Additional file 3 of Altitude-dependent agro-ecologies impact the microbiome diversity of scavenging indigenous chicken in Ethiopia
Creators: name: Glendinning, Laura
name: Jia, Xinzheng
name: Kebede, Adebabay
name: Oyola, Samuel O.
name: Park, Jong-Eun
name: Park, Woncheoul
name: Assiri, Abdulwahab
name: Holm, Jacob Bak
name: Kristiansen, Karsten
name: Han, Jianlin
name: Hanotte, Olivier
Publisher: name: figshare
Year: 2024
Subjects: subject: Space Science
subject: Evolutionary Biology
subject: FOS: Biological sciences
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: Biological Sciences not elsewhere classified

description: Additional file 3: Fig. S1. Construction of a chicken caecal reference gene catalogue. The 3,629,587 non-redundant genes contained in the catalogue represent the metagenomes of 240 chicken caecal contents samples. Non-redundant genes were assigned to different taxon levels based on their last common ancestor in the Uniprot database (version 2019_03). Fig. S2. Description of the gene catalogue constructed from the caecal microbiota of Ethiopian indigenous chickens. A) Rarefaction analysis of the number of non-redundant genes vs sampling number. B-E) Break down of the taxa identified in the Ethiopian chicken caecal microbial gene catalogue, by Kingdom. Fig. S3. Genome statistics of high-quality, non-redundant strain-level (A-E) and species-level (F-J) metagenome-assembled genomes, as defined by CheckM. A and F: Completeness and contamination – dashed red lines indicate cutoffs for defining genomes as high-quality. B and G: Percentage GC content. C and H: log10 number of contigs per genome. D and I: Genome size (mb). E and J: log10 N50 of contigs. Fig. S4. The proportions of annotated read after mapping raw sequencing reads to the non-redundant gene catalogue (A) and MAGs (B). Fig. S5. Violin plot showing the number of CAZyme genes per strain-level MAG by dataset. A) Total unique CAZyme families. B) Total unique Glycoside Hydrolases (GH) families. C) Total CAZyme genes. D) Total GH genes. Fig. S6. Heatmap showing the percentage of species-level MAGs within genera with particular metabolic pathways. Genera were clustered at 40% AAI using the output from comparem. The uniqueness of genera in comparison to previous datasets is indicated. Genus-level clusters were not unique based on GTDB if any MAGs within that cluster were assigned a taxonomy at the genus level. MAGs were defined as not unique when compared to previous chicken microbial datasets (“not_unique_drep”) if they clustered at 99% (strain) or 95% (species) ANI with any non-scavenging chickens (NSC) microbial genome. Genera were defined as not unique when compared to previous chicken microbial datasets (not_unique_comparem) if they clustered at 60% AAI with any NSC microbial genome. Fig. S7. Principle coordinate analysis showing the clustering of samples by autosomal SNPs. Samples are labelled by the region in which the sample was collected. Fig. S8. Five climate zones, clustered using Kmeans according to annual temperature, annual precipitation and precipitation of the driest quarter of the sampling location between 1970 - 2000. Components 1 and 2 explain 85.1% of sampling site variation. Table S1: Diversity of CAZymes in strain level MAGs.