Metagenome Assembled Genomes (MAGs) from faecal microbiomes of great tits and blue tits

Overview:

The vertebrate gut microbiome plays crucial roles in host health and disease. However, there is limited data on the microbiomes of wild birds, most of which is restricted to barcode sequences. We therefore explored the use of shotgun metagenomics on the faecal microbiomes of two wild bird species widely used as model organisms in ecological studies: the great tit (Parus major) and the Eurasian blue tit (Cyanistes caeruleus). High and Medium quality Metagenome Assembled Genomes (MAGs) were assembled from these metagenomes and are made available as a catalogue in this archive.

Methods:

Metagenomic reads were trimmed, and quality controlled using FastP configured to a minimum phred score of 20 and minimum length of 50 bp. In order to avoid contamination of the bins by eukaryotic sequences, Tiara v1.0.3  was used to classify contigs longer than 3.000 kb into their high-level kingdoms, allowing to exclude sequences of a eukaryotic or of an organelle origin, and only retaining all unclassified contigs and prokaryotic contigs for the binning step. 
Contigs were binned using MaxBin2 v2.2.7 , SemiBin2 v2.1.0 and Metabat2 v 2.15 independently. The bins were refined using DasTool v 1.1.7 using a min score threshold of 0.3. The quality of the refined bins was obtained using CheckM2 v 1.0.2, and any bin with a contamination above 10% were excluded. The final MAGs were classified as Low-quality (<50% completeness, <10% contamination), medium-quality (>50% completeness, <10% contamination) and high-quality (>90% completeness, <5% contamination), as recommended by the MIMAG specification . Finally, the MAGs were dereplicated using an dRep v 3.4.3 with an ANI of 95% and classified using gtdb-tk v2.4.0 using the gtdb database release220.

Files:

• The MAGs_sequences_v1.0.0 contains the fasta sequence for the individual MAGs assembled in this project
• The MAGs_catalogue_v1.0.0.xlsx contains a description of the quality, taxonomic annotation and characteristics of each MAGs in the dataset