LM Rodriguez-R

EMBO Metagenomics 360° 2025

2025-03-11T00:00:00+00:00

Genome-Resolved Metagenomics

All the data used here is already available in your Virtual Machines, but you can also obtain it here if you’d like to reproduce these tasks in your own machines:

wget 'https://disc-genomics.uibk.ac.at/data/came24.tar.gz' -O - | tar -zx
cd SLMU

0. Nonpareil

First, let us define where the data is, so we don’t have to type it every time:

DATA=/mnt/data/Rodriguez

Now, we can start by running nonpareil locally:

mkdir -p 00_nonpareil
nonpareil -T kmer -s $DATA/SLMU.1.fastq.gz -f fastq -b 00_nonpareil/SLMU
NonpareilCurves.R --pdf 00_nonpareil/SLMU.pdf --no-observed 00_nonpareil/SLMU.npo
xdg-open 00_nonpareil/SLMU.pdf

1. Assembly

# IMPORTANT: This step might use significant RAM and take a very long time
spades.py --meta -1 $DATA/SLMU.1.fastq.gz -2 $DATA/SLMU.2.fastq.gz -o 01_assembly

If this is taking too long, you can kill the process by using Ctrl + C, and instead copy the scaffolds we provide:

rm -rf 01_assembly
mkdir -p 01_assembly
cp $DATA/SLMU.scaffolds.fasta 01_assembly/scaffolds.fasta

2. Mapping reads to the assembly

mkdir -p 02_mapping
bowtie2-build 01_assembly/scaffolds.fasta 02_mapping/SLMU.idx
bowtie2 -1 $DATA/SLMU.1.fastq.gz -2 $DATA/SLMU.2.fastq.gz \
      -S 02_mapping/SLMU.sam -x 02_mapping/SLMU.idx --no-unal
samtools view -b 02_mapping/SLMU.sam | samtools sort -o 02_mapping/SLMU.bam -
ls 02_mapping

3. Binning

mkdir -p 03_binning
jgi_summarize_bam_contig_depths --outputDepth 03_binning/SLMU.abund \
      02_mapping/SLMU.bam
metabat2 -i 01_assembly/scaffolds.fasta -a 03_binning/SLMU.abund \
      -o 03_binning/SLMU_bin
ls 03_binning/SLMU_bin.*.fa

4. Comparing a MAG against a collection of close relatives

# IMPORTANT: This step is currently causing problems because NCBI has blocked
# the internal network. If if fails, you can use our local copy (see below)
miga new -v -P 04_classification/Sal -t genomes
miga gtdb_get -v -P 04_classification/Sal -T g__Salinibacter --ref

If the genomes cannot be downloaded, you can also use our local copy instead:

# IMPORTANT: This will remove the results from the above commands, so make sure
# you use it only if the above is failing
rm -rf 04_classification
mkdir -p 04_classification
cp -R $DATA/Sal 04_classification/Sal

And finally, you can add your own genomes and process all the data:

miga add -v -P 04_classification/Sal -i assembly -t popgenome \
      03_binning/SLMU_bin.*.fa
miga index_wf -o 04_classification/Sal -v

That last command will take some time, but you can continue to part 5 and revisit the results from MiGA later.

Once the run is complete, you can open the file:

firefox 04_classification/Sal/index.html

If you want the taxonomic table, you can use:

miga ls -P 04_classification/Sal -o 04_classification/Sal/taxonomy.tsv \
      --active -m tax --tab
miga browse -P 04_classification/Sal

And reload the page in Firefox, which should now have a taxonomy link.

5. Mapping reads to the genome(s)

mkdir -p 05_genome_mapping
bowtie2-build 03_binning/SLMU_bin.5.fa 05_genome_mapping/SLMU_bin5.idx
bowtie2 -1 $DATA/SLMU.1.fastq.gz -2 $DATA/SLMU.2.fastq.gz \
      -S 05_genome_mapping/SLMU_bin5.sam \
      -x 05_genome_mapping/SLMU_bin5.idx --no-unal

6. Recruitment plots

mkdir -p 06_recplot
rpe build -d 06_recplot/SLMU_5.db -r 05_genome_mapping/SLMU_bin5.sam \
    -g 03_binning/SLMU_bin.5.fa --mag
rpe plot -d 06_recplot/SLMU_5.db -o 06_recplot/plot

Open the file manager and open the following file to explore some of the results locally:

firefox 06_recplot/plot/SLMU_bin5_sam/SLMU_bin_recruitment_plot.html

Software Used

And lots of Software used by those above!

