bioRxiv preprint doi: https://doi.org/10.1101/779587; this version posted September 23, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Defining data-driven primary transcript annotations with primaryTranscriptAnnotation in R Warren D. Andersona, , Fabiana M. Duarteb, Mete Civeleka,c,d,*, and Michael J. Guertina,d,* aCenter for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America bDepartment of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, United States of America cDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America dBiochemistry and Molecular Genetics Department, University of Virginia, Charlottesville, Virginia, United States of America *co-senior authors Nascent transcript measurements derived from run-on se- transcripts and determining the RNA polymerase location quencing experiments are critical for the investigation of relative to gene features. Analyses of run-on data indicates transcriptional mechanisms and regulatory networks. How- that annotated transcription start sites (TSSs) are often in- ever, conventional gene annotations specify the boundaries accurate (Link et al., 2018). Similarly, it is well established of mRNAs, which significantly differ from the boundaries of that transcription extends beyond the 30 polyadenylation primary transcripts. Moreover, transcript isoforms with dis- region (Proudfoot, 2016), thereby rendering transcription tinct transcription start and end coordinates can vary between termination sites (TTSs) distinct from annotated mRNA cell types. Therefore, new primary transcript annotations are needed to accurately interpret run-on data. We developed the ends. Identifying more accurate TSSs and TTSs for primary primaryTranscriptAnnotation R package to infer the transcripts is important for accurate transcript quantifica- 0 transcriptional start and termination sites of annotated genes tion from run-on data. Experimental techniques such as 5 from genomic run-on data. We then used these inferred co- GRO-seq, PRO-cap, and Start-seq can directly estimate TSS ordinates to annotate transcriptional units identified de novo. coordinates (Link et al., 2018; Mahat et al., 2016; Scruggs Hence, this package provides the novel utility to integrate data- et al., 2015), however, data-driven methods for improved driven primary transcript annotations with transcriptional annotations are of considerable practical interest. unit coordinates identified in an unbiased manner. Our anal- Efforts in de novo transcript identification from run-on yses demonstrated that this new methodology increases the data have partially addressed problems related to TSS/TTS sensitivity for detecting differentially expressed transcripts annotation. The R package groHMM and the command line and provides more accurate quantification of RNA polymerase pause indices, consistent with the importance of using accu- tool HOMER identify transcriptional unit (TU) coordinates rate primary transcript coordinates for interpreting genomic and have been successfully applied to run-on data (Chae nascent transcription data. et al., 2015; Heinz et al., 2010). However, these existing Availability: https://github.com/ methods do not facilitate the assignment of gene identifiers WarrenDavidAnderson/genomicsRpackage/ to the identified TUs, which are generically defined by their tree/master/primaryTranscriptAnnotation chromosomal coordinates. PRO-seq/GRO-seq analysis | transcript annotation | RNA Polymerase Paus- Here we present the R package ing primaryTranscriptAnnotation for annotat- Correspondence: [email protected] ing primary transcripts. We directly infer TSSs and TTSs for annotated genes, then we integrate the identified coordinates with TUs identified de novo. Our improved Introduction annotations increase the sensitivity and accuracy of detect- Quantification of nascent transcription is critical for resolv- ing differential transcript expression and quantifying RNA ing temporal patterns of gene regulation and defining gene polymerase pausing. This package improves precision in regulatory networks. Processed mRNA levels are influenced analyses of critical phenomena related to transcriptional by numerous factors that coordinate the mRNA production regulation and can be easily incorporated into standard and degradation rates (Blumberg et al., 2019; Honkela et al., genomic run-on analysis workflows. 