold (-r option). The two joined contigs/scaffolds are then deleted and the RFs between the new scaffolds and all other contigs/scaffolds are updated. The joining progress continues till the minimum RF is greater than the predefined threshold or no more contigs/scaffolds to join. The output is a file named ${out_prefix}.par to describe the super scaffolds.
Input genetic linkage map file (generated by program map), contig/scaffold sequences in FASTA format, assembly error file (optional, generated by program asmerr)
Output one AGP and two FASTA files describe the pseudomolecules
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar chromosomer -i ${map_mct_file} -a ${seq_fasta_file} -e ${asm_err_file} -n 1000 -o ${out_prefix}
The program takes input the genetic linkage map file generated by program map (-i option), the FASTA file contains contig/scaffolds sequences (-a option) and optionally, the file contains assembly errors generated by program asmerr (-e option), and outputs several files to describe the pseudomolecules including two FASTA files containing sequences for raw contigs/scaffolds (after splitting for misassembly) and pseudomolecules, respectively, and an AGP file describing the architecture of pseudomolecules. To construct pseudomolecules, neighbouring raw contigs/scaffolds are joined with a poly 'n' of size defined by -n option.
Input output files for haplotype phasing, reference VCF file contains true phased genotypes
Output statistics for phasing accuracy
Command
$ java -jar polyGembler-${version}-jar-with-dependencies.jar evaluator -i ${haps_out_dir} -p ${true_phased_genotype_file} -ex ${experiment_id}
This is an auxiliary program independent from pseudomolecule construction. It is provided in order to evaluate the phasing accuracy of program haplotyper. It calculates the correct phasing rate which is defined as the proportion of the correctly phased loci with respect to the true haplotypes. The true haplotypes are provided with a VCF file (-p option) containing phased genotypes.
popsimulation Simulate a full-sib mapping population. gbssimulation Simulate GBS data. datapreparation Prepare data for haplotype phasing. haplotyper Haplotype phasing from a mapping population. asmerr Correct assembly errors. singlepoint Signle-point analysis: estimate recombination fraction between markers within contigs/scaffolds. twopoint Two-point analysis: estimate pairwise recombination fraction between contigs/scaffolds. map Construct linkage maps. superscaffold Construct superscaffold using nearest neighbour joining. chromosomer Construct pseudo chromosomes. evaluator Haplotype phasing accuracy evaluation with konwn haplotypes. gembler Run PolyGembler pipeline to construct genetic linkage maps/pseudomolecules.
-r/--reference Reference (fasta file format).
-n/--pop-size Population size including parents (default 96).
-p/--ploidy Copy number of chromosomes (default 2).
-c/--centimorgan Total genetic length to simulate. Assume the physical length
and genetic length has linear correlation (default calculated
from the reference, 1cM per 1Mbp).
-t/--threads Number of threads (default 1).
-s/--run-id Unique run id (default Sc1).
-o/--prefix Output directory (default current directory).
-f/--fasta-file Directory contains genome fasta files to be sequenced. -e/--enzyme Enzyme(default PstI). -l/--library GBS protocol library preparation file (default null). -t/--threads Number of threads (default 1). -b/--barcode-file GBS protocol barcode file (default null). -m/--avg-depth Depth of coverage (default 5). -s/--sdev Standard deviation of depth of coverage (default 5). -S/--random-seed Random seed (default system nano time). -q/--quality-file Markov chain parameter file for quality scores (default null). -o/--output-prefix Output directory (default current directory).
-i/--vcf Input VCF file. -s/--id Unique id of this run (default: input VCF file name prefix). -l/--min-depth Minimum depth to keep a SNP (0). -u/--max-depth Maximum depth to keep a SNP (2147483647). -q/--min-qual Minimum quality to keep a SNP (0). -f/--min-maf Minimum minor allele frequency to keep a SNP (default 0). -m/--max-missing Maximum proportion of missing data to keep a SNP (default 1.0). -o/--prefix Prefix for output files (default: input VCF file folder).
-i/--input Input zipped file.
-o/--prefix Output file location.
-ex/--experiment-id Common prefix of haplotype files for this experiment.
-c/--scaffold The scaffold/contig/chromosome id will run.
-cs/--start-position The start position of the scaffold/contig/chromosome.
-ce/--end-position The end position of the scaffold/contig/chromosome.
-x/--max-iter Maxmium rounds for EM optimization (default 1000).
-p/--ploidy Ploidy of genome (default 2).
-f/--parent Parent samples (separated by a ":").
-s/--initial-seperation Initialisations of distances between the adjacent scaffolds
if multiple scaffolds will be jointly inferred. The separation
could be either physical distances or recombination frequencies,
i.e., if all values provided is below 0.5, the
program will take them as recombination frequencies.
Distances should be separated by ":".
-r/--reverse Take either 'true' or 'false', indicating whetherr the
scaffold is reversed before inferring haplotypes. Multiple
scaffolds are separated by ":".
-G/--genotype Use genotypes to infer haplotypes. Mutually exclusive with
option -D/--allele-depth.
