OmniSplice: a framework-free splicing event reporter

OmniSplice: a framework-free splicing event reporter | 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 OmniSplice: a framework-free splicing event reporter View ORCID Profile Romain Lannes , Jaclyn M Fingerhut , View ORCID Profile Yukiko M. Yamashita doi: https://doi.org/10.1101/2025.04.06.647416 Romain Lannes 1 Whitehead Institute for Biomedical Research Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Romain Lannes For correspondence: rlannes{at}wi.mit.edu yukikomy{at}wi.mit.edu Jaclyn M Fingerhut 1 Whitehead Institute for Biomedical Research 2 Howard Hughes Medical Institute Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yukiko M. Yamashita 1 Whitehead Institute for Biomedical Research 2 Howard Hughes Medical Institute 3 Massachusetts Institute of Technology, Department of Biology Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yukiko M. Yamashita For correspondence: rlannes{at}wi.mit.edu yukikomy{at}wi.mit.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract We report the development of a framework-free program to assess splicing events: OmniSplice. This new method comprehensively identifies the end of every exon, and categorizes the RNA sequence read based on the sequence that follows the exon ends. OmniSplice detects not only ‘spliced’ and ‘nascent’ reads but also unpredicted reads. Using this method, we detected aberrant splicing events, including backsplicing that we failed to detect in our previous analysis upon depletion of a splicing factor U2AF. Introduction Introns are prevalent in eukaryotic genes, and their removal by splicing is essential to yield a mature mRNA ( Rogozin et al., 2012 ). Introns and their splicing add complexity to eukaryotic gene regulation, as introns often contain cis-regulatory information and alternative splicing can generate multiple protein isoforms from a single gene ( Gehring & Roignant, 2021 ). Importantly, defective splicing is often associated with various human diseases, such as cancer ( Bradley & Anczuków, 2023 ) and neurodegenerative diseases ( Li et al., 2021 ). Various methodologies, from in vitro biochemical assays to RNA sequencing, have been employed to study splicing ( Movassat et al., 2014 ). In recent years, RNA sequencing has often been utilized to infer splicing status. For example, short-read RNA sequencing is used to infer alternative splicing (AS) events ( Jiang et al., 2023 ; Q. Wang & Rio, 2018 ; Y. Wang et al., 2024 ). AS analysis is a well-established framework capable of identifying splicing events, including cassette alternative exons, alternative 3’ or 5’ splicing sites, and intron retention. However, these categories of splicing events do not encompass all the significant biological events that may happen at a splice site. For example, in recursive splicing, an intron is removed in a ‘piecemeal’ manner ( Sibley et al., 2015a ): the intermediate products of recursive splicing, which join the end of an exon to a sequence in the middle of the intron, are not captured by the AS framework. Recursive splicing was first identified from the study of the Ultrabithorax gene in D. melanogaster (Hatton et al., 1998). However, the detection of recursive splicing at a large scale required a new approach that considers RNA sequencing reads (specifically reads in which exon ends are connected to a sequence in the middle of an intron), which would be discarded in the AS framework( Sibley et al., 2015b ). Likewise, backsplicing, the joining of an exon end with an upstream exon, has been reported to occur ( Liu & Chen, 2022 ), but such events will not be detected by the AS framework, either, because the AS framework discards RNA reads that do not align linearly with regard to the genomic sequence. RNA sequencing has also been used to infer splicing efficiency by comparing the frequency of spliced reads vs. nascent (unspliced) reads: Splicing efficiency (SE) is typically calculated as the number of spliced-reads divided by the total number of reads (nascent + spliced reads) ( de Melo Costa et al., 2021 ). However, such a method may not accurately represent the extent of splicing defects, if the defects result in splicing products that join an exon end to something that is not the expected splicing acceptor. Whereas