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Functional Annotation – How to Tackle the Bottleneck in Plant Genomics

Submitted:

10 February 2024

Posted:

14 February 2024

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Abstract
This review provides an overview of advancements in plant genomics, emphasizing key stages in genomics projects and addressing associated challenges. Long read sequencing enables the cost-effective sequencing of plant DNA and assembly of highly continuous genome sequences - often even separating haplophases. Incorporating external hints, such as cDNA sequences from RNA-seq or full length cDNA sequencing, enhances the identification of gene models. While these steps enable high-throughput exploration of numerous plant genomes, a significant bottleneck lies in elucidating gene functions. The classical approach based on wet lab methods is impractical when dealing with thousands of genes in a new genome sequence. To overcome this challenge, computational tools harnessing existing information for cross species knowledge transfer are essential for expediting the functional annotation process. In support of researchers entering the field of plant genomics, a collection of recommended tools has been curated and is accessible at https://github.com/bpucker/ToolOverview.
Keywords: 
plant genomics
;  
functional genomics
;  
gene function
;  
sequence comparison
;  
orthology
;  
synteny
Subject: 
Biology and Life Sciences  -   Plant Sciences

How to Obtain a Gene Sequence?

Access to the gene repertoire of a plant species is usually gained through a transcriptome assembly or a genome sequence. Given the large size of plant genomes and the large proportion of non-genic elements, transcriptome assemblies have been a cost-effective way to obtain protein encoding sequences of a species of interest (Haak et al., 2018). The rapid technological development of long read sequencing made the generation of high quality genome sequences affordable and feasible for many plant species (Marks et al., 2021; Pucker et al., 2022). Oxford Nanopore Technologies and Pacific Biosciences offer sequencing instruments that enable the continuous sequencing of long DNA molecules with high raw read accuracy of over 99%. The portability and affordable prices of ONT sequencers enable an ever increasing number of scientists to actively participate in plant genomics (Pucker et al., 2022). Sequencing data are often stored in FASTQ files that combine sequence and quality information (Cock et al., 2010). Long reads are processed by assemblers like HiCanu (Nurk et al., 2020), Flye (Kolmogorov et al., 2019), Shasta (Shafin et al., 2020), or NextDenovo2 (GrandOmics, 2023) to produce highly continuous genome sequences. These assembled sequences are called contigs and stored in a FASTA file (Lipman & Pearson, 1985). Continuous sequences (contigs) produced in the assembly process often represent chromosome arms or even entire chromosomes in some cases. The quality and completeness of the assembled sequences can be assessed with Merqury (Rhie et al., 2020) which checks the assembly for all k-mers that have been observed in the reads. Most prominent in plant genomes are repeats and transposable elements (TEs) that can be identified by tools like RepeatMasker (Smit et al., 2015), Extensive de-novo TE Annotator (EDTA) (Ou et al., 2019), or TransposonUltimate (Riehl et al., 2022). When multiple genome sequences of a species are investigated at the same time, panEDTA can be applied to benefit from the pangenome context (Ou et al., 2022). As repeats, especially in the centromeres, become more accessible with long reads, many studies are now investigating these parts of plant genomes (Naish et al., 2021; Wlodzimierz et al., 2023). Excluding positions of repeats and transposable elements (TEs) in the subsequent step of identifying protein-encoding plant genes is a common practice. This is done to ensure that transposon genes are not included in the annotation, maintaining the accuracy of the results. The gene prediction process can be performed by BRAKER3 (Gabriel et al., 2023), GeMoMa (Keilwagen et al., 2016, 2019), CAT (Fiddes et al., 2018), or Funannotate (Palmer, 2019) and results in gene models also known as structural annotation. Details about the positions and structures of all genes are typically stored in a GFF3 file. While the aforementioned tools predict only monocistronic gene models, OpenProt2021 supports polycistronic gene models in the annotation of eukaryotic genome sequences (Brunet et al., 2021). While polycistronic genes have been described in Chlamydomonas reinhardtii (Gallaher et al., 2021), there is currently limited