Using native and syntenically mapped cDNA alignments to improve de novo gene finding

By Mario Stanke, Mark Diekhans, Robert Baertsch, David Haussler. Bioinform., 2008

2,184 citations recorded by the source corpus.

MOTIVATION Computational annotation of protein coding genes in genomic DNA is a widely used and essential tool for analyzing newly sequenced genomes. However, current methods suffer from inaccuracy and do poorly with certain types of genes. Including additional sources of evidence of the existence and structure of genes can improve the quality of gene predictions. For many eukaryotic genomes, expressed sequence tags (ESTs) are available as evidence for genes. Related genomes that have been sequenced, annotated, and aligned to the target genome provide evidence of existence and structure of genes. RESULTS We incorporate several different evidence sources into the gene finder AUGUSTUS. The sources of evidence are gene and transcript annotations from related species syntenically mapped to the target genome using TransMap, evolutionary conservation of DNA, mRNA and ESTs of the target species, and retroposed genes. The predictions include alternative splice variants where evidence supports it. Using only ESTs we were able to correctly predict at least one splice form exactly correct in 57% of human genes. Also using evidence from other species and human mRNAs, this number rises to 77%. Syntenic mapping is well-suited to annotate genomes closely related to genomes that are already annotated or for which extensive transcript evidence is available. Native cDNA evidence is most helpful when the alignments are used as compound information rather than independent positionwise information. AVAILABILITY AUGUSTUS is open source and available at http://augustus.gobics.de. The gene predictions for human can be browsed and downloaded at the UCSC Genome Browser (http://genome.ucsc.edu).

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ce8b8b36eca3641710e7640ad273b5ff9b8c4392

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Citations in the corpus

BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes (2021, methodology): “The Augustus gene predictor (Stanke et al. 2008) is still available in BUSCO and can be selected by specifying “–augustus” when running the analysis in “genome” mode.”
Predicting Genes in Single Genomes with AUGUSTUS (2018, background): “However, the option --alternatives-from-evidence=onmakes AUGUSTUS report alternative transcripts when they are suggested by hints (Stanke et al., 2008).”
TSEBRA: transcript selector for BRAKER (2021, methodology): “BRAKER1 uses GeneMark-ET [10–12] together with AUGUSTUS [13–17] and relies on RNA-seq data to support gene finder training and accurate prediction of gene structures.”
A Snapshot of the Emerging Tomato Genome Sequence (2009, methodology): “Analyses such as repeat masking, EST alignment, and gene predictions using diff erent gene fi nders such as GeneID (Parra et al., 2000), GeneMark (Isono et al., 1994), and Augustus (Stanke et al., 2008) are performed on those BACs.”
Nanopore sequencing and assembly of a human genome with ultra-long reads (2017, positive): “The gene finding tool AUGUSTUS is used to clean up these transcript projections and a combined gene set is generated 62 .”
A novel hybrid gene prediction method employing protein multiple sequence alignments (2011, methodology): “…in AUGUSTUS is completely generic as to the source of the hints and the way they have been generated; in practice, however, they are almost always derived from alignment-based methods (Stanke et al., 2006a, 2008), including the use of peptides from proteomics experiments (Castellana et al., 2008).”
MAKER-P: A Tool Kit for the Rapid Creation, Management, and Quality Control of Plant Genome Annotations1[W][OPEN] (2013, methodology): “We ﬁ rst used the TAIR10 annotations as a gold standard with which to determine gene-level accuracies of the ab initio gene ﬁ nders Semi Hidden Markov model [HMM]-Based Nucleic Acid Parser (SNAP; Korf, 2004) and Augustus (Stanke and Waack, 2003; Stanke et al., 2008).”
Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, methodology): “…(Trapnell et al., 2010; Pertea et al., 2015), ab-initio gene prediction based on general features of (protein-coding) genes (Solovyev et al., 2006; Stanke et al., 2008), and homology-based gene prediction relying on gene models of (closely) related, well-annotated species (Slater and Birney,…”
BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database (2020, neutral): “On a parallel avenue, yet another algorithm, AUGUSTUS [9-14], was demonstrated to be one of the most accurate gene prediction tools [15-17].”
Genome Sequencing and Comparative Transcriptomics of the Model Entomopathogenic Fungi Metarhizium anisopliae and M. acridum (2011, methodology): “Annotation Annotations of the genomic sequences of M. anisopliae and M. acridum were performed with Augustus [111], specifically trained with .6000 unique sequenced Metarhizium ESTs, and the annotated information of F. graminearum was incorporated as a reference.”
Phylogenomics provides a robust topology of the major cnidarian lineages and insights on the origins of key organismal traits (2018, positive): “3 [109] with default parameters to generate Renilla reniformis protein predictions.”
Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. (2008, result): “We consider the public annotation of genes in C. neoformans genome as one of the best in our sample (Tenney et al. 2004), similar in accuracy to annotation of the wellstudied S. pombe genome.”