How to Illuminate the Dark Proteome Using the Multi-omic OpenProt Resource

Ten of thousands of open reading frames (ORFs) are hidden within genomes. These alternative ORFs, or small ORFs, have eluded annotations because they are either small or within unsuspected locations. They are found in untranslated regions or overlap a known coding sequence in messenger RNA and anywhere in a “non-coding” RNA. Serendipitous discoveries have highlighted these ORFs’ importance in biological functions and pathways. With their discovery came the need for deeper ORF annotation and large-scale mining of public repositories to gather supporting experimental evidence. OpenProt, accessible at https://openprot.org/, is the first proteogenomic resource enforcing a polycistronic model of annotation across an exhaustive transcriptome for 10 species. Moreover, OpenProt reports experimental evidence cumulated across a re-analysis of 114 mass spectrometry and 87 ribosome profiling datasets. The multi-omics OpenProt resource also includes the identification of predicted functional domains and evaluation of conservation for all predicted ORFs. The OpenProt web server provides two query interfaces and one genome browser. The query interfaces allow for exploration of the coding potential of genes or transcripts of interest as well as custom downloads of all information contained in OpenProt. © 2020 The Authors.

Basic Protocol 1 : Using the Search interface

Basic Protocol 2 : Using the Downloads interface

Historically, open reading frames (ORFs) shorter than 100 codons were discarded from genome annotations unless previously characterized, as they were deemed too short to be functional (Cheng et al., 2011). This length criterion, alongside requirement of an ATG start codon and the restriction of a single coding sequence per transcript, has considerably shaped and limited the exploration of the proteome (Brunet, Levesque, Hunting, Cohen, & Roucou, 2018; Hellens, Brown, Chisnall, Waterhouse, & Macknight, 2016; Olexiouk & Menschaert, 2016; Orr, Mao, Storz, & Qian, 2019). Deeper ORF annotation is key to functional proteomic discoveries and a better understanding of physiological and pathological mechanisms (Brunet et al., 2018; Ma et al., 2014; Menschaert et al., 2013; Samandi et al., 2017). With the development of ribosome profiling (Ingolia, 2014), a technique detecting ribosome-protected fragments (footprints) originating from translating ribosomes, all translation events across the genome can potentially be captured (Ingolia, 2016). This observation led the community to use ribosome profiling to capture a deeper ORF landscape and identify small ORF (sORF) candidates for functional characterization (Andreev et al., 2015a, 2015b; Bazzini et al., 2014; Chen et al., 2020; Menschaert et al., 2013). Several repositories of sORFs have been published, all relying on ribosome profiling data for ORF annotation (Hao et al., 2017; Olexiouk, Van Criekinge, & Menschaert, 2018; Xie et al., 2016). Despite being an undeniable resource for novel ORF identification, the ribosome profiling technique still presents biases that can hinder detection of functional yet non-annotated ORFs, including ORFs in low-abundance transcripts or in repetitive regions (Brar & Weissman, 2015; Brunet et al., 2018; Ingolia, 2014; Ingolia, Ghaemmaghami, Newman, & Weissman, 2009; Raj et al., 2016).

At OpenProt (Brunet et al., 2019), we computationally predict all possible alternative ORFs (altORFs) from an exhaustive transcriptome. All known transcripts are retrieved from both Ensembl and NCBI RefSeq annotations (O'Leary et al., 2016; Yates et al., 2020), and after in silico translation, all ORFs starting with an ATG and longer than 30 codons are listed. The predicted proteins are then divided into three categories (Table 1): known proteins are called refProts, non-annotated proteins similar to a known protein in the same gene are called novel isoforms, and non-annotated proteins with no significant similarity to a known protein in the same gene are called altProts. Such a computational strategy allows for annotation of an exhaustive set of ORFs, yet it also certainly results in false positives. Thus, OpenProt evaluates protein conservation and mines ribosome profiling and proteomics datasets to cumulate experimental evidence for all predicted proteins (Barrett et al., 2013; Deutsch et al., 2020; Perez-Riverol et al., 2019). All evidence is listed in the OpenProt resource, allowing the user an in-depth review of evidence for any protein supported by OpenProt. OpenProt currently supports 10 species and explores 114 proteomics and 87 ribosome profiling datasets. For a complete overview of the OpenProt resource, including the computational and analytic methods, we refer the user to the original publication (Brunet et al., 2019). The web server also contains a detailed help section with tutorials and frequently asked questions (https://openprot.org/p/help).

Basic Protocol 1 described here guides the novice user in how to explore ORFs using the Search interface. This protocol is designed to provide a rapid view of the coding potential and translation products of genes of interest. Basic Protocol 2 describes how to download custom data from the OpenProt resource. Guidelines for investigation of a specific altORF are provided afterward, alongside discussion on critical parameters.