information about the relevance of polycistronic genes in land plants (García-Ríos et al., 1997; Wang et al., 2019). RNA-seq reads are generated by sequencing fragments of cDNAs, which essentially consist of concatenated exons without the intervening presence of introns. When aligned to a genome sequence, gaps in the alignment of RNA-seq reads span the positions of introns. Therefore, RNA-seq reads can reveal the exon/intron structure of a gene and indicate which exons belong to the same gene. This process requires dedicated tools like STAR (Dobin et al., 2013; Dobin & Gingeras, 2015) or HISAT2 (Kim et al., 2019), which can accurately split alignments around introns. Reads derived from direct RNA sequencing or full length cDNA sequencing enable the annotation of distinct transcript isoforms that can be the result of alternative splicing (Amarasinghe et al., 2020; Guizard et al., 2023). Similarly, polypeptide sequences from databases can be aligned to a genome sequence to inform the gene prediction process. However, the inclusion of polypeptide sequence derived hints might lead to high rates of false-positive predictions (Vuruputoor et al., 2023). These types of external information already indicate a major limitation of today’s gene prediction approaches: the structural annotation is restricted to the transcribed region of a gene and does not cover the regulatory elements in the promoter which would also be part of a plant gene. The completeness of a genome sequence and the corresponding structural annotation can be assessed with BUSCO (Simão et al., 2015; Manni et al., 2021) that checks for the presence of highly conserved single copy genes. The completeness reported by BUSCO for a genome sequence usually exceeds the reported completeness reported for the corresponding annotation. This might be due to the inclusion of pseudogenes in the completeness analysis of genome sequences, while gene prediction tools would filter out such sequences. However, both values are often >95% suggesting that high quality genomic resources are routinely generated and that missing genes are an exception. In the gene prediction process, each gene receives a unique ID that enables access to all information collected about this gene. As this ID is specific for a structural annotation, different annotation versions and sources might use different IDs for the same biological entity. Matching the IDs between different annotation versions is a frequent task that requires specific rules for complicated cases where the annotation versions deviate substantially from each other. The result of this process is a mapping table that connects each gene ID from one annotation version to zero, one, or many IDs of another annotation version. Such mapping tables are particularly important if many different annotation versions exist for the same species and are used as reference by different research groups or consortia. An example would be Vitis vinifera, for which a range of different reference genome sequences and annotation versions were developed, sometimes in parallel by different groups, and favored by different parts of the community (Velasco et al., 2007; Muñoz et al., 2014; Grimplet et al., 2014; Velt et al., 2023; Shi et al., 2023). It is important to enable the connection of biological insights reported in scientific publications based on different annotation versions to integrate all knowledge in the body of literature and to avoid redundant research endeavors. Another purpose of a gene ID is to enable users to retrieve the underlying sequence together with any attached information from a database. A practical solution to make genomic data accessible is a genome browser as implemented in jbrowse (Buels et al., 2016; Diesh et al., 2023) or gbrowse (Stein, 2013). A graphical user interface and access via the internet enable users to retrieve the desired sequences of a gene of interest. Famous examples are The Arabidopsis Information Resource (TAIR) (Lamesch et al., 2012; Berardini et al., 2015), Banana Genome Hub (Droc et al., 2022), Sol Genomics Network (Fernandez-Pozo et al., 2015), and Coffee Genome Hub (Dereeper et al., 2015) that also provide additional information besides gene and genome sequences.
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Figure 1. Workflow of a plant genomics project leading to a genome sequence and corresponding annotation that can be accessed through a genome browser.
Figure 1. Workflow of a plant genomics project leading to a genome sequence and corresponding annotation that can be accessed through a genome browser.
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How to Understand the Function of a Gene?

