STRkit: precise, read-level genotyping of short tandem repeats using long reads and single-nucleotide variation

STRkit: precise, read-level genotyping of short tandem repeats using long reads and single-nucleotide variation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results STRkit: precise, read-level genotyping of short tandem repeats using long reads and single-nucleotide variation View ORCID Profile David R Lougheed , View ORCID Profile Tomi Pastinen , View ORCID Profile Guillaume Bourque doi: https://doi.org/10.1101/2025.03.25.645269 David R Lougheed 1 Canadian Centre for Computational Genomics , Montréal, QC, Canada 2 Department of Human Genetics, McGill University , Montréal, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David R Lougheed For correspondence: david.lougheed{at}mail.mcgill.ca guil.bourque{at}mcgill.ca Tomi Pastinen 3 Genomic Medicine Center, Children’s Mercy Hospital and Research Institute , KC, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tomi Pastinen Guillaume Bourque 1 Canadian Centre for Computational Genomics , Montréal, QC, Canada 2 Department of Human Genetics, McGill University , Montréal, QC, Canada 4 Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University , Montréal, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guillaume Bourque For correspondence: david.lougheed{at}mail.mcgill.ca guil.bourque{at}mcgill.ca Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Variation in short tandem repeats (STRs) is implicated in Mendelian disease and complex traits, but can be difficult to resolve with short-read genome sequencing. We present STRkit , a software package for genotyping STRs using long read sequencing (LRS) that uses nearby single-nucleotide variants to improve genotyping accuracy without a priori haplotype information. We show that STRkit has unique strengths versus other methods: it can use data from both major LRS technologies (Pacific Biosciences HiFi [PB] and Oxford Nanopore [ONT]) to output both allele and read-level copy number and sequence, performs best in benchmarking with F1 scores of 0.9633 and 0.9056 with PB and ONT data respectively, achieves a Mendelian inheritance rate of 97.86% with PB data, and is open source software. STRkit ’s features open up new possibilities for association testing, assessing patterns of STR inheritance, and better understanding the functional effects of these notable repeat elements. BACKGROUND Short tandem repeats (STRs), or microsatellites, are repetitive genomic elements found in both eukaryotes and prokaryotes where a small motif, from 1 or 2 to 6-13 base pairs long ( Lander et al . 2001 , Ellegren 2004 , Chiu et al . 2021 ), is repeated several times in a row. These regions cover around 3% of the human genome ( Lander et al . 2001 ), are often multi-allelic, and are significant contributors to total genomic variation ( Ellegren 2004 , English et al . 2024 , Liao et al . 2023 , Hannan et al . 2018). STRs typically have high rates of mutation, usually via stepwise repeat expansion or contraction ( Fan & Chu 2007 , Ellegren 2004 ). Variation in STRs is associated with over 60 inherited and sporadic diseases in humans ( Gall-Duncan et al . 2022 ). In Huntington’s disease, fragile X syndrome (FXS), and others, copy number expansion beyond a threshold causes the disease, and further expansion is associated with phenotypic severity ( Igarashi et al . 1992 ; Duyao et al . 1993 ; Brinkman et al ., 1997 ; Matsuura et al ., 2000 ; Blauw et al . 2012 ; Bragg et al . 2017 ). The relationship between copy number and disease severity is not always linear; for example, with fragile X-associated primary ovarian insufficiency, peak severity occurs at an intermediate copy number ( Allen et al . 2021 ). STR motif composition can also affect phenotype: insertion of a stretch of a non-canonical motif into a non-coding STR in the DAB1 gene is responsible for a form of spinocerebellar ataxia (Seixas et al . 2017). Beyond expansion detection, precisely determining copy number and motif composition in disease-causing STR expansions may be required to fully quantify their relationship with phenotype. Other phenotypic relationships with STRs are not limited to a single locus: autism is associated with a genome-wide increase in rare STR expansions ( Trost et al . 2020 ) and small de novo STR mutations ( Mitra et al . 2021 ). Expansion and somatic instability in STRs feature in several cancers ( Erwin et al . 2022 , Yamamoto & Imai 2019 ). STRs are implicated in gene expression and regulation ( Gymrek et al . 2016 ; Hannan, 2018 ; Cui et al . 2025 ), including by acting as binding sites for transcription factors ( Horton et al . 2023 ). Direct STR genotyping at a genome scale is thus valuable; STRs cannot always be imputed using SNVs ( Gymrek et al . 2016 , Saini et al . 2018 ), as SNV-STR linkage disequilibrium (LD) is lower than SNV-SNV pair LD ( Willems et al . 2014 , Press et al . 2018 ). Examination of STR variation in the human genome may help to address the “missing heritability” problem, explaining a portion of complex trait heritability not yet identified ( Hannan 2018 ). However, STRs are difficult to characterize with short read sequencing (SRS) technologies due to often long repeat tracts and lack of flanking sequence within reads ( Narzisi & Schatz 2015 , Mousavi et al . 