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FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

Background Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. Results We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. Conclusions FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision—ideal for bench researchers with limited experience in handling computational tools. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04120-9.

Multiple groups working on the same species have different and oftentimes conflicting annotations that are difficult to merge into a common consensus.
This issue is moderately mitigated by BRAKER2 [64,65], which uses read splice information instead of full-length assemblies to predict gene structures and has been shown to perform better than de novo approaches [66]. BRAKER2 entails a round of unsupervised gene predictions using GeneMark-ET [67] generating ab-initio gene predictions followed by a second round of training by AUGUSTUS [68] using a subset of the gene models created by GeneMark-ET [64]. All variations of MAKER (MAKER, MAKER2 and MAKER-P) use a combination of AUGUSTUS [68] and SNAP [69] to generate gene predictions. Unlike BRAKER2 or PASA, users need to run MAKER for multiple rounds to improve annotation. With no standard technique to optimize the number of rounds, users often undertake a trial-and-error approach to decide what data is supplied to MAKER in each execution round. These unguided choices can create different annotations based on the same data sets. Thus, current approaches report either incomplete genes and/or derive annotations that are missing alternatively spliced transcripts.
To overcome the drawbacks described above, we developed FINDER, a new, automated annotation pipeline that downloads RNA-Seq data from NCBI SRA [70], conducts genome-guided assembly of short reads, predicts gene structure, and annotates genes. FINDER annotates both untranslated and coding regions of genes, categorizes transcripts based on the tissue/conditions where they are expressed, and outputs a complete set of alternatively spliced transcripts. FINDER analyzes the spatial expression profile of each transcript to redefine its boundaries and/or even create newer transcripts and employs an optimized strategy to locate transcripts housing microexons. Finally, gene models predicted by BRAKER2 are incorporated into the annotation along with assemblies generated by PsiCLASS [63]. We show that FINDER outperforms state-of-the-art annotation tools in constructing accurate gene structures, when executed with the same expression data.

Implementation
The detailed workflow of FINDER is outlined in Fig. 1. The pipeline accepts metadata via a comma-separated values (csv) file (see Additional file 2: Table S1). Users can verify the input data using the `verifyInputsToFINDER` utility (Please check section 1.5.1 of Additional file 9). Both single-end and paired-end data are accepted. The pipeline automatically downloads RNA-Seq data from NCBI SRA or the samples can be accessed locally. Multiple rounds of alignment are conducted using STAR [71,72] with short reads, thus ensuring the capture of tissue-specific splice junctions and ultimately generates the most comprehensive set of alternatively spliced transcripts. FINDER uses PsiCLASS [63] to generate transcripts both at the tissue level and consolidates them to produce a consensus annotation. It employs change-point detection (CPD) using coverage data to polish intron/exon boundaries if needed. Polished transcripts are then supplied to GeneMarkS-T [73] to predict protein coding regions. In addition to constructing genes from expression data, FINDER uses BRAKER2 [65] to predict genes de novo. Finally, gene models are assigned scores that reflect the confidence of prediction and evidence across different data sets. Throughout the pipeline run, intermediate temporary data is removed to optimize space usage. Proper logging of executions is implemented through ruffus [74]. FINDER workflow. FINDER assembles short reads from RNA-Seq expression data, collected from multiple tissues and conditions, to generate full-length transcripts using PsiCLASS. Short read coverage profile is used to polish the structure of the transcripts to enhance the quality of annotation. GeneMarkS-T is used to predict coding regions of the transcripts. Gene models predicted by BRAKER2 and models obtained by mapping proteins are added to the gene models constructed from RNA-Seq data. Additionally, FINDER outputs the tissues where each transcript is expressed allowing users to work with tissue-specific transcripts. FINDER categorizes transcripts into two confidence levels depending on the available supporting evidence and depth of coverage.