This protocol details the use of the Search interface to query specific genes, transcripts, or proteins. The interface is optimized to accommodate many questions that a researcher may have. For example, a researcher may want to know if a specific gene contains novel ORFs with supporting experimental evidence or whether a given transcript may contain several ORFs. The protocol will guide novice users in how to exploit the Search interface of the OpenProt resource. First, the protocol describes how to navigate to the Search interface from the homepage. Then, it details the features available on the interface to tailor the results to any query. Finally, the protocol explains how to investigate a specific ORF of interest.

Necessary Resources

• OpenProt is accessible via all major web browsers supporting JavaScript, such as Safari, Firefox, Chrome, or Internet Explorer. All pages can be viewed on mobile phones, but the interfaces have been optimized for display on computers or tablets. The Search interface is designed for exploration of specific genes, transcripts, and/or proteins. The interface is accessible via the homepage by clicking on the “Search” tab or can be accessed directly at https://openprot.org/p/altorfDbView. The Downloads interface is designed for custom downloads of any data in the OpenProt resource (see Basic Protocol 2). The interface is accessible via the homepage by clicking on the “Downloads” tab or can be accessed directly at https://openprot.org/p/download. Each protein annotated in OpenProt has a dedicated page containing a genome browser and all supporting information.

Navigating from the homepage to the Search interface

1.Visit OpenProt homepage (https://openprot.org/).

2.Hover cursor over each tab of the navigation bar at the top of the page to highlight it and click to navigate to Search interface (see steps 3 to 5) or another desired page (for Downloads, see Basic Protocol 2).

Exploring the Search interface

3.Use main filters to define a search.

4.Use additional filters to refine the output of the query. Click on “edit search criteria” to open advanced criteria (framed by a gray dashed line in Fig. 2).

5.Use “order by” and “column settings” filters to sort and arrange the table of results.

Exploring the table of results

6.Click on blue box “update search results” (Fig. 2) to view the table of results (Fig. 3).

7.Go to bottom of the table to navigate between the different pages of results.

8.Click “share” button, which appears at the top right of the table, next to the sorting and download options, to display a shareable link to this specific search result.

Inspecting a specific protein

9.Click “details” link in the main table of results to navigate to a page dedicated to the queried protein.

10.Use genome browser to visualize the queried protein and the associated transcript.

11.See summary table located below the genome browser to view general information on the queried protein.

12.Click on “mass spectrometry” tab to review experimental detection of the queried protein in mass spectrometry–based proteomic datasets.

13.Click on “translation” tab to review experimental detection of translation of the queried ORF in ribosome profiling datasets.

14.Click on “domains” tab to review prediction of functional domains for the queried protein using multiple domain annotation databases.

15.Click on “conservation” tab to review conservation of the queried protein across species supported by OpenProt.

This protocol details the use of the Downloads interface to retrieve a large amount of data stored in the OpenProt resource. The interface is optimized to obtain custom downloads for specific research questions. For example, a researcher may want to download a FASTA file containing only the sequences of altProts and novel isoforms with experimental evidence or a BED file containing the genomic coordinates of all proteins predicted by OpenProt. This protocol will guide novice users in how to exploit the Downloads interface of the OpenProt resource. First, the protocol describes how to navigate to the Downloads interface from the homepage. Then, it details the features available on the interface to tailor results to any query. Finally, the protocol explains the different file formats available.

Necessary Resources

• See Basic Protocol 1.

Navigating from the homepage to the Downloads interface

1.Navigate to Downloads interface according to Basic Protocol 1, steps 1 and 2.

Exploring the Downloads interface

2.Use query filters to define a search.

3.Click on desired file name to start the download.

OpenProt is a proteogenomic resource that seeks experimental evidence for predicted novel proteins from non-annotated ORFs (Brunet et al., 2019). OpenProt is open source, all methods and codes are published and freely available (Brunet et al., 2019; Samandi et al., 2017), and all supported data are freely accessible and downloadable (Basic Protocols 1 and 2). At OpenProt, we predict all possible ORFs longer than 30 codons throughout the annotated transcriptome for 10 species. This approach was chosen to be as inclusive as possible for predictions and to then retrieve experimental evidence for each prediction. Thus, OpenProt is not dependent on a specific experimental bias, but the user has to be aware that false positives are a reality with such design. Not all predicted proteins in OpenProt are likely expressed. Thus, because noise and nonspecific detections vary across experimental datasets and designs, we encourage users to seek experimental detection across multiple datasets to increase confidence in an altProt and/or the existence of a novel isoform.

Broadly, there are two major usages of the OpenProt resource. First, users may be interested in a specific gene or transcript and wonder whether they are capturing its full coding potential. To that end, users should use the OpenProt Search interface (Basic Protocol 1) and investigate each predicted protein in detail (Fig. 5). Second, users may be interested in analyzing their mass spectrometry–based proteomic data with the OpenProt database. Users should use the OpenProt Downloads interface for such a query (Basic Protocol 2). If users wish to tailor their mass spectrometry database to a specific set of transcripts, we encourage them to download the full database and keep only entries of interest based on the transcript accession (TA field in the FASTA header). Users may also use the OpenProt Search interface to query specific transcripts and download the results as a FASTA file. Please note, however, that for computational reasons, such queries are limited to 2000 genes (or transcripts) at a time.

