Wolves’ diet based on DNA metabarcoding

Here is a tutorial on how to analyze DNA metabarcoding data produced on Illumina sequencers using:

  • the OBITools
  • some basic Unix commands

The data used in this tutorial correspond to the analysis of four wolf scats, using the protocol published in Shehzad et al. (2012) for assessing carnivore diet. After extracting DNA from the faeces, the DNA amplifications were carried out using the primers TTAGATACCCCACTATGC and TAGAACAGGCTCCTCTAG amplifiying the 12S-V5 region (Riaz et al. 2011), together with a wolf blocking oligonucleotide.

The complete data set can be downloaded here: the tutorial dataset

Good to remember: I am working with tons of sequences

It is always a good idea to have a look at the intermediate results or to evaluate the best parameter for each step. Some commands are designed for that purpose, for example you can use :

  • obicount to count the number of sequence records in a file
  • obihead and obitail to view the first or last sequence records of a file
  • obistat to get some basic statistics (count, mean, standard deviation) on the attributes (key=value combinations) in the header of each sequence record (see The extended OBITools fasta format in the fasta format description)
  • any Unix command such as less, awk, sort, wc to check your files


The data needed to run the tutorial are the following:

  • fastq files resulting of a GA IIx (Illumina) paired-end (2 x 108 bp) sequencing assay of DNA extracted and amplified from four wolf faeces:

    • wolf_F.fastq
    • wolf_R.fastq
  • the file describing the primers and tags used for all samples sequenced:

    • wolf_diet_ngsfilter.txt The tags correspond to short and specific sequences added on the 5’ end of each primer to distinguish the different samples
  • the file containing the reference database in a fasta format:

    • db_v05_r117.fasta This reference database has been extracted from the release 117 of EMBL using ecoPCR
  • the NCBI taxonomy formatted in the ecoPCR format (see the obiconvert utility for details) :

    • embl_r117.ndx
    • embl_r117.rdx
    • embl_r117.tdx

Step by step analysis

Recover full sequence reads from forward and reverse partial reads

When using the result of a paired-end sequencing assay with supposedly overlapping forward and reverse reads, the first step is to recover the assembled sequence.

The forward and reverse reads of the same fragment are at the same line position in the two fastq files obtained after sequencing. Based on these two files, the assembly of the forward and reverse reads is done with the illuminapairedend utility that aligns the two reads and returns the reconstructed sequence.

In our case, the command is:

> illuminapairedend --score-min=40 -r wolf_R.fastq wolf_F.fastq > wolf.fastq

The --score-min option allows discarding sequences with low alignment quality. If the alignment score is below 40, the forward and reverse reads are not aligned but concatenated, and the value of the mode attribute in the sequence header is set to joined instead of alignment

Remove unaligned sequence records

Unaligned sequences (mode=joined) cannot be used. The following command allows removing them from the dataset:

> obigrep -p 'mode!="joined"' wolf.fastq > wolf.ali.fastq

The -p requires a python expression. mode!="joined" means that if the value of the mode attribute is different from joined, the corresponding sequence record will be kept.

The first sequence record of wolf.ali.fastq can be obtained using the following command line:

> obihead --without-progress-bar -n 1 wolf.ali.fastq

And the result is:

@HELIUM_000100422_612GNAAXX:7:119:14871:19157#0/1_CONS ali_length=61;
direction=left; seq_ab_match=47; sminR=40.0; seq_a_mismatch=7; seq_b_deletion=1;
seq_b_mismatch=7; seq_a_deletion=1; score_norm=1.89772607661;
score=115.761290673; seq_a_insertion=0; mode=alignment; sminL=40.0;
seq_a_single=46; seq_b_single=46; seq_b_insertion=0;

Assign each sequence record to the corresponding sample/marker combination

Each sequence record is assigned to its corresponding sample and marker using the data provided in a text file (here wolf_diet_ngsfilter.txt). This text file contains one line per sample, with the name of the experiment (several experiments can be included in the same file), the name of the tags (for example: aattaac if the same tag has been used on each extremity of the PCR products, or aattaac:gaagtag if the tags were different), the sequence of the forward primer, the sequence of the reverse primer, the letter T or F for sample identification using the forward primer and tag only or using both primers and both tags, respectively (see ngsfilter for details).