Read alignments to the genome
Reads from each sample are aligned to the genome using STAR [72]. FINDER accepts the location of the genomic STAR indices. If indices are not provided, then FINDER will generate them locally. FINDER implements multiple strategies to detect as many correct splice-junctions as possible. Several studies use a multi-step approach where splice junctions are detected in the first pass and then those junctions are used to guide the alignments in future passes [75,76]. FINDER employs a similar strategy to align reads and obtain the most confident splice junctions in each tissue type and/or condition by conducting mapping in four passes (Please check section 1.3 of Additional file 9 for more details).

Generating exon-exon transcript structure annotation with PsiCLASS
Alignments reported by STAR and OLego are combined and provided as input to PsiCLASS [63].  FINDER is configured to use changepoint detection (CPD) analysis to detect the descent in read coverage at the junction of two overlapping transcripts. Statistical CPD is a procedure to detect changes in the probability distribution of a stochastic process.
Typically, CPD is widely used to detect changes in time series [92][93][94][95][96], but can be extended to other applications as well [97,98]. We have found that even though CPD was developed under the assumption of normality, it can also be used where normality is violated.
In the first step in FINDER's CPD, short read alignments to the genome are converted into number of read counts per nucleotide using bedtools [99]. A custom python script is is probed to detect changepoints. The data has been modeled using an exponential distribution, and binary segmentation has been used to determines the changepoints in the exonic coverage using the 'changepoints' package [100]. Read coverage of exons mimics a time series where each nucleotide position of an exon can be assumed to be a single unit of time. Coverage patterns of exons, suspected to be merged, contain a characteristic depression in the signal to split the gene models ( Fig. 2A). Overlapping transcripts on opposite strands sometimes share a common exon (Fig. 2B). This negatively impacts precision since the boundaries of the predicted transcript exceed the boundaries of the transcript in the reference annotation. FINDER trims the transcript boundaries, using the changepoints, to better model the RNA-Seq coverage (Fig. 2B).
These strategies improve the annotation by increasing the transcript F1 scores ( Table   1). Certain genes are expressed only under specific tissues and conditions [101]. However, constructing an exhaustive set of genes expressed across all possible tissues and conditions is a daunting task due to the mammoth volume of potential expression data.
Hence, approaches that can predict structures of unknown genes using information obtained from known genes are needed. Within the FINDER framework, we used BRAKER2 [65] to predict the structure of protein coding genes. The pipeline is provided with alignment files generated by STAR and an optional, user-provided protein data file.
If the previous execution fails, a second execution of BRAKER2 is launched without protein information. Genes predicted by BRAKER2 are compared to the genes obtained from expression data. To prevent too many false positives, predictions made by BRAKER2 are considered high confidence, only if those are supported by expression level or protein level evidence.

Prediction of coding regions
We leveraged GeneMarkS-T [73] to predict protein-coding regions of genes constructed from expression data. GTF files are first converted to FASTA files using the provided genome. Those FASTA files are supplied to GeneMarkS-T as inputs. GeneMarkS-T outputs coding sequence for the transcripts. CDS annotations are incorporated into the final GTF file by converting the transcriptomic coordinates to genomic coordinates.

Using proteins to annotate more genes
In addition to RNA-Seq data, FINDER also uses protein data (when provided), in two ways (1) to assess the veracity of the transcript models generated by BRAKER2, and (2) to align those proteins not recognized by BRAKER2 or PsiCLASS. Protein coding genes obtained from expression data and predicted by BRAKER2 are BLASTed [102] to the protein set provided by the user. Proteins not encountering any hits are aligned to the genome using exonerate [103] with a minimum threshold of 90% similarity. These alignments are augmented to the final set of gene predictions. Since these transcripts are obtained solely from proteins, they lack UTR sequences.

Scoring gene models
FINDER groups genes into multiple categories based on supporting evidence. Genes that are expressed in RNA-Seq datasets, predicted by BRAKER2, and have protein evidence, are put into the high-confidence gene set. BRAKER2-predicted genes with no evidence of expression and/or proteins are treated as low confidence genes. FINDER expects a soft masked genome since it is a BRAKER2 requirement. Genes which are located in the repeat regions are marked as such and moved to the set of lowconfidence genes.

