TSSpredator-Web: A web-application for transcription start site prediction and exploration

TSSpredator-Web: A web-application for transcription start site prediction and exploration | 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 TSSpredator-Web: A web-application for transcription start site prediction and exploration View ORCID Profile Mathias Witte Paz , View ORCID Profile Alexander Herbig , View ORCID Profile Kay Nieselt doi: https://doi.org/10.1101/2025.05.29.656934 Mathias Witte Paz 1 Institute for Bioinformatics and Medical Informatics, University of Tübingen , 72076, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mathias Witte Paz For correspondence: mathias-alexander.witte-paz{at}uni-tuebingen.de kay.nieselt{at}uni-tuebingen.de Alexander Herbig 2 Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology , 04103, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexander Herbig Kay Nieselt 1 Institute for Bioinformatics and Medical Informatics, University of Tübingen , 72076, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kay Nieselt For correspondence: mathias-alexander.witte-paz{at}uni-tuebingen.de kay.nieselt{at}uni-tuebingen.de Abstract Full Text Info/History Metrics Preview PDF Abstract Background With the rapid development of high-throughput RNA-seq technologies, the transcriptome of prokaryotes can now be studied in unprecedented detail. Transcription start site (TSS) identification provides critical insights into transcriptional regulation, yet current command-line tools for the prediction of TSS remain challenging with respect to their usability and lack of integrated exploration features. Results We introduce TSSpredator-Web an interactive web application that enhances the usability of the established yet unpublished tool, TSSpredator. TSSpredator-Web facilitates TSS prediction from non-enriched and enriched RNA-seq data, classifies TSS relative to annotated genes, and allows users to explore results through dynamic visualizations and interactive tables. For the visualizations we provide an UpSet plot summarizing TSS distribution across experiments or classes, and a genome viewer that integrates transcriptomic and genomic data that contextualizes the insights of the TSS predictions. To illustrate the usage of TSSpredator-Web, we provide a use case with Cappable-seq data from Escherichia coli . TSSpredator-Web is available at the TueVis visualization web-server at https://tsspredator-tuevis.cs.uni-tuebingen.de/ . Conclusions By combining user-friendly accessibility with interactive data exploration, TSSpredator-Web significantly facilitates genome-wide TSS analysis and interpretation in prokaryotes, empowering a broader range of researchers to generate biological insights from transcriptomic data. 1 Introduction Due to the development of high-throughput RNA-seq technologies, the transcriptome of a prokaryotic organism can now be studied in unprecedented detail. Far beyond the quantification of known transcripts, the precision of the data allows for the detection of novel transcripts or the definition of transcript starting boundaries [ 4 , 5 ]. To clearly define the base-specific starting boundary of a transcript, researchers require the identification of transcription start sites (TSS). Their identification plays a crucial role in understanding prokaryotic transcriptional regulation, as they define the exact positions where RNA polymerase initiates transcription as well as the location of the promoter region. Promoter regions contain essential regulatory elements such as binding sites for various sigma and transcription factors [ 6 , 7 ]. This facilitates the generation of insights on how gene expression is regulated under varying environmental conditions [ 8 , 9 ]. Beyond this, the base-specific TSS identification defines also untranslated regions (UTRs), which contain riboswitches, RNA thermometers [ 10 ] and helps identifying other regulatory instances in bacteria, such as non-coding RNAs (ncRNAs) or antisense RNAs [ 11 ]. Despite the critical role of TSS identification, accurately predicting these sites remains challenging. Most genome annotations only provide translation start sites and coding regions, and relying solely on standard RNA-seq data for genome-wide TSS prediction does not produce comprehensive results [ 12 ]. To overcome these challenges, special library preparation protocols have been developed for the determination of genome-wide TSS maps. In 2010, Sharma et al. presented differential RNA-seq (dRNA-seq), an experimental approach to enrich for reads originating from the 5’ ends of primary transcripts in prokaryotes (i.e., transcripts containing a 5 ′ -triphosphate instead of a monophosphate) [ 4 ]. This is achieved by treating the cDNA with a terminator exonuclease (TEX), which specifically degrades processed RNAs with a 5’-monophosphate. A more recent method for enrichment is Cappable-seq [ 5 ]. Instead of degrading processed RNAs, a vaccinia capping enzyme (VCE) is used to positively enrich primary RNAs with a 5’-triphosphate end. A more general approach is tagRNA-seq [ 3 ], which attaches specific tags to primary and processed RNA molecules. Besides the difference in the methodology, all enriched libraries can be sequenced to produce enriched expression profiles, hence increasing the sensitivity and specificity of TSS annotation strategies, making a genome-wide TSS identification possible. Such a comprehensive view of transcriptional activity enables the identification of not only local regulatory elements but also global regulatory patterns throughout the organism, such