CAME 2024

2024-09-01T00:00:00+00:00

VirtualBox

The Virtual Machine is available from USB sticks, or for download here: https://disc-genomics.uibk.ac.at/data/CAME2024_v5.ova

The user is vmuser and the password is vmuser.

16S Ribosomal RNA Gene Amplicons

0. Download the data

wget 'https://disc-genomics.uibk.ac.at/data/CAME_SSU.tar.gz' -O - | tar -zx
cd VM_CAME

1. Download the LTP database

wget 'https://disc-genomics.uibk.ac.at/data/LTP_10_2024.arb'

Genome-Resolved Metagenomics

0. Download the data

wget 'https://disc-genomics.uibk.ac.at/data/came24.tar.gz' -O - | tar -zx
cd SLMU

You can start by running Nonpareil locally:

mkdir -p 00_nonpareil
nonpareil -T kmer -s SLMU.1.fastq.gz -f fastq -b 00_nonpareil/SLMU
NonpareilCurves.R --pdf 00_nonpareil/SLMU.pdf --no-observed 00_nonpareil/SLMU.npo
open 00_nonpareil/SLMU.pdf

1. Assembly

# IMPORTANT: This step might use significant RAM and take a very long time
spades.py --meta -1 SLMU.1.fastq.gz -2 SLMU.2.fastq.gz -o 01_assembly

If this is taking too long, you can kill the process by using Ctrl + C, and instead copy the scaffolds we provide:

rm -rf 01_assembly
mkdir -p 01_assembly
cp SLMU.scaffolds.fasta 01_assembly/scaffolds.fasta

2. Mapping reads to the assembly

mkdir -p 02_mapping
bowtie2-build 01_assembly/scaffolds.fasta 02_mapping/SLMU.idx
bowtie2 -1 SLMU.1.fastq.gz -2 SLMU.2.fastq.gz \
      -S 02_mapping/SLMU.sam -x 02_mapping/SLMU.idx --no-unal
samtools view -b 02_mapping/SLMU.sam | samtools sort -o 02_mapping/SLMU.bam -
ls 02_mapping

3. Binning

mkdir -p 03_binning
jgi_summarize_bam_contig_depths --outputDepth 03_binning/SLMU.abund \
      02_mapping/SLMU.bam
metabat2 -i 01_assembly/scaffolds.fasta -a 03_binning/SLMU.abund \
      -o 03_binning/SLMU_bin
ls 03_binning/SLMU_bin.*.fa

4. Comparing a MAG against a collection of close relatives

This step is optional, and it’s intended to demonstrate the capabilities of MiGA in the command line. Feel free to skip it.

miga new -P 04_classification/Sal -t genomes
miga gtdb_get -P 04_classification/Sal -T g__Salinibacter --ref -v
miga add -P 04_classification/Sal -i assembly -t popgenome \
      03_binning/SLMU_bin.*.fa
miga index_wf -o 04_classification/Sal -v

That last command will take some time, but you can continue to part 5 and revisit the results from MiGA later.

Once the run is complete, you can open the file:

04_classification/Sal/index.html

5. Mapping reads to the genome(s)

mkdir -p 05_genome_mapping
bowtie2-build 03_binning/SLMU_bin.5.fa 05_genome_mapping/SLMU_bin5.idx
bowtie2 -1 SLMU.1.fastq.gz -2 SLMU.2.fastq.gz \
      -S 05_genome_mapping/SLMU_bin5.sam \
      -x 05_genome_mapping/SLMU_bin5.idx --no-unal

6. Recruitment plots

mkdir -p 06_recplot
rpe build -d 06_recplot/SLMU_5.db -r 05_genome_mapping/SLMU_bin5.sam \
    -g 03_binning/SLMU_bin.5.fa --mag
rpe plot -d 06_recplot/SLMU_5.db
mv recruitment_plots 06_recplot/

Open the file manager and open the following file to explore some of the results locally:

06_recplot/recruitment_plots/SLMU_bin5_sam/SLMU_bin_recruitment_plot.html

Software Used

And lots of Software used by those above!