Choice of species for comparison
We tested the performance of FINDER primarily on three well-annotated plant organisms -Arabidopsis thaliana [104], Oryza sativa [105][106][107] and Zea mays [108,109]. The genomes assemblies of these model organisms have been frequently updated and are almost complete with telomere-to-telomere sequences with fewer gaps and unknown nucleotides. In addition, their gene annotations have undergone regular improvement by mining the large number of RNA-Seq datasets available in the literature. Also, The Arabidopsis Information Resource (TAIR) provides a five-star rating system based on available evidence for each gene. Such a system offers a platform to test the quality of gene annotation software. For further evaluation, and to ensure that FINDER is able to annotate a wider range of genome types, we selected the following additional species to test: Caenorhabditis elegans [110], Drosophila melanogaster [111,112], Homo sapiens [113,114], Hordeum vulgare [115], and Triticum aestivum [115][116][117][118] [109,120] and AGPv4 [108,121].

Metrics to assess quality of annotation
We used four metrics to compare the quality of annotations generated by each pipeline: 1) Annotation Edit Distance (AED) [42,43,122], 2) sensitivity, 3) specificity, and 4) F1 score. Although these metrics could be computed both at the nucleotide-and exon-level we chose to make comparisons at the transcript level since it encompasses bases, exons, and introns. An AED score of 0 indicates complete agreement of the predicted annotation with the reference, and a score of 1 denotes that the reference has not been identified in the annotation. A transcript is considered to be "recognized" only when all its intron definitions agree with at least one transcript from the predicted set. We used the Mikado "compare" utility to compare the predictions with the reference annotations [123]. A highly sensitive annotation is one that can correctly recognize more reference transcripts. A set of annotations has high specificity when it reports minimal incorrect transcripts. For an annotation to be of good quality, both sensitivity and specificity should be high. A balanced metric is the F1 score which is the harmonic mean of sensitivity and specificity. While AED provides a good numeric assessment of how well the ground truth evidence is represented in an annotation, when individually used, it fails to capture the extent to which false positives are reported. Hence, F1 score complements AED since it incorporates both specificity and sensitivity. For evaluation purposes, we assume that the annotations achieved through community efforts are the ground truth and contain no errors.
FINDER generates more accurate gene models than BRAKER2, MAKER2 and PASA FINDER leverages expression data to construct transcript models and employs statistical changepoint detection to enhance their structures (see Implementation). Both MAKER2 and PASA were run with transcript sequences reported by PsiCLASS. Distribution of AED scores. Violin plots wider at the base indicate high density of annotations with lower AED. FINDER was able to create gene models having lowest AED resulting in a wide base. Gene models generated by FINDER were enhanced by adding predictions made by BRAKER and including protein evidence. Wilcoxon's signed rank test was used to compare the AED scores between FINDER and other annotating pipelines. The "***" symbol implies that the AED scores of FINDER gene models were significantly lesser (p_value<0.01) than the AED scores of the gene models reported by other pipelines. (B, E & H) Bar plot of F1 score of multiple approaches of annotation. Having a high nucleotide F1 (Base F1) or a high exon F1 score is not sufficient to conclude a good annotation. High value of transcript F1 score is indicative of good gene models with high sensitivity and high specificity. (C, F & I) Stacked bar plot showing percentage of transcripts in each of the four groups of AEDs. Higher number of transcripts to low AED denotes better annotation. In each of the three species, FINDER was able to generate a higher percentage of transcripts with low AED compared to other techniques of annotation.
To assess FINDER's performance, we compared the AED scores of transcript models generated by FINDER with those generated by other commonly used annotation methods. As shown in High-quality exhaustive annotations predict the fewest false positives thereby boosting the transcript F1 score. The transcript F1 scores of the gene models that were reported by FINDER for A. thaliana, O. sativa and Z. mays were higher than the models generated by BRAKER2, MAKER, and PASA (Fig. 3B, 3E & 3H). This same trend is observed for other tested organisms where FINDER was successful in detecting nucleotides, exons, introns, transcripts and genes ( Table 1, Additional file 1: Figure   S2-S5 and Additional file 3: Table S2). MAKER2 and BRAKER2 registered a high specificity for most of the organisms because fewer transcripts were reported than FINDER. MAKER2 and BRAKER2 also had lower F1 scores, indicating less sensitivity than FINDER. Additionally, we compared the CDS regions of genes reported by FINDER with those of BRAKER2. For most of the organisms, FINDER generated transcript models with a higher F1 score (Additional file 4: Table S3). These results show that the better performance of FINDER is ensured not only due to the presence of UTRs but also due to enhanced CDS structure of gene models.
Finally, including BRAKER2 predictions and protein sequences to FINDER enhanced the gene model predictions. About 15% of the gene models reported by BRAKER2, those having high sequence similarity with the provided protein sequences were included in the final annotations ( Table 2). As shown in Table 1 and Additional file 5:

