predictions with RNA evidence

[xvazquezc]

Hi there,

I'm annotating a genome and I have RNAseq data. I already have assembled transcripts (via Trinity) and I was wondering if there was any preference and/or differences if using the --transcripts or --bam with funannotate2 predict.

In the same line, are the --weights applicable to the evidences or just to the gene predictors? I've only seen how to specify them in #43 , e.g. here. So wondering about the appropriate usage.

Cheers,
Xabi

[xvazquezc]

Hi again,

After overcoming the issue described in #93, I ran the prediction to completion like this (btw, conda install with funannotate2 v26.6.21 in Rocky Linux):

funannotate2 predict --cpus $NCPUS --memory-limit 120 \
--fasta "${MSKGENOME}" \
--species "${SPECIES}" \
--strain $ID \
--locus-tag $ID \
--input-dir $TRAINOUT \
--external $HLXROUT \
--proteins ${FUNANNOTATE2_DB}/uniprot_sprot.fasta \
--transcripts $TRANSCRIPTS \
--out $PREDICTOUT

This was after running funannotate2 train (note the genome is basically T2T):

funannotate2 train \
--fasta "${MSKGENOME}" \
--species "${SPECIES}" \
--min-contig-length 50000 \
--genemark-mode guided \
--augustus-species aspergillus_fumigatus \
--busco-lineage dothideomycetes \
--strain $ID \
--out $TRAINOUT \
--cpus $NCPUS

Looking at the resuts I was wondering how the model weighting is managed. For example, these were the models scoring (tbh, surprised by the low values for gapmm2-gene:

{
  "augustus": {
    "busco": 0.9813542688910697,
    "train": {
      "tool": "augustus",
      "model": "908c72a4-3a9a-419b-ba56-9b3f1f5c37ab",
      "n_test_genes": 200,
      "ref_genes_found": 200,
      "ref_genes_missed": 0,
      "extra_query_genes": 154,
      "average_aed": 0.11906437352332498,
      "nucleotide_sensitivity": 0.9071259860882048,
      "nucleotide_precision": 0.9068441978367979,
      "exon_sensitivity": 0.6522842639593909,
      "exon_precision": 0.6594085271750247,
      "gene_sensitivity": 1.0,
      "gene_precision": 0.3418803418803419
    }
  },
  "gapmm2-gene": {
    "busco": 0.48380765456329733
  },
  "genemark": {
    "busco": 0.9960745829244357,
    "train": {
      "tool": "genemark",
      "model": "gmhmm.mod",
      "n_test_genes": 200,
      "ref_genes_found": 200,
      "ref_genes_missed": 0,
      "extra_query_genes": 217,
      "average_aed": 0.10446726703519064,
      "nucleotide_sensitivity": 0.8814030905419991,
      "nucleotide_precision": 0.9487698187664156,
      "exon_sensitivity": 0.6363636363636364,
      "exon_precision": 0.6772126022126022,
      "gene_sensitivity": 1.0,
      "gene_precision": 0.2668918918918919
    }
  },
  "glimmerhmm": {
    "busco": 0.9283611383709519,
    "train": {
      "tool": "glimmerhmm",
      "model": "train",
      "n_test_genes": 200,
      "ref_genes_found": 200,
      "ref_genes_missed": 0,
      "extra_query_genes": 147,
      "average_aed": 0.12894121641414472,
      "nucleotide_sensitivity": 0.8979005764266919,
      "nucleotide_precision": 0.8888833915488055,
      "exon_sensitivity": 0.6096938775510204,
      "exon_precision": 0.6076166180758017,
      "gene_sensitivity": 1.0,
      "gene_precision": 0.30660377358490565
    }
  },
  "helixer": {
    "busco": 0.9990186457311089
  },
  "miniprot-gene": {
    "busco": 0.13052011776251227
  "snap": {
    "busco": 0.9087340529931305,
    "train": {
      "tool": "snap",
      "model": "snap-trained.hmm",
      "n_test_genes": 200,
      "ref_genes_found": 200,
      "ref_genes_missed": 0,
      "extra_query_genes": 241,
      "average_aed": 0.12325902516548182,
      "nucleotide_sensitivity": 0.8673618102316233,
      "nucleotide_precision": 0.9277418342864765,
      "exon_sensitivity": 0.6005154639175257,
      "exon_precision": 0.6240549828178694,
      "gene_sensitivity": 1.0,
      "gene_precision": 0.22258064516129034
    }
  }
}

Led to the following weights:

{"augustus-hiq": 4, "genemark": 3, "helixer": 2, "augustus": 2, "gapmm2-gene": 1, "snap": 1, "glimmerhmm": 1, "miniprot-gene": 1}

And consensus gene models:

[["genemark", 5340], ["augustus-hiq", 2053], ["helixer", 370], ["augustus", 199], ["gapmm2-gene", 179], ["glimmerhmm", 178], ["snap", 116], ["miniprot-gene", 1]]

I don't know if the low weighting of the transcript evidence is bc of the way the transcripts were provided (Trinity-assembled transcripts) but I found it a bit surprising to see the best evidence source so lowly scored. Also, HelixerLite predictions had the highest busco score (0.999...). Wondering if there are fewer parameters to evaluate the sources/models when they don't come from the training models generated with funannotate2 train.

I leave the funannotate2 predict log in case it helps