Gene annotation III: DRAM distillation¶
Objectives
Overview of DRAM.py annotate output¶
The submitted jobs from the previous session should now be completed. If we examine the output directory 10.gene_annotation_and_coverage/dram_annotations/ we will see the following files:
| File name | Description |
|---|---|
genes.faa and genes.fna |
FASTA files with all the genes called by Prodigal, with additional header information gained from the annotation as nucleotide and amino acid records, respectively |
genes.gff |
GFF3 file with the same annotation information as well as gene locations |
scaffolds.fna |
A collection of all scaffolds/contigs given as input to DRAM.py annotate with added bin information |
annotations.tsv |
This file includes all annotation information about every gene from all MAGs |
trnas.tsv |
Summary of the tRNAs found in each MAG |
rrnas.tsv |
Summary of the rRNAs found in each MAG |
If we inspect the head of the annotation file we will see the following
code
Terminal output
fasta scaffold gene_position start_position end_position strandedness rank ko_id kegg_hit peptidase_id peptidase_family peptidase_hit peptidase_RBH peptidase_identity peptidase_bitScore peptidase_eVal pfam_hits cazy_id cazy_hits heme_regulatory_motif_count bin_taxonomy bin_completeness bin_contamination
bin_0.filtered_bin_0_NODE_11_length_361162_cov_0.994424_1 bin_0.filtered bin_0_NODE_11_length_361162_cov_0.994424 1 2 277 -1 D Fumarate hydratase (Fumerase) [PF05681.17] 0 d__Bacteria;p__Campylobacterota;c__Campylobacteria;o__Campylobacterales;f__Arcobacteraceae;g__Arcobacter;s__Arcobacter nitrofigilis 99.59 3.79
bin_0.filtered_bin_0_NODE_11_length_361162_cov_0.994424_2 bin_0.filtered bin_0_NODE_11_length_361162_cov_0.994424 2 474 764 1 E 0d__Bacteria;p__Campylobacterota;c__Campylobacteria;o__Campylobacterales;f__Arcobacteraceae;g__Arcobacter;s__Arcobacter nitrofigilis 99.59 3.79
bin_0.filtered_bin_0_NODE_11_length_361162_cov_0.994424_3 bin_0.filtered bin_0_NODE_11_length_361162_cov_0.994424 3 775 1452 1 D Response regulator receiver domain [PF00072.27]; Transcriptional regulatory protein, C terminal [PF00486.31] 0 d__Bacteria;p__Campylobacterota;c__Campylobacteria;o__Campylobacterales;f__Arcobacteraceae;g__Arcobacter;s__Arcobacter nitrofigilis 99.59 3.79
bin_0.filtered_bin_0_NODE_11_length_361162_cov_0.994424_4 bin_0.filtered bin_0_NODE_11_length_361162_cov_0.994424 4 1449 2696 1 D GHKL domain [PF14501.9] 0 d__Bacteria;p__Campylobacterota;c__Campylobacteria;o__Campylobacterales;f__Arcobacteraceae;g__Arcobacter;s__Arcobacter nitrofigilis 99.59 3.79
For each gene annotated, DRAM provides a summary rank (from A to E), representing the confidence of the annotation based on reciprocal best hits (RBH). The following figure briefly explains how this summary rank is calculated:
Overview of DRAM-v.py annotate output¶
DRAM-v generates the same output files as DRAM, but this time for the viral contigs. These files can be viewed in the output directory 10.gene_annotation_and_coverage/dramv_annotations/. In this case, annotations.tsv also includes some viral-specific columns, including viral gene database matches (vogdb), and categories that are used by DRAM-v.py distill to identify putative auxiliary metabolic genes (AMGs) (virsorter_category, auxiliary_score, is_transposon, amg_flags)
DRAM and DRAM-v distillation of the results¶
After the annotation is finished, we will summarise and visualise these annotations with the so-called distillation step. We do so by running the following commands directly in the terminal. This will generate the distillate and liquor files for each dataset.