SeqCode Registry

2023-01-01T00:00:00+00:00

The SeqCode Registry is a portal for the registration, tracking, curation, and validation of prokaryotic names on the basis of genome sequences. It features several automated checks, including nomenclatural and genomic reviews, as well as a multi-scheme taxonomic browser.

The SeqCode Registry can be accessed at https://seqco.de/ and it’s part of The SeqCode Initiative.

Lignocellulolytic consortium

2022-09-26T00:00:00+00:00

The understanding and manipulation of microbial communities toward the conversion of lignocellulose and plastics are topics of interest in microbial ecology and biotechnology. In this study, the polymer-degrading capability of a minimal lignocellulolytic microbial consortium (MELMC) was explored by genome-resolved metagenomics. The MELMC was mostly composed (>90%) of three bacterial members (Pseudomonas protegens; Pristimantibacillus lignocellulolyticus gen. nov., sp. nov; and Ochrobactrum gambitense sp. nov) recognized by their high-quality metagenome-assembled genomes (MAGs). Functional annotation of these MAGs revealed that Pr. lignocellulolyticus could be involved in cellulose and xylan deconstruction, whereas Ps. protegens could catabolize lignin-derived chemical compounds. The capacity of the MELMC to transform synthetic plastics was assessed by two strategies: (i) annotation of MAGs against databases containing plastic-transforming enzymes; and (ii) predicting enzymatic activity based on chemical structural similarities between lignin- and plastics-derived chemical compounds, using Simplified Molecular-Input Line-Entry System and Tanimoto coefficients. Enzymes involved in the depolymerization of polyurethane and polybutylene adipate terephthalate were found to be encoded by Ps. protegens, which could catabolize phthalates and terephthalic acid. The axenic culture of Ps. protegens grew on polyhydroxyalkanoate (PHA) nanoparticles and might be a suitable species for the industrial production of PHAs in the context of lignin and plastic upcycling.

65,000 names

2022-09-20T00:00:00+00:00

Thousands of new bacterial and archaeal species and higher-level taxa are discovered each year through the analysis of genomes and metagenomes. The Genome Taxonomy Database (GTDB) provides hierarchical sequence-based descriptions and classifications for new and as-yet-unnamed taxa. However, bacterial nomenclature, as currently configured, cannot keep up with the need for new well-formed names. Instead, microbiologists have been forced to use hard-to-remember alphanumeric placeholder labels. Here, we exploit an approach to the generation of well-formed arbitrary Latinate names at a scale sufficient to name tens of thousands of unnamed taxa within GTDB. These newly created names represent an important resource for the microbiology community, facilitating communication between bioinformaticians, microbiologists and taxonomists, while populating the emerging landscape of microbial taxonomic and functional discovery with accessible and memorable linguistic labels.

SeqCode

2022-09-19T00:00:00+00:00

Most prokaryotes are not available as pure cultures and therefore ineligible for naming under the rules and recommendations of the International Code of Nomenclature of Prokaryotes (ICNP). Here we summarize the development of the SeqCode, a code of nomenclature under which genome sequences serve as nomenclatural types. This code enables valid publication of names of prokaryotes based upon isolate genome, metagenome-assembled genome or single-amplified genome sequences. Otherwise, it is similar to the ICNP with regard to the formation of names and rules of priority. It operates through the SeqCode Registry (https://seqco.de/), a registration portal through which names and nomenclatural types are registered, validated and linked to metadata. We describe the two paths currently available within SeqCode to register and validate names, including Candidatus names, and provide examples for both. Recommendations on minimal standards for DNA sequences are provided. Thus, the SeqCode provides a reproducible and objective framework for the nomenclature of all prokaryotes regardless of cultivability and facilitates communication across microbiological disciplines.