> ngsfilter -t wolf_diet_ngsfilter.txt -u unidentified.fastq wolf.ali.fastq > \

This command creates two files:

  • unidentified.fastq containing all the sequence records that were not assigned to a sample/marker combination
  • wolf.ali.assigned.fastq containing all the sequence records that were properly assigned to a sample/marker combination

Note that each sequence record of the wolf.ali.assigned.fastq file contains only the barcode sequence as the sequences of primers and tags are removed by the ngsfilter program. Information concerning the experiment, sample, primers and tags is added as attributes in the sequence header.

For instance, the first sequence record of wolf.ali.assigned.fastq is:

@HELIUM_000100422_612GNAAXX:7:119:14871:19157#0/1_CONS_SUB_SUB status=full;
seq_ab_match=47; sminR=40.0; ali_length=61; tail_quality=67.0;
reverse_match=tagaacaggctcctctag; seq_a_deletion=1; sample=29a_F260619;
forward_match=ttagataccccactatgc; forward_primer=ttagataccccactatgc;
reverse_primer=tagaacaggctcctctag; sminL=40.0; forward_score=72.0;
score=115.761290673; seq_a_mismatch=7; forward_tag=gcctcct; seq_b_mismatch=7;
experiment=wolf_diet; mid_quality=69.4210526316; avg_quality=69.1045751634;
seq_a_single=46; score_norm=1.89772607661; reverse_score=72.0;
direction=forward; seq_b_insertion=0; seq_b_deletion=1; seq_a_insertion=0;
seq_length_ori=153; reverse_tag=gcctcct; seq_length=99; mode=alignment;
head_quality=67.0; seq_b_single=46;

Dereplicate reads into uniq sequences

The same DNA molecule can be sequenced several times. In order to reduce both file size and computations time, and to get easier interpretable results, it is convenient to work with unique sequences instead of reads. To dereplicate such reads into unique sequences, we use the obiuniq command.

Definition: Dereplicate reads into unique sequences
  1. compare all the reads in a data set to each other
  2. group strictly identical reads together
  3. output the sequence for each group and its count in the original dataset (in this way, all duplicated reads are removed)

Definition adapted from Seguritan and Rohwer (2001)

For dereplication, we use the obiuniq command with the -m sample. The -m sample option is used to keep the information of the samples of origin for each unique sequence.

> obiuniq -m sample wolf.ali.assigned.fastq > wolf.ali.assigned.uniq.fasta

Note that obiuniq returns a fasta file.

The first sequence record of wolf.ali.assigned.uniq.fasta is:

>HELIUM_000100422_612GNAAXX:7:119:14871:19157#0/1_CONS_SUB_SUB_CMP ali_length=61;
seq_ab_match=47; sminR=40.0; tail_quality=67.0; reverse_match=ttagataccccactatgc;
seq_a_deletion=1; forward_match=tagaacaggctcctctag; forward_primer=tagaacaggctcctctag;
reverse_primer=ttagataccccactatgc; sminL=40.0; merged_sample={'29a_F260619': 1};
forward_score=72.0; seq_a_mismatch=7; forward_tag=gcctcct; seq_b_mismatch=7;
score=115.761290673; mid_quality=69.4210526316; avg_quality=69.1045751634;
seq_a_single=46; score_norm=1.89772607661; reverse_score=72.0; direction=reverse;
seq_b_insertion=0; experiment=wolf_diet; seq_b_deletion=1; seq_a_insertion=0;
seq_length_ori=153; reverse_tag=gcctcct; count=1; seq_length=99; status=full;
mode=alignment; head_quality=67.0; seq_b_single=46;

The run of obiuniq has added two key=values entries in the header of the fasta sequence:

  • merged_sample={'29a_F260619': 1}: this sequence have been found once in a single sample called 29a_F260619
  • count=1 : the total count for this sequence is 1

To keep only these two key=value attributes, we can use the obiannotate command:

> obiannotate -k count -k merged_sample \
  wolf.ali.assigned.uniq.fasta > $$ ; mv $$ wolf.ali.assigned.uniq.fasta

The first five sequence records of wolf.ali.assigned.uniq.fasta become:

>HELIUM_000100422_612GNAAXX:7:119:14871:19157#0/1_CONS_SUB_SUB_CMP merged_sample={'29a_F260619': 1}; count=1;
>HELIUM_000100422_612GNAAXX:7:108:5640:3823#0/1_CONS_SUB_SUB_CMP merged_sample={'29a_F260619': 7, '15a_F730814': 2}; count=9;
>HELIUM_000100422_612GNAAXX:7:97:14311:19299#0/1_CONS_SUB_SUB_CMP merged_sample={'29a_F260619': 5, '15a_F730814': 4}; count=9;
>HELIUM_000100422_612GNAAXX:7:22:8540:14708#0/1_CONS_SUB_SUB merged_sample={'29a_F260619': 4697, '15a_F730814': 7638}; count=12335;
>HELIUM_000100422_612GNAAXX:7:57:18459:16145#0/1_CONS_SUB_SUB_CMP merged_sample={'26a_F040644': 10490}; count=10490;

Denoise the sequence dataset

To have a set of sequences assigned to their corresponding samples does not mean that all sequences are biologically meaningful i.e. some of these sequences can contains PCR and/or sequencing errors, or chimeras. To remove such sequences as much as possible, we first discard rare sequences and then rsequence variants that likely correspond to artifacts.

Get the count statistics

In that case, we use obistat to get the counting statistics on the ‘count’ attribute (the count attribute has been added by the obiuniq command). By piping the result in the Unix commands sort and head, we keep only the count statistics for the 20 lowest values of the ‘count’ attribute.

> obistat -c count wolf.ali.assigned.uniq.fasta |  \
  sort -nk1 | head -20

This print the output:

count      count     total
1          3504      3504
2           228       456
3           136       408
4            73       292
5            61       305
6            47       282
7            34       238
8            27       216
9            26       234
10           25       250
11           13       143
12           14       168
13           10       130
14            5        70
15            9       135
16            8       128
17            4        68
18            9       162
19            5        95

The dataset contains 3504 sequences occurring only once.

Keep only the sequences having a count greater or equal to 10 and a length shorter than 80 bp

Based on the previous observation, we set the cut-off for keeping sequences for further analysis to a count of 10. To do this, we use the obigrep command. The -p 'count>=10' option means that the python expression count>=10 must be evaluated to True for each sequence to be kept. Based on previous knowledge we also remove sequences with a length shorter than 80 bp (option -l) as we know that the amplified 12S-V5 barcode for vertebrates must have a length around 100bp.

> obigrep -l 80 -p 'count>=10' wolf.ali.assigned.uniq.fasta \
    > wolf.ali.assigned.uniq.c10.l80.fasta

The first sequence record of wolf.ali.assigned.uniq.c10.l80.fasta is:

>HELIUM_000100422_612GNAAXX:7:22:8540:14708#0/1_CONS_SUB_SUB count=12335; merged_sample={'29a_F260619': 4697, '15a_F730814': 7638};

Clean the sequences for PCR/sequencing errors (sequence variants)

As a final denoising step, using the obiclean program, we keep the head sequences (-H option) that are sequences with no variants with a count greater than 5% of their own count (-r 0.05 option).

> obiclean -s merged_sample -r 0.05 -H \
  wolf.ali.assigned.uniq.c10.l80.fasta > wolf.ali.assigned.uniq.c10.l80.clean.fasta

The first sequence record of wolf.ali.assigned.uniq.c10.l80.clean.fasta is:

merged_sample={'29a_F260619': 4697, '15a_F730814': 7638};
obiclean_count={'29a_F260619': 5438, '15a_F730814': 8642}; obiclean_head=True;
'HELIUM_000100422_612GNAAXX:7:22:8540:14708#0/1_CONS_SUB_SUB', '15a_F730814':
count=12335; obiclean_internalcount=0; obiclean_status={'29a_F260619': 'h', '15a_F730814': 'h'};
obiclean_samplecount=2; obiclean_headcount=2; obiclean_singletoncount=0;

Taxonomic assignment of sequences

Once denoising has been done, the next step in diet analysis is to assign the barcodes to the corresponding species in order to get the complete list of species associated to each sample.

Taxonomic assignment of sequences requires a reference database compiling all possible species to be identified in the sample. Assignment is then done based on sequence comparison between sample sequences and reference sequences.