tools for AS and SE analyses have been widely utilized to analyze the specific subset of splicing events defined by their framework, they do not comprehensively capture splicing events outside their framework. Recent studies introduced a reference-free and unbiased approach, SPLASH/SPLASH2, that uses k-mers ( Chaung et al., 2023 ; Kokot et al., 2024 ). However, these methods require the comparison of multiple samples to identify ‘unconventional’ RNA species, preventing the detection of splicing events that occur in unperturbed (‘wild type’) conditions. Most methods described above require prior knowledge/hypothesis on the type of splicing events/errors present in the sample. However, such foresight is not always possible. Motivated by the need to analyze RNA species/splicing status without prior knowledge of the expected splicing events, we developed OmniSplice, a framework-free method to report splicing events. In short, this software specifically identifies RNA sequencing reads that overlap with the end of an exon and then classifies each of those reads based on the adjoining RNA sequence. This approach allows for the identification of a broader range of splicing events, including previously unknown AS events, recursive splicing sites, back-splicing, and other unexpected splicing products. OmniSplice requires an annotated genome, but does not require prior knowledge of expected spliced products or comparison between two conditions. Using OmniSplice, we identified splice junction reads that were previously overlooked. This includes unexpected splicing events in unperturbed (wild type) conditions, as well as defective splicing events, which would have been missed by other splicing analysis methods that focus on alternative splicing or splicing efficiency. In our earlier work, we studied the impact of RNAi-mediated knockdown of U2af38 , a critical splicing factor ( Shao et al., 2014 ), on gene expression in the Drosophila testis ( Fingerhut et al., 2024 ). With OmniSplice, we now report a broader range of defective splicing events in this condition, extending our previous analysis, which underestimated splicing defects because of the limited scope of splicing defects that the AS framework can identify. Results In a broad range of species from Drosophila to mammals, a handful genes in the genome have a unique feature called intron gigantism, where some intron exceed hundreds of kilobases to megabases. A well-known example of intron gigantism is found in mammalian dystrophin gene ( Hildyard & Piercy, 2023 ; Hiramuki et al., 2025 ; Lopes et al., 2021 ; Tharp et al., 2019 ). Some of fertility genes located on the Y chromosome of Drosophila also exhibit intron gigantism, and they are expressed during spermatogenesis ( Fingerhut et al., 2024 ). Transcription and splicing of these gigantic genes with megabase-sized introns is highly complex. Transcription of these genes takes a few daysClick or tap here to enter text., and they are co-transcriptionally spliced ( Fingerhut et al., 2024 ) Previously, we found that RNAi-mediated knockdown of splicing factor ( U2af38 ) resulted in global defective splicing, affecting both Y-linked gigantic genes and regular-sized genes ( Fingerhut et al., 2024 ). In addition, depletion of U2af38 led to a unique effect that was specific to gigantic genes: apparent transcriptional attenuation of gigantic genes, associated with a significant drop in RNA sequencing read depth within the gigantic introns ( Fingerhut et al., 2024 ). Moreover, gigantic genes were more misspliced than smaller genes upon depletion of U2af38 . These results led us to speculate that gigantic introns may pose a great difficulty for the splicing machinery. JUM, a splicing analysis software that uses an AS framework (Q. Wang & Rio, 2018 ), detected splicing defects in U2af38 RNAi testes, including intron retention and aberrant 5’ and 3’ splice sites. However, considering the expected complexity associated with gigantic introns, we suspected that there might be more splicing defects that have been left undetected in U2af38 mutants. This motivated us to design a new workflow to examine additional splicing defects that may not be captured by an AS splicing framework. Design of OmniSplice Workflow To characterize possible splicing defects more comprehensively, we