Modtector: Ultra-Fast Modification Signal Mining on Mapped Sequencing Reads

Modtector: Ultra-Fast Modification Signal Mining on Mapped Sequencing Reads | 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 Modtector: Ultra-Fast Modification Signal Mining on Mapped Sequencing Reads View ORCID Profile Tong Zhou , Yifan Hong , Panfeng Li , Xitong Liu , Ang Li , Lei Sun doi: https://doi.org/10.1101/2025.10.08.681300 Tong Zhou 1 Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University , Qingdao 266237, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tong Zhou For correspondence: tongzhou{at}sdu.edu.cn angli41{at}sdu.edu.cn sunlei0227{at}sdu.edu.cn Yifan Hong 1 Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University , Qingdao 266237, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Panfeng Li 1 Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University , Qingdao 266237, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xitong Liu 1 Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University , Qingdao 266237, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ang Li 1 Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University , Qingdao 266237, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: tongzhou{at}sdu.edu.cn angli41{at}sdu.edu.cn sunlei0227{at}sdu.edu.cn Lei Sun 1 Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University , Qingdao 266237, China 2 State Key Laboratory of Microbial Technology, Shandong University , Qingdao 266237, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: tongzhou{at}sdu.edu.cn angli41{at}sdu.edu.cn sunlei0227{at}sdu.edu.cn Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Summary Current tools for RNA epitranscriptomic modification and structural feature analysis are often fragmented, focusing on single signal types and struggling with processing efficiency, especially as sequencing data volumes increase and single-cell technologies advance. To address this challenge, we developed Modtector, an efficient and versatile tool for unified extraction of modification signals from aligned sequencing data. By integrating dual-signal recognition within a single framework, Modtector employs a “count-then-correct” approach to handle both mutation and stop signals, significantly reducing computational complexity. This results in a multi-fold performance improvement compared to existing tools, particularly when processing large-genome, high-coverage datasets—such as completing the analysis of HEK293 22G data in 5 minutes— demonstrating its potential for large-scale data analysis. Availability and implementation The manual is available at Readthedocs ( https://modtector.readthedocs.io/ ), and source code is available at GitHub ( https://github.com/TongZhou2017/modtector ) and Crate.io ( https://crates.io/crates/modtector ). Download figure Open in new tab 1. Introduction Chemical probing techniques are crucial for studying biomolecular structure and function through high-throughput sequencing ( Xu et al ., 2022 ; Liu et al ., 2023 ). While various computational tools exist for analyzing the resulting signals, most are limited in their ability to jointly process multiple signal types, focusing exclusively on either mutation patterns ( Busan and Weeks, 2018 ) or termination events (Li et al ., 2020), requiring researchers to use separate tools and manually integrate the results—leading to potential inconsistencies and computational overhead. This fragmentation becomes particularly problematic when analyzing complex modification patterns involving both mutation and stop signals, such as those seen with RNA structure probing ( Sexton et al ., 2017 ) and m6A modifications (Liu et al ., 2023). The absence of unified frameworks complicates analytical workflows and hinders direct comparisons between different signal types under consistent statistical conditions ( Sexton et al ., 2017 ). Furthermore, as sequencing data grows exponentially, computational efficiency has become a key consideration for large-scale analyses ( Mu et al ., 2025 ). For instance, RASP, which collects over a thousand datasets, struggles to perform genome-wide reanalysis within a reasonable time due to the inefficiency of existing tools ( Li et al ., 2021 ). Similarly, single-cell data—often generating