2019 ), affecting mappability to a reference genome. Some SRS STR genotyping tools can estimate larger-than-read-length allele sizes ( Dolzhenko et al . 2017 , 2019 , Mousavi et al . 2019 ) but cannot capture variation in motif structure nor precisely genotype these long expansions ( Mousavi et al . 2019 ). By contrast, modern long read sequencing (LRS) technologies like Oxford Nanopore’s nanopore sequencing (ONT) with newer chemistry, or Pacific Biosciences (PacBio) high-fidelity circular consensus sequencing (HiFi), can give accurate reads of sizes in the tens of kilobases ( Gustafson et al . 2024 ; Tanudisastro et al . 2024 ; Wenger et al . 2019 ). These reads span pathogenic expansions that exceed SRS read length ( Hannan 2018 ) and include more flanking sequence. These flanking sequences may include single-nucleotide variants (SNVs) that could be used to cluster STR-spanning reads into alleles, as SNVs are the most reliably-called form of variation with LRS ( Wenger et al . 2019 , Gustafson et al . 2024 ). Several LRS STR genotyping tools have been recently developed, including Straglr ( Chiu et al . 2021 ), TRGT ( Dolzhenko et al . 2024 ), LongTR ( Ziaei Jam et al . 2024 ), and STRdust ( De Coster et al ., 2024 ). These methods take standard BAM/CRAM alignments as input and output variants as VCF files. LongTR and TRGT have been tested on whole-genome catalogs of STRs, meaning they should be suitable for association testing and novel expansion discovery, among other whole-genome-scale applications. However, they do not take full advantage of LRS data, or have additional restrictions which limit their use – TRGT can use nearby SNVs to aid genotyping, but is license-restricted to only work with PacBio data; Straglr does not output consensus sequences for alleles; and neither LongTR nor STRdust output read-level data, limiting their use for motif composition analysis or detecting within-allele somatic instability. Here, we introduce a new STR genotyping software, STRkit , designed to operate primarily with accurate LRS data. STRkit can optionally call proximate SNVs and use them to cluster and locally phase STR alleles without needing a phased SNV call-set a priori. Data can be output at the read level, enabling analysis of intra-allele STR copy number and/or motif composition to analyse somatic instability. We also evaluate STRkit ’s performance for STR genotyping, and demonstrate the value of incorporating SNVs into the process by benchmarking STRkit against existing LRS STR genotyping methods using the Genome-in-a-Bottle (GIAB) tandem repeats benchmark ( English et al . 2024 ) for the HG002 individual of the well-characterized GIAB Ashkenazi trio. We evaluated performance on an STR-only subset of this benchmark using Truvari ( English et al . 2022 ) and Laytr ( English et al . 2024 ), and by assessing rates of Mendelian inheritance in trio genotypes. Finally, we demonstrate how read-level STR genotype information produced by our tool replicates a known pattern of somatic repeat instability in targeted sequencing of a pathogenic expansion. RESULTS The STRkit software package We created an STR genotyping method, STRkit , as an easily-installable Python package. Our algorithm takes aligned long-read sequencing (LRS) BAM or CRAM files as input, with an optional realignment step for mis-mapped reads ( Figure 1A and Methods ). It then determines STR allele copy number and optionally genotypes nearby SNVs, whose locations are extracted from a user-provided VCF file. Depending on what SNV data are present, STRkit either uses a Gaussian mixture model allele-calling approach with copy numbers, segregates alleles based on SNV haplotype groups, or uses a mix of both information sources. If two STRs have common SNV haplotype groups, they will be locally phased. If a phased alignment file is provided, STRkit can use read haplotype data instead. Genotypes are then reported in either VCF, TSV, or JSON formats. JSON output can be explored in an included web-based visualization tool ( Figure 1B ). Our software package also includes a Mendelian inheritance (MI) analysis tool, for benchmarking callers or detecting candidate de novo mutations. We compared the features of STRkit against those of other LRS STR genotyping packages to highlight its unique features, including the MI tool, SNV incorporation and output during STR genotyping, and read-level copy number output ( Table 1 ). Download figure Open in new tab Figure 1: Flowchart of the STRkit genotyping and visualization workflow. A. The genotyping workflow (also see Methods ). Our approach includes an optional SNV incorporation step, which can find heterozygous SNVs proximate to STRs and use them to cluster reads to call STR alleles. B. Users can explore the generated report in the STRkit visualization tool. The read count histogram shows the read-level distribution of copy numbers for the locus. Here, a pathogenic expansion in the HTT gene is shown. The k-mer distribution plots show motif-sized k-mer sequence diversity among read STR sequences for each allele peak. View this table: View inline View popup Download powerpoint Table 1: Feature comparison of long-read sequencing STR genotyping tools Precise STR genotyping with long reads using proximate SNVs To evaluate the performance of STRkit , we first obtained PacBio HiFi reads at ∼32x coverage and ONT R10 Duplex reads at ∼12x coverage for the Genome-in-a-Bottle (GIAB) HG002 individual from the GIAB Ashkenazi trio, and aligned them to the HG38 reference genome (see Methods ). We then genotyped 914 676 short tandem repeat regions derived from a ∼1.7 million tandem repeat benchmark variant set developed for this individual ( English et al . 