GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes.

Large-scale genomic initiatives, such as the Earth BioGenome Project, require efficient methods for eukaryotic genome annotation. Here we present an automatic gene finder, GeneMark-ETP, integrating genomic-, transcriptomic-, and protein-derived evidence that has been developed with a focus on large plant and animal genomes. GeneMark-ETP first identifies genomic loci where extrinsic data are sufficient for making gene predictions with "high confidence." The genes situated in the genomic space between the high-confidence genes are predicted in the next stage. The set of high-confidence genes serves as an initial training set for the statistical model. Further on, the model parameters are iteratively updated in the rounds of gene prediction and parameter re-estimation. Upon reaching convergence, GeneMark-ETP makes the final predictions and delivers the whole complement of predicted genes. GeneMark-ETP outperforms gene finders using a single type of extrinsic evidence. Comparisons with gene finders MAKER2 and TSEBRA, those that use both transcript- and protein-derived extrinsic evidence, show that GeneMark-ETP delivers state-of-the-art gene-prediction accuracy, with the margin of outperforming existing approaches increasing in its application to larger and more complex eukaryotic genomes.

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA.

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

GeneMark-ETP: Automatic Gene Finding in Eukaryotic Genomes in Consistency with Extrinsic Data.

New large-scale genomic initiatives, such as the Earth BioGenome Project, require efficient methods for eukaryotic genome annotation. A new automatic tool, GeneMark-ETP, presented here, finds genes by integration of genomic-, transcriptomic- and protein-derived evidence. The algorithm was developed with a focus on large plant and animal genomes. GeneMark-ETP first identifies genomic loci where extrinsic data is sufficient for gene prediction with 'high confidence' and then proceeds with finding the remaining genes across the whole genome. The initial set of parameters of the statistical model is estimated on the training set made from the high confidence genes. Subsequently, the model parameters are iteratively updated in the rounds of gene prediction and parameter re-estimation. Upon reaching convergence, GeneMark-ETP makes the final predictions of the whole complement of genes. The GeneMark-ETP performance was expectably better than the performance of GeneMark-ET or GeneMark-EP+, the gene finders using a single type of extrinsic evidence, either short RNA-seq reads or mapped to genome homologous proteins. Subsequently, for comparisons with the tools utilizing both transcript- and protein-derived extrinsic evidence, we have chosen MAKER2 and a more recent tool, TSEBRA, combining BRAKER1 and BRAKER2. The results demonstrated that GeneMark-ETP delivered state-of-the-art gene prediction accuracy with the margin of outperforming existing approaches increasing for larger and more complex eukaryotic genomes.

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS and TSEBRA.

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement was made by the recently released GeneMark-ETP integrating all three data types. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperformed BRAKER1 and BRAKER2. The average transcript-level F1-score was increased by ~20 percentage points on average, while the difference was most pronounced for species with large and complex genomes. BRAKER3 also outperformed other existing tools, MAKER2, Funannotate and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

MgCod: Gene Prediction in Phage Genomes with Multiple Genetic Codes.

Massive sequencing of microbiomes has led to the discovery of a large number of phage genomes with intermittent stop codon recoding. We have developed a computational tool, MgCod, that identifies genomic regions (blocks) with distinct stop codon recoding simultaneously with the prediction of protein-coding regions. When MgCod was used to scan a large volume of human metagenomic contigs hundreds of viral contigs with intermittent stop codon recoding were revealed. Many of these contigs originated from genomes of known crAssphages. Further analyses had shown that intermittent recoding was associated with subtle patterns in the organization of protein-coding genes, such as 'single-coding' and 'dual-coding'. The dual-coding genes, clustered into blocks, could be translated by two alternative codes producing nearly identical proteins. It was observed that the dual-coded blocks were enriched with the early-stage phage genes, while the late-stage genes were residing in the single-coded blocks. MgCod can identify types of stop codon recoding in novel genomic sequences in parallel with gene prediction. It is available for download from https://github.com/gatech-genemark/MgCod.

A chromosome-length genome assembly and annotation of blackberry (Rubus argutus, cv. "Hillquist").