as unannotated ncRNAs throughout a genome [ 13 ]. Furthermore, genome-wide TSS mapping enables comparative transcriptomics, allowing the investigation of transcriptional differences not only between conditions but also between bacterial strains to shed light on evolutionary adaptations in gene expression [ 14 ]. However, as the scale of the data generated by these approaches grows, manual curation becomes impractical, necessitating the development of fully automated computational methods to process and analyze TSS data efficiently. Various tools have been developed to automate the prediction of TSS from enriched RNA sequencing libraries. Among them, statistical and probabilistic approaches such as TSSAR [ 15 ], TSSer [ 16 ], and ToNER [ 17 ] identify expression changes indicative of TSS locations. Moreover, a support vector machine learning-based approach has also been explored to improve predictive accuracy [ 18 ]. Here, we focus on one of the first methods developed for TSS prediction, TSSpredator [ 14 ], that has been significantly expanded since its initial release. The latest version of TSSpredator now includes support for three prominent experimental protocols (dRNA-seq [ 4 ], Cappable-seq [ 5 ], and tagRNA-seq [ 3 ]), alongside new features, such as the analysis of multi-contig assemblies. While neither TSSpredator nor its further developments have been released as a stand-alone publication, the tool has proven effective in various studies. For instance, it has been used to compare the transcriptome of one organism under different conditions (e.g., [ 1 , 9 ]), as well as cross-strain analyses to identify conserved and divergent transcriptional characteristics (e.g., [ 2 , 14 ]). Still, given the complexity and volume of genome-wide TSS data, TSSpredator as well as the other command-line tools mentioned above, often fail to provide the efficient data exploration needed for meaningful interpretation and insight generation. Interactive web applications address this challenge by simplifying both the use of the tool and the exploration of results through user-friendly interfaces. By including dynamic and interactive representations of TSS distributions, promoter sequences, and expression patterns, such platforms enable users to quickly inspect genomic regions, compare transcriptional landscapes, and identify patterns that might otherwise remain hidden. Features such as summary visualizations, interactive genome browsers, expression data overlays, and customizable filtering options enhance accessibility and facilitate hypothesis generation, making genome-wide TSS analysis more accessible, efficient and informative. To achieve this, we introduce TSSpredator-Web, a web application designed to predict and explore TSS identified by TSSpredator. The web interface facilitates the interaction with TSSpredator, for example, by reducing the hurdles of dependencies installation and by providing enhanced interactions for data upload and data sharing. Moreover, TSSpredator-Web enhances the exploration of results by offering interactive visualizations, providing an overview of genome-wide TSS maps, as well as detailed views on the TSS predictions, thus facilitating deeper insights into transcriptional regulation. 2 Methods 2.1 Comparative detection of TSS Before introducing TSSpredator-Web, we introduce the underlying algorithm of TSSpredator for TSS detection. TSSpredator is able to analyze different sequencing protocol for TSS detection in prokaryotes, as long as it produces a control and an enriched library. So far, it has been tested with data provided by all established protocols mentioned above: dRNAseq [ 4 ], Cappable-seq [ 5 ] and tagRNA-seq [ 3 ]. The sequencing results need to be processed to produce coverage profiles for both libraries, either in wiggle or in bedGraph format, with mapping workflows such as READemption [ 19 ]. TSSpredator then mimics the manual TSS annotation process originally described by Sharma et al. [ 4 ], by comparing both profiles and expecting an increase in expression for TSS in the enriched library versus its non-enriched counterpart, independently for each genomic strand (forward and reverse). For this, TSSpredator expects the expression data pairs (enriched and non-enriched) to be pre-normalized. TSSpredator then conducts a normalization between libraries: For each enriched library, by default, the 90th percentile is computed and used as a factor for a percentile normalization of both libraries – enriched and its corresponding non-enriched counterpart. To recover the original data range, the minimal value across samples is multiplied by the normalized values. Moreover, to normalize for different enrichment factors across replicates and sets, for all library pairs (enriched and non-enriched), by default, the median enrichment factor (i.e. enriched value divided by non-enriched library) is computed. The largest factor is then used to normalize all non-enriched libraries. After normalization, for each position i in each enriched expression graph TSSpredator calculates an expression height, e ( i ), and compares that expression height to the preceding position by calculating the height change, e ( i ) − e ( i − 1) and the factor of height change e ( i ) ( Fig 1 ). These values are compared to predefined thresholds. If a position i exceeds the step height and step factor thresholds, it is considered a TSS candidate and classified as detected . Here, it is important to note that the step height threshold is relative to the 90th percentile value used for the inter-library normalization. If too many of the