The classical reverse genetics approaches towards gene function elucidation are studying a knock-out or overexpression line. As a targeted integration of DNA into the plant genome through homologous recombination is not feasible, researchers had to rely on randomly introduced mutations during the last decades. Large collections of knock-out lines were established for model organisms like Arabidopsis thaliana (SALK, GABI-Kat) (Alonso et al., 2003; Rosso et al., 2003; O’Malley et al., 2015). These lines are based on a random integration of T-DNAs into the plant genome and a localization of the integration site in the genome (Rosso et al., 2003; Kleinboelting et al., 2012). Scientists can order a knock-out line that harbors a T-DNA inside their gene of interest through the NASC and ABRC. The floral dip method, which involves a Agrobacterium tumefaciens-mediated transfer of DNA into generative A. thaliana cells (Clough & Bent, 1998), has been a frequently applied method to generate stable transgenic lines. A careful characterization of T-DNA insertion lines is necessary as multiple T-DNA copies might affect different genes and can also trigger large-scale genomic rearrangements (Pucker et al., 2021). T-DNA based integration of genes into mutant lines can also serve as a method to characterize genes of non-model organisms in A. thaliana through complementation experiments (Lee et al., 2013; Schilbert et al., 2021; Aslam et al., 2022). The development of different CRISPR-Cas9-derived systems enables plant biologists to generate mutants in a targeted way (Grützner et al., 2021). However, this still requires the transformation of plants with the necessary constructs and a following validation of the introduced genomic changes. A gene knock-out is not possible for essential genes as their loss would be lethal. This makes knock-down a powerful alternative strategy. Initially triggered by chance in Petunia engineering experiments (Napoli et al., 1990), knock-down, i.e., reduction of transcript abundance, developed into a strategy in functional plant genomics (Samuilov et al., 2018; Debladis et al., 2020; Demirer & Landry, 2021). Virus-induced gene silencing (VIGS) emerged as a convenient tool to also knock-down genes in non-model organisms (Ruiz et al., 1998; Lu et al., 2003; Dommes et al., 2019).
Forward genetics is the opposite approach that can reveal the gene responsible for a certain phenotype. Frequently deployed methods to identify causal genes are genome-wide association studies (GWAS) (Lee & Lee, 2021; Gloss et al., 2022) and mapping-by-sequencing (MBS) (Schneeberger & Weigel, 2011; James et al., 2013) followed by an in-depth investigation of an identified quantitative trait locus (QTL). The concept of these approaches is to find systematic genetic differences between two groups of individuals that have been pooled based on their phenotype. These systematic genetic differences can be small sequence variants or presence/absence variants affecting entire genes. Tools like SnpEff (Cingolani et al., 2012) and NAVIP (Baasner et al., 2024) enable a prediction of the functional consequences of a sequence variant. GWAS and MBS have been deployed to study A. thaliana gene functions and to provide insights into crop genes determining important traits (Mascher et al., 2014; Sasaki et al., 2021; Schilbert et al., 2022; Naake et al., 2023; Sielemann et al., 2023).
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Figure 2. Schematic illustration of forward and reverse genetics. Forward genetics starts from the phenotype and identifies the underlying genotype. Reverse genetics starts from the genotype and aims to understand the resulting phenotype.
Figure 2. Schematic illustration of forward and reverse genetics. Forward genetics starts from the phenotype and identifies the underlying genotype. Reverse genetics starts from the genotype and aims to understand the resulting phenotype.
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How to Understand the Function of All Genes in a Genome?

The gene function elucidation approaches described above suffer from poor scalability and are therefore not suitable for investigation of all gene functions in a newly sequenced plant genome. These have two major disadvantages: they are time intensive and costly. This restricts the investigation of gene functions through knock-out, overexpression, or knock-down lines to small numbers of genes and a small number of genetically accessible species. Given that a substantial amount of information about genes and their functions is already available in databases, individual users can access this through sequence comparison. This cross-species annotation transfer is based on the assumption that similar sequences have similar functions. While this holds true in most cases, some very similar sequences, paralogs, can differ in their function. It is generally assumed that only orthologs, i.e., the same gene in different species, are highly likely to harbor the same function (Eisen, 1998). The major challenge in functional annotation is therefore the reliable and efficient identification of orthologs i.e., to clearly distinguish between orthologs and paralogs. Three conceptually different types of analyses can be distinguished that allow the connection of genes between species. The first type utilizes only the sequence of a gene or the derived polypeptide sequence for basic similarity analyses. The second type performs a phylogenetic analysis to place the sequence of interest in an evolutionary context with similar sequences. The third type screens for genomic regions where a number of flanking genes, not just the gene of interest, show similarity between two species. This last approach utilizes information beyond the borders of the gene itself to establish a more reliable pair of orthologs.