large volumes of independent datasets for multiple cells—presents challenges for tools like bam-readcount, which is slow and can only process mutation signals, limiting the ability to support future stop signal-based single-cell RNA structural analysis ( Wang et al ., 2024 ). To address these challenges, we developed Modtector (Mod ification de tector) , a unified computational framework that simultaneously extracts and correlates multiple modification signals from aligned sequencing reads. By integrating dual-signal quantification within a single statistical model, Modtector enables researchers to obtain a comprehensive view of modification events in a single execution, ensuring consistent analytical conditions across signal types while delivering superior computational performance. 2. Features 2.1 Unified Framework for Dual-Signal Analysis Modtector introduces an integrated pipeline ( Figure 1A ) that simultaneously processes two key signal types from SAM/BAM files: nucleotide mismatches (mutation) and reverse transcription truncation events (stop). This unified framework includes core functions such as signal counting, reactivity analysis, normalization, evaluation, and visualization, supporting both primary signal calling and downstream analysis methods. Unlike existing tools that typically require separate analyses for each signal type ( Table S1 ), Modtector ensures consistent statistical treatment of both signals by using shared genomic coordinates. Download figure Open in new tab Figure 1. Overview of Modtector’s dual-signal analysis framework and benchmarking results. (A) Workflow of Modtector’s analysis pipeline, illustrating the key steps in processing mapped SAM/BAM files to generate CSV files for mutation and stop signal, applicable to RNA structure and modification data analysis. The flowchart includes command-line examples and key parameters for core functions. (B-C) Runtime comparisons (thread = 1) of Modtector with other tools for RNA structure data analysis using E. coli (B) and human HEK293 cell line (C) datasets. Each point represents a tool, with colors indicating the types of signals processed: orange for mutation signals, blue for stop signals, and red for tools supporting both signals. The point size represents the number of features collected. (D) Performance scaling analysis comparing Modtector to samtools and bedtools with varying thread counts (1 to 64). The x-axis represents the number of threads, and the y-axis represents runtime, with log10 scaling applied. Modtector shows significant speed improvements across different thread counts (arrows from data points to the dashed line represent the speedup factor). (E) Visualization of secondary structure of 18S rRNA (icSHAPE dataset: SRR11164863) by Modtector plot command. The reactivity scores calculated by Modtector are overlaid on the structures, with color gradients representing different levels of reactivity: red for high reactivity, yellow for medium reactivity, and grey for low reactivity. (F) Dual-signal distribution of MALAT1 Chr11:65506159 m1A site (m1A-seq dataset: SRR5418425), comparing the consistency between samtools + bedtools and Modtector methods. (G-H) Dual-signal distribution and secondary structure mapping for 18S rRNA 1832 m6A site (G; miCLIP dataset: SRR2003406) and 28S rRNA 1322 m1A site (H; dataset: SRR5418425). The miCLIP method is characterized by mutations or truncations occurring at the previous C position during reverse transcription at m6A sites. The secondary structure backbone downloads from the RNAcentral database (URS0000726FAB_9606 for 18S rRNA, URS000075EC78_9606 for 28S rRNA), and the loop structure scores are mapped through Modtector plot command, with red for high reactivity, yellow for medium reactivity, and grey for low reactivity. Filled circles represent RNA modification sites, with red for m6A modification and orange for m1A modification. Modtector applies an efficient “count-then-correct” approach to handle stop signal shifts, minimizing redundant file operations. For the core function signal detection ( Figure 1A , Step 1), Modtector simultaneously quantifies mutation and stop signals across both strands. For stop signal, it first distinguishes strand-specific reads and uses the read start position as a raw signal. Signal