developed OmniSplice, which aims to identify and categorize RNA sequencing reads that may have been missed or discarded by other frameworks. OmniSplice reports and classifies all reads at an exon extremity. OmniSplice takes an indexed bam file and a gtf file as inputs. From the gtf file, it extracts all exons and uses them to build an interval tree ( Fig 1 ). Then OmniSplice reads the bam file, recovering reads that overlap with exon ends (by referencing the interval tree), and filtering out reads that fail quality control. Based on read alignment, we identified five primary read categories, with junction reads being further refined into four subcategories: junction read (exon end was connected to a downstream sequence) spliced (correctly spliced based on genome annotation), exon-intron (exon end is connected to a sequence within the next intron), exon-other (exon end is connected to a sequence downstream of the next exon), isoform (junction reads compatible with alternative isoform) Unspliced (nascent RNA sequence) Exon-clipped: Soft Clipping of at least 10 bp at the exon end (i.e. reads with alignment truncation, with at least 10 bp of ‘unaligned’ sequence following the exon end). Skipped: the read is a junction read that skip the exon’s end. Wrong strand: read comes from a fragment with the wrong strand orientation relative to the exon (only available for RNA-seq stranded library). Download figure Open in new tab Figure 1. OmniSplice overview: The input for OmniSplice is an indexed bam file and a gtf. From the gtf, it extracts all exons and builds an interval tree. Then OmniSplice reads the bam file and recovers reads that overlap with exon ends and pass QC. Reads are subsequently categorized. Junction Reads are a special case and are subdivided again based on the other end of the read. Finally. OmniSplice outputs three main files: 1) .cat file (raw data), 2) .table file (read counts for categorized Junction Reads), and 3) a splicing efficiency file (where one can select which category of read to consider). Lastly, OmniSplice can return a file containing reads from a selected category for further inspection. Download figure Open in new tab Figure 2. Splicing defect plot: Splicing defect plot in control condition vs U2af38 condition for kl-2 (A), kl-3 (B), kl-5 (C). After analyzing sequence reads based on these criteria, OmniSplice outputs two main files, one with unprocessed junction data and one detailing the read counts of each junction read sub-category (spliced, exon-intron, exon-other, isoform). Optionally, OmniSplice can return the categorized read into a separate file for further inspection. In addition, we generated two additional modules (splicing efficiency and backsplicing) as examples of how to further utilize the OmniSplice output. OmniSplice can be used as a tool to estimate splicing efficiency, accounting for a broader range of splicing defects. The equation to compute splicing efficiency can be modulated by the user. By default, splicing efficiency is computed using the standard approach (correctly spliced/ (unspliced + correctly spliced)), but the user may modify the equation, e.g., correctly spliced/ (unspliced + correctly spliced + erroneously spliced). Similarly, the equation can be modified to compute the proportion of reads with splicing defects. It should be noted that not all of the reads detected by OmniSplice may be biological. Indeed, many software that analyzes a specific framework of splicing events filter out unexpected reads (e.g., an exon is joined to unexpected sequences) to avoid the inclusion of biologically irrelevant reads, such as artifacts due to accidental ligation during library preparation. Therefore, non-canonical reads identified by OmniSplice should be validated experimentally. OmniSplice is coded in Rust and uses bindings to the HTS-lib (a C library for working with bam files). Rust is a low-level, compiled programming language with high-level features and abstractions. It enforces specific rules at compilation, protecting against most memory errors. Rust and the HTS-lib allow us to achieve both good performance and full control of the parameters and filters used. Thus, it is fast, has a low memory footprint, and can be run on a laptop. In addition, we have developed pre-made