Example Data for "Tutorial: annotation and interpretation of mammalian genomes"

This dataset is used to perform end-to-end genome annotation using the Genome Annotation Tutorial (https://github.com/BaderLab/GenomeAnnotationTutorial) in a manner that is less resource and time intensive than annotating an entire genome assembly. End-to-end, this pipeline should take ~48 hours to run without any parallelization of jobs. With parallelization (i.e., using a Snakemake pipeline), the workflow with this example data will be under 24 hours.

Abstract from: Tutorial: annotation and interpretation of mammalian genomes.

As DNA sequencing technologies improve, it is becoming easier to sequence and assemble the genomes of non-model organisms. However, before a genome sequence can be used as a reference it must be annotated with genes and other features, which can now be performed by individual labs using public software. Modern genome annotations integrate gene predictions from the assembled DNA sequence with gene homology information from a high-quality reference and functional evidence (e.g. protein sequences, RNA sequencing). Many genome annotation pipelines exist that vary in accuracy and how resource-intensive and user-friendly they are. This tutorial covers a streamlined genome annotation pipeline that can create high-quality mammalian genome annotations in-lab. Our pipeline integrates existing state-of-the-art genome-annotation tools capable of annotating protein-coding and non-coding RNA genes. This tutorial also guides the user on assigning gene symbols and annotating repeat regions. We lastly describe additional tools to assess annotation quality and combine and format the results.

This dataset contains a small chromosome from a recent naked mole rat assembly, short read RNA-seq of three tissues, and ISO-seq data from two tissues. This example dataset was generated to allow users to complete the five major steps of genome annotation: (1) identifying repetitive elements and masking repeats that can interfere with gene finding, (2) identifying protein-coding/messenger RNA (mRNA) gene models, (3) optimizing gene models using multiple lines of evidence, (4) adding non-coding RNA (ncRNA) gene models (5) labelling gene models with the likely gene identity (i.e. gene symbol).

The naked mole-rat assembly and ISO-seq data are derived from:

Sokolowski, D. J., Miclăuș, M., Nater, A., Faykoo-Martinez, M., Hoekzema, K., Zuzarte, P., ... & Wilson, M. D. (2024). An updated reference genome sequence and annotation reveals gene losses and gains underlying naked mole-rat biology. bioRxiv, 2024-11.

The short read RNA-seq data are derived from:

Bens, M., Szafranski, K., Holtze, S., Sahm, A., Groth, M., Kestler, H. A., ... & Platzer, M. (2018). Naked mole-rat transcriptome signatures of socially suppressed sexual maturation and links of reproduction to aging. BMC biology, 16, 1-13.