2015). In contrast, the levels of nascent RNAs reflect the genome-wide distribution of transcriptionally engaged RNA Description polymerases at the time of measurement (Wissink et al., 2019). Precision run-on sequencing (PRO-seq, Kwak et al. We distinguish two related tasks performed by our package: (2013)) and global run-on sequencing (GRO-seq, Core et al. (1) Integration of run-on data and existing gene annotations (2008)) have standardized experimental protocols and are to refine estimates of TSSs and TTSs, and (2) combining the commonly used to quantify nascent transcription (Lopes results of the first task with the results of an unsupervised et al., 2017; Mahat et al., 2016). Genome-wide analyses of TU identification method (groHMM or HOMER) to annotate nascent transcription require accurate annotations of gene the TUs. We accept the data-driven annotations from (1) as boundaries. While ongoing efforts aim to increase the qual- a ‘ground truth’ and we use these coordinates to segment ity of genome annotations (Haft et al., 2018), existing gene and assign identifiers to the de novo TUs (Figure 1a). For annotations are inadequate for both quantifying nascent demonstration of the package functions, we use PRO-seq Anderson et al. | bioRχiv | September 23, 2019 | 1–19 bioRxiv preprint doi: https://doi.org/10.1101/779587; this version posted September 23, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. largest interval b annotation c TTS a n inferred o annotation GENCODE PRO/GRO data annotation .bigWig reads eads .bed tributi r s spline 200 di 20 kb -200 0 200 chr coordinate get.TSS() dist, TSS to read max (bp) Data-driven get.TTS() gene annotation .bed single.overlaps() largest interval annotation e multi.overlaps() d de novo tu gene annotation inferred annotation gene x eads r o De novo Transcript unit l=start-start r=end-end gene annotation annotation 25 25 kb .bed .bed minus if |l|<200bp & |r|<1kb Zfp800 tu = gene x f else o = gene x ... unann_102 unann_104 unann_105 unann_108 unann_103 unann_106 unann_107 tu_class1_1 tu_class1_2 Atp6v1h Lypla1 Rgs20 tu_class5_4 Tcea1 tu_class5_1 Mrpl15 tu_class5_5 tu_class5_2 Scale Mrpl15 Lypla1 Rgs20 Atp6v1h chr1: All HMMs Tcea1 20 _ Un-annotated Single-overlap TUs Multi-overlap TUs ig 50 kb Inferred minus 1 _ 80 _ igWig plus 1 _ Fig. 1. The primaryTranscriptAnnotation package accurately annotates gene features and assigns gene names to transcriptional units. (a) Aligned run on data and gene annotations are inputs to redefine gene annotations. De novo-identified transcription units can be assigned gene identifiers using refined gene annotations. (b) Promoter-proximal paused RNA polymerases are more constrained to the canonical 20-80 bases downstream of the refined transcription start sites, compared to con- ventional annotations. (c) TTS inference involves 1) detection of higher density peaks in the 30 end of the gene, corresponding to the slowing of RNA polymerase and 2) determining the genomic position when the read density decays towards zero. (d) These methods generate improved TSS and TTS estimates for the Zfp800 gene. (e) An- notation of de novo-defined transcription units with gene identifiers is based upon degree of overlap. (f) This approach produces gene boundaries with improved accuracy, while maintaining gene identifiers. data from adipogenesis time-series experiments available 500 bases. Consistent with paused RNA polymerase accu- through GEO accession record GSE133147. Extensive im- mulation in close proximity and downstream of transcription plementation details are provided in the vignette associated initiation sites, the distribution of highest RNA polymerase with the publicly available R package. densities is more focused immediately downstream for the refined TSS annotation as compared to the ‘largest interval’ Data-driven gene annotation. annotation (Figure 1b and Figure S1). We used GENCODE gene annotations as a reference point To infer TTSs, we examined evidence of transcriptional for inferring TSSs and TTSs (Harrow et al., 2012). To in- termination in regions extending from a 30 interval of the fer TSSs, we considered all first exons of each gene isoform gene to a selected number of base pairs downstream of and defined the TSS as the 50 end of the annotated first exon the most distal annotated gene end (Figure S2). We based that contains the maximal read density within a specified our method for TTS inference on data demonstrating that range downstream. Such regions of peak read density, typ- transcription rates are attenuated at gene ends (Lian et al., ically between 20 and 80 bp downstream from a TSS, exist 2008). This phenomena is manifested as elevated poly- at RNA polymerase ‘pause