Build a reference database

One way to build the reference database is to use the ecoPCR program to simulate a PCR and to extract all sequences from the EMBL that may be amplified in silico by the two primers (TTAGATACCCCACTATGC and TAGAACAGGCTCCTCTAG) used for PCR amplification.

The full list of steps for building this reference database would then be:

  1. Download the whole set of EMBL sequences (available from: ftp://ftp.ebi.ac.uk/pub/databases/embl/release/)
  2. Download the NCBI taxonomy (available from: ftp://ftp.ncbi.nih.gov/pub/taxonomy/taxdump.tar.gz)
  3. Format them into the ecoPCR format (see obiconvert for how you can produce ecoPCR compatible files)
  4. Use ecoPCR to simulate amplification and build a reference database based on putatively amplified barcodes together with their recorded taxonomic information

As step 1 and step 3 can be really time-consuming (about one day), we alredy provide the reference database produced by the following commands so that you can skip its construction. Note that as the EMBL database and taxonomic data can evolve daily, if you run the following commands you may end up with quite different results.

Any utility allowing file downloading from a ftp site can be used. In the following commands, we use the commonly used wget Unix command.

Download the sequences
> mkdir EMBL
> cd EMBL
> wget -nH --cut-dirs=4 -Arel_std_\*.dat.gz -m ftp://ftp.ebi.ac.uk/pub/databases/embl/release/
> cd ..
Download the taxonomy
> mkdir TAXO
> cd TAXO
> wget ftp://ftp.ncbi.nih.gov/pub/taxonomy/taxdump.tar.gz
> tar -zxvf taxdump.tar.gz
> cd ..
Format the data
> obiconvert --embl -t ./TAXO --ecopcrDB-output=embl_last ./EMBL/*.dat
Use ecoPCR to simulate an in silico` PCR
> ecoPCR -d ./ECODB/embl_last -e 3 -l 50 -L 150 \

Note that the primers must be in the same order both in wolf_diet_ngsfilter.txt and in the ecoPCR command.

Clean the database
  1. filter sequences so that they have a good taxonomic description at the species, genus, and family levels (obigrep command below).
  2. remove redundant sequences (obiuniq command below).
  3. ensure that the dereplicated sequences have a taxid at the family level (obigrep command below).
  4. ensure that sequences each have a unique identification (obiannotate command below)
> obigrep -d embl_last --require-rank=species \
  --require-rank=genus --require-rank=family v05.ecopcr > v05_clean.fasta

> obiuniq -d embl_last \
  v05_clean.fasta > v05_clean_uniq.fasta

> obigrep -d embl_last --require-rank=family \
  v05_clean_uniq.fasta > v05_clean_uniq_clean.fasta

> obiannotate --uniq-id v05_clean_uniq_clean.fasta > db_v05.fasta


From now on, for the sake of clarity, the following commands will use the filenames of the files provided with the tutorial. If you decided to run the last steps and use the files you have produced, you’ll have to use db_v05.fasta instead of db_v05_r117.fasta and embl_last instead of embl_r117

Assign each sequence to a taxon

Once the reference database is built, taxonomic assignment can be carried out using the ecotag command.

> ecotag -d embl_r117 -R db_v05_r117.fasta wolf.ali.assigned.uniq.c10.l80.clean.fasta > \

The ecotag adds several key=value attributes in the sequence record header, among them:

  • best_match=ACCESSION where ACCESSION is the id of hte sequence in the reference database that best aligns to the query sequence;
  • best_identity=FLOAT where FLOAT*100 is the percentage of identity between the best match sequence and the query sequence;
  • taxid=TAXID where TAXID is the final assignation of the sequence by ecotag
  • scientific_name=NAME where NAME is the scientific name of the assigned taxid.

