TOGA vs. hg38 Track Settings
 
TOGA annotations using human/hg38 as reference   (All Genes and Gene Predictions tracks)

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Data last updated at UCSC: 2022-06-20 05:19:26

Description

TOGA (Tool to infer Orthologs from Genome Alignments) is a homology-based method that integrates gene annotation, inferring orthologs and classifying genes as intact or lost.

Methods

As input, TOGA uses a gene annotation of a reference species (human/hg38 for mammals, chicken/galGal6 for birds) and a whole genome alignment between the reference and query genome.

TOGA implements a novel paradigm that relies on alignments of intronic and intergenic regions and uses machine learning to accurately distinguish orthologs from paralogs or processed pseudogenes.

To annotate genes, CESAR 2.0 is used to determine the positions and boundaries of coding exons of a reference transcript in the orthologous genomic locus in the query species.

Display Conventions and Configuration

Each annotated transcript is shown in a color-coded classification as

  •   "intact": middle 80% of the CDS (coding sequence) is present and exhibits no gene-inactivating mutation. These transcripts likely encode functional proteins.
  •   "partially intact": 50% of the CDS is present in the query and the middle 80% of the CDS exhibits no inactivating mutation. These transcripts may also encode functional proteins, but the evidence is weaker as parts of the CDS are missing, often due to assembly gaps.
  •   "missing": <50% of the CDS is present in the query and the middle 80% of the CDS exhibits no inactivating mutation.
  •   "uncertain loss": there is 1 inactivating mutation in the middle 80% of the CDS, but evidence is not strong enough to classify the transcript as lost. These transcripts may or may not encode a functional protein.
  •   "lost": typically several inactivating mutations are present, thus there is strong evidence that the transcript is unlikely to encode a functional protein.

Clicking on a transcript provides additional information about the orthology classification, inactivating mutations, the protein sequence and protein/exon alignments.

Credits

This data was prepared by the Michael Hiller Lab

References

The TOGA software is available from github.com/hillerlab/TOGA

Kirilenko BM, Munegowda C, Osipova E, Jebb D, Sharma V, Blumer M, Morales AE, Ahmed AW, Kontopoulos DG, Hilgers L et al. Integrating gene annotation with orthology inference at scale. Science. 2023 Apr 28;380(6643):eabn3107. PMID: 37104600; PMC: PMC10193443