detected TSS are found close to each other within a window, they are reduced by selecting either only the first TSS or the one with the highest expression. This produces a set of putative TSS per strand and per replicate. Download figure Open in new tab Fig 1. Sketch of the computation of the three parameters step height , step factor and enrichment factor required for TSS prediction. The black line represents the non-enriched RNA-seq profile ( e non−enriched ), covering the whole length of an annotated gene. The yellow line shows the enriched profile ( e enriched ), with an increased expression value upstream of the annotated gene. In case of more than one replicate per experiment (that is, an organism’s genome or a tested condition), predicted TSS that are within a distance of 1bp when comparing replicates are considered the same to allow a cross-replicate shift. This default value can be changed by the user. If a TSS is labeled as detected in one replicate, the corresponding positions in the other replicates are re-evaluated by reducing the thresholds by predefined reduction values for the step height and step factor. This increases the number of detected TSS across replicates. A TSS needs to be detected in a minimal number of replicates (default: 1) to be included in the next steps. However, this lower threshold can be increased for higher specificity. For all detected TSS at any position j , the enrichment factor ( Fig 1 ) is computed with respect to the same position in the non-enriched library and compared to a threshold. For example, if the selected threshold is 2, it means that the double expression value is expected in the enriched library compared to the control library for the TSS to be labeled as enriched. Analog to the cross-replicate shift, all TSS found within the cross-condition shift value (default: 1bp), are considered equal. This returns one set of detected and enriched TSS per strand and experiment. Lastly, the sets of enriched TSS per experiment are compared to each other. The comparison of TSS in a multiple-condition experiment is straightforward, since the TSS are already in the same coordinate system. For a multiple strain analysis, a common coordinate system is computed using the concept of the SuperGenome based on a whole-genome alignment [ 14 ], such as the XMFA file provided by Mauve [ 20 ]. With the alignment, TSS are compared to each other and clustered together, if they are found in close distance. This returns per experiment a set of detected and enriched TSS that can be further analyzed. All mentioned thresholds can be modified by the user to lower or increase the specificity and/or sensitivity of the prediction. Finally, all detected TSS are classified according to their location relative to annotated genes. While other classification methods exist [ 21 ], here we classify TSS as defined in [ 4 ] ( Fig 2 ). 5 different classes are defined: Primary and secondary TSS are found up to 300 nt upstream of a gene’s annotated translation start, where the strongest or the first signal are classified as primaries, and the rest of signals as secondary TSS. Internal TSS are found within an annotated genes, and antisense TSS are found on the antisense strand within a distance of less than 150 nt to an annotated gene. Lastly, TSS that are not associated with either of the other four classes are called orphan TSS. Note that a TSS can also get more than one class assignment (e.g., it can be a primary TSS of a gene and at the same time an antisense TSS for a gene on the opposite strand). The resulting predictions are stored in a TSV-file called MasterTable , which summarizes all relevant information per TSS. This table describes in detail all predicted TSS positions, such as their enrichment factor, their class, the gene to which they might be associated, among others. This is reported for every experiment in the analysis, showing multiple lines per TSS and classification. Moreover, TSSpredator provides GFF files for each experiment aligned into one coordinate system. This is especially useful when analyzing multiple strains of a bacterium. Download figure Open in new tab Fig 2. Classification of TSS based on the distance to annotated genes as defined by [ 4 ]. Primary and secondary TSS are located upstream of annotated genes, where secondary TSS show a lower enriched expression signal compared to the respective primary TSS. Internal TSS are located within the genes themselves, while antisense TSS are located on the antisense strand close to a gene (within 150 bp) or within the gene itself. Lastly, all other identified TSS are called orphan TSS. 2.2 Design process of TSSpredator-Web The workflow for TSS prediction and their association with a gene is run independently for each replicate, condition, and strand, as described above. For each replicate, four input files are required—one for each strand (forward and reverse) and experimental type (non-enriched control and enriched). Since TSSpredator expects each file to be correctly categorized, every file must be uploaded separately. To facilitate this interaction, the latest version of TSSpredator offers a JAVA-based GUI to assist users in allocating files and setting the required parameters for the TSS prediction ( https://it.inf.uni-tuebingen.de/tsspredator , accessed on May 2025). From this GUI, the data processing can be initiated, and the predicted results are generated and saved in external files for downstream exploration. While effective for TSS prediction, this process presents several usability challenges. First, since TSSpredator is implemented in