post-analysis modules to visualize junctions, detect backsplicing, and compare two conditions to identify splicing differences. OmniSplice is able to work on paired-end and single-end reads, it is strand aware, and it supports multiple stranded and un-stranded RNA-seq library layouts. OmniSplice is straightforward to use and can be run with the default parameters using this command: omni_splice -g -i -o . Analysis of splicing defects upon depletion of U2af38 during Drosophila spermatogenesis using OmniSplice Using OmniSplice, we reanalyzed RNA sequencing reads obtained from the Drosophila testis (control and U2af38-RNAi samples). OmniSplice recovered signals (sequence reads) that we would have otherwise missed by other programs. OmniSplice detected various ‘defective’ splicing events of three Y-linked gigantic genes, kl-2, -3, -5 upon U2af38 depletion (Fig2 A-C). There was a dramatic increase in not only unspliced reads, but also unexpected reads such as ‘exon-clipped’ and ‘exon-other’. Inspection of exon-clipped reads showed that they likely represent backsplicing events, where the 3’ end of an exon (preceding a gigantic intron) was connected to the beginning of the earlier exon. Although biological validation is required, these reads likely represent biological RNA species, considering that these reads exactly join the end of an exon to the beginning of the earlier exon. To facilitate this analysis of identifying backsplicing events, we have generated a backsplicing module (an optional add on package) for OmniSplice. Using this module, backsplicing events can be easily identified without the need of a manual inspection of exon-clipped reads. Briefly, OmniSplice’s backsplicing module extracts and aligns the clipped fragment to the genome and reports reads that align to the precise location of a upstream splicing acceptor site. These RNA sequencing reads detected by OmniSplice would have been missed by the AS program (such as JUM or rMats) and SE programs (splice-q). Conclusion OmniSplice is a framework-free method to recover and analyze splicing events. Using OmniSplice, we were able to detect a wide range of splicing events and defects that would have required the use of multiple programs. OmniSplice is particularly suited when the nature of the splicing perturbation is unknown. Method The code for OmniSplice is available at: https://github.com/rLannes/OmniSplice . First, OmniSplice parses a gtf (gene annotation) and builds an interval tree with the exon start-end coordinates. For each chromosome in the gtf, it fetches all reads mapping to this chromosome. Then, it discards read that do not pass a basic quality check. By default, it excludes read with specific BAM flag set (e.g. not a primary alignment, read fails platform/vendor quality checks, read is PCR or optical duplicate, supplementary alignment) or read with mapq < 13). Then, for every read passing the QC, only if it is possible to recover the read’s strand, OmniSplice queries the interval tree with the read start and end position. For every exon, the Interval Tree recovers from a read, OmniSplice determines the read’s category in the following order and keep track of the count: ∘ Check that the read overlaps one or both the exon end. (Else discard the read) ∘ Check if the read comes from a fragment which strand matches the exon strand => Else assign WrongStrand ∘ Check if the read is a junction read where the junction skips the exon end’s => assign Skipped ∘ Check if the overhang fail => assign OverhangFail ∘ Check if the read map continuously from the last base of the exon to the first base of the intron => assign ReadThrough (a.k.a. Unspliced) ∘ Check if the read is SoftClipped and the alignment stops at the exon end => assign Exon-Clipped ∘ Check if the read is a junction read with one end of the junction at the exon ends => Assign JunctionRead(start, end) ∘ Lastly, if it reaches this code, something wrong happened and the Tag unexpected is returned. If this tag is seen in the results, one needs to investigate and/or to report a bug. (negative control) => Assign Unexpected Then the data associated with the exon in the interval Tree is updated to count the number of reads of any category found. Finally, OmniSplice writes those results in the .cat file. This first pass is the Core of