EmpiricalReads2Map_v1.4.0

1.4.0

• STACKs included
• support to pair-end reads
• defined defaults
• runtimes adapted to run with Caper
• new tutorial

EmpiricalMaps_v1.2.4

1.2.4

• runtimes adapted to run with Caper
• perform the genotype calling with updog, SuperMASSA and polyRAD with complete data set (not only for the selected chromosome)
• new tutorial

EmpiricalSNPCalling_v1.2.1

1.2.1

• Adjustments in runtime

EmpiricalMaps_v1.2.3

1.2.3

• Supermassa has smaller probability threshold (bugfix)

EmpiricalMaps_v1.2.2

1.2.2

• Supermassa has smaller probability threshold

EmpiricalMaps_v1.2.1

1.2.1

• Avoid estimating multipoint genetic distances to save time
• Adjustments in runtime

EmpiricalSNPCalling_v1.2.0

1.2.0

• Run freebayes parallelizing in nodes according to chromosomes and cores splitting in genomic regions
• Adjust runtimes
• Add polyploid dataset for tests

EmpiricalMaps_v1.2.0

1.2.0

• Add MAPpoly to build linkage maps for polyploid species
• Adjust runtimes
• Add polyploid dataset for tests

SimulatedReads2Map_v1.0.0

1.0.0

Initial release

This workflow perform simulations of one or more (defined by number_of_families) bi-parental outcrossing population haplotypes using PedigreeSim software based on a provided linkage map and SNP markers. It uses RADinitio software, the simulated haplotypes and a reference genome to also simulate genotyping-by-sequencing read sequences. After, it performs the SNP and genotype calling and builds 68 linkage maps from the combinations:

• SNP calling: GATK and Freebayes
• Dosage/genotype calling: updog, polyRAD and SuperMASSA
• Linkage map build software: OneMap 3.0 and GUSMap
• Using genotype probabilities from GATK, Freebayes, updog, polyRAD and SuperMASSA, and a global error rate of 5% and 0.001% in the OneMap HMM.

It also has the options to:

• Include or not multiallelic (MNP) markers
• Apply filters using VCFtools

This workflow uses:

• A reference linkage map
• A reference VCF file
• A single chromosome from a reference genome
• Diploid bi-parental F1 population
• Genomic positions for markers order

EmpiricalReads2Map_v1.0.0

1.0.0

Initial release

This workflow build linkage maps from genotyping-by-sequencing (GBS) data. The GBS samples are splitted into chunks to be run in different nodes and optimize the analyses. Set the number of samples by chunk in the 'chunk_size' input. Use 'max_cores' to define number of cores to be used in each node. The workflow runs the combinations:

• SNP calling: GATK and Freebayes
• Dosage/genotype calling: updog, polyRAD and SuperMASSA
• Linkage map build software: OneMap 3.0 and GUSMap
• Using genotype probabilities from GATK, Freebayes, updog, polyRAD and SuperMASSA, and a global error rate of 5% and 0.001% in the OneMap HMM.

Resulting in 34 linkage maps.

The workflow include de options to:

• Remove or not the read duplicates
• Perform the Hard Filtering in GATK results
• Replace the VCF AD format field by counts from BAM files
• Run MCHap software to build haplotypes based on GATK called markers
• Include or not multiallelic (MNP) markers
• Apply filters using VCFtools

This workflow requires:

• Diploid bi-parental F1 population
• Single-end reads
• A reference genome
• Genomic positions for markers order
• Selection of a single chromosome from a reference genome to build the linkage maps