For the viral annotations, we will also include the parameters --remove_transposons ("Do not consider genes on scaffolds with transposons as potential AMGs") and --remove_fs ("Do not consider genes near ends of scaffolds as potential AMGs") to filter out some potential false positives for auxiliary metabolic gene identification.
code
module purge
module load DRAM/1.3.5-Miniconda3
cd /nesi/nobackup/nesi02659/MGSS_U/<YOUR FOLDER>/10.gene_annotation_and_coverage/
# Prokaryote annotations
DRAM.py distill -i dram_annotations/annotations.tsv -o dram_distillation --trna_path dram_annotations/trnas.tsv --rrna_path dram_annotations/rrnas.tsv
# viral annotations
DRAM-v.py distill --remove_transposons --remove_fs \
-i dramv_annotations/annotations.tsv \
-o dramv_distillation
DRAM.py distill output files¶
The DRAM distillation step generates the following files that can be found within the dram_distillation directory :
| File name | Description |
|---|---|
genome_stats.tsv |
Genome quality information required for MIMAG |
metabolism_summary.xlsx |
Summarised metabolism table containing number of genes with specific metabolic function identifiers |
product.html |
HTML file displaying a heatmap summarising pathway coverage, electron transport chain component completion, and presence/absence of specific functions |
product.tsv |
Data table visualised in product.html |
First, let's have a look at the genome_stats.tsv file to check the assembly quality of our bins by double-clicking the file within the Jupyter environment, viewing from the terminal via less or cat, or downloading the files from here and opening locally (e.g. via Excel).
To finish, we visualize the Product, an .HTML file produced in the distillation step, by double-clicking on it in our Jupyter lab notebook or downloading from here. The Product has three primary parts:
Product visualisation
Central metabolism pathways coverage. Completion of pathways is based on the structure of KEGG modules, with the pathway coverage calculated as the percent of steps with at least one gene present.
Electron Transport Chain component completion
Presence of specific functions, including CAZy, Nitrogen metabolism, Sulfur metabolism and Photosynthesis. Note that the taxonomic classification of each of the bins is also shown in the first figure
DRAM-v.py distill output files¶
The DRAM-v distillation step for the viral contigs generates the following files that can be found within the dramv_distillation/ directory :
File name |
Description |
|---|---|
vMAG_stats.tsv |
"Genome" (in this case viral contigs of varying completeness) information including: total gene counts, viral vs host gene counts, and counts of genes related to viral replication, structure, and those with presumed viral or host benefits |
amg_summary.tsv |
Genes identified as putative auxiliary metabolic genes (AMGs) and various columns for metabolic characterisation of each gene |
product.html |
HTML file displaying a heatmap summarising AMG counts and presence/absence for different broad metabolic categories for each viral contig |
When viewing these files, see if you can find the following information:
- What are some annotations of interest within the output annotations file?
- NOTE: the *VirSorter2 annotations file includes multiple columns for both prokaryote and viral protein predictions. Be careful as to which column you are looking at (as well as its associated confidence score) when assessing viral annotations vs. AMGs*.
- Among these annotations, how many were flagged as AMGs by
DRAM-v? - What broad metabolic categories did the AMGs fall into?
- Discussion point: How might we investigate whether identified putative AMGs are actually within the viral genomes, rather than residual contaminating host genomic sequence attached to the end of integrated prophage (but incompletely trimmed off in the excision process)?
Tie findings to your initial goal¶
It is now time to explore the genomes and try to address your original goal!
You were tasked with identifying one of the following.
- Denitrification (Nitrate or nitrite to nitrogen)
- Ammonia oxidation (Ammonia to nitrite or nitrate)
- Anammox (Ammonia and nitrite to nitrogen)
- Sulfur oxidation (SOX pathway, thiosulfate to sulfate)
- Sulfur reduction (DSR pathway, sulfate to sulfide)
- Photosynthetic carbon fixation
- Non-photosynthetic carbon fixation (Reverse TCA or Wood-Ljundahl)
- Non-polar flagella expression due to a chromosomal deletion
- Plasmid-encoded antibiotic resistance
- Aerobic (versus anaerobic) metabolism
Depending on what you are looking for, you will either be trying to find gene(s) of relevance to a particular functional pathway, or the omission of genes that might be critical in function. In either case, make sure to use the taxonomy of each MAG to determine whether it is likely to be a worthwhile candidate for exploration, as some of these traits are quite restricted in terms of which organisms carry them.
To conduct this exersise, you should use the information generated with DRAM as well as the annotation files we created yesterday and that are available in the directory 10.gene_annotation_and_coverage/gene_annotations.
Please note that we have also provided further annotation files within the directory 10.gene_annotation_and_coverage/example_annotation_tables that contain information obtained after annotating the MAGs against additional databases (UniProt, UniRef100, KEGG, PFAM and TIGRfam). These example files can also be downloaded from here.