Lederhosen

Lederhosen is a set of tools for OTU clustering rRNA amplicons using Robert Edgar's USEARCH and is simple, robust, and fast. Lederhosen was designed from the beginning to handle lots of data from lots of samples, specifically from data generated by multiplexed Illumina Hi/Mi-Seq sequencing.

No assumptions are made about the design of your experiment. Therefore, there are no tools for read pre-processing and data analysis or statistics. Insert reads, receive data.

Lederhosen is free and open source under the MIT license. Except for the USEARCH license, Lederhosen is free for commercial use.

Badges

Features

• Referenced-based OTU clustering to via USEARCH.
• Multiple Database Support (RDP, GreenGenes, TaxCollector, Silva).
• Parallel support (USEARCH, MapReduce or Compute Cluster).
• Generation and filtering of OTU abundancy matrices.

Installation

Tested using Ruby 1.9.3 and 2.0.0 (1.8.7 not supported)

0. Obtain & Install USEARCH version 7.
1. Get a database:

• TaxCollector
• GreenGenes 13.5 Rep. Set 16S database (recommended)
• File an issue report or pull request ;) to request support for a different database.

2. Install Lederhosen by typing:

   sudo gem install lederhosen

3. Check installation by typing lederhosen. You should see some help text.

Need Help?

Tweets: @heyaudy.

Tasks

Lederhosen is invoked by typing lederhosen [TASK]

Trim Reads

Trimming removed. I think you should use Sickle, or Trimmomatic. You can use FastQC to inspect read quality.

Create Database

The 16S database can optionally be in USEARCH database format (udb). This speeds things up if you are clustering sequences in multiple FASTA files.

lederhosen make_udb \
  --input=taxcollector.fa \
  --output=taxcollector.udb

Cluster Reads using USEARCH

Cluster reads using USEARCH. Output is a uc file.

lederhosen cluster \
  --input=trimmed/sequences.fasta \
  --identity=0.95 \
  --output=clusters_95.uc \
  --database=taxcollector.udb

The optional --dry-run parameter prints the USEARCH command to standard out. Instead of actually running the command. This is useful if you want to run jobs in parallel and/or on a cluster.

for reads_file in reads/*.fasta;
do
    echo lederhosen cluster \
                    --input=$reads_file \
                    --identity=0.95 \
                    --output=$(basename $reads_file .fasta).95.uc \
                    --database=taxcollector.udb \
                    --threads 1 \
                    --dry-run
done > jobs.sh

# send jobs to queue system
cat jobs.sh | parallel -j 24 # run 24 parallel jobs

Generate taxonomy counts tables

Before generating OTU tables, you must generate taxonomy counts (.tax) tables.

A taxonomy count table looks something like this

# taxonomy, number_of_reads
[0]Bacteria[1];...;[8]Akkermansia_municipalia, 28
...

From there, you can generate OTU abundance matrices at the different levels of classification (domain, phylum, ..., genus, species).

lederhosen count_taxonomies \
  --input=clusters.uc \
  --output=clusters_taxonomies.txt

Generate OTU tables

Create an OTU abundance table where rows are samples and columns are clusters. The entries are the number of reads for that cluster in a sample.

lederhosen otu_table \
  --files=clusters_taxonomies.strict.genus.*.txt \
  --output=my_poop_samples_genus_strict.95.txt \
  --level=genus

This will create the file my_poop_samples_genus_strict.95.txt containing the clusters as columns and the samples as rows.

If your database doesn't have taxonomic descriptions, use --level=original.

Filter OTU tables

Sometimes, clustering high-throughput reads at stringent identities can create many, small clusters. In fact, these clusters represent the vast majority (>99%) of the created clusters but the minority (<1%>) of the reads. In other words, 1% of the reads have 99% of the clusters.

If you want to filter out these small clusters which are composed of inseparable sequencing error or actual biodiversity, you can do so with the otu_filter task.

lederhosen otu_filter \
  --input=table.csv \
  --output=filtere.csv \
  --reads=50 \
  --samples=50

This will remove any clusters that do not appear in at least 10 samples with at least 50 reads. The read counts for filtered clusters will be moved to the noise psuedocluster.

Get representative sequences

You can get the representative sequences for each cluster using the get_reps tasks. This will extract the representative sequence from the database you ran usearch with. Make sure you use the same database that you used when running usearch.

lederhosen get_reps \
  --input=clusters.uc \
  --database=taxcollector.fa \
  --output=representatives.fasta

You can get the representatives from more than one cluster file using a glob:

lederhosen get_reps \
  --input=*.uc \
  --database=taxcollector.fa \
  --output=representatives.fasta

Get unclassified sequences

lederhosen separate_unclassified \
  --uc-file=my_results.uc \
  --reads=reads_that_were_used_to_generate_results.fasta
  --output=unclassified_reads.fasta

separate_unclassified has support for strict pairing

lederhosen separate_unclassified \
  --uc-file=my_results.uc \
  --reads=reads_that_were_used_to_generate_results.fasta
  --strict=phylum
  --output=unclassified_reads.fasta

Acknowledgements

• Sinbad Richardson for the Lederhosen Guy artwork
• Lexi, and Kevin for beta-testing and putting up with bugs.
• The QIIME project for inspiration.

Please Cite

Please cite this GitHub repo (https://github.com/audy/lederhosen) with the version you used (type lederhosen version) unless I publish a paper. Then cite that.