Group 4 Transcripts with microexons
Transcripts where at least one exon has length less than 50 nucleotides

Group 5 Transcripts with long introns
Transcripts where at least one intron has a length greater than 10,000 bp

Group 6
Closely placed transcripts on same strand Transcripts on the same strand having less than 250 nucleotides between each other

Group 7
Closely placed transcripts on opposite strand Transcripts on the opposite strands having less than 250 nucleotides between each other

Group 8
Multi transcript gene Transcripts of a gene that have multiple transcripts

Group 9
Single transcript gene Transcripts of a gene that have single transcript

Distinct gene groups are accurately annotated with FINDER
Although eukaryotic genes differ from one another in terms of location, structure and the isoforms they encode, most annotation pipelines annotate and evaluate gene predictions with a global and uniform approach. The problem arises when these variances prompt each pipeline to perform differently on dissimilar groups of genes. To avoid this pitfall, we created groups of genes and transcripts based on various criteria ( Table 3) and compared the performance of FINDER with BRAKER2, MAKER, and PASA for each of these sets.

FINDER vs other pipelines on different groups of genes in three model species -(A) A. thaliana, (B) O. sativa and (C) Z. mays.
F1 score is the harmonic mean between sensitivity and specificity. Higher F1 score indicates better agreement with the reference transcript models. We created groups of transcripts that have similar characteristics as shown in the y-axis legend. A pool of transcripts was created containing multi-exonic transcript predictions, from each pipeline, that has a complete intron chain match with at least one reference annotation. Mono exonic transcripts were considered if at least 80% of the nucleotides overlap with one reference annotation. Transcript F1 scores, for each of the annotation pipelines, have been plotted as a bar graph. Even though all annotation pipelines are designed to serve the same purpose of annotating genomes, each pipeline adopts a different strategy. Each strategy has its own merits and demerits that lead to better annotation of a certain category of genes. This plot helps understand the performance of each annotation pipeline on different categories. The symbol "#" denotes the best annotator in each gene group.
On the set of UTR-containing transcripts, FINDER reported the best transcript F1 scores (Fig. 4, Additional file 1: Figure S6, S7). Unlike BRAKER2, FINDER uses GeneMark S/T to predict CDS from the transcript sequences assembled by PsiCLASS and can hence annotate UTR regions. For most of the organisms, BRAKER2 and MAKER2 gene models register a low transcript F1 score in this category of genes. Next, we tested the performance of the annotation pipelines on transcripts that are closely located in the genome. On this set of transcripts, FINDER reported the best F1 transcript score for A. thaliana, O. sativa, and Z. mays (Fig. 4), and comparable scores for D. melanogaster (Additional file 1: Figure S6), H. vulgare (Additional file 1: Figure S8), and C. elegans (Additional file 1: Figure S7) with BRAKER2. Most eukaryotic genes have multiple isoforms which differ from one another by their exonintron definition. Splice sites and coverage information provides clues to construct such alternatively spliced transcripts. We selected genes with more than one transcript to check how well each annotation pipeline was able to detect transcript isoforms. For this case, FINDER was able to generate the best transcript structures with the highest transcript F1 score among all the pipelines gene annotation software applications ( Fig.   4 and Additional file 1: Figure S6-S9). BRAKER2 generated the best transcript annotation for the set of transcripts with a single exon (Fig. 4A&B and Additional file 1: Figure S6-S9). Such transcripts, devoid of any introns, are difficult to construct from RNA-Seq alone. Also, the direction of the splice sites infers the direction of a transcript. Without any introns, such a single-exon transcript has to be probed for a CDS sequences' presence to infer directionality.
BRAKER2 was configured to optimally predict only CDS regions of genes, hence, it performs well with the set of transcripts that have missing UTRs for organisms with small and moderate sized genomes ( Fig. 4A&B and Additional file 1: Figure S6-S9).
The average number of transcripts per gene reported by BRAKER2 is lower than FINDER. While this boosts specificity, it compromises recall since BRAKER2 is not sensitive to detecting alternatively spliced transcripts. Hence, BRAKER2 accomplishes the best F1 score when tested on a set of single-transcript genes but performs poorly on a set of multi-transcript genes ( Fig. 4A&B and Additional file 1: Figure S6-S9).