shift corrections, such as those needed for RNA structure modifications (e.g., occurring at +1), are applied at the site level. The framework supports both gene-level and genome-level calculations, with genome-wide analysis benefiting from sliding window segmentation and dynamic task deployment to optimize computational resource usage. Overlapping windows (based on stop signal correction lengths) ensure no data loss, addressing issues observed in tools like JACUSA2 ( Figure S1A ). For other downstream analysis functions, Modtector offers multiple reactivity calculation and data normalization options. It also provides two distinct approaches for both stop ( Ding et al ., 2014 ; Rouskin et al ., 2014 ) and mutation ( Siegfried et al ., 2014 ; Zubradt et al ., 2017 ) signal ( Figure 1A , Step 2&3). Additionally, Modtector supports differential site statistics at both single-base and region (deltaSHAPE ( Smola et al ., 2015 ) and Diffscan ( Yu et al ., 2022 )) levels for different sample groups ( Figure 1A , Step 4). The evaluate function assesses accuracy among base type seperately, providing multiple performance metrics (eg. AUC, F1-score) to ensure the reliability of the extracted signals ( Figure 1A , Step 5). For the signal score and RNA modification site annotation, Modtector can visualize in secondary structure in SVG format ( Figure 1A , Step 6). 2.2 Enhanced Performance Compared to State-of-the-Art Tools Comprehensive benchmarking comparing Modtector with leading tools ( Table S1 ) in RNA structure task, such as RNA Framework ( Incarnato et al ., 2018 ), JACUSA2 ( Piechotta et al ., 2022 ), ShapeMapper2 ( Busan and Weeks, 2018 ), bam-readcount ( Khanna et al ., 2022 ), and icshape-pipe ( Li et al ., 2020 ), reveals significant runtime advantages on E. coli small genome and human datasets ( Figure 1B,C ). Notably, it demonstrated a speedup of over 100-fold compared to bam-readcount, which is often used for single-cell data analysis ( Wang et al ., 2024 ), highlighting Modtector’s potential for large-scale single-cell data processing. When compared with classic tools in RNA modification task, Modtector achieved a twofold speedup over conventional samtools ( Danecek et al ., 2021 ) and bedtools ( Quinlan and Hall, 2010 ) pipelines ( Figure 1D ). In typical workflows, samtools/bedtools provide predominantly single-threaded core operations and produce intermediate outputs that require additional, user-written scripts to compute extended features, which limits scalability on large-genome, high-coverage datasets. This constraint also indirectly affects tools that depend on these utilities (e.g., RNA Framework; ShapeMapper2). By contrast, Modtector employs a fully Rust-based, unified dual-signal recognition algorithm and leverages multi-threading throughout, including direct bindings to the htslib crate, thereby maximizing computational efficiency and throughput. For example, using only 8 threads, Modtector can process a 22G human dataset in just 5 minutes, whereas other multi-threaded tools require 64 threads to achieve comparable efficiency ( Table S2 ). 2.3 Methodological Consistency and Biological Accuracy In addition to its speed, Modtector demonstrates high consistency with results from other established methods. Most notably, it showed perfect correlation (Pearson’s r = 1.0) with samtools-based approaches for RNA modification data analysis ( Figure S2 ), confirming that the computational advantages do not come at the cost of accuracy. This consistency is further evidenced by Modtector’s ability to produce biologically meaningful RNA secondary and structural mappings ( Figure 1E , S3 ). For example, base-resolution accuracy assessments revealed higher detection rates for adenosine and cytidine ( Figure S3A ), consistent with DMS-based probing methods ( Xu et al ., 2022 ). In addition, Modtector’s RNA modification signal detection is highly consistent, as shown in Figure 1F , where it simultaneously identifies dual-signal distributions in the human lncRNA MALAT1 m1A site (Chr11: 65506159). Due to its ability to process both RNA structure and modification signals, Modtector can focus on specific RNA modifications within particular structures, such as the m6A modification on 18S rRNA 1832 ( Figure 1E,G ), which is involved in translation regulation ( Sepich-Poore et al ., 2022 ), or the m1A modification on 28S rRNA 1322 ( Figure 1H , S3B ), which plays a role in ribosomal structure and function regulation ( Sharma et al ., 2018 ). This combination of biological accuracy and methodological consistency makes Modtector a robust and reliable solution for modern chemical probing data analysis. 