JAVA, it requires users to install additional dependencies, limiting platform independence and posing difficulties for users with limited technical expertise. Second, the manual file allocation process becomes increasingly cumbersome, and possible error-prone, as the number of replicates and conditions grows. Finally, downstream analysis is not integrated into the tool, so that users must export the results to other platforms to gain a comprehensive overview or biological insight. By reviewing several genome-wide TSS studies, we identified common strategies used to explore such data sets. For example, many studies provide overview visualizations with respect to the TSS distribution across their classes or analyzed conditions [ 9 , 18 , 22 ]. Additionally, these studies often integrate transcriptomic and genomic data, especially gene annotations and upstream regulatory regions, within one view for enhanced data exploration [ 4 ]. Based on the limitations of TSSpredator, and the evaluation of published TSS studies, we identified eight requirements to improve the TSS analysis workflow (see Fig 3 ). These requirements can be classified with respect to the step of the TSS prediction workflow they can improve: the input allocation (R1, R2), data processing (R3, R4) and result exploration (R5, R6, R7, R8). Download figure Open in new tab Fig 3. Requirements identified for the improvement of the TSS prediction workflow. Each of them will be tackled with the implementation of TSSpredator-Web. Building on these requirements, we have defined a set of goals to be addressed by TSSpredator-Web: G1 Accessibility (R3): Ensure platform independence by eliminating installation requirements, allowing users to easily access the tool. G2 Usability (R1, R4–R7): Simplify the user experience by providing easy ways of uploading and exploring the data, as well as making long waiting times bearable. G3 Exploration (R5–R7): Support data exploration through interactive visualizations that present TSS predictions across levels of detail. G4 Data Integration (R6, R7): Facilitate exploratory analyses by combining genomic and transcriptomic information into an interactive view, providing genomic context of the results. G5 Reproducibility and Data Sharing (R2, R8): Provide mechanisms to reproduce and share both the prediction results and exploration processes with others. 2.3 Back-end of TSSpredator-Web To facilitate the accessibility for the TSS prediction workflow, TSSpredator-Web has been designed as a web-application. It is freely accessible via the TueVis visualization platform ( https://tsspredator-tuevis.cs.uni-tuebingen.de ). The back-end was designed with Flask (v2.3.3) and runs a JAVA-compiled version of TSSpredator for the TSS prediction. For multiple simultaneous asynchronous requests, a worker manager based on Celery (v5.3.4) and redis (v6.2.0) has been integrated in the back-end. The results of the TSS prediction are preprocessed using Python scripts and bedGraphToBigWig [ 23 ] for the subsequent interactive exploration in the web interface. 2.4 Web-interface The front end of TSSpredator-Web has been developed using React and is structured into three distinct pages, each corresponding to one step in the TSS prediction workflow. The first step, input allocation , offers an improved file upload experience. Users can upload all necessary files at once and conveniently organize them using a drag & drop interface. As described previously, the analysis for the identification of TSS requires different parameters to adapt the specificity and sensitivity of the analysis. We provide five different sets of parameters—ranging from very sensitive to very specific prediction—which can be adapted at will. Once all parameters have been set, a configuration file that encompasses all the chosen parameters can be saved for future use, ensuring future reproducibility. If any user is provided with such a configuration file, they only need to upload it along with a folder containing all needed files, for TSSpredator-Web to allocate the inputs automatically, enabling a faster start of the analysis. Upon starting the TSS prediction, users are redirected to the status page, where information on the data processing is regularly updated. This allows an effortless monitoring of the analysis without needing to keep the browser window open, as well as facilitating the sharing of ongoing predictions. Additionally, if any result have already been computed with TSSpredator-Web, the users are able to upload a ZIP-file of these results. This skips the need to rerun the prediction and still enabling access to the downstream data exploration features of TSSpredator-Web. 2.5 Data exploration & integration To support the goals of data exploration & integration, as well as the usability improvement, TSSpredator-Web completes the TSS prediction workflow by providing an interactive result analysis. For this, the MasterTable as computed by TSSpredator is presented as an interactive table, complemented by two visualization approaches: an UpSet plot [ 24 ] offering a high-level overview of the dataset, and a genomic viewer for contextualization of the TSS predictions. The interactive MasterTable includes common features of exploration, such as searching, filtering, and sorting. Since TSS can be associated with more than one gene and therefore can be classified into more than one of the classes mentioned above, they correspond to one row in the MasterTable per classification. The TSS distribution among the different classes or between experiments is visually summarized by an UpSet plot [ 24 ]. By