Performance comparison on TAIR's 5-star System
In order to assess the performance of the annotation pipelines on groups of genes constructed from varying levels of evidence, we used the TAIR10 5-star system. TAIR associates a quality score to each A. thaliana transcript based on the evidence used to construct the models, with five stars designating the best evidence and zero stars the least [125]. The three categories with limited evidence (<3 stars) have fewer than 3,000 transcripts each. BRAKER2's performance, on the genes in these three categories, was slightly better than the rest of the annotation pipelines (Fig. 5). The other two categories (five star and four star) have 9,067 and 18,374 transcripts respectively. In both of these categories, FINDER was able to detect more transcripts than any other annotation pipeline. 51.5% and 86.4% of genes in the 5-star and 4-star category respectively were multi-exonic. In both these categories, FINDER correctly constructed more gene models compared to any other annotation pipeline (Fig. 5). FINDER reported 80% of the gene models belonging to the 4-star category -18% more than BRAKER2 (Fig. 5). Hence, it is evident from this analysis that FINDER can reconstruct the structures of most of the genes that are well-supported by underlying evidence. Performance of annotation pipelines on gene groups of Arabidopsis thaliana generated by TAIR10. The Arabidopsis Information Resource (TAIR) group has created a quality ranking system to indicate the level of confidence in an annotated gene/transcript. The ranking system has five levels (denoted by stars). Higher number of stars denote the availability of more information to generate the gene structure. Here we display the percentage of transcripts in each category that was identified by a particular annotation pipeline. A high percentage of identified transcripts indicate higher sensitivity and hence a better annotation. The number below each legend in the x-axis denote the number of genes in that respective group. The "#" denotes the predictor which detected the maximum number of transcripts within each group.

Improving transcript annotations using changepoint analysis
The co-location of multiple overlapping genes on the genome strands makes it difficult to correctly annotate their structures (see Methods Polishing gene structures to optimize gene discovery). FINDER employs changepoint detection (CPD) [100] to split the merged transcripts reported by PsiCLASS (Fig. 2). To gauge the magnitude of  Table 4 and Additional file 6: Table S5, implementing the CPD improved both specificity and sensitivity in organisms with small or medium-sized genomes. In A. thaliana, the transcript F1 scores increased from 40.78 to 45.95 ( Table 4 and Additional file 6: Table S5) and in C. elegans it increased from 40 to 50. In large genomes, the improvement was not as significant, mainly because there are only a few genes that overlap with one another.