Gene Sequence Similarity Analyses

Basic Local Alignment Search Tool (BLAST) is the most frequently used tool in life sciences and enables a quick comparison of a sequence against a comprehensive database (Altschul et al., 1990, 1997). The availability of BLAST through websites hosted by the NCBI, Phytozome (Goodstein et al., 2012), or TAIR (Lamesch et al., 2012; Berardini et al., 2015) enables life scientists to explore a gene function without computational biology skills. Annotation terms associated with the best BLAST hits can give a first impression about the function of the gene of interest. There is a large number of databases that contain valuable information about sequence functions. Examples are Plant Reactome (Naithani et al., 2020), MetaCyc (Caspi et al., 2020), Gene Ontology (GO) (Ashburner et al., 2000; Gene Ontology Consortium, 2021), Protein family (Pfam) (Mistry et al., 2021), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa & Goto, 2000; Kanehisa et al., 2023), and the large collection of sequences hosted by the International Nucleotide Sequence Database Collaboration, a consortium formed by GenBank, ENA, and DDBJ (Clark et al., 2016; Sayers et al., 2020; Arita et al., 2021). There are specific tools that can assign information of these databases to polypeptide sequences derived from a novel genome sequence. Examples are BLAST2GO (Conesa et al., 2005) that attaches GO terms and KAAS (Moriya et al., 2007) that assigns KEGG identifiers to sequences. InterProScan5 (Jones et al., 2014) assigns a range of different identifiers to novel polypeptide sequences including GO terms, KEGG identifier, Pfams, and PANTHER annotation terms. Mercator4 (Lohse et al., 2014) is available as a web service and can efficiently annotate provided polypeptide sequences by assigning them to clusters of similar sequences. BLAST is one of the most frequently used tools for the identification of similar sequences, but it is not a reliable method to identify orthologs, because only small stretches of highly similar sequences are considered for the alignment (Altschul et al., 1990). The chances of identifying bona fide orthologs can be increased by performing a reciprocal best BLAST hit analysis, which adds an additional filter layer (Pucker et al., 2016). Comparing all polypeptide sequences of a species against another species or even an entire database has huge computational costs and can result in long run times. Such tasks usually require a parallel analysis of sequences in batches on a high performance compute cluster. A faster BLAST alternative is DIAMOND (Buchfink et al., 2015, 2021), but this speed increase comes at the expense of higher memory consumption. If a large number of sequences needs to be compared against a database, users might want to use DIAMOND instead of BLAST. Eukaryotic Non-Model Transcriptome Annotation Pipeline (ENTAP) is a dedicated tool for the functional annotation of transcriptome assembly sequences through comparison against several databases (Hart et al., 2020). There are also dedicated tools for the identification of orthologs. JustOrthologs identifies putative orthologs through characteristics like gene structure, CDS length, and dinucleotide percentages in a computationally efficient way (Miller et al., 2019). SwiftOrtho is a graph-based tool for the identification of orthologs that was also developed with a focus on minimizing the computational costs (Hu & Friedberg, 2019). Since there are often not 1:1-relationships between species, a number of orthologous sequences must be collected in an orthogroup. OrthoFinder2 (Emms & Kelly, 2019) enables an automatic identification of orthogroups based on a number of polypeptide sequence sets belonging to different species. An advantage of this analysis is the ability to integrate polypeptide sequences derived from de novo transcriptome assemblies or short read-based genome sequence assemblies. Unfortunately, orthogroups might be too large and consequently not helpful in many scenarios where large gene families are of interest. Examples are the large transcription factor gene families MYB and bHLH that can have >100 members per plant species (Stracke et al., 2001; Zimmermann et al., 2004; Dubos et al., 2010; Thoben & Pucker, 2023) and are often clustered into a small number of orthogroups. As members of these gene families belong to dozens of subgroups that have individual functions (Dubos et al., 2010; Pucker et al., 2020a), a fine separation is required for accurate functional annotation transfer.

