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usearch_qf (usearch quality filter) is a pipeline script built using USEARCH to perform filtering of noisy sequences, chimera checking, and OTU picking on a set of de-multiplexed (i.e. post split_libraries.py) sequences. This tutorial explains how to use usearch_qf through QIIME, with details about each of the steps performed and a brief description of basic parameters and their effect.
Usearch quality filtering is modeled after the OTUPipe series of scripts by Robert Edgar. For details, please check the website OTUPIPE where you can also find some benchmark results using an artificial bacterial community http://www.drive5.com/usearch/perf/mock_results.html.
To use usearch_qf in QIIME, you will need a FASTA file resulting from split_libraries.py. In this tutorial we will use data from the main QIIME tutorial, so our input file will be seqs.fna. From the directory where this file is located, type:
pick_otus.py -i seqs.fna -m usearch --db_filepath=/path/to/gold.fa -o usearch_qf_results/ --word_length 64
where /path/to/gold.fa specifies the full path to the location of the reference set that will be used when doing chimera checking. A copy of this file can be found here (remember to uncompress the file). After executing this command, several files will be created in the usearch_qf_results/ directory. The only file that you will need at this point is usearch_qf_results/seqs_otus.txt (the OTU map file), which can then be used to pick a set of representative sequences with pick_rep_set.py as you would do after running pick_otus.py with default options (i.e. using uclust). The word length is the optimized value for the mock community results http://www.drive5.com/usearch/perf/mock_results.html.
usearch_qf performs 10 steps to process the input reads. We will assume log files are created at each step, which is the default setting in QIIME. File names correspond to those you will see if you use the command specified in the section Basic usage. The value of certain parameters might be different depending on what you specified.
Sequences are initially sorted by length, and the result is stored in the file usearch_qf_results/len_sorted.fasta.
Sequences are de-replicated, so that the resulting file will contain unique sequences only. Results are stored in the file usearch_qf_results/dereplicated_seqs.fasta. In this file each sequence description now includes information on how many sequences match exactly this one, so for instance a sequence with two exact copies would appear as:
By default, de-replication is performed using –max_rejects=500, which can be time demanding if your input data set is large. Reducing this value to, for instance, 64, can greatly improve the speed of this step and still produce very similar results.
De-replicated sequences are then sorted by abundance using the information generated in the previous step, the result being stored in the file usearch_qf_results/abundance_sorted.fasta.
Sequences are clustered at the specified identity (by default, 97%) to filter noisy reads, and the resulting consensus sequences are written to usearch_qf_results/clustered_error_corrected.fasta. Each sequence header contains a new identifier (a unique cluster number) and the size of the cluster:
The identity percentage specified for error correction can be set with the option -j or –percent_id_err, by default 0.97. Higher values of this parameter will result in less reads being merged together at this point; “good” reads that are similar to each other other won’t be clustered as a unique read (i.e. you are not artificially reducing diversity), but some “noisy” reads will escape detection, thus artificially inflating diversity estimates. In general we have not found cases where this parameter needs to be modified. Additionally, running time can be affected by larger values of the parameter –max_rejects in this step.
Once the sequences have been corrected for errors, chimera checking is performed using UCHIME (1). In this step “de novo” checking is performed, without using any external set of reference sequences. This is particularly useful when are using data for which a good reference set does not exist. However, “de novo” chimera checking can be computationally expensive for large datasets, and we suggest to disable it in such cases using the parameter -k or –de_novo_chimera_detection. Results from this step are stored in files de_novo_non_chimeras.fasta and de_novo_chimeras.fasta.
The parameter -a or –abundance_skew can be used to control the abundance skew for chimera detection.
Reference-based chimera checking is performed against gold.fa (or another user-provide set of sequences), and results are stored in files reference_non_chimeras.fasta and reference_novo_chimeras.fasta.
The parameter -f or –db_filepath can be used to specify the path to the sequence set to be used for ref-based chimera checking. To skip this step altogether, use the option -x or –reference_chimera_detection.
Sequences tagged as non-chimeric during steps 6 and 7 can be combined either by taking the intersection (only sequences flagged as non-chimeric by both methods) or union (sequences recognized by one of the methods as non-chimeric). Results are stored in combined_non_chimeras.fasta.
The parameter -F or –non_chimeras_retention is used to set the merging as the union or intersection of the sets of non-chimeric sequences obtained from “de novo” and reference-based chimera checking.
Example Assume there are 4 sequences (A, B, C, D) before chimera checking and “de novo” tags sequence A and B as chimeric while ref-based tags sequences B and C. Using –non_chimeras_retention=union will result in sequence B tagged as chimeric and A, C, and D as non-chimeric, while –non_chimeras_retention=intersection will tag A, B, and C as chimeras and only D as a non chimera.
Once sequences tagged as chimeras have been removed, the sequences are again sorted by abundance and clusters with less than MINSIZE reads are discarded. Results are stored in abundance_sorted_minsize_4.fasta (this assume MINSIZE is set to the default value of 4). To modify the minimum number of reads that a cluster can have, use the parameter -g or –minsize. A value of 2, for instance, would remove all singletons (clusters of size 1). To skip this step use the parameter -l or –cluster_size_filtering.
This step corresponds to what is usually known as “OTU picking”, i.e. sequences are clustered at the desired identity level. Different to regular OTU picking, by using usearch_qf you have also performed error correction and chimera checking, producing a ‘cleaner’ set of OTUs that will contain less artifacts. Results are stored in clustered_seqs.fasta.
The identity percentage to cluster reads can be specified with the parameter -s or –similarity. In general the default of 0.97 works well for most datasets. The parameter –max_rejects can be modified to reduce running time during this step.
The OTUs calculated in the previous step get their IDs replaced by a sequential number, and the result is stored in enumerated_otus.fasta.
Each non-chimeric reads is assigned to the specific OTU identifier it belongs to. This creates the OTU map file (seqs_otus.txt), which can be later used by pick_rep_set.py.
|||Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011 Aug 15;27(16):2194-200. Epub 2011 Jun 23. (link)|
|||Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010 Aug 12;26(19):2460-2461. (link)|