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Dendroctonus ponderosae (Mountain pine beetle) (DendPond_male_gca000355655v1.0)

About Dendroctonus ponderosae

Dendroctonus ponderosae, the mountain pine beetle, is a destructive pest of pine trees in increasingly large regions of North America [1]. It has a hard black exoskeleton, and measures approximately 5 millimetres, about the size of a grain of rice. The beetle carries blue-stain fungus (Grosmannia clavigera [2]), a symbiont that enables the beetles to extract nutrients from the tree. Infestation by many beetles (which coordinate attacks with pheromones) can be fatal to the tree because the fungus produces filaments that block water transport. The Tria project provides genomic resources for the mountain pine beetle, blue-stain fungus, pine tree, and related-species, to assist researchers in understanding the ecological dynamics of these species.

The mountain pine beetle is the second coleopteran genome to be sequenced (the other is Tribolium castaneum), and provides a valuable comparative resource for this inordinately speciose order.

Picture credit: Public domain via Wikimedia Commons (Image source)

Taxonomy ID 77166

More information General information about this species can be found in Wikipedia

Taxonomy ID 77166

Data source The Tria Project: Mountain Pine Beetle System Genomics

More information and statistics

Genome assembly: DendPond_male_gca000355655v1.0

More information and statistics

Download DNA sequence (FASTA)

Display your data in Ensembl Metazoa

Gene annotation

What can I find? Protein-coding and non-coding genes, splice variants, cDNA and protein sequences, non-coding RNAs.

More about this genebuild

Download genes, cDNAs, ncRNA, proteins - FASTA - GFF3

Update your old Ensembl IDs

Comparative genomics

What can I find? Homologues, gene trees, and whole genome alignments across multiple species.

More about comparative analyses

Phylogenetic overview of gene families

Download alignments (EMF)


This species currently has no variation database. However you can process your own variants using the Variant Effect Predictor:

Variant Effect Predictor