Provided by: axe-demultiplexer_0.3.3+dfsg-5_amd64 
NAME
axe - axe Documentation
Axe is a read de-multiplexer, useful in situations where sequence reads contain the indexes that uniquely
distinguish samples. Axe uses a rapid and accurate algorithm based on hamming mismatch tries to
competitively match the prefix of a sequencing read against a set of indexes. Axe supports combinatorial
indexing schemes.
Contents:
AXE TUTORIAL
In this tutorial, we'll use Axe to demultiplex some paired-end, combinatorially-index
Genotyping-by-Sequencing reads. The data for this tutorial is available from figshare: <https://figshare
.com/articles/axe-tutorial_tar/6143720> .
Axe should be run as the initial step of any analysis: don't use sequence QC tools like AdapterRemoval or
Trimmomatic before using axe, as indexes may be trimmed away, or pairing information removed.
Step 0: Download the trial data
This will download the trial data, and extract it on the fly:
curl -LS https://ndownloader.figshare.com/files/11094782 | tar xv
Step 1: prepare a key file
The key file associates index sequences with sample names. A key file can be prepared in a spreadsheet
editor, like LibreOffice Calc, or Excel. The format is quite strict, and is described in detail in the
online usage documentation.
Let's now inspect the keyfile I have provided for the tutorial.
head axe-keyfile.tsv
Step 2: Demultiplex with Axe
In this step, we will demultiplex our interleaved input file to per-sample interleaved output files. To
see a full range of Axe's options, please run axe-demux -h, or inspect the online usage documentation.
First, let's inspect the input.
zcat axe-tutorial.fastq.gz | head -n 8
Then, we need to ensure that axe has somewhere to put the demultiplexed reads. Axe outputs one file (or
more, depending on pairing) per sample. Axe does so by appending the sample name to some prefix (as given
by the -I, -F, and/or -R options). If this prefix is a directory, then sample fastq files will be created
in that sub-directory, but the directory must exist. Let's make an output directory:
mkdir -p output
Now, let's demultiplex the reads!
axe-demux -i axe-tutorial.fastq.gz -I output/ \
-c -b axe-keyfile.tsv -t demux-stats.tsv -z 1
The command above demultiplexes reads from axe-tutorial.fastq.gz into separate files under output, based
on the combinatorial (-c) sample-to-index-sequence mapping described in axe-keyfile.tsv, and saves a file
of