setting which variable should be used for the plot (either TSS classes or different experiments), the users can identify how many TSS positions have multiple TSS classes or how many of them occur in specific combinations of experiments. To get more information on these subsets of TSS, these subsets in the UpSet plot are interactive, such that users can select them, and only their corresponding rows are shown in the MasterTable . For the exploration of TSS in a genomic context, a genome browser has been implemented. Based on the visualization grammar Gosling [ 25 ], the genome browser provides aggregated and detailed views of the data for each strand independently, following Shneiderman’s mantra: Overview first, zoom and filter, details on demand [ 26 ]. The aggregated view ( Fig 4 , top) consists of multiple visualization components. The main component shows a stacked bar chart that bins the TSS according to their position in the genome and their assigned class. Depending on the zoom level, the view is aggregated with bins of 50kbp, 10kbp or 5kbp. A further track below the stacked bar charts indicates annotated genes by including gray rectangles at the corresponding position. Such an aggregated view is shown until a full window size resolution of 50,000 bp is reached. From this point on, the detailed view ( Fig 4 , bottom) is displayed, showing each individual TSS and the surrounding annotated genes. Within this view, the main track also includes the normalized expression values for the enriched and control libraries at each genomic position. To provide information on gene regulation, the 50 bp-long sequence upstream of the TSS is visualized on a third track. A tool-tip can provide more detailed information of each TSS, gene, and expression value. Download figure Open in new tab Fig 4. Sketch of different views of the genome browser of TSSpredator-Web. The top section (aggregated view) displays the aggregated stacked bar chart. Each bar shows the count of TSS of a specific class in the respective genomic region bin. Below, the gene track provides a hints for the location of annotated genes. The bottom section illustrates how the plot changes upon falling below the 50,000 bp threshold (detailed view). Individual TSS locations are represented by colored glyphs, and genes are shown in their entirety with their corresponding name or locus tag . The visualization also includes expression data from both control (in gray) and enriched libraries (in yellow), along with a track that illustrates the upstream region of a TSS. For simplicity, only 10 bp of the upstream region are shown on this representation instead of the actual 50 bp. The described tracks visualize the data separately for each strand, with a difference in the orientation of the plots and glyphs ( Fig 5 ). This mimics the visualization method used in other genomic browsers such as the Integrated Genome Browser ( IGB ) [ 27 ] or the Integrative Genomics Viewer (IGV) [ 28 ]. The genome browser of TSSpredator-Web provides two view arrangements to facilitate the exploration of the data within one experiment, and also the comparison across experiments ( Fig 5 ). The single view mode groups the tracks of each experiment together (see Fig 5 , top row), thus facilitating the exploration of single experiments, under different conditions, or single strains of one organism. Furthermore, the aligned view mode (see Fig 5 , bottom row) groups the components vertically with respect to their strand, hence facilitating the comparison between conditions or strains. Regardless of the chosen arrangement, all views are synchronized, so that the same zoom level is shown in all instances. Moreover, a synced cross-line appears on hover to identify the position over all views. The genome browser can be freely used for exploration at any level of detail of the genomic coordinates. If users require specific TSS positions, these can be searched in the MasterTable and then directly visualized in the genome browser . Download figure Open in new tab Fig 5. Visual representation of the two modes of the genome browser to show data of multiple experiments. Each experiment consists of one component per strand, differing on the orientation of the data (for example, the reverse strand is flipped vertically). The single view mode (top section) groups the components based on the experiments for a direct exploration within each experiment. Differently, the aligned view (bottom section) groups the visualization components vertically with respect to the two strands. This allows an easier comparison across experiments. Regardless of the chosen view, a synced crossline is shown on hover. For simplicity, in this figure this line is only shown in the single view. The complete predicted dataset as well as each single visualization can be downloaded from the interface. Moreover, as each prediction of TSSpredator receives a unique URL, the TSS predictions can be easily shared and accessed up to seven days after the corresponding TSS prediction was run. 3 Use Case To provide an example of how TSSpredator-Web can be used to generate and analyze genome-wide TSS data, a dataset for Escherichia coli K-12 MG1655 published by Balkin et al. [ 29 ] [GEO Acession No. GSE215300] is used in the following section. In this study, the authors treated the E. coli strain with three different antibiotics (novobiocin, rifampicin and tetracycline). All three antibiotics have different modes of actions: novobiocin and rifampicin inhibit DNA [ 30 ] and RNA synthesis [ 31 ] respectively, while tetracycline inhibits protein synthesis [ 32 ]. For all three treatments and a control condition, the authors measured gene expression using three replicates per condition, following the Cappable-seq protocol [ 5 ] to generate 5 ′ -enriched reads. Non-enriched VCE-capped reads were generated using the NEBNext Ultra™ II Directional RNA prep kit are also available under the same GEO accession number. READemption [ 19 ] was used to align the reads and to compute the coverage plots in wiggle format. Enriched and non-enriched reads were aligned independently to the E. coli reference genome [NCBI Accession No. GCF 000005845.2], taking into account their different protocols for library preparations. The READemption coverage command provides different normalized wiggle files. To make both independent runs comparable, the tnoar mil normalized coverage plots were used for further analysis, consisting of 24 wiggle files (that is, 4 conditions × 3 replicates × 2 strands) per library protocol (non-enriched and enriched). These 48 wiggle files, together with the genome and annotation file obtained from NCBI, are the basis for the prediction of TSSpredator-Web. Since one of the goals of TSSpredator-Web is to provide a user-friendly way to predict and explore genome-wide TSS maps, this starts already with the upload of the data. The 50 required files can be easily uploaded to TSSpredator-Web using the drag & drop functionality (S1 Fig). Here, the files were distributed among the four conditions and three replicates. To increase the confidence of the results, the predetermined very specific parameters were chosen for the TSS prediction step. For clustering after prediction, a cross-condition shift of 3bp and a cross-replicate shift of 2bp were allowed. Based on these parameters, TSSpredator identified a total of 7, 194 genomic positions as enriched TSS. The genome-wide TSS exploration process starts with an overview of the TSS across classes and analyzed conditions. Taking into account only the genomic position of a TSS, the UpSet plot shows that most TSS are classified as internal, directly followed by primary TSS ( Fig 6A ). However, this distribution does not account for how often a TSS occurs across conditions, meaning that a TSS can be enriched only in one condition. This can be verified by considering both, the genomic position and the condition in which the TSS occur for the UpSet plot ( Fig 6B ). The results show that primary TSS are, in fact, the predominant class in the results. A similar result can be seen by looking at the genome browser , where all TSS positions are aggregated by class and genomic position per condition ( Fig 6C ), where a predominance of primary TSS can also be observed. Download figure Open in new tab Fig 6. Analysis of the overall distribution of TSS across conditions, classes and location in the genome for data collected for E. coli on four different conditions (one control and three treatment with antibiotics). (a) UpSet plot showing the distribution of enriched TSS aggregated only by their location (i.e., position and strand). (b) UpSet plot showing the distribution of enriched TSS aggregated by their location and the condition they occur. For this UpSet plot, each TSS is counted for each condition separately. (c) Aggregated view of the genome browser showing the distribution of TSS colored by class and binned by their position in the genome for the control condition. To analyze the distribution of primary TSS even further, one can visualize the occurrence of this class across conditions. This can also be inspected via the UpSet plot ( Fig 7A ) and provides a glimpse to an interesting subset of TSS: those enriched only in the presence of an antibiotic (43 TSS, highlighted in Fig 7A ). These positions can be analyzed in more detail by interacting with the UpSet plot to filter the MasterTable for this specific subset. From here, the MasterTable can be sorted by step height (that is, the increase in the enriched library at position i compared to the previous position i − 1) to show the most prominent positions (an excerpt of the MasterTable shown in Table 1 ). Based on these results, users can search for further information, for example, by searching for more information about genes in known databases, such as the EcoCyc database [ 33 ] for E. coli . Some of the genes found in Table 1 were manually searched in EcoCyc to exemplify this exploration workflow. For example, the genes ugd , arnB and ais , are described as responsible for changes in membrane lipopolysaccharides (LPS), indicating a reaction against the hostile environments [ 34 – 36 ] as already shown for another antibiotic polymyxin [ 35 ]. In addition, the gene dinI indicates DNA damage, which can be caused by antibiotics, even though it is not part of their active mode of action [ 37 ]. In summary, some of the TSS with the highest step height and their associated genes reflect how E. coli reacts to the high stress caused by antibiotics. Download figure Open in new tab Fig 7. Analysis of primary TSS for E. coli across conditions, especially those TSS occurring only under the treatment with each antibiotic. (a) UpSet plot showing the distribution of primary enriched TSS across conditions, aggregated only by their location. The highlighted set refers to those TSS positions enriched only under the treatment with each antibiotic. (b) Aligned mode of the genome browser showing a primary TSS position on the reverse strand (shown via the red background, the direction of the glyphs and the bars of the expression profiles) occurring in all conditions with antibiotic treatment, but not in the control