-D/--allele-depth Use allele depth to infer haplotypes. Mutually exclusive
with option -G/--genotype.(default)
-S/--random-seed Random seed for this run.
-i/--hap-file Directory with input haplotype files.
-o/--prefix Output file prefix.
-vi/--vcf-in Input VCF file. If provided, will generate a VCF file contains variants from split scaffolds.
-vo/--vcf-out Output VCF file. If not provided, will use output file prefix (-o) option.
-r/--rf-thresh Recombination frequency threshold for assembly error detection (default 0.1).
-wbp/-windows-bp Window size (#basepairs) for RF estimation for marker pairs on
same scaffold (default 30000).
-wnm/-windows-nm Window size (#markers) for RF estimation for marker pairs on
same scaffold (default 30).
-ex/--experiment-id Common prefix of haplotype files for this experiment.
-nb/--best The most likely nb haplotypes will be used (default 30).
-phi/--skew-phi For a haplotype inference, the frequencies of parental
haplotypes need to be in the interval [1/phi, phi],
otherwise will be discared (default 2).
-nd/--drop At least nd haplotype inferences are required for
a contig/scaffold to be analysed (default 1).
-t/--threads #threads (default 1).
-i/--hap-file Directory with input haplotype files.
-o/--prefix Output file prefix.
-wbp/-windows-bp Window size (#basepairs) for RF estimation for marker pairs on
same scaffold (default 30000).
-wnm/-windows-nm Window size (#markers) for RF estimation for marker pairs on
same scaffold (default 30).
-ex/--experiment-id Common prefix of haplotype files for this experiment.
-nb/--best The most likely nb haplotypes will be used (default 10).
-phi/--skew-phi For a haplotype inference, the frequencies of parental
haplotypes need to be in the interval [1/phi, phi],
otherwise will be discared (default 2).
-nd/--drop At least nd haplotype inferences are required for
a contig/scaffold to be analysed (default 1).
-t/--threads #threads (default 1).
-i/--hap-file Directory with input haplotype files.
-o/--prefix Output file prefix.
-ex/--experiment-id Common prefix of haplotype files for this experiment.
-nb/--best The most likely nb haplotypes will be used (default 10).
-phi/--skew-phi For a haplotype inference, the frequencies of parental
haplotypes need to be in the interval [1/phi, phi],
otherwise will be discared (default 2.0).
-nd/--drop At least nd haplotype inferences are required for
a contig/scaffold to be analysed (default 1).
-t/--threads #threads (default 1).
-i/--input Recombination frequency file. -m/--map Recombination map file. -l/--lod LOD score threshold (default: 3). -r/--rf Recombination frequency threshold (default: 0.38). -1/--one-group Keep all in one group. Do not do clustering (default: false). -c/--check-chimeric Check chimeric joins during grouping (default: false). -rlib/--R-external-libs R external library path. -t/--threads #threads (default 1). -o/--prefix Output file prefix.
-i/--input Recombination frequency file. -r/--rf Recombination frequency threshold (default: 0.27). -o/--prefix Output file prefix.
-i/--map Input genetic linkage map file.
-a/--assembly Input assembly FASTA file.
-e/--error Assembly error file.
-n/--gap Gap size between sequences (default 1000).
The gaps will be filled with character 'n'.
-o/--prefix Output pseudomolecule file.
-i/--hap-file Directory with input haplotype files.
-p/--phase-file Phased haplotype file.
-ex/--experiment-id Common prefix of haplotype files for this experiment.
-phi/--skew-phi For a haplotype inference, the frequencies of parental
haplotypes need to be in the interval [1/phi, phi],
otherwise will be discared (default 2).
-t/--threads #threads (default 1).
Common:
-i/--input-vcf Input VCF file.
-o/--prefix Output file location, create the directory if not exist.
-p/--ploidy Ploidy of genome (default 2).
-t/--threads Threads (default 1).
-rlib/--R-external-libs External library paths that you want R to search for packages.
This could be useful if you are not root users and install R
packages in directories other than default.
Multiple paths separated by ':' could be provided.
Data preparation:
-l/--min-depth Minimum depth to keep a SNP (0).
-u/--max-depth Maximum depth to keep a SNP (2147483647).
-q/--min-qual Minimum quality to keep a SNP (0).
-f/--min-maf Minimum minor allele frequency to keep a SNP (default 0).
-m/--max-missing Maximum proportion of missing data to keep a SNP (default 1.0).
Haplotype inferring and pseudomolecule refinement:
-x/--max-iter Maxmium rounds for EM optimization (default 1000).
-parent/--parent Parent samples (separated by a ":").
-G/--genotype Use genotypes to infer haplotypes. Mutually exclusive with
option -D/--allele-depth.
-D/--allele-depth Use allele depth to infer haplotypes. Mutually exclusive
with option -G/--genotype (default).
-c/--min-snp-count Minimum number of SNPs on a scaffold to run (default 5).
-r/--repeat Repeat haplotype inferring for multiple times as EM algorithm
could be trapped in local optima. The program takes three values
from here, i.e., for scaffold, for superscaffold and for genetic
linkage map refinement. Three values should be separated by ','
(default 30,30,10).