Blackberries (Rubus spp.) are the fourth most economically important berry crop worldwide. Genome assemblies and annotations have been developed for Rubus species in subgenus Idaeobatus, including black raspberry (R. occidentalis), red raspberry (R. idaeus), and R. chingii, but very few genomic resources exist for blackberries and their relatives in subgenus Rubus. Here we present a chromosome-length assembly and annotation of the diploid blackberry germplasm accession "Hillquist" (R. argutus). "Hillquist" is the only known source of primocane-fruiting (annual-fruiting) in tetraploid fresh-market blackberry breeding programs and is represented in the pedigree of many important cultivars worldwide. The "Hillquist" assembly, generated using Pacific Biosciences long reads scaffolded with high-throughput chromosome conformation capture sequencing, consisted of 298 Mb, of which 270 Mb (90%) was placed on 7 chromosome-length scaffolds with an average length of 38.6 Mb. Approximately 52.8% of the genome was composed of repetitive elements. The genome sequence was highly collinear with a novel maternal haplotype-resolved linkage map of the tetraploid blackberry selection A-2551TN and genome assemblies of R. chingii and red raspberry. A total of 38,503 protein-coding genes were predicted, of which 72% were functionally annotated. Eighteen flowering gene homologs within a previously mapped locus aligning to an 11.2 Mb region on chromosome Ra02 were identified as potential candidate genes for primocane-fruiting. The utility of the "Hillquist" genome has been demonstrated here by the development of the first genotyping-by-sequencing-based linkage map of tetraploid blackberry and the identification of possible candidate genes for primocane-fruiting. This chromosome-length assembly will facilitate future studies in Rubus biology, genetics, and genomics and strengthen applied breeding programs.

TSEBRA: transcript selector for BRAKER.

BACKGROUND: BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. RESULTS: We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. CONCLUSION: TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

GeneMark-HM: improving gene prediction in DNA sequences of human microbiome.

Computational reconstruction of nearly complete genomes from metagenomic reads may identify thousands of new uncultured candidate bacterial species. We have shown that reconstructed prokaryotic genomes along with genomes of sequenced microbial isolates can be used to support more accurate gene prediction in novel metagenomic sequences. We have proposed an approach that used three types of gene prediction algorithms and found for all contigs in a metagenome nearly optimal models of protein-coding regions either in libraries of pre-computed models or constructed de novo. The model selection process and gene annotation were done by the new GeneMark-HM pipeline. We have created a database of the species level pan-genomes for the human microbiome. To create a library of models representing each pan-genome we used a self-training algorithm GeneMarkS-2. Genes initially predicted in each contig served as queries for a fast similarity search through the pan-genome database. The best matches led to selection of the model for gene prediction. Contigs not assigned to pan-genomes were analyzed by crude, but still accurate models designed for sequences with particular GC compositions. Tests of GeneMark-HM on simulated metagenomes demonstrated improvement in gene annotation of human metagenomic sequences in comparison with the current state-of-the-art gene prediction tools.

BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database.

The task of eukaryotic genome annotation remains challenging. Only a few genomes could serve as standards of annotation achieved through a tremendous investment of human curation efforts. Still, the correctness of all alternative isoforms, even in the best-annotated genomes, could be a good subject for further investigation. The new BRAKER2 pipeline generates and integrates external protein support into the iterative process of training and gene prediction by GeneMark-EP+ and AUGUSTUS. BRAKER2 continues the line started by BRAKER1 where self-training GeneMark-ET and AUGUSTUS made gene predictions supported by transcriptomic data. Among the challenges addressed by the new pipeline was a generation of reliable hints to protein-coding exon boundaries from likely homologous but evolutionarily distant proteins. In comparison with other pipelines for eukaryotic genome annotation, BRAKER2 is fully automatic. It is favorably compared under equal conditions with other pipelines, e.g. MAKER2, in terms of accuracy and performance. Development of BRAKER2 should facilitate solving the task of harmonization of annotation of protein-coding genes in genomes of different eukaryotic species. However, we fully understand that several more innovations are needed in transcriptomic and proteomic technologies as well as in algorithmic development to reach the goal of highly accurate annotation of eukaryotic genomes.

Disproportionate presence of adenosine in mitochondrial and chloroplast DNA of Chlamydomonas reinhardtii.

Ribonucleoside monophosphates (rNMPs) represent the most common non-standard nucleotides found in the genome of cells. The distribution of rNMPs in DNA has been studied only in limited genomes. Using the ribose-seq protocol and the Ribose-Map bioinformatics toolkit, we reveal the distribution of rNMPs incorporated into the whole genome of a photosynthetic unicellular green alga, Chlamydomonas reinhardtii. We discovered a disproportionate incorporation of adenosine in the mitochondrial and chloroplast DNA, in contrast to the nuclear DNA, relative to the corresponding nucleotide content of these C. reinhardtii organelle genomes. Our results demonstrate that the rNMP content in the DNA of the algal organelles reflects an elevated ATP level present in the algal cells. We reveal specific biases and patterns in rNMP distributions in the algal mitochondrial, chloroplast, and nuclear DNA. Moreover, we identified the C. reinhardtii orthologous genes for all three subunits of the RNase H2 enzyme using GeneMark-EP + gene finder.