Phylogenetic Analyses

The construction of phylogenetic trees has been reported to be more accurate when predicting protein functions than the application of basic sequence similarity analyses, because they enable a more reliable identification of orthologs (Eisen, 1998; Sjölander, 2004; Brown & Sjölander, 2006; Pucker et al., 2020b). While other studies suggest that the inclusion of paralogs improves the accuracy in some pairwise comparisons (Nehrt et al., 2011; Stamboulian et al., 2020), an accurate assignment of homologous sequences across species forms the basis of the annotation transfer.
Databases like Phytozome (Goodstein et al., 2012), PANTHER (Thomas et al., 2022), OMA (Altenhoff et al., 2024), PhylomeDB (Fuentes et al., 2022), GreenPhylDB (Guignon et al., 2021), or OrthoDB (Kuznetsov et al., 2023) provide the phylogenetic relationships of all sequences belonging to the included organisms. Specific advantages and details about the underlying tools for the generation of many of these resources have been recently reviewed (de Boissier & Habermann, 2020). These phylogenies can be utilized to transfer annotation information between the included species, but would require additional processing to obtain a functional annotation file for a species of interest. A comprehensive and up-to-date API documentation facilitates the efficient utilization of these databases. Unfortunately, the representation of plant species in these databases is generally sparse.
Many computational tools can be run locally to compare sequence data sets with the objective of reliable ortholog identification for the following annotation transfer. A phylogeny-based analysis of all candidates enables an accurate annotation of members belonging to large gene families like MYBs and bHLHs (Pucker, 2022; Thoben & Pucker, 2023). The eggNOG-mapper v2 can functionally annotate all predicted polypeptide sequences of a new species through comparison against a large collection of previously computed orthogroups, the eggNOG database (Huerta-Cepas et al., 2019; Cantalapiedra et al., 2021). Unfortunately, this database does only contain a very limited number of plant datasets yet (Huerta-Cepas et al., 2019). Recently, SHOOT (Emms & Kelly, 2022) was released as a phylogenetics-informed alternative to BLAST. While the assignment of orthologs would be more reliable through SHOOT, it does not cover the entire dataset accessible through the NCBI-hosted BLAST web service. PharaohFUN assigns functional annotation information to supplied sequences based on gene trees and a data set covering a wide taxonomic diversity of plants (Ramos-González et al., 2023). FastOMA is another tool that was developed to efficiently identify orthologs in huge datasets that will be produced by the rapid generation of complete genome sequences (Majidian et al., 2024). Users might want to inspect the intermediate results that lead to the annotation of selected genes. If phylogenetic trees are generated as intermediate files by the above mentioned tools, these can be visualized in iTOL (Letunic & Bork, 2021). PhyloProfile (Tran et al., 2018) also enables users to visualize phylogenetic information associated with a gene and could provide another starting point for the manual inspection of selected cases. While online tools like PhyloFacts (Krishnamurthy et al., 2006) or OrthoVenn2 (Xu et al., 2019) could make the functional annotation process convenient, the throughput is often limited and the risk of becoming inaccessible due to broken links in the original publication is huge.

Synteny Analyses

An annotation transfer solution with very high resolution is the identification of syntelogs, i.e., genes that are located at the same genomic position (Lyons et al., 2008). This approach relies on synteny i.e., the order of genes being roughly the same in the compared plant genomes. As the order and orientation of genes is changing during evolution, this approach is limited to the comparison of species within a certain phylogenetic distance. The simultaneous analysis of multiple neighboring genes enables a more specific assignment of orthologs across species borders by including information outside the gene of interest. Tools for a synteny analysis are MCscan/JCVI (Tang et al., 2008), TBtools-II (Chen et al., 2023), and TOGA (Kirilenko et al., 2023). Synteny analyses are an excellent way to identify orthologs with high reliability and resolution, but the computational costs exceed those of a simple analysis via BLAST (Figure 3).

How Can Artificial Intelligence Improve the Functional Annotation Process?