Development of the SeqCode

2022-09-01T00:00:00+00:00

Over the last fifteen years, genomics has become fully integrated into prokaryotic systematics. The genomes of most type strains have been sequenced, genome sequence similarity is widely used for delineation of species, and phylogenomic methods are commonly used for classification of higher taxonomic ranks. Additionally, environmental genomics has revealed a vast diversity of as-yet-uncultivated taxa. In response to these developments, a new code of nomenclature, the Code of Nomenclature of Prokaryotes Described from Sequence Data (SeqCode), has been developed over the last two years to allow naming of Archaea and Bacteria using DNA sequences as the nomenclatural types. The SeqCode also allows naming of cultured organisms, including fastidious prokaryotes that cannot be deposited into culture collections. Several simplifications relative to the International Code of Nomenclature of Prokaryotes (ICNP) are implemented to make nomenclature more accessible, easier to apply and more readily communicated. By simplifying nomenclature with the goal of a unified classification, inclusive of both cultured and uncultured taxa, the SeqCode will facilitate the naming of taxa in every biome on Earth, encourage the isolation and characterization of as-yet-uncultivated taxa, and promote synergies between the ecological, environmental, physiological, biochemical, and molecular biological disciplines to more fully describe prokaryotes.

Maputo children: comparative fecal microbiome

2022-08-18T00:00:00+00:00

The infant gut microbiome has lifelong implications on health and immunity but there is still limited understanding of the microbiome differences and similarities between children in low- and middle-income countries (LMICs) vs. high-income countries (HICs). Here, we describe and compare the microbiome profile of children aged under 48 months in two urban areas: Maputo, Mozambique and Atlanta, USA using shotgun metagenomics. The gut microbiome of American children showed distinct development, characterized by higher alpha diversity after infancy, compared to the same age group of African children, and the microbiomes clustered separately based on geographic location or age. The abundances of antibiotic resistance genes (ARGs) and virulence factors (VFs) were significantly higher in Maputo children, driven primarily by several primary and opportunistic pathogens. Most notably, about 50% of Maputo children under the age of two were positive for enterotoxigenic (ETEC) and typical enteropathogenic (EPEC) Escherichia coli diagnostic genes while none of the Atlanta age-matched children showed such a positive signal. In contrast, commensal species such as Phocaeicola vulgatus and Bacteroides caccae were more abundant in Atlanta, potentially reflecting diets rich in animal protein and susceptibility to inflammatory diseases. Overall, our results suggest that the different environments characterizing the two cities have significant, distinctive signatures on the microbiota of children and its development over time. Lack of safe water, sanitation, and hygiene (WASH) conditions and/or unsafe food sources may explain the higher enteric pathogen load among children in Maputo.

RecruitPlotEasy Announcement

2022-01-27T00:00:00+00:00

Mapping of short metagenomic (or metatranscriptomic) read data to reference isolate or single-cell genomes or metagenome-assembled genomes (MAGs) to assess microbial population relative abundance and/or structure represents an essential task of many studies across environmental and clinical settings. The filtering for the quality of the read match and assessment of read mapping results are frequently performed without visual aids or with the assistance of visualizations produced through ad-hoc, in-house approaches. Here, we introduce RecruitPlotEasy, a fully automated, user-friendly pipeline for these purposes that integrates statistical approaches to quantify intra-population sequence and gene-content diversity and identify co-occurring relative populations in the sample. Hence, RecruitPlotEasy should also greatly facilitate population genetics studies.

RecruitPlotEasy is implemented in Python and R languages and is freely available open source software under the Artistic License 2.0 from https://github.com/KGerhardt/RecruitPlotEasy.

RecruitPlotEasy

2022-01-01T00:00:00+00:00

RecruitPlotEasy is a fully automated, user-friendly pipeline for these purposes that integrates statistical approaches to quantify intra-population sequence and gene-content diversity and identify co-occurring relative populations in the sample.

RecruitPlotEasy is a standalone application inspired by the Recruitment Plot v2 tools from the Enveomics Collection.