Crucial OpenProt features and considerations heavily depend on the research question behind the query. For any question or additional information on data analysis and interpretation, contact the OpenProt team via the light blue “contact us” button at the bottom of all OpenProt pages (https://groups.google.com/forum/#!forum/openprot).

Background Information

The premises of the OpenProt resource were first published in 2013 (Vanderperre et al., 2013). The former HAltORF database was a mere list of altORFs within the human transcriptome (based on the NCBI RefSeq annotation). Community-driven requests and serendipitous discoveries contributed to the desire and need to develop OpenProt as the first proteogenomic resource to enforce a polycistronic annotation model on both coding RNA (messenger RNA, or mRNA) and non-coding RNA (ncRNA) transcripts (Samandi et al., 2017). The OpenProt resource was first officially released in 2019, contains 10 species, and cumulates experimental evidence using mass spectrometry and ribosome profiling data (Brunet et al., 2019). Using cutting-edge algorithms for ribosome profiling and mass spectrometry data mining (Erhard et al., 2018; Vaudel, Barsnes, Berven, Sickmann, & Martens, 2011, 2015), OpenProt re-analyzed 87 and 114 datasets, respectively. OpenProt not only lists novel proteins with experimental evidence but also allows critical assessment of the evidence by the user. OpenProt is constantly re-analyzing datasets and adding new features, but all data are continuously available thanks to the release-based structure of the resource. Suggestions of new features or additional species or datasets from the community are always welcome and can be submitted via the OpenProt discussion forum (https://groups.google.com/forum/#!forum/openprot).

Critical Parameters and Troubleshooting

We refer the user to the original article for explanation of the mass spectrometry pipeline enforced by OpenProt (Brunet et al., 2019), yet one needs to acknowledge the stringent 0.001% false discovery rate (FDR). Such an FDR balances the use of a large database that can affect the false positive rate in proteomics analyses (Jeong, Kim, & Bandeira, 2012; Nesvizhskii, 2014). Thus, an absence of detection by mass spectrometry in OpenProt does not necessarily mean that the protein does not exist. Such a pipeline will heavily hinder the detection of some proteins. As a guideline, the same standard mass spectrometry analysis filtered at a usual 1% FDR or the stringent 0.001% FDR may only share 40 to 80% of identifications depending on the spectral quality of the dataset (unpub. observ.). Similarly, an absence of detection by ribosome profiling does not mean that there is no evidence of translation. At the moment, OpenProt only incorporates ORFs predicted by the translation analysis pipeline (PRICE) that have a perfect overlap with the ORF predicted by OpenProt. Thus, if a start codon is a non-canonical codon upstream or downstream of the ATG predicted by OpenProt, no translation evidence will be reported by OpenProt. Implementation of such cases will be available in the next OpenProt release. Additionally, one should note that the p-value reported by the PRICE algorithm for each detected ORF is the result of a generalized binomial test (not corrected for multiple comparisons). Hence, the p-value indicates the confidence in the given ORF not being attributable to noise.

Finally, for each protein, a list of paralogs and orthologs is provided in the “conservation” tab (described in Fig. 5). The user should note that this list is restricted to species currently supported by OpenProt (listed in Table 3). For a more exhaustive list, the user may want to use the BLASTp tool (Madden, Tatusov, & Zhang, 1996) to search a specific protein against a reference database such as the non-redundant NCBI or the UniProtKB protein database (Bateman et al., 2017; O'Leary et al., 2016). This analysis may identify proteins with significant sequence similarity in various species.

Acknowledgments

X.R. is a member of the Fonds de Recherche du Québec Santé (FRQS)-supported Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke. This work was supported by a Canada Research Chair in Functional Proteomics and Discovery of Novel Proteins to X.R. We thank M. Brunelle, J-F. Lucier, M. Levesque, and everybody else involved in the continuous development of the OpenProt resource. We thank the team at Calcul Québec and Compute Canada for their support with the use of the supercomputer mp2 from Université de Sherbrooke. Operation of the mp2 supercomputer is funded by the Canada Foundation of Innovation (CFI), le ministère de l’Économie, de la science et de l'innovation du Québec (MESI), and les Fonds de Recherche du Québec.

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• Brunet et al. (2019). See above.

Corresponds to the official release of OpenProt and explains all the underlying methods in detail.

• Orr et al. (2019). See above.

Recent review painting an exhaustive picture of altORFs and small ORFs.

• Brunet et al. (2018). See above.

Complete review of the impact of altORF omission in experimental design and investigation of pathologies.

• Nesvizhskii (2014). See above.

Reviews the concepts of proteogenomics methods to better explore the proteome.

• Ingolia (2016). See above.

Exhaustive presentation of the ribosome profiling technique.

• https://openprot.org/

OpenProt.

Number of times cited according to CrossRef: 3

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