2024 ) and released as an official benchmark by the GIAB consortium. The benchmark covers ∼8.1% of the HG38 human reference genome, but not all of these benchmark regions are STRs; some have homopolymers or larger TRs, which we excluded to provide more precise estimates of performance (see Methods ). We also removed regions with more than one tandem repeat because the tool we used to evaluate performance on the benchmark, Truvari ( English et al . 2022 ), cannot handle proximate unphased variants. Our final set of benchmark STRs covers ∼0.87% of HG38. Separately, we confirmed the accuracy of STRkit ’s local SNV phasing step by comparing it to alignment files for the Ashkenazi trio, which were haplotype-tagged using DeepVariant ( Poplin et al . 2018 ) and WhatsHap ( Martin et al . 2016 ). Across the trio, we found a phase error rate of approximately 0.12% in phase sets with more than one locus. To evaluate STRkit against other tools, we also genotyped our benchmark set of STR loci using LongTR ( Ziaei Jam et al . 2024 ), Straglr ( Chiu et al . 2021 ), STRdust ( De Coster et al . 2024 ), and TRGT ( Dolzhenko et al . 2024 ). We then assessed their respective performance using Truvari and Laytr ( English et al . 2024 ); except for Straglr , whose output is not compatible with Truvari . These results are summarized in Table 2 . With HiFi data, STRkit performed best overall in most metrics (accuracy = 0.9767, F1 = 0.9633), although TRGT achieved marginally better recall (0.9697 versus 0.9552 for STRkit ). With ONT data, STRkit (accuracy = 0.9438, F1 = 0.9056) performed better than LongTR (accuracy = 0.9296, F1 = 0.8786) and STRdust (accuracy = 0.8443, F1 = 0.7693) in all metrics. View this table: View inline View popup Download powerpoint Table 2: Performance metrics for long-read STR genotypers on the HG002 benchmark subset Panel A: HG002, PacBio HiFi, ∼32x coverage Truvari also reports a compound variant quality score, called the “TruScore”, combining overlap, size similarity, and sequence similarity between a given call and the benchmark into an overall score ( English et al . 2022 ). With HiFi data, STRkit achieved the best average TruScore of 98.87, closely followed by LongTR with 98.80 and TRGT with 98.66 and beating STRdust ’s average score (95.73) by a larger margin. With ONT data, a similarly small average TruScore difference was observed between STRkit (96.03) and LongTR (95.71), followed again by STRdust (94.11). Using the Laytr tool, we stratified performance metrics (F1 score, precision, recall) by the locus-maximum absolute change (Δ) in allele size from the reference genome ( Figure S1 ). Most STR Δ allele sizes are small (95.5% of allele size changes are within 50 base pairs (bp) of the HG38 reference genome) and that is where STRkit with HiFi data shows a slight but measurable improvement over other tools in terms of F1 score and precision. With ONT data for small and intermediate allele size changes versus the reference (within 200 bp of HG38), STRkit performed markedly better versus other tools. With larger Δ allele size, all tools performed worse with both sequencing technologies, with TRGT appearing to perform the best with these loci when it could be used (PacBio only). It was with these larger Δ allele sizes where STRkit ’s SNV incorporation helps its genotyping performance the most. Our evaluation of STRkit without the SNV incorporation step confirmed that using SNV data improves STR genotyping. All metrics output by Truvari for STRkit improved when the SNV step was included, for both HiFi and ONT data ( Table 2 ). For most loci in the call-set (∼72% across the trio) STRkit used information from at least one heterozygous SNV for clustering reads into alleles ( Table S1 ). High-quality genotypes track STR allele inheritance in trios STRkit also includes a tool for calculating the rate of Mendelian inheritance (MI) of alleles from parents to offspring, in terms of copy number and allele sequence/length. Loci are considered to follow MI if allele sequences from the child can be found in the parents. This process also identifies loci that do not meet expectations of MI in the trio – sites of potential de novo mutation. Versions of this metric have been used to evaluate other STR genotyping software, e.g., GangSTR ( Mousavi et al . 