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**findmap.f90**
Align sequence reads to reference map, call previous variants, and identify new variantsDownloads
Version 2.2 programs, example and test outputs, and executables*(released December 10, 2018)*
Findmap alignment and Findvar variant calling series
Programs mapsim.f90, storemap.f90, map2seq.f90, findmap.f90, findvar.f90, leftmost.f90, and depth2vcf.c included
Skip program mapsim when using actual map and variant files
Skip simulation program map2seq when using actual fastq reads
Programs leftmost and depth2vcf optional for converting to other formats
To save space, zip download does not include actual reference genome, variant list, or fastq sequence read files
Download findmapV2.2.zip onto a computer with the Unix operating system
Type unzip findmapV2.2.zip, and hit enter
After unzipping package, type runsim.script to run program series script and generate an example simulated reference map, a simulated variant file, simulated fastq files, and other program input and output files; type runmap.script to run alignment and variant calling programs
Programs will execute in the Test_Output directory using options files there.
Options files give more detail on available options and recommended values
Output files in Test_Output should match those in Example_Output fairly closely
Each new project should be run in a new directory because standard file names are used
For quick testing, mapsim.options and map2seq.options are set to just 1 chromosome, and you must then reset them to 30 chromosome pairs for cattle (or other numbers to match your reference.fa file); similarly, maxhash is set to 87654321 and maxdup to 10 million in storemap.options to reduce memory in initial testing, but values of 287654321 and 300 million are recommended for a 3-Gbase genome
Findmap output formats .found and .lost now include an extra column for map quality score
findvar is not backward compatible to input data aligned by earlier findmap versions
Map quality scores are accurate if detection option is set to 2 but quadruples run time
Do not include all of the unmapped contigs in the fasta map file as separate chromosomes because those would increase memory for little gain; from the human reference hg38.fa, we used 25 chromosomes (including 22 autosomes plus X, Y, and mitochondrial DNA) and reformatted the file using fasta.sas (included)
Variant list from the the 1000 Bull Genomes Project (Daetwyler et al., 2014) should be concatenated across chromosomes and then reformatted to variants.prior using program variant.sas.
Main reason to reformat is that indel notation and locations used in findmap differ from vcf notation
variant.sas program can also reformat the 00-common_all.vcf human variant file
Simulation options
For markersim.f90, there is an optional input file, genetic.cor, in the subdirectory Example_Output.
For storemap.f90, there is an optional input file, flank.location, in the subdirectory Example_Output; its only purpose is to output the flanking sequence for each location in the input file
Mapsim option newvar should be 0 when processing actual data so that all known variants will be used
In mapsim.options, newvar can be set to 5 (or other odd number) to exclude every 5th variant and demonstrate detection of those "new" variants
Program testvar.sas then tests accuracy of variant calls, both for previously known and newly discovered variants
Program map2seq simulates genotypes for variant list in groups of 4, with every 4th variant homozygous alternate allele, every 2nd variant homozygous reference, and 1st and 3rd variants heterozygous or optionally can read file genotypes.true instead to declare variant genotypes for each DNA sourceVersion 2.1 programs, example outputs, and executables *(released October 1, 2018)*Version 2 programs, example outputs, and executables*(released May 31, 2018)*Version 1 programs, example outputs, and executables*(released July 19, 2016; last updated July 28, 2016)*Version 0 programs, example outputs, and executables*(beta version; released January 8, 2016)*Inputs
reference.fa
Standard fasta format for reference genome:> as 1st character in line for each new chromosome50-byte lines of ACGT (or N for unknown bases), or acgt for repeated sectionsAll programs in this series treat lower and uppercase as the same because storemap identifies, counts, stores, and links repeated k-mers to each other while hashing reference mapvariants.prior
Lists all previously known SNPs and indelsInsertions reported 1 base to left of 1st base where they differ from reference genome, reading left to rightDeletions reported at their detected location, not 1 base to leftUse variants.sas to reformat the 1000 Bull Genomes variant fileFormat: chr# location vartype (SNP, INS, or DEL)  variant#  length  alternate_allelefastq.filelist
List of DNA source names such as source1, source2, etc., along with numeric IDssource1.1.fq,source1.2.fq,source2.1.fq,source2.2.fq, etc.