3 Usage and document Modtector is implemented in Rust and freely available under the MIT License at https://github.com/TongZhou2017/modtector . The software provides a unified command-line interface for simultaneous processing of mutation and stop signals, with subcommands for specialized tasks. Designed for Unix-like environments, the binary executable of Modtector can be directly copied and used without the need for installation, making it easy to integrate into existing bioinformatics workflows. Comprehensive documentation, example datasets, and benchmarking scripts are included to facilitate adoption and reproducibility at readthedocs website ( https://modtector.readthedocs.io/ ). Conflict of interest None declared. Data availability All data are incorporated into the article and its online supplementary material. Funding This work was supported by the National Natural Science Foundation of China (No.82341086, No.32300521, and No.32422013 to L.S.); the Open Grant from the Pingyuan Laboratory (No.2023PY-OP-0104 to L.S.); the State Key Laboratory of Microbial Technology Open Projects Fund (No.M2023-20 to L.S.); the Intramural Joint Program Fund of the State Key Laboratory of Microbial Technology (NO.SKLMTIJP-2024-02 to L.S. and T.Z.); the Shandong Province Postdoctoral Innovation Project (NO. SDCX-ZG-202400146 to T.Z.), the Qingdao Postdoctoral Science Foundation (NO. QDBSH20240102200 to T.Z.), the Double-First Class Initiative of Shandong University School of Life Sciences; the Young Innovation Team of Shandong Higher Education Institutions, the Taishan Scholars Youth Expert Program of Shandong Province, and the Program of Shandong University Qilu Young Scholars. Figure Legends Download figure Open in new tab Figure S1. Result consistency comparison. (A) JACUSA2 loses data in multi-thread models due to overlapping signal corrections. The x-axis represents the number of threads (1 to 64), and the y-axis represents the number of output file rows. (B-C) Pearson’s correlation analysis between Modtector and samtools-based m6A (B) and m1A (C) RNA modification data. The x-axis represents the proportion of A bases at genomic single-base sites for Modtector, and the y-axis represents the results from samtools. The dark of the hexagonal color blocks represents site density. Download figure Open in new tab Figure S2. Visualization of secondary structure of RNA by Modtector plot command. (A-B) E.coli 16S rRNA (DMS-seq dataset: SRR3710405) and human 28S rRNA (icSHAPE dataset: SRR11164863) reactivity scores calculated by Modtector are overlaid on the structures, with color gradients representing different levels of reactivity: red for high reactivity, yellow for medium reactivity, and white for low reactivity. Table Legends Table S1. List of tools for benchmark comparison . Table S2. Runtime performance in multi-thread model . Acknowledgements We would like to thank Yongkang Tang and Ruobin Zhao for valuable suggestions on functionality, Shaozhen Yin for assistance with accuracy assessments, and Yuning Liu for testing the code and providing insightful feedback. Special thanks to the Computing Platform at the Core Facility and Service Platform, School of Life Sciences, Shandong University, for providing computational resources and support. Funder Information Declared National Natural Science Foundation of China , 82341086 , 32300521 , 32422013 Open Grant from the Pingyuan Laboratory , 2023PY-OP-0104 State Key Laboratory of Microbial Technology Open Projects Fund , M2023-20 Intramural Joint Program Fund of the State Key Laboratory of Microbial Technology , SKLMTIJP-2024-02 Shandong Province Postdoctoral Innovation Project , SDCX-ZG-202400146 Qingdao Postdoctoral Science Foundation , QDBSH20240102200 Double-First Class Initiative of Shandong University School of Life Sciences the Young Innovation Team of Shandong Higher Education Institutions Taishan Scholars Youth Expert Program of Shandong