2019 ). On the Ashkenazi trio, STRkit achieves a sequence-wise (seq.) MI rate of 97.85% (98.07% in terms of seq. length, 99.08% in terms of seq. length ±1 base pair). Without SNVs, STRkit achieves a lower seq. MI rate of 93.65% (95.68% seq. len., 98.43% ±1bp), indicating that incorporating SNVs to cluster reads improves allele sequence quality. LongTR and TRGT again perform almost as well; LongTR achieves a seq. MI rate of 94.52% (94.85% seq. len., 97.54% ±1bp), and TRGT achieves a seq. MI rate of 94.68% (97.10% seq. len., 98.98% ±1bp). Straglr and STRdust achieve significantly lower rates of MI. These results are summarized in Table S2 . We then stratified sequence-wise MI by reference genome locus length ( Figure 2 ). The bulk of loci are small, which is again where STRkit has the clearest advantage over other methods when incorporating SNVs. Above ∼200 base pairs in length, the rates drop off, as STRs become harder to sequence accurately and are more likely to accumulate mutations in their passage from parent to child. Download figure Open in new tab Figure 2: Rates of sequence Mendelian inheritance (MI), and number of trio-called loci, by reference genome locus length. Expectations of MI are met if both child allele sequences can be seen in the two parents (one in the mother and one in the father). The vast majority of short tandem repeat regions in the catalogue are below ∼200 base pairs long, where STRkit (with SNV incorporation) achieves the highest rate. Computational performance of SNV-informed STR genotyping Most genotyping tools tested ( LongTR , STRkit , and TRGT ) can analyze the filtered HG002 benchmark catalogue in under a day measured in core-minutes using ∼32x coverage PacBio HiFi data and ∼12x ONT R10 Duplex data ( Table S3 ). STRkit is slower than TRGT and LongTR , but is on the same order of magnitude (in the 100s of core-minutes); in contrast, Straglr is in the 1000s of core-minutes, and STRdust is the slowest at ∼10 000 core-minutes on average for the GIAB trio individuals sequenced with HiFi. Where possible, we used 8 cores of an Intel Gold 6148 processor; LongTR does not support using multiple CPU cores, but is the fastest in terms of core-minutes used ( Table S3 ). STRkit was the fastest multi-core program for ONT. STRkit consumes more memory than TRGT or LongTR with HiFi data on average, but less than STRdust and Straglr , and the SNV incorporation step increases its memory usage. STRkit without SNV incorporation is the most memory-efficient STR caller for ONT. Read-level genotyping of targeted long-read sequencing data captures known instability in a pathogenic expansion We ran STRkit and other tandem repeat callers on targeted CCS sequencing data of pathogenic repeat expansions in the HTT and FMR1 genes ( Table S4 and Methods ). We ran STRkit using its targeted sequencing mode, without SNV incorporation as the targeted sequencing data lacks sufficient flanking sequence. The callers successfully detected the repeat expansions in almost all cases, except for one HTT expansion missed by Straglr , and one FMR1 expansion missed by STRdust . The genotypes overall were highly concordant. STRkit , Straglr , and TRGT output copy numbers directly, and the former two have read-level copy number output, allowing further expansion analysis. De Luca et al . (2021) found that one of these samples, NA20253, has three copy number peaks, i.e., mosaic alleles, which they measured at 107, 134, and 175 CAG repeats, with the first being the most prominent. Figure 3 shows the the read-level data for this expanded allele from STRkit ’s output, along with annotations showing the peaks found by De Luca et al . (2021) ; we observe three read peaks which mirror those found using their triplet repeat-primed PCR assay, but slightly shifted towards a higher copy number. Download figure Open in new tab Figure 3: HTT allele copy number distribution in targeted sequencing of the NA20253 sample. Vertical lines show the three expansion copy number peaks found by De Luca et al . (2021) , which fall near read peaks in STRkit output – two mosaic alleles, at 134 and 175 repeats, with the main allele peak (with the majority of reads) at 107 repeats. DISCUSSION Here, we present STRkit , a new tool for calling short tandem repeat genotypes from long read sequencing (LRS) data. Our tool has a unique combination of features, filling a niche in the STR genotyping analytical space: an STR caller which can operate at a genome-wide scale with both major LRS technologies, precisely genotype both sequence and copy number, incorporate proximate SNVs into the calling process and use them to phase STRs locally, and report read-level data ( Table 1 ). Both STR copy number and sequence are phenotypically relevant properties ( Olson et al . 2023 ); however LongTR and STRdust only output sequence, and Straglr outputs only copy number. Of the tested tools, all are LRS technology-independent except TRGT , which can only be used with PacBio data due to a licensing restriction. Technology-independent tools are critical, because they enable repeatable research and community-inclusive development, allow for confirmation of findings across technologies, and allow the best genotyping approach to be evaluated and applied regardless of data source – for example the cross-technology benchmarking we performed to evaluate STRkit. Our benchmarking with STRkit , when incorporating proximate SNVs into the genotyping process, demonstrates state-of-the-art performance in several metrics with both PacBio HiFi data and modern nanopore sequencing data (ONT R10 Duplex). While performance gains are modest with HiFi data versus the next best-performing tool, LongTR ( Table 2 ), maximizing precision and accuracy has direct benefits for genome-wide analysis, where statistical power in correlation tests would be reduced by lower-quality genotypes. With ONT data, our performance gains were larger, achieving a ∼6% higher F1 score than LongTR and narrowing the performance gap between 30x HiFi and 12x ONT data. Our SNV incorporation process allows clustering STR-overlapping reads into alleles without relying solely on copy number. This aids our genotyping approach to a small but measurable degree ( Table 2 ), and for most loci we can incorporate SNV information into the STR calling process ( Table S1 ), although it may not be the primary explanation for STRkit ’s improved benchmark metrics versus other tools, especially with ONT data ( Table 2 , Figure S1 ). SNV data captured by STRkit may prove useful for more than genotyping; in downstream analysis, they could aid, for example, trio phasing or STR mutation modeling, although our current approach only calls heterozygous STR-proximate SNVs, and we have not extensively validated these calls against dedicated SNV callers like bcftools ( Danecek et al . 