condition. The TSS is located upstream of the gene udp (locus tag: b2028). The enriched libraries (orange bars) can be seen increased in all conditions. However, the treatment with novobiocin shows the highest expression value. For simplicity, the empty genome track has been removed from the figure. View this table: View inline View popup Download powerpoint Table 1. Excerpt of the MasterTable showing the top 10 enriched primary TSS enriched based on step height and their associated genes. These TSS are enriched in all antibiotic treatment conditions for E. coli . Although providing an overview of the most prominent TSS can be helpful, combining this information with the transcriptomic layer provides even more insight. This can be achieved through the genome browser . Here, we inspect the most prominent TSS with respect to the step height: position 2, 099, 734, the primary TSS of gene ugd (locus tag: b2028, Fig 7B ). Here, it can be seen that enrichment libraries show the highest expression for the TSS under the treatment with novobiocin , in comparison to the other two antibiotics. A recent study identified that among these three antibiotics, the membrane LPS modification triggered by ugd , among other genes, is most effective against novobiocin [ 38 ]. A further step to analyze this region beyond TSSpredator-Web would be to extract the promoter and/or the UTR region of this gene to analyze putative regulatory elements in detail. Here, the UTR region of ugd was manually extracted using the coordinates provided by TSSpredator-Web, and compared to the RFAM [ 39 ] database outside of the presented interface. Though no hit was identified, the secondary structure of the sequence was computed using RNAfold [ 40 ] and returned a stable secondary structure (see S2 Fig). Another set of interesting TSS are orphan TSS (that is, positions that cannot be associated with the transcription of an annotated gene). To investigate putative overlooked genes, we analyzed the top 10 orphan TSS present under all conditions ranked by their step height. A prominent region is close to the orphan TSS at position 2, 904, 461 on the reverse strand ( Fig 8A ). Upon zooming in on this region in the genome browser , a noticeable increase in the enriched library can be observed, with expression levels increasing up to 350.Moreover, the upstream sequence contains a subsequence similar to the Pribnow box (TATAAA) at -9 nt upstream of the TSS ( Fig 8B ), suggesting a binding site of the RNA polymerase. When exploring the nearby regions, a second orphan TSS is identified on the forward strand at position 2, 903, 986 ( Fig 8C ). This TSS also shows a clear Pribnow box at position -13 nt upstream of the TSS. Together, these two orphan TSS may represent previously unannotated transcriptional units. Their pronounced step heights and well-defined upstream promoter motifs make them strong candidates for further computational and experimental validation, with the potential to contribute improving the genomic annotation of the organism. For example, one could analyze the downstream regions of the TSS to identify possibly overlooked open reading frames. Download figure Open in new tab Fig 8. Usage of the genome browser for the exploration and characterization of orphan TSS in E. coli . For simplicity, empty visualization tracks have been removed from the figures. (a) Genome viewer on Single view mode visualizing a region with two orphan TSS that occur in all conditions, shown here only for the rifampicin treatment. On the reverse strand (bottom), the TSS position 2, 904, 461 shows a prominent step height rising up to an expression of around 350. Interestingly, on the forward strand at position 2, 903, 986 a further orphan TSS position was identified. (b) Zoomed view of TSS at position 2, 904, 461 of the reverse strand with an expression value of the enriched library (orange bars) around 350. The upstream region shows a putative Pribnow box (TATAAA) starting at -9 nt upstream of the TSS. (c) Zoomed view of TSS at position 2, 903, 986 with an expression value of the enriched library (orange bars) around 25. The upstream region shows again a clear putative Pribnow box (TAATATAA) starting at -13 nt upstream of the TSS. 4 Discussion & Conclusion Genome-wide TSS maps provide important information for the analysis of the architecture of the prokaryotic transcriptome [ 9 , 29 ] or even recent studies on bacteriophages [ 41 , 42 ]. These studies facilitate the definition of the regulatory promoter region of genes, and provide clear signals for the identification of unannotated genes [ 6 , 7 , 13 ]. Due to the large complexity of the underlying data, computational methods are required to analyze the data to produce insight. The currently available methods tackle the prediction of TSS with different underlying methodologies [ 15 – 18 ] and most commonly provide only a command-line tool. Yet, the usage via the command-line demands technical expertise, which should not be expected from researchers with a biological background, essentially since they are the individuals who generate insights from the data. Moreover, the data exploration and integration is a key step on the insight generation. However, this step has been neglected by many of the current existing tools. Therefore, we defined requirements and goals to close the gap in insight generation for TSS prediction workflows at every step through the implementation of TSSpredator-Web. As a web-application, the platform-independent usage and the user-friendly GUI of TSSpredator-Web enhances the accessibility of the prediction workflow (our