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bioconductor-doubletrouble

doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a s

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  • SKILL.md3.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

doubletrouble

Workflows

Standard Workflow

library(doubletrouble)
library(syntenet)

# Load example data
data(yeast_seq)
data(yeast_annot)
data(diamond_intra)

# Prepare input data
pdata <- process_input(yeast_seq, yeast_annot)

# Classify gene pairs using the standard scheme
c_standard <- classify_gene_pairs(
  annotation = pdata$annotation,
  blast_list = diamond_intra,
  scheme = "standard"
)

# Calculate duplicate frequencies
table(c_standard$Scerevisiae$type)

Note: Inputs are processed protein sequences and gene annotations along with intraspecies DIAMOND search results; output is a list of classified duplicated gene pairs.

When to Use

  • To identify and classify duplicated genes from whole-genome protein sequences using classify_gene_pairs.
  • To classify duplicates into different schemes: binary (SD vs SSD), standard (SD, TD, PD, DD), or extended (SD, TD, PD, TRD, DD).

When NOT to Use

  • For finding synteny or running sequence similarity searches directly; use syntenet functions like process_input and run_diamond instead.
  • When whole-genome protein sequences (proteome) and gene annotations (GFF3/GTF) are not available.

Data Requirements

  • Proteome: List of AAStringSet objects containing translated sequences of the primary transcripts.
  • Annotation: List of GRanges objects containing genomic coordinates of all features.
  • Intraspecies BLAST/DIAMOND results: A list of data frames with tabular output (e.g., from run_diamond).

Key Parameters

  • annotation: Processed annotation list (a GRangesList object).
  • blast_list: A list of data frames with DIAMOND/BLAST tabular output for intraspecies comparisons.
  • scheme ("standard"): Classification scheme to use ("binary", "standard", "extended", or "full").
  • blast_inter: List of data frames with DIAMOND/BLAST tabular output for interspecies comparisons (required for "extended" and "full" schemes).

Best Practices

  • Ensure list names in the annotation match the list names in the sequence object using setequal(names(seqs), names(annotation)).
  • Keep only the longest sequence for each protein-coding gene to avoid isoforms.
  • Perform bidirectional similarity searches for interspecies comparisons and collapse them using collapse_bidirectional_hits before classification.

Common Pitfalls

  • Mismatching list names between sequences and annotations: Ensure names are consistent across lists.
  • Including multiple isoforms per gene: Filter the input sequences to keep only the longest sequence per gene.

Alternatives

  • syntenet for synteny detection and running DIAMOND alignments.

Citations

  • Ohno 2013, Springer Science & Business Media.
  • Buchfink, Reuter, and Drost 2021, Nature Methods.

References

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