2021 ). When metrics are faceted by allele size difference (Δ allele size) versus the reference genome, tools generally performed well until Δ allele size reached ∼2.5k base pairs or longer ( Figure S1 ), except for a noticeable dip in genotyping quality in the the 3-500 base pair range. Manual inspection indicates that some of these may be regions with non-canonical motifs or non-tandem-repeat sequences embedded within a tandem repeat locus, which may impact STRkit ’s modeling of candidate repeat tract sequences or result in reads being filtered out to a greater degree versus other tools. Of the five callers, STRkit , LongTR , and TRGT best accommodated the large catalogue of ∼900 000 loci, with relatively quick runtimes (in the 100s of core-minutes) and low memory requirements ( Table S3 ). Our general approach to genotyping does add a computational overhead, as it is slower to genotype the benchmark catalogue versus LongTR and TRGT . We believe that this is due to our additional read-level data collection and copy-number determination approach. The SNV incorporation step in STRkit appears to speed up genotyping (654 versus 978 core-minutes on average in the trio), indicating that future performance improvements could come primarily from optimizing other procedures (e.g., read processing or copy number determination) within our method. STRdust and Straglr both had significantly longer runtimes (1000s of core-minutes), and Straglr used an order of magnitude more memory than the other tools. STRkit is the only one of the three most performant tools (in terms of both genotype quality and computation speed) which can report full read-level copy number data, meaning it may be useful for genome-wide somatic instability detection. LongTR ’s lack of parallel processing support means it avoids parallel processing overhead, at the cost of longer per-sample runtime, whereas other methods were evaluated using eight CPU cores, requiring inter-process communication. STRkit ’s shared SNV context, used for local phasing of STR genotypes, likely results in slower-than-linear performance gains with more CPU cores, trading off additional computation for increased genotype quality. In our trio analysis for Mendelian inheritance (MI), we quantified the proportion of STR loci where both alleles in the HG002 child were found in the parents. STRkit had the best performance across the three versions of the metric we tested ( Table S2 ). MI is limited as a benchmarking metric, however, because systematic errors such as off-by-one errors which affect both parent and child may not be adequately considered. Despite this, our high MI rates, when combined with high absolute scores on the benchmark, indicate that STRkit should be the most accurate currently-available tool for genome-scale STRs genotyping, and potentially for detecting the most common category of de novo STR mutation events: ±1 repeat unit changes. However, without a more complete mutation detection procedure with statistical confidence measures to control for false discovery, we cannot claim that all loci which do not respect MI are true de novo mutations. So far, we have discussed genome-wide benchmarking results across ∼900 000 tandem repeat loci. Very few STR loci have known occurrences of pathogenic expansions – Gall-Duncan et al . (2022) reported 63 as of 2021. STRkit provides a unique feature-set useful for close examination of specific expansion loci. To demonstrate this, we used its targeted sequencing mode to genotype STRs with known expansions in the HTT or FMR1 genes ( Table S4 ), where, like other tools, it correctly detects the expanded allele. Unlike other tools, STRkit can both generate a whole-allele consensus sequence and report read-level copy number and sequence data, including motif-sized k-mer distributions, at the same time ( Figure 3 ). STRkit does not directly detect somatic instability, unlike a tool such as prancSTR ( Sehgal et al . 2024 ); however, STRkit ’s read-level copy number data replicates a previously-captured multi-peak HTT mosaic expansion in a cell line sample ( De Luca et al . 