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The best tandem repeat (TR) genotyping software;
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The highest accuracy (99%) and fastest speed in seconds;
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For any short and long Next Generation Sequencing reads ;
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Tested for forensic core 20 forensic core STRs, 38 XY STRs and 61 known disease-causing TRs in human.
Nweest version : v2.0 TRcaller 2.0 has been updated to the new version 2.0 for a better performance, higher accuracy, and easy usage.
The fastest and most accurate tandem repeat genotyping tool by far for both short and long Next Generation Sequencing reads from Illumina, PacBio and Nanopore.
The software can be downloaded for a direct use. No additional compiling and installation. Get it from Github:
git clone https://github.com/XuewenWangUGA/TRcaller
or download the zip compressed files and then unzip to TRcaller
Update Java run environment if necessary. The software will use the Java runtime environment (SE) V17.
If your computer has an old version of Java runtime, please install the newest Java or Java SE 17 or higher from https://www.oracle.com/java/technologies/downloads/. Either Java or Java SE should work. In this case, you should put path before java. e.g. the dwonloaded java binary is in c:/java21/bin; then type the command to run TRcaller as the followings:
javaPath=c:/java21/bin
$javaPath/java -jar TRcaller.jar
After downloading the tool. Run with the test data set coming with TRcaller. Type the following command beblow in your command terminal: The v2.0 after TRcaller will be version number, TRcallerv2.0, you can remove the version number as needed. Just two files are required, the bed file with position of TR in a reference genome and the read alignment file. If the user will use the default bed file "ForensicCODIS_v1.1.bed", the only mandatory file will be the alignment file.
java -jar TRcaller.jar -i HG002.GRCh38.2x250.subset.bam or
java -jar TRcaller.jar -b ForensicCODIS_v1.1.bed -i HG002.GRCh38.2x250.subset.bam or
java -Xmx2G -jar TRcaller.jar -b ForensicCODIS_v1.1.bed -i HG002.GRCh38.2x250.subset.bam
The memery option is optional. For a very large file, the user can increase the memory, e.g. -Xmx10G
The following data will be generated. The test output files from Human sample HG002 are available on Github.
Result files:
Output raw haplotype: HG002.GRCh38.2x250.subset.bam.TRcaller_Hap.raw.txt
Output report: HG002.GRCh38.2x250.subset.bam.TRcaller_Hap.rept.txt
Output statistical summary: HG002.GRCh38.2x250.subset.bam.TRcaller_Hap.stat.txt
All in one Excel file: HG002.GRCh38.2x250.subset.bam.TRcaller.xlsx
The ".Hap.raw.txt" file has all TR allele candidates in the input read alignment file;
The ".Hap.rept.txt" and Microsoft Excel format ".xlsx" file has the same content of TR alleles after filtering noisy canidatates; Data in Tab separated format looks like
#Report is generated by TRcaller v2.0
#The general report with details
#Marker Count Read_proportion Sample_hap_length Ref_hap_length Sample_allele Ref_allele Haplotype Validation
D1S1656 31 0.6078431372549019 52 68 13 17 CCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
D1S1656 19 0.37254901960784315 56 68 14 17 CCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
TPOX 53 0.8688524590163934 32 32 8 8 AATGAATGAATGAATGAATGAATGAATGAATG PASS
D2S441 25 0.49019607843137253 44 48 11 12 TCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
D2S441 21 0.4117647058823529 60 48 15 12 TCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATTTATCTATCTA PASS
D2S1338 15 0.4411764705882353 88 92 22 23 GGAAGGAAGGACGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGCAGGCAGGCAGGCAGGCAGGCAGGCA PASS
D2S1338 14 0.4117647058823529 96 92 24 23 GGAAGGAAGGACGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGCAGGCAGGCAGGCAGGCAGGCA PASS
D3S1358 34 0.5074626865671642 64 64 16 16 TCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
D3S1358 30 0.44776119402985076 60 64 15 16 TCTATCTGTCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
FGA 19 0.4634146341463415 80 88 20 22 GGAAGGAAGGAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAGAAAAAAGAAAGAAAGAAA PASS
FGA 18 0.43902439024390244 92 88 23 22 GGAAGGAAGGAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAGAAAAAAGAAAGAAAGAAA PASS
D5S818 34 0.5483870967741935 48 44 12 11 ATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCT PASS
D5S818 26 0.41935483870967744 44 44 11 11 ATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCT PASS
CSF1PO 33 0.6346153846153846 48 52 12 13 ATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCT PASS
CSF1PO 17 0.3269230769230769 40 52 10 13 ATCTATCTATCTATCTATCTATCTATCTATCTATCTATCT PASS
D7S820 24 0.46153846153846156 48 52 12 13 TATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATC PASS
D7S820 23 0.4423076923076923 44 52 11 13 TATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATC PASS
D8S1179 27 0.5 52 52 13 13 TCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
D8S1179 24 0.4444444444444444 64 52 16 13 TCTATCTATCTGTCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