StartLink and StartLink+: Prediction of Gene Starts in Prokaryotic Genomes.

State-of-the-art algorithms of ab initio gene prediction for prokaryotic genomes were shown to be sufficiently accurate. A pair of algorithms would agree on predictions of gene 3'ends. Nonetheless, predictions of gene starts would not match for 15-25% of genes in a genome. This discrepancy is a serious issue that is difficult to be resolved due to the absence of sufficiently large sets of genes with experimentally verified starts. We have introduced StartLink that infers gene starts from conservation patterns revealed by multiple alignments of homologous nucleotide sequences. We also have introduced StartLink+ combining both ab initio and alignment-based methods. The ability of StartLink to predict the start of a given gene is restricted by the availability of homologs in a database. We observed that StartLink made predictions for 85% of genes per genome on average. The StartLink+ accuracy was shown to be 98-99% on the sets of genes with experimentally verified starts. In comparison with database annotations, we observed that the annotated gene starts deviated from the StartLink+ predictions for ∼5% of genes in AT-rich genomes and for 10-15% of genes in GC-rich genomes on average. The use of StartLink+ has a potential to significantly improve gene start annotation in genomic databases.

Bioinformatics Pipeline for Human Papillomavirus Short Read Genomic Sequences Classification Using Support Vector Machine.

We recently developed a test based on the Agilent SureSelect target enrichment system capturing genomic fragments from 191 human papillomaviruses (HPV) types for Illumina sequencing. This enriched whole genome sequencing (eWGS) assay provides an approach to identify all HPV types in a sample. Here we present a machine learning algorithm that calls HPV types based on the eWGS output. The algorithm based on the support vector machine (SVM) technique was trained on eWGS data from 122 control samples with known HPV types. The new algorithm demonstrated good performance in HPV type detection for designed samples with 25 or greater HPV plasmid copies per sample. We compared the results of HPV typing made by the new algorithm for 261 residual epidemiologic samples with the results of the typing delivered by the standard HPV Linear Array (LA). The agreement between methods (97.4%) was substantial (kappa= 0.783). However, the new algorithm identified additionally 428 instances of HPV types not detectable by the LA assay by design. Overall, we have demonstrated that the bioinformatics pipeline is an accurate tool for calling HPV types by analyzing data generated by eWGS processing of DNA fragments extracted from control and epidemiological samples.

GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins.

We have made several steps toward creating a fast and accurate algorithm for gene prediction in eukaryotic genomes. First, we introduced an automated method for efficient ab initio gene finding, GeneMark-ES, with parameters trained in iterative unsupervised mode. Next, in GeneMark-ET we proposed a method of integration of unsupervised training with information on intron positions revealed by mapping short RNA reads. Now we describe GeneMark-EP, a tool that utilizes another source of external information, a protein database, readily available prior to the start of a sequencing project. A new specialized pipeline, ProtHint, initiates massive protein mapping to genome and extracts hints to splice sites and translation start and stop sites of potential genes. GeneMark-EP uses the hints to improve estimation of model parameters as well as to adjust coordinates of predicted genes if they disagree with the most reliable hints (the -EP+ mode). Tests of GeneMark-EP and -EP+ demonstrated improvements in gene prediction accuracy in comparison with GeneMark-ES, while the GeneMark-EP+ showed higher accuracy than GeneMark-ET. We have observed that the most pronounced improvements in gene prediction accuracy happened in large eukaryotic genomes.

Development of a workflow for identification of nuclear genotyping markers for Cyclospora cayetanensis.

Cyclospora cayetanensis is an intestinal parasite responsible for the diarrheal illness, cyclosporiasis. Molecular genotyping, using targeted amplicon sequencing, provides a complementary tool for outbreak investigations, especially when epidemiological data are insufficient for linking cases and identifying clusters. The goal of this study was to identify candidate genotyping markers using a novel workflow for detection of segregating single nucleotide polymorphisms (SNPs) in C. cayetanensis genomes. Four whole C. cayetanensis genomes were compared using this workflow and four candidate markers were selected for evaluation of their genotyping utility by PCR and Sanger sequencing. These four markers covered 13 SNPs and resolved parasites from 57 stool specimens, differentiating C. cayetanensis into 19 new unique genotypes.