2021 ). Our peaks are slightly offset from the reported peaks in this expansion, possibly due to the passage of the sampled cell line. This pattern revealed itself only with high-depth targeted sequencing: this may be critical for fully characterizing expansion alleles. Beyond somatic instability detection, there are other STR genotyping features STRkit does not yet implement. Methylation reporting using HiFi’s base methylation data, for example, remains exclusive to TRGT . Our calling approach also has limitations: reads are primarily clustered based on motif copy number if SNV or haplotype information is not available; this is useful for obtaining copy number confidence intervals, but may miss more subtle allelic sequence variation. In the absence of SNV data, our tool may also miss variation when faced with large allele differences versus the HG38 reference genome, to a greater degree than other methods ( Figure S1 ). Finally, STRkit , like most of the other tools tested, is a catalogue-based genotyper – it requires a list of loci with reference genome coordinates and a motif. This limits what can be genotyped to only what is already present in the reference, and may not function well with non-reference motifs within known loci or may miss certain disease-causing expansions completely ( Tanudisastro et al . 2024 ). Annotating an assembly or using a de novo approach such as ExpansionHunter Denovo ( Dolzhenko et al . 2020 ) or Straglr without a provided catalogue can find repeats that STRkit and others cannot. CONCLUSIONS Short tandem repeats (STRs) cover around 3% of the human genome, are implicated in genetic disorders and transcriptional regulation, and are major contributors to overall genomic variation. STRs are important to genotype directly, as they cannot always be imputed with single-nucleotide variation (SNVs), but these elements are difficult to resolve with traditional sequencing methods and complex to analyze – they are multi-allelic and defined by both their length and motif composition, rather than a simple binary genotype. STRkit uses accurate long-read data and a strategy which incorporates both SNVs and copy-number genotyping into the STR genotyping process to achieve better performance in several metrics. The tool also includes read-level copy number and motif composition data for these loci, while many other methods do not. The approach fills a niche in the STR genotyping software ecosystem and opens up new possibilities for association testing, assessing patterns of STR inheritance, and better understanding the functional effects of these repeat elements. METHODS STR genotyping approach The general genotyping approach used in STRkit is as follows: The user provides a BAM or CRAM-formatted long-read sequencing (LRS) alignment file, generated using an aligner such as minimap2 ( Li, 2018 ), as well as a BED-formatted catalogue of STRs to genotype. Optionally, they also provide a VCF with a list of SNVs to genotype if proximate to an STR. For each STR in the catalogue, we optionally re-determine the copy number of the STR in the reference genome to allow for imperfect reference motif copies and adjust the catalogue coordinates to correct for small boundary errors. The set of all reads spanning each STR region (including flanking sequences on either side for realignment) is extracted using the PySAM library (v0.22.0; https://github.com/pysam-developers/pysam ). Low-quality or badly-aligned reads are filtered out. If realignment is enabled, badly-aligned reads may be realigned to the reference genome. For each read, a series of candidate STR tracts of varying copy number, starting with a candidate closest in length to the aligned read’s STR segment size, are generated using the catalogued motif. Each of these candidate STR tracts are aligned to the read using the parasail library’s semi-global alignment algorithm ( Daily 2016 ), incorporating 5’ and 3’ flanking sequences to ‘anchor’ the STR candidate and to allow for complete STR tract deletion. The best-scoring alignment candidate is kept for each read in question. After we have finished this process, we have approximate copy numbers for each read. If the read-level k-mer functionality is enabled, motif-sized k-mers are computed for each read . If an SNV VCF catalogue is provided: For each read, SNVs that differ from the reference are collected; these are then filtered to those which occur in many reads and can split the reads into two groups, i.e., likely heterozygotic. For each read, a re-sampling weight is generated, corresponding to the estimated inverse probability of observing a read encompassing an STR tract of the same size. This process adds weight to reads with longer STR tracts. Given random read localization on a reference and a read size distribution, longer STR alleles are less likely to be entirely ‘captured’ by a read; in this process, allele size bias is corrected for, and long expansion tracts can be better captured. Reads (with associated copy numbers and SNV calls) are then resampled a number of times (defaulting to 100), according to the re-sampling weight. From here, multiple possibilities are available to call allele peaks: If there is haplotype information in the alignment file and --use-hp is specified: The haplotype information for the reads is used if available. Otherwise, one of the next three methods is used. If there is a lot of informational SNVs for the locus: SNVs are used to split the reads into allele peaks, since SNV assessment is generally very accurate in NGS data. A distance matrix is calculated, and an agglomerative clustering function from the scikit-learn library ( Pedregosa et al ., 2011 ) is applied to group the reads into alleles. If there is some SNV information available: SNV data are used, but copy number is