Impact of missing untranslated region on annotation of transcripts
Gene transcription is triggered by adherence of a transcription factor in the promoter region of a gene. Promoters are typically located within 1,000 bp upstream of a gene's transcription start site (TSS) [145][146][147]. Determining the TSS from sequencing data is best facilitated by RAMPAGE [148,149] or CAGE-Seq [150], but this data is usually unavailable due to constraints imposed by cost and time. Nevertheless, a good estimate can be obtained from RNA-Seq data by assuming the start coordinates of the assembled genes as the TSS. Thus, researchers often localize their investigation to a section 500-1,000 bp upstream of the assumed TSS [151,152]. Without 5' UTR annotation it is impossible to deduce a good approximation of the TSS. This leads to conducting promoter mining in a completely incorrect genome location. To assess the quality of 5' UTR annotation, we plotted the difference of TSS between the reference genes and the genes reported by BRAKER2 and FINDER using a violin plot (Fig. 6).
Further, we applied Wilcoxon's rank-sum test and found that the TSS distances reported by FINDER were significantly less than that of BRAKER2 for A. thaliana and Z.    Comparison of distance between transcription start sites of gene models predicted by BRAKER2 and FINDER. Violin plots of the distribution of the distance between the actual transcription start site (TSS) and the predicted transcription start site. In a set of well annotation complete gene structures, a higher fraction of genes is expected to have low deviation from the actual TSS. We considered genes that were reported in either BRAKER or FINDER for this analysis. Wilcoxon's rank sum test was used to compare the TSS distances between FINDER and BRAKER2. The "***" symbol implies that TSS distance for FINDER gene models was significantly less than BRAKER2 gene models.

Enhancing ground truth annotations by extending untranslated regions
Official annotations of several model organisms, used as ground truth for this study, contain transcripts with missing UTR sequences. Even though UTRs do not code for proteins, they are relevant segments of a transcript involved in several important biological processes like mRNA translation [153][154][155], regulation of expression [156][157][158][159][160]] and a number of diseases [161][162][163][164][165]. In the A. thaliana TAIR10 annotations, there are 7,888 transcripts missing either UTR; 50% of these had a rating below 2 stars.
PacBio (Menlo Park, CA) offers long-read sequencing that contain both CDS and UTRs.

Evaluating FINDER on different clades reported by Phylostratr
Genes in each organism can be categorized by their evolutionary history [172,173]. We used Phylostratr [174] to classify genes into evolutionary strata. Here we present our results on the three model organisms -A. thaliana, O. sativa, and Z. mays. For all three, FINDER was able to accurately detect more genes in highly populated strata (Fig. 7). The performance of FINDER and PASA was comparable in strata with few genes. It was surprising to note that BRAKER2 was unable to identify highly conserved genes (those from the "cellular organisms" strata) since those would be easier to predict than organism specific genes. This demonstrates that FINDER is capable of effectively constructing genes from different evolutionary backgrounds. Numbers below each stratum indicate the number of genes allocated to that strata.
Strata having genes fewer than 500 are not shown in the graph.

Conclusion
Identifying genes on chromosomes and deducing their structures from a plethora of evidence has been undertaken in multiple ways, with each method having advantages and disadvantages. Herein, we propose FINDER -an entirely automated, generalpurpose pipeline to annotate genes in eukaryotic genomes. FINDER (1) implements an optimized mapping strategy that reduces the number of spurious mappings, (2) produces complete full-length transcripts comprising UTRs while identifying transcripts with micro-exons, (3) employs statistical CPD to modify gene boundaries and construct new genes, (4) reports more alternatively spliced transcripts as compared to other stateof-the-art annotation pipelines, and (5) assigns confidence classes to each transcript based on the evidence(s) that were used to construct those.
With a wide variety of available data for annotation, researchers often struggle to manage and optimize their usage. Several gene annotation software also offer users complicated configurations without providing substantial guidance. FINDER makes the job of gene annotation easy for bench scientists by automating the entire process from RNA-Seq data processing to gene prediction. Since FINDER does not assume the ploidy or the nucleotide composition of a genome, it could be applied to derive gene structures for a wide range of species, including non-model organisms. FINDER constructs gene models primarily from RNA-Seq data and is therefore capable of constructing tissue-and/or condition-specific isoforms which would have been impossible to obtain from ESTs only. FINDER supersedes the performance of existing software applications by utilizing read coverage information to fine-tune gene model boundaries. Instead of removing low-quality transcripts, FINDER flags them as low confidence -giving users the choice of using them as they seem fit. As a proof of concept, we provided evidence that using read coverage signal indeed enhances gene structures in a diverse set of organisms. Thus, we are confident that FINDER will pave the way for improved gene structure annotation in the future.