Province Program of Shandong University Qilu Young Scholars Footnotes https://github.com/TongZhou2017/modtector https://modtector.readthedocs.io/ https://crates.io/crates/modtector References ↵ Busan , S. and Weeks , K.M. ( 2018 ) Accurate detection of chemical modifications in RNA by mutational profiling (MaP) with ShapeMapper 2 . RNA , 24 , 143 – 148 . OpenUrl Abstract / FREE Full Text ↵ Danecek , P. et al. ( 2021 ) Twelve years of SAMtools and BCFtools . GigaScience , 10 , giab008 . OpenUrl CrossRef PubMed ↵ Ding , Y. et al. ( 2014 ) In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features . Nature , 505 , 696 – 700 . OpenUrl CrossRef PubMed Web of Science ↵ Incarnato , D. et al. ( 2018 ) RNA Framework: an all-in-one toolkit for the analysis of RNA structures and post-transcriptional modifications . Nucleic Acids Research , 46 , e97 – e97 . OpenUrl CrossRef PubMed ↵ Khanna , A. et al. ( 2022 ) Bam-readcount-rapid generation of basepair-resolution sequence metrics . JOSS , 7 , 3722 . OpenUrl ↵ Li , P. et al. ( 2020 ) icSHAPE-pipe: A comprehensive toolkit for icSHAPE data analysis and evaluation . Methods , 178 , 96 – 103 . OpenUrl CrossRef PubMed ↵ Li , P. et al. ( 2021 ) RASP: an atlas of transcriptome-wide RNA secondary structure probing data . Nucleic Acids Research , 49 , D183 – D191 . OpenUrl CrossRef PubMed ↵ Liu , C. et al. ( 2023 ) Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI . Nat Biotechnol , 41 , 355 – 366 . OpenUrl CrossRef PubMed ↵ Mu , K. et al. ( 2025 ) RASP v2.0: an updated atlas for RNA structure probing data . Nucleic Acids Research , 53 , D211 – D219 . OpenUrl PubMed ↵ Piechotta , M. et al. ( 2022 ) RNA modification mapping with JACUSA2 . Genome Biol , 23 , 115 . OpenUrl CrossRef PubMed ↵ Quinlan , A.R. and Hall , I.M. ( 2010 ) BEDTools: a flexible suite of utilities for comparing genomic features . Bioinformatics , 26 , 841 – 842 . OpenUrl CrossRef PubMed Web of Science ↵ Rouskin , S. et al. ( 2014 ) Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo . Nature , 505 , 701 – 705 . OpenUrl CrossRef PubMed Web of Science ↵ Sepich-Poore , C. et al. ( 2022 ) The METTL5-TRMT112 N6-methyladenosine methyltransferase complex regulates mRNA translation via 18S rRNA methylation . Journal of Biological Chemistry , 298 , 101590 . OpenUrl CrossRef PubMed ↵ Sexton , A.N. et al. ( 2017 ) Interpreting Reverse Transcriptase Termination and Mutation Events for Greater Insight into the Chemical Probing of RNA . Biochemistry , 56 , 4713 – 4721 . OpenUrl CrossRef PubMed ↵ Sharma , S. et al. ( 2018 ) A single N1-methyladenosine on the large ribosomal subunit rRNA impacts locally its structure and the translation of key metabolic enzymes . Sci Rep , 8 , 11904 . OpenUrl CrossRef PubMed ↵ Siegfried , N.A. et al. ( 2014 ) RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP) . Nat Methods , 11 , 959 – 965 . OpenUrl CrossRef PubMed Web of Science ↵ Smola , M.J. et al. ( 2015 ) Detection of RNA–Protein Interactions in Living Cells with SHAPE . Biochemistry , 54 , 6867 – 6875 . OpenUrl CrossRef PubMed ↵ Wang , J. et al. ( 2024 ) RNA structure profiling at single-cell resolution reveals new determinants of cell identity . Nat Methods , 21 , 411 – 422 . OpenUrl PubMed ↵ Xu , B. et al. ( 2022 ) Recent advances in RNA structurome . Sci. China Life Sci ., 65 , 1285 – 1324 . OpenUrl CrossRef PubMed ↵ Yu , B. et al. ( 2022 ) Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure . Nat Commun , 13 , 4227 . OpenUrl PubMed ↵ Zubradt , M. et al. ( 2017 ) DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo . Nat Methods , 14 , 75 – 82 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted October 10, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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Share Modtector: Ultra-Fast Modification Signal Mining on Mapped Sequencing Reads Tong Zhou , Yifan Hong , Panfeng Li , Xitong Liu , Ang Li , Lei Sun bioRxiv 2025.10.08.681300; doi: https://doi.org/10.1101/2025.10.08.681300 Share This Article: Copy Citation Tools Modtector: Ultra-Fast Modification Signal Mining on Mapped Sequencing Reads Tong Zhou , Yifan Hong , Panfeng Li , Xitong Liu , Ang Li , Lei Sun bioRxiv 2025.10.08.681300; doi: https://doi.org/10.1101/2025.10.08.681300 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)