incorporated into a compound distance function, where SNV differences are weighed more heavily (5:1 or 10:1) versus copy number differences. Agglomerative clustering is used here as well. If no or limited SNV information is available: A Gaussian Mixture Model (GMM) from scikit-learn is applied to each re-sampling to derive estimates of allele copy numbers via a bootstrap process. Each allele starts with a two-peak GMM; if one peak is assigned very little explanatory weight after fitting, we switch to a single-peak GMM (i.e., a homozygous model). If the organism is diploid, reads are assigned to peaks based on SNV genotypes (if available) or bootstrapped estimates of peak mean, standard deviation, and weight – the complete set of parameters characterizing a peak in a GMM. If the peaks overlap to a high degree and no informative SNV genotypes are available, reads are instead randomly assigned to peaks. Final copy number genotype estimates, consensus sequences, and copy number confidence intervals are computed. If the k-mer functionality is enabled, motif-sized k-mers are computed for each allele . A final report is generated with STR genotypes, parameters used, peak assignments (and peak assignment method used) for each read, copy numbers determined for each read, standard deviations for peaks, and optionally: Read-level SNV calls. Read- and/or peak-level motif-size k-mer quantification. Benchmarking genome-scale tandem repeat genotyping We first obtained unaligned HiFi and ONT R10 Duplex LRS data for the HG002 individual (see Availability of data and methods ). We aligned these reads to the UCSC HG38 analysis set reference genome. The reads were aligned using minimap2 v2.28, using the map-hifi and lr:hq presets for the HiFi and ONT R10 duplex data, respectively. English et al . (2024) have published a genome-wide tandem repeat benchmark via the Genome-in-a-Bottle consortium (GIAB) for the HG002 Ashkenazim individual. We used version 1.0 of this benchmark, which includes 1 784 804 regions, some with multiple tandem repeat loci defined in the same region. The authors extended the Truvari software ( English et al ., 2022 ), which can be used to benchmark structural variant calls, to support region-based STR calls. For benchmarking, we used Truvari version 4.3.1. We filtered out some complex or non-STR tandem repeat definitions from their benchmark region catalogue, using the following steps: Motifs were normalized (e.g., CACACA → CA). Homopolymers and tandem repeats with motif length >10bp were removed. Benchmark regions with more than one tandem repeat were removed, since unphased proximate haplotypes cannot be correctly evaluated with Truvari . Additionally, homopolymers were filtered from the HG002 benchmark variant VCF to mitigate erroneous false negatives when homopolymers were present near other STR regions. To combine and visualize the benchmarking results after running Truvari , we used Laytr (commit c2eccbf ; https://github.com/ACEnglish/laytr ). Applying this filtering, we ended up with a catalogue of 914 676 loci. For benchmarking STRkit , we chose four recently-developed general-purpose LRS STR genotyping tools, all of which take standard BAM/CRAM-formatted alignment files and can output VCFs, for use with Truvari . The set of tools, with their versions used for benchmarking, are as follows: LongTR v1.1 ( Ziaei Jam et al . 2024 ) Straglr v1.5.3 ( Chiu et al . 2021 ) with TRF v4.09.1 ( Benson 1999 ) STRkit v0.20.0 ( our method, benchmarked with and without SNV incorporation ) STRdust ( De Coster et al . 2024 ) commit 3f3ebf0 (Aug. 22, 2024) TRGT v1.4.1 ( Dolzhenko et al . 2024 ) Parameters used for benchmarking can be found in the tool batch scripts located at https://github.com/davidlougheed/strkit_paper/tree/main/2_giab_calls . This folder also contains scripts used to generate Figure S1 and compute the summary data found in Table 2 and supplemental tables. To calculate the approximate phasing error rate in STRkit ’s SNV calling approach, we compared multi-locus phase sets derived from SNVs with haplotype-resolved output from STRkit (via the --use-hp flag) using haplotype-tagged aligned PacBio HiFi reads. A phase set was counted as containing an error if heterozygous loci did not match in terms of allele size or copy number ordering. Assessing computational performance All STR genotyping tools were run on the Digital Research Alliance of Canada’s Béluga cluster, using 8 cores of an Intel Gold 6148 Skylake processor, or 1 core where parallel processing was not supported (i.e., LongTR ). Compute time was normalized into CPU-minutes by multiplying runtime by the number of cores used. Calculating Mendelian inheritance in call sets We first obtained unaligned HiFi reads for the Ashkenazi trio from PacBio. We aligned these reads to the same UCSC HG38 analysis set used for benchmarking, using minimap2 v2.28 with the map-hifi preset. In STRkit , we include a module named strkit mi to calculate rates of trio Mendelian inheritance (MI) in autosomal loci from the output of all STR genotyping tools we benchmarked. We calculated MI in terms of binary ‘yes/no’ inheritance, inheritance ±1 repeat unit, and parent-offspring 95% confidence interval overlap. The tool performs the following steps: For each locus in the STR callset for the child in the trio, check if a corresponding call