One major challenge in genomics is the efficient exploitation of available data sets for the annotation of novel sequences. Various sequence databases show an exponential growth, which provides an excellent resource for data reuse (Sielemann et al., 2020; Marks et al., 2021). Simultaneously, it is also feasible and rewarding to automate processes that have been performed by human experts during the last years, e.g., the annotation of enzyme sequences. This increases reproducibility and allows scale-up of annotation processes. One example is Knowledge-based Identification of Pathway Enzymes (KIPEs) that automates all steps a researcher would conduct when exploring biosynthesis genes of a generally well-known pathway in a novel species (Pucker et al., 2020b; Rempel et al., 2023). This implementation of analysis steps needs to be combined with expert knowledge about the pathway of interest. When investigating enzymes, details about the functionally important amino acid residues (in the active center) could be a valuable source of information to predict whether an enzyme is active (Pucker et al., 2020b). So far, this requires an extensive body of literature about the pathway of interest, which is only available for widespread pathways like the flavonoid biosynthesis (Pucker et al., 2020b) or the carotenoid biosynthesis (Rempel et al., 2023). With increasing data availability, more pathways will be accessible through automatic processes. Also, literature research had to be done manually during the last years, but current artificial intelligence (AI) developments might enable automatic screening of publications in the near future (de la Torre-López et al., 2023). Open access publishing and the release of scientific publication in machine readable formats will pave the way to a more comprehensive cross-species transfer of knowledge. Whenever expert behavior can be described by clear rules and is not relying on ‘gut feeling’, an automation is feasible. Harnessing the full power of the scientific literature for upcoming annotation projects holds great promise.
Transcriptomic data sets can be a powerful resource to connect a candidate gene to members of a co-expression network if no information about any orthologs is available. JGI Plant Gene Atlas is a prime example of utilizing gene expression data to assign functional information to uncharacterized sequences (Sreedasyam et al., 2023). Coexpression networks conserved across species borders and characteristic responses of gene expression to stress treatments can be informative (Sreedasyam et al., 2023). As the resource can be updated continuously in the future, relevance will gain as more data becomes available. Well established tools to perform a co-expression analysis locally are WGCNA (Langfelder & Horvath, 2008) and GENIE3 (Huynh-Thu et al., 2010)/dynGENIE3 (Huynh-Thu & Geurts, 2018). These tools would require count tables that contain information about the activity of all genes. Downloading all RNA-seq datasets of a species and processing them is computationally intensive. Precomputed datasets might be available through Gene Expression Omnibus (GEO) (Barrett et al., 2013), which does collect some preprocessed datasets in addition to the raw RNA-seq reads. Once a co-expression network is constructed by any of the above mentioned tools, Cytoscape (Shannon et al., 2003) could be utilized to visualize the network for manual in-depth inspection. Connecting gene expression data to other omics or phenotypic data can also support the functional annotation process (Singh et al., 2022). Genes associated with a specific metabolite or a particular trait can be identified in this way. Examples are the discovery of the podophyllotoxin biosynthesis pathway in Podophyllum hexandrum (Lau & Sattely, 2015) or the montbretin A biosynthesis pathway in Crocosmia × crocosmiiflora (Irmisch et al., 2018, 2019). It is important to follow-up on these connections to distinguish between correlation and causation in these cases.
It might be feasible to exploit convergent evolution events for the transfer of annotation information. If the structure of two proteins is similar without orthology, the function might be similar too. FASSO (Andorf et al., 2022) identifies reciprocal best protein structure alignments in the identification of orthologs between two species. It might be feasible to extend such approaches to screen for 3D structural similarity rather than protein sequence similarity. Additionally, protein-protein interaction information can also help to understand the function of a protein encoding gene. STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) (Szklarczyk et al., 2015) harbors a comprehensive collection of this information, but does not cover the full taxonomic diversity of plants yet.
Large language models (LLMs) are useful in constructing annotation text that is easily accessible by humans. Given the amount of annotation terms that can be retrieved from various databases, generating a concise and accurate string of human-readable information is a major challenge. While tools like InterProScan5 (Jones et al., 2014) already compile a set of annotation details based on different databases, the tabular output requires re-structuring for readability. Previously, many annotation terms were stored as English words, but LLMs could easily enable multi language support. Generating more detailed annotation information by processing all of the available literature is another approach to advance gene function understanding. Databases connecting important pieces of information from scientific articles to genes are crucial functional genomics. The most striking example is TAIR that was built by experts curating the data (Huala et al., 2001; Lamesch et al., 2012; Berardini et al., 2015). Recently, PlantConnectome was constructed by screening over 100,000 abstracts of plant biology publications for information about the functions of genes (Fo et al., 2023). While the innovation potential of LLMs is enormous, there are also potential risks and challenges associated with their use, e.g., the requirement for appropriate filtering when collecting the database or ensuring the accuracy of generated output. A particular challenge is the avoidance of circular conclusions when research data generated with LLMs serves as a basis for the development of future LLMs.

Summary

Rapid development of long read sequencing technologies over the last years have enabled an almost routine generation of high quality genome sequences and structural annotations. The major bottleneck is currently the elucidation of gene functions. A range of different approaches transfer knowledge between orthologs across species borders. Large transcriptomic resources enable a reference-independent assignment of novel genes to biosynthesis pathways or processes based on comprehensive co-expression analyses. In the future, additional connections with other omics datasets and prediction of protein structures could establish novel annotation approaches.

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Preprints 98686 g003
Figure 3. Methods for the high-throughput assignment of functional annotation to novel genes. Three different concepts can be distinguished: (A) analysis of basic sequence similarity, (B) analysis in phylogenetic context, and (C) analysis of synteny. The listed tools are only examples. See GitHub repository https://github.com/bpucker/ToolOverview for an extended list of tools for the functional annotation.
Figure 3. Methods for the high-throughput assignment of functional annotation to novel genes. Three different concepts can be distinguished: (A) analysis of basic sequence similarity, (B) analysis in phylogenetic context, and (C) analysis of synteny. The listed tools are only examples. See GitHub repository https://github.com/bpucker/ToolOverview for an extended list of tools for the functional annotation.
Preprints 98686 g003
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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