TITLE: Développement d'un flux de travail pour l'identification de marqueurs de génotypage nucléaire pour Cyclospora cayetanensis. ABSTRACT: Cyclospora cayetanensis est un parasite intestinal responsable de la cyclosporose, maladie diarrhéique. Le génotypage moléculaire, utilisant le séquençage ciblé des amplicons, fournit un outil complémentaire pour les enquêtes sur les épidémies, en particulier lorsque les données épidémiologiques sont insuffisantes pour relier les cas et identifier les grappes. Le but de cette étude était d'identifier des marqueurs candidats de génotypage à l'aide d'un nouveau flux de travail pour la détection des polymorphismes d'un seul nucléotide (SNP) différentiateurs dans les génomes de C. cayetanensis. Quatre génomes entiers de C. cayetanensis ont été comparés à l'aide de ce flux de travail et quatre marqueurs candidats ont été sélectionnés pour l'évaluation de leur utilité de génotypage par PCR et séquençage Sanger. Ces quatre marqueurs couvraient 13 SNP et ont résolu les parasites provenant de 57 spécimens de selles, différenciant C. cayetanensis en 19 nouveaux génotypes uniques.

Whole-Genome Annotation with BRAKER.

BRAKER is a pipeline for highly accurate and fully automated gene prediction in novel eukaryotic genomes. It combines two major tools: GeneMark-ES/ET and AUGUSTUS. GeneMark-ES/ET learns its parameters from a novel genomic sequence in a fully automated fashion; if available, it uses extrinsic evidence for model refinement. From the protein-coding genes predicted by GeneMark-ES/ET, we select a set for training AUGUSTUS, one of the most accurate gene finding tools that, in contrast to GeneMark-ES/ET, integrates extrinsic evidence already into the gene prediction step. The first published version, BRAKER1, integrated genomic footprints of unassembled RNA-Seq reads into the training as well as into the prediction steps. The pipeline has since been extended to the integration of data on mapped cross-species proteins, and to the usage of heterogeneous extrinsic evidence, both RNA-Seq and protein alignments. In this book chapter, we briefly summarize the pipeline methodology and describe how to apply BRAKER in environments characterized by various combinations of external evidence.

Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools.

The genomes of mammalian species are pervasively transcribed producing as many noncoding as protein-coding RNAs. There is a growing body of evidence supporting their functional role. Long noncoding RNA (lncRNA) can bind both nucleic acids and proteins through several mechanisms. A reliable computational prediction of the most probable mechanism of lncRNA interaction can facilitate experimental validation of its function. In this study, we benchmarked computational tools capable to discriminate lncRNA from mRNA and predict lncRNA interactions with other nucleic acids. We assessed the performance of 9 tools for distinguishing protein-coding from noncoding RNAs, as well as 19 tools for prediction of RNA-RNA and RNA-DNA interactions. Our conclusions about the considered tools were based on their performances on the entire genome/transcriptome level, as it is the most common task nowadays. We found that FEELnc and CPAT distinguish between coding and noncoding mammalian transcripts in the most accurate manner. ASSA, RIBlast and LASTAL, as well as Triplexator, turned out to be the best predictors of RNA-RNA and RNA-DNA interactions, respectively. We showed that the normalization of the predicted interaction strength to the transcript length and GC content may improve the accuracy of inferring RNA interactions. Yet, all the current tools have difficulties to make accurate predictions of short-trans RNA-RNA interactions-stretches of sparse contacts. All over, there is still room for improvement in each category, especially for predictions of RNA interactions.

Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes.

In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of precomputed "heuristic" models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as noncanonical RBS patterns. To assess the accuracy of GeneMarkS-2, we used genes validated by COG (Clusters of Orthologous Groups) annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ∼5000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts.

The genome sequence and transcriptome of Potentilla micrantha and their comparison to Fragaria vesca (the woodland strawberry).

Background: The genus Potentilla is closely related to that of Fragaria, the economically important strawberry genus. Potentilla micrantha is a species that does not develop berries but shares numerous morphological and ecological characteristics with Fragaria vesca. These similarities make P. micrantha an attractive choice for comparative genomics studies with F. vesca. Findings: In this study, the P. micrantha genome was sequenced and annotated, and RNA-Seq data from the different developmental stages of flowering and fruiting were used to develop a set of gene predictions. A 327 Mbp sequence and annotation of the genome of P. micrantha, spanning 2674 sequence contigs, with an N50 size of 335,712, estimated to cover 80% of the total genome size of the species was developed. The genus Potentilla has a characteristically larger genome size than Fragaria, but the recovered sequence scaffolds were remarkably collinear at the micro-syntenic level with the genome of F. vesca, its closest sequenced relative. A total of 33,602 genes were predicted, and 95.1% of bench-marking universal single-copy orthologous genes were complete within the presented sequence. Thus, we argue that the majority of the gene-rich regions of the genome have been sequenced. Conclusions: Comparisons of RNA-Seq data from the stages of floral and fruit development revealed genes differentially expressed between P. micrantha and F. vesca.The data presented are a valuable resource for future studies of berry development in Fragaria and the Rosaceae and they also shed light on the evolution of genome size and organization in this family.