can be found in both parent callsets. If not, skip the locus. If the locus is in a user-specified exclusion set, e.g., for removing known de novo mutation, skip it. Add this locus to the set S of seen loci for this trio. If (slightly different from step 1) a call failed in any of the three individuals, skip the locus (after it has been added to set S.) If the calls for the locus are consistent with Mendelian inheritance (for each version of the MI metric X ∈ {strict, ±1 repeat, 95% CI, sequence, seq. len., seq. len. ±1 base pair}), add one to a counter c X of consistent loci for metric X. Some versions of the metric are not supported by some of the genotyping methods; LongTR and STRdust do not provide copy numbers or 95% copy number confidence intervals, and Straglr does not provide 95% copy number confidence intervals or consensus allele sequences. After all loci have been processed, return the total rate of MI as c/|S|. Genotyping expansions from targeted sequencing data We accessed a dataset of Pacific Biosciences targeted sequencing of expansions from the following URL: https://downloads.pacbcloud.com/public/dataset/RepeatExpansionDisorders_NoAmp/ . The same STR genotyping tool versions used in our benchmarking were compared with this targeted disease expansion sequencing dataset. Batch scripts with specific parameters, as well as the script used to generate Figure 3 , are available at https://github.com/davidlougheed/strkit_paper/tree/main/4_pathogenic_exp . Coriell genotypes were manually collected from https://www.coriell.org/ Oct 4, 2022. DECLARATIONS Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The source code for the STRkit package is available on GitHub at: https://github.com/davidlougheed/strkit/ and in Zenodo under the DOI https://doi.org/10.5281/zenodo.12689906 , and is obtainable as a Python package in the Python Package Index (PyPI) under the name strkit. The Genome-in-a-Bottle (GIAB) Tandem Repeats V1.0 benchmark is available from the NCBI at: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG00 2_NA24385_son/TandemRepeats_v1.0/GRCh38/. Corresponding benchmark region annotations are available from Zenodo at https://doi.org/10.5281/zenodo.6930201 . The Genome-in-a-Bottle Ashkenazi trio sequencing HiFi data (both unaligned and aligned phased reads) used for benchmarking and Mendelian inheritance analysis are available from PacBio at: https://downloads.pacbcloud.com/public/revio/2022Q4/ . Oxford Nanopore R10 stereo duplex sequencing data for the same trio are available from the Human Pangenomics Project at: https://human-pangenomics.s3.amazonaws.com/index.html?prefix=submissions/0CB93 1D5-AE0C-4187-8BD8-B3A9C9BFDADE--UCSC_HG002_R1041_Duplex_Dorado/Dorado_v0.1.1/stereo_duplex/. The HG38 human reference genome (analysis version) is available from UCSC at: https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/analysisSet/hg38.analysisSe t.fa.gz. Targeted expansion sequencing data used for assessing expansion genotyping are available from PacBio at: https://downloads.pacbcloud.com/public/dataset/RepeatExpansionDisorders_NoAmp/ . Coriell genotypes used for assessing expansion genotyping were manually collected from https://www.coriell.org/ Oct 4, 2022. The scripts used to analyze the data for this manuscript are available in GitHub at https://github.com/davidlougheed/strkit_paper/ . Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2024-05329) and the Canadian Institutes of Health Research (PJT-191707). G.B. is supported by a Canada Research Chair Tier 1 award and a FRQ-S, Distinguished Research Scholar award. Authors’ contributions D.R.L. developed the software package and performed the benchmarking analyses. G.B. and D.R.L. conceived of the project. T.P. gave guidance and insight during the development of the project. G.B. supervised the project. All authors read and approved the final manuscript. Acknowledgements We would like to thank Rob Eveleigh and Mathieu Bourgey for providing valuable instruction on long read data and benchmarking approaches, José Héctor Gálvez López for his important feedback on our genotyping approach, Elia Afanasiev for his help increasing computational performance and for proofreading the manuscript, and Boriana Apostolov and Stephen C. Lougheed for proofreading the manuscript. 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Share STRkit: precise, read-level genotyping of short tandem repeats using long reads and single-nucleotide variation David R Lougheed , Tomi Pastinen , Guillaume Bourque bioRxiv 2025.03.25.645269; doi: https://doi.org/10.1101/2025.03.25.645269 Share This Article: Copy Citation Tools STRkit: precise, read-level genotyping of short tandem repeats using long reads and single-nucleotide variation David R Lougheed , Tomi Pastinen , Guillaume Bourque bioRxiv 2025.03.25.645269; doi: https://doi.org/10.1101/2025.03.25.645269 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13894) Bioinformatics (41951) Biophysics (21455) Cancer Biology (18592) Cell Biology (25507) Clinical Trials (138) Developmental Biology (13380) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24321) Genetics (15610) Genomics (22509) Immunology (17737) Microbiology (40398) Molecular Biology (17182) Neuroscience (88618) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7641) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)