D10S1248 24 0.4444444444444444 64 52 16 13 GGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAA PASS
D10S1248 22 0.4074074074074074 56 52 14 13 GGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAA PASS
TH01 30 0.6122448979591837 36 28 9 7 AATGAATGAATGAATGAATGAATGAATGAATGAATG PASS
TH01 17 0.3469387755102041 39 28 9.3 7 AATGAATGAATGAATGAATGAATGATGAATGAATGAATG PASS
vWA 26 0.49056603773584906 72 68 18 17 TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGACAGACAGATAGA PASS
vWA 25 0.4716981132075472 64 68 16 17 TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGACAGACAGATAGA PASS
D12S391 18 0.46153846153846156 88 76 22 19 AGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGACAGACAGACAGACAGACAGACAGAC PASS
D12S391 18 0.46153846153846156 88 76 22 19 AGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGACAGACAGACAGACAGACAGACAGAT PASS
D13S317 24 0.46153846153846156 52 44 13 11 TATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATC PASS
D13S317 24 0.46153846153846156 44 44 11 11 TATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATC PASS
D16S539 64 0.9142857142857143 44 44 11 11 GATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATA PASS
D18S51 29 0.5686274509803921 52 72 13 18 AGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAA PASS
D18S51 21 0.4117647058823529 64 72 16 18 AGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAA PASS
D19S433 16 0.48484848484848486 56 64 14 14 CCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTACCTTCTTTCCTT PASS
D19S433 10 0.30303030303030304 66 64 16.2 14 CCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTTCCTACCTTTTCCTT PASS
D21S11 22 0.6875 126 127 31.2 29 TCTATCTATCTATCTATCTATCTGTCTGTCTGTCTGTCTGTCTGTCTATCTATCTATATCTATCTATCTATCATCTATCTATCCATATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATATCTA PASS
D21S11 6 0.1875 120 127 30 29 TCTATCTATCTATCTATCTGTCTGTCTGTCTGTCTGTCTGTCTATCTATCTATATCTATCTATCTATCATCTATCTATCCATATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTATCTA PASS
D22S1045 49 0.9245283018867925 48 51 16 17 ATTATTATTATTATTATTATTATTATTATTATTATTATTACTATTATT PASS
The "Hap.stat.txt" file has the statistical information.
For help and more advance options, type the following command below:
java -jar TRcallerv2.0.jar
TRcaller v2.0
usage: java -jar -Xmx10G TRcaller.jar [options]
-b,--bed <arg> required, .bed format configure file with a path
-c,--count <arg> integer, minimum count of supported reads for report TR alleles,default [2]
-i,--input <arg> required, input BAM file with a path
-l,--log <arg> string, log file name, default [log.txt]
-o,--output <arg> prefix of output file name ofr saving result
-r,--ratio <arg> float, minimum value of supported read ratio of all reads at each loci, default [0.05]
-s,--source <arg> integer, source type of DNA, 1 for single individual, 2 or higher for DNA mixture, default [1]. for XY chromosomes, it should be set to 3
-t,--thread <arg> integer, the number of computing threads, default [2]
The scripts and setting files for TRcaller paper (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1227176/full) are in the folder: TRcaller_paper_files, scripts for TRcaller versions between 2.0
TRcaller will generated the identical results for 20 core CODIS STRs independent of which forensic sequencing kits are used.
The free testing account is opened at www.trcaller.com/index.aspx
Abstract of the coming paper:
Calling tandem repeat (TR) variants from DNA sequences is of both theoretical and practical significance. Some bioinformatics tools have been developed for detecting or genotyping TRs. However, little study has been done to genotyping TR alleles from long-read sequencing data, and the accuracy of genotyping TR alleles from next generation sequencing data still needs to be improved. Herein, a novel algorithm is described to retrieve TR regions from sequence alignment, and a software program TRcaller has been developed and integrated into a web portal to call TR alleles from both short- and long-read sequences, both whole genome and targeted sequences generated from multiple sequencing platforms. All TR alleles are genotyped as haplotypes and the robust alleles will be reported, even multiple alleles in a DNA mixture. TRcaller could provide substantially higher accuracy (> 99% in 289 human individuals) in detecting TR alleles with magnitudes faster (e.g., ~2 seconds for 300x human sequence data) than the mainstream software tools. The web portal preselected 119 TR loci from forensics and disease plus customer giving TR loci. TRcaller is validated to be scalable in various applications, such as DNA forensics and disease diagnosis, which can be expanded into other fields like breeding programs.
Availability: TRcaller is available at www.trcaller.com/index.aspx.
More detailed information is on https://github.com/Ge-Lab/TRcaller
Manual: Manual_TRcaller_aug28_2023.pdf
Fig 1. D8S1179 colorful STR Alleles
Fig 2. D19S433 colorful STR Alleles
X Wang, H Meng, B Budowle, J Ge. 2023, TRcaller: a novel tool for precise and ultrafast tandem repeat variant genotyping in massively parallel sequencing reads, Frontiers in genetics , https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1227176/full