OmniSplice. Although the .cat file contains all the information, OmniSplice outputs a .table file as well, summarizing the read count for every exon end of every transcript to facilitate further analysis. The table file consists mainly of the read count of “Skipped”, “Clipped”, and “WrongStrand” categories. And the subcategory computed from the “JunctionRead” based on the junction ends: - Spliced: the junction read describes the expected splicing based on the gtf file. - Isoform: the junction read describes the expected splicing of an alternative isoform based on the gtf file. In some case genes can share the same exons, those junction are considered as isoforms. - Exon-intron: the junction read joins the end of the exon to the next intron for donor site and the previous intron for acceptor site. - Exon-other: the junction read joins the end of the exon to any other places. To prevent counting reads that are part of an overlapping exon ending downstream, OmniSplice marks an exon’s end overlapping another exon as ambiguous. Exons ending at the same position while overlapping are not counted as ambiguous. Finally, OmniSplice will compute the splicing efficiency and write the results in the .sd file. Acknowledgment This work was funded by Howard Hughes Medical Institute (YY). We thank Maiko Kitaoka for helpful discussion. Footnotes https://github.com/rLannes/OmniSplice Reference ↵ Bradley , R. K. , & Anczuków , O. ( 2023 ). RNA splicing dysregulation and the hallmarks of cancer . Nature Reviews Cancer 2023 23:3 , 23 ( 3 ), 135 – 155 . doi: 10.1038/s41568-022-00541-7 OpenUrl CrossRef PubMed ↵ Chaung , K. , Baharav , T. Z. , Henderson , G. , Zheludev , I. N. , Wang , P. L. , & Salzman , J. ( 2023 ). SPLASH: A statistical, reference-free genomic algorithm unifies biological discovery . 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Anything but Ordinary – Emerging Splicing Mechanisms in Eukaryotic Gene Regulation . Trends in Genetics , 37 ( 4 ), 355 – 372 . doi: 10.1016/J.TIG.2020.10.008/ASSET/9F03839A-E3CC-447F-BAA4-9EDFC8792B44/MAIN.ASSETS/GR3.JPG OpenUrl CrossRef PubMed ↵ Hildyard , J. C. W. , & Piercy , R. J. ( 2023 ). When Size Really Matters: The Eccentricities of Dystrophin Transcription and the Hazards of Quantifying mRNA from Very Long Genes . Biomedicines , 11 ( 7 ). doi: 10.3390/BIOMEDICINES11072082 OpenUrl CrossRef ↵ Hiramuki , Y. , Hosokawa , M. , Osawa , K. , Shirakawa , T. , Watanabe , Y. , Hanajima , R. , Kugoh , H. , Awano , H. , Matsuo , M. , & Kazuki , Y. ( 2025 ). Titin fragment is a sensitive biomarker in Duchenne muscular dystrophy model mice carrying full-length human dystrophin gene on human artificial chromosome . Scientific Reports 2025 15:1 , 15 ( 1 ), 1 – 9 . doi: 10.1038/s41598-025-85369-5 OpenUrl CrossRef PubMed ↵ Jiang , M. , Zhang , S. , Yin , H. , Zhuo , Z. , & Meng , G. ( 2023 ). A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level . Briefings in Bioinformatics , 24 ( 3 ), 1 – 13 . doi: 10.1093/BIB/BBAD121 OpenUrl CrossRef ↵ Kokot , M. , Dehghannasiri , R. , Baharav , T. , Salzman , J. , & Deorowicz , S. ( 2024 ). SPLASH2 provides ultra-efficient, scalable, and unsupervised discovery on raw sequencing reads . BioRxiv , 2023.03.17.533189. doi: 10.1101/2023.03.17.533189 OpenUrl Abstract / FREE Full Text ↵ Li , D. , McIntosh , C. S. , Mastaglia , F. L. , Wilton , S. D. , & Aung-Htut , M. T. ( 2021 ). Neurodegenerative diseases: a hotbed for splicing defects and the potential therapies . Translational Neurodegeneration 2021 10:1 , 10 ( 1 ), 1 – 18 . doi: 10.1186/S40035-021-00240-7 OpenUrl CrossRef PubMed ↵ Liu , C. X. , & Chen , L. L. ( 2022 ). Circular RNAs: Characterization, cellular roles, and applications . 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Nature Protocols 2024 19:4 , 19 ( 4 ), 1083 – 1104 . doi: 10.1038/s41596-023-00944-2 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted April 08, 2025. Download PDF 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. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following OmniSplice: a framework-free splicing event reporter Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share OmniSplice: a framework-free splicing event reporter Romain Lannes , Jaclyn M Fingerhut , Yukiko M. 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