defined goal G1 ). Moreover, it provides a solid base to use up-to-date user-friendly approaches to tackle other defined goals. For example, it facilitates the reproducibility and data sharing ( G5 ) of the workflow by facilitating a link-sharing of the results as well as their reproducibility via the upload of a configuration file of all the input files. Besides reproducibility, TSSpredator-Web was developed to increase the usability of the TSS prediction workflow ( G2 ). From one side, this relates with the direct interaction with TSSpredator, for example, via the enhanced file uploading and allocation, or via the asynchronous prediction. However, a clear usability improvement also corresponds to the possibility of exploring genomic and transcriptomic data within the interface. Users are now able to not only use TSSpredator-Web for data prediction, but also for data exploration ( G3 ), as seen in the previous use case. The exploration can be pursued from different angles. The MasterTable provides access to all predicted results of TSSpredator, while the visualizations provide a more comprehensive view of the data. For example, a quick overview of the TSS distribution among classes, experiments, and location in the genome, can be achieved either by using the UpSet plot or by the aggregated view of the genome browser . As shown in the use case, users can identify specific TSS sets of interest, for example, those that occur only under specific conditions, in this case, where E. coli was treated with antibiotics. The implementation of the genome browser tackles the goal of integrating genomic and transcriptomic data into one view ( G4 ). Prior studies had to completely rely on external tools, such as MEME, to provide a genomic context of a TSS. Though our implementation of TSSpredator-Web does not provide any statistical information on the occurrence of the sequences upstream of a TSS, it allows a general exploration and contextualization of the data. With this, the users are not only able to identify TSS with high confidence values, but also dig deeper into their potential regulation, such as sequences present in their promoter regions. Moreover, the ability to characterize orphan TSS, those TSS not linked to annotated transcriptional units, opens the door to discovering previously overlooked genomic elements, such as ncRNAs [ 43 , 44 ]. Using the genome browser of TSSpredator-Web, the confidence in such sites can be evaluated based on expression, high step height values or the presence of a Pribnow box. Other tools, such as the recently developed pipeline TSS-Captur [ 13 ], focus on the further characterization of these TSS sites to provide a hint on the functionality of the transcript and close the gap of missing gene annotations. Both TSSpredator-Web and TSS-Captur are part of the Tübingen Visualization Server (TueVis) initiative, a visualization server for user-friendly tools. In the future it is planned to directly link TSSpredator-Web with TSS-Captur to allow a seamless transition from exploratory TSS prediction to detailed characterization of uncharacterized TSS. Moreover, future developments of TSSpredator-Web could be tested or adapted to process newer sequencing protocols such as Cappable-ONT [ 42 ] for TSS prediction based on long-read data, and Term-seq [ 45 ] for the characterization of transcription termination sites (TTS). This would provide deeper insights into RNA processing, regulation and boundary detection, further extending the depth and applicability of TSS analyses in prokaryotes. In conclusion, TSSpredator-Web provides an accessible and interactive platform for genome-wide TSS exploration, improving the discovery and interpretation of bacterial transcriptomics. Its user-friendly focus combined with the capability of visual analysis of the data provides users with a good basis for the analysis of the prokaryotic transcriptome architecture. 5 Funding MWP and KN are supported by infrastructural funding from the Cluster of Excellence EXC 2124 ‘Controlling Microbes to Fight Infections’ [project ID 390838134] from the DFG (Deutsche Forschungsgemeinschaft, German Research Foundation). We acknowledge support from the Open Access Publication Fund of the University of Tübingen. 6 Author’s Contributions AH and KN conceived the idea of TSSpredator. AH developed TSSpredator. MWP and KN conceived the idea of TSSpredator-Web. MWP developed TSSpredator-Web, together with its deployment and visualizations. MWP and KN wrote the initial manuscript. All authors reviewed the final manuscript. Supporting information S1 Fig. Screenshot of drag & drop process for file distribution. 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Share TSSpredator-Web: A web-application for transcription start site prediction and exploration Mathias Witte Paz , Alexander Herbig , Kay Nieselt bioRxiv 2025.05.29.656934; doi: https://doi.org/10.1101/2025.05.29.656934 Share This Article: Copy Citation Tools TSSpredator-Web: A web-application for transcription start site prediction and exploration Mathias Witte Paz , Alexander Herbig , Kay Nieselt bioRxiv 2025.05.29.656934; doi: https://doi.org/10.1101/2025.05.29.656934 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 (13895) Bioinformatics (41953) Biophysics (21456) Cancer Biology (18595) Cell Biology (25521) Clinical Trials (138) Developmental Biology (13381) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24323) Genetics (15612) Genomics (22511) Immunology (17738) Microbiology (40401) Molecular Biology (17184) Neuroscience (88623) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)