Standard fastq format for paired end reads, with reads 1 and 2 of each pair at same position in 2 separate files for each DNA source*.options
Program control file with user-defined optionsOutputs
storemap.unf
Hash table, etc., for reference mapreference.unf
Unformatted map for faster inputvariant.readdepth
Number of ref and alt alleles, 1 row/variantFormat: variant# chr# var_location ref# alt#individual.readdepth
Format: ID#  chip#  #SNPsRead counts for A and B alleles stored in 1-byte hexadecimal format (input format for imputation programfindhap4;VanRaden et al., 2015)segments.found
Alignments, errors, and known variant locations for segments where paired end locations differ by <fraglenFormat: segment# pair# direction chr# segment_location num_alts num_errs (var_locations var_type) (err_locations err_base)segments.lost
Same format, but for segments where paired end locations do not matchsegments.newindels
Locations and properties of new indels detected (those not already in variants.txt)Format: segment# pair# direction chr# seg_location indel_size indel_location bases (inserted or deleted)SNPs.new
Summary of new SNPs including read depth and number of alternate alleles foundLocations can have >1 row if differing alternate alleles are observedFormat: chr# SNP_location read_depth num_alt ref_allele alt_alleleindels.new
Summary of new indels including read depth and number of alternate alleles foundLocations can have >1 row if differing alternate alleles are observedFormat: chr# indel_location read_depth num_alt ref_allele alt_allelevariants.all
Combined list of prior and new variants in same format as variants.priorReferences
2019
VanRaden, P.M., Bickhart, D.M., and O'Connell, J.R. Calling known variants and identifying new variants while rapidly aligning sequence data. J. Dairy Sci. 102:3216–3229.2016
VanRaden, P.M., and D.M. Bickhart. Fast single-pass alignment and variant calling using sequencing data. Plant Anim. Genome XXIV Conf., San Diego, CA, Jan. 9–13, W161.  |  Presentation slidesVanRaden, P.M., D.M. Bickhart, and J.R. O'Connell. Identifying and calling insertions, deletions, and single-base mutations efficiently from sequence data. J. Dairy Sci. 99(E-Suppl. 1):140(abstr. 0302).  |  Presentation slides2015
VanRaden, P.M., C. Sun, and J.R. O'Connell. Fast imputation using medium- or low-coverage sequence data. BMC Genet. 16:82.2014
Daetwyler, H.D., A. Capitan, H. Pausch, P. Stothard, R. van Binsbergen, R.F. Brøndum, X. Liao, A. Djari, S.C. Rodriguez, C. Grohs, D. Esquerré, O. Bouchez, M.N. Rossignol, C. Klopp, D. Rocha, S. Fritz, A. Eggen, P.J. Bowman, D. Coote, A.J. Chamberlain, C. Anderson, C.P. Van Tassell, I. Hulsegge, M.E. Goddard, B. Guldbrandtsen, M.S. Lund, R.F. Veerkamp, D.A. Boichard, R. Fries, and B.J. Hayes. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genet. 46:858–865.VanRaden, P.M., and C. Sun. Fast imputation using medium- or low-coverage sequence data. Proc. 10th World Congr. Genet. Appl. Livest. Prod., 179.  |  Presentation slidesLicense
Fortran package findmap.f90 is public domain and was developed with U.S. taxpayer funding. Accurate results are not guaranteed. Please report any bugs to paul.vanraden@usda.gov. You may modify, improve, use, and redistribute the code to anyone for any purpose. Or, you can ask Paul to make changes that could benefit U.S. evaluations and other users. **Paul VanRaden**Animal Genomics and Improvement LaboratoryAgricultural Research Service, USDA


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