The first sequence record of wolf.ali.assigned.uniq.c10.l80.clean.tag.fasta is:

species_name=Capreolus capreolus; family=9850; scientific_name=Capreolus
capreolus; rank=species; taxid=9858; best_identity={'db_v05_r117': 1.0};
scientific_name_by_db={'db_v05_r117': 'Capreolus capreolus'};
obiclean_samplecount=2; species=9858; merged_sample={'29a_F260619': 4697,
'15a_F730814': 7638}; obiclean_count={'29a_F260619': 5438, '15a_F730814': 8642};
obiclean_singletoncount=0; obiclean_cluster={'29a_F260619':
species_list={'db_v05_r117': ['Capreolus capreolus']}; obiclean_internalcount=0;
match_count={'db_v05_r117': 1}; obiclean_head=True; taxid_by_db={'db_v05_r117':
9858}; family_name=Cervidae; genus_name=Capreolus;
obiclean_status={'29a_F260619': 'h', '15a_F730814': 'h'}; obiclean_headcount=2;
count=12335; id_status={'db_v05_r117': True}; best_match={'db_v05_r117':
'AJ885202'}; order_name=None; rank_by_db={'db_v05_r117': 'species'}; genus=9857;

Generate the final result table

Some unuseful attributes can be removed at this stage.

> obiannotate  --delete-tag=scientific_name_by_db --delete-tag=obiclean_samplecount \
  --delete-tag=obiclean_count --delete-tag=obiclean_singletoncount \
  --delete-tag=obiclean_cluster --delete-tag=obiclean_internalcount \
  --delete-tag=obiclean_head --delete-tag=taxid_by_db --delete-tag=obiclean_headcount \
  --delete-tag=id_status --delete-tag=rank_by_db --delete-tag=order_name \
  --delete-tag=order wolf.ali.assigned.uniq.c10.l80.clean.tag.fasta > \

The first sequence record of wolf.ali.assigned.uniq.c10.l80.clean.tag.ann.fasta is then:

match_count={'db_v05_r117': 1}; count=12335; species_name=Capreolus capreolus;
best_match={'db_v05_r117': 'AJ885202'}; family=9850; family_name=Cervidae;
scientific_name=Capreolus capreolus; taxid=9858; rank=species;
obiclean_status={'29a_F260619': 'h', '15a_F730814': 'h'};
best_identity={'db_v05_r117': 1.0}; merged_sample={'29a_F260619': 4697,
'15a_F730814': 7638}; genus_name=Capreolus; genus=9857; species=9858;
species_list={'db_v05_r117': ['Capreolus capreolus']};

The sequences can be sorted by decreasing order of count.

> obisort -k count -r wolf.ali.assigned.uniq.c10.l80.clean.tag.ann.fasta >  \

The first sequence record of wolf.ali.assigned.uniq.c10.l80.clean.tag.ann.sort.fasta is then:

>HELIUM_000100422_612GNAAXX:7:22:8540:14708#0/1_CONS_SUB_SUB_CMP count=12335;
match_count={'db_v05_r117': 1}; species_name=Capreolus capreolus;
best_match={'db_v05_r117': 'AJ885202'}; family=9850; family_name=Cervidae;
scientific_name=Capreolus capreolus; taxid=9858; rank=species;
obiclean_status={'29a_F260619': 'h', '15a_F730814': 'h'};
best_identity={'db_v05_r117': 1.0}; merged_sample={'29a_F260619': 4697,
'15a_F730814': 7638}; genus_name=Capreolus; genus=9857; species=9858;
species_list={'db_v05_r117': ['Capreolus capreolus']};

Finally, a tab-delimited file that can be open by excel or R is generated.

> obitab -o wolf.ali.assigned.uniq.c10.l80.clean.tag.ann.sort.fasta > \
This file contains 26 sequences. You can deduce the diet of each sample:
  • 13a_F730603: Cervus elaphus
  • 15a_F730814: Capreolus capreolus
  • 26a_F040644: Marmota sp. (according to the location, it is Marmota marmota)
  • 29a_F260619: Capreolus capreolus

Note that we also obtained a few wolf sequences although a wolf-blocking oligonucleotide was used.


  • Shehzad W, Riaz T, Nawaz MA, Miquel C, Poillot C, Shah SA, Pompanon F, Coissac E, Taberlet P (2012) Carnivore diet analysis based on next generation sequencing: application to the leopard cat (Prionailurus bengalensis) in Pakistan. Molecular Ecology, 21, 1951-1965.
  • Riaz T, Shehzad W, Viari A, Pompanon F, Taberlet P, Coissac E (2011) ecoPrimers: inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Research, 39, e145.
  • Seguritan V, Rohwer F. (2001) FastGroup: a program to dereplicate libraries of 16S rDNA sequences. BMC Bioinformatics. 2001;2:9. Epub 2001 Oct 16.


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