-rr/--refinement-round Number of rounds to refine pseudomelecules (default 3).
Recombination frequency estimation and assembly error detection:
-asmr/--asmr-thresh Recombination frequency threshold for assembly error detection (default 0.1).
-wbp/--windows-bp Window size (#basepairs) for RF estimation for marker pairs on
same scaffold (default 30000).
-wnm/--windows-nm Window size (#markers) for RF estimation for marker pairs on
same scaffold (default 30).
-nb/--best The most likely nb haplotypes will be used (default 30).
-phi/--skew-phi For a haplotype inference, the frequencies of parental
haplotypes need to be in the interval [1/phi, phi],
otherwise will be discared (default 2).
-nd/--drop At least nd haplotype inferences are required for
a contig/scaffold to be analysed (default 1).
Genetic linkage map construction:
-l/--lod LOD score threshold (default: 3).
-lr/--lr-thresh Recombination frequency threshold for linkage mapping (default: 0.38).
-c/--check-chimeric Check chimeric joins during grouping (default: false).
Superscaffold construction by nearest neighbor joining:
-ns/--no-superscaffold Do NOT construct superscaffold for RF refinement.
-sr/--sr-thresh Recombination frequency threshold for superscaffold (default: 0.27).
Pseudomolecule construction:
-a/--contig-file Input assembly FASTA file.
-g/--gap Gap size between sequences (default 1000).
The gaps will be filled with character 'n'.
a. ${out_dir}/Sc1/*.fasta.gz Gzipped files with genome sequences. One for each sample.
b. ${out_dir}/*.* Meta files used and generated by PedigreeSim V2.0.
a. ${out_dir}/*.gz Gzipped files with GBS reads. One for each sample.
b. ${out_dir}/*_key.txt The key file for the GBS.
a. ${out_dir}/*.${run_id}.vcf A preprocessed VCF files. Variants are filtered.
b. ${out_dir}/*.${run_id}.zip A zip file can be read by polyGembler programs.
a. ${out_dir}/*.zip A zip file contains all related results for haplotype phasing.
|- snp.txt SNPs that used in this run.
|- emission.txt Emission probabilities of each allele for each parental haplotype at each locus.
|- transition.txt Transition probabilities (RFs) between parental haplotypes for adjacent loci.
|- haplotype.txt Inferred inheritance pattern or haplotypes for all samples.
|- genotype.txt Phased genotypes for all samples.
|- dosage.txt Allele dosages for all samples.
|- runinfo.txt A log file for this run.
a. ${out_prefix}.err A file describes the assembly errors.
b. ${out_prefix}.vcf A VCF file for new contigs/scaffolds after splitting the misassembled ones.
a. ${out_prefix}.map A file describes the RFs for SNP pairs within contigs/scaffolds.
a. ${out_prefix}.txt A file describes the pairwise RFs for contigs/scaffolds.
a. ${out_prefix}.mct A file describe the genetic linkage maps.
b. ${out_prefix}.par Anther format to describe genetic linkage maps. Each line in this file could
be used as an input to program `haplotype` for whole linkage group haplotype
phasing, which is essential for linkage map polishing.
c. ${out_prefix}.RData A R data object storing the related data (for debug only).
d. ${out_prefix}.log A log file.
a. ${out_prefix}.nns A file describes the super scaffolds. It has the same format as `.par` file
generated by program `map`.
a. ${out_prefix}.mol.fa A FASTA file of pseudomolecules.
b. ${out_prefix}.raw.fa A FASTA file of raw contigs/scaffolds for constructing pseudomolecules.
c. ${out_prefix}.agp A file describes the architecture of pseudomolecules, i.e., how raw
contigs/scaffolds are joined.
statistics in `stdout`, no output files.
a. ${out_dir}/final.mol.fa Output by program `chromosomer`.
b. ${out_dir}/final.raw.fa Output by program `chromosomer`.
c. ${out_dir}/final.agp Output by program `chromosomer`.
d. ${out_dir}/final.err Output by program `asmerr`.
e. ${out_dir}/final.map Output by program `singlepoint`.
f. ${out_dir}/final.txt Output by program `twopoint`.
g. ${out_dir}/final.mct Output by program `map`.
h. ${out_dir}/final.par Output by program `map`.
i. ${out_dir}/h1/* Haplotype phasing results for the first run.
j. ${out_dir}/h2/* Haplotype phasing results for the superscaffolds.
- may not exist if skip the superscaffold step
k. ${out_dir}/herr/* Haplotype phasing results for the misassembled contigs/scaffolds.
- may not exist if no misassembly detected
l. ${out_dir}/hlg/* Haplotype phasing results for the linkage group polishing.
- may not exist if skip the polishing step
*others There are also other intermediate files which are less essential.
The method is described in:
Zhou, C., Olukolu, B., Gemenet, D.C. et al. Assembly of whole-chromosome pseudomolecules for polyploid plant genomes using outbred mapping populations. Nat Genet 52, 1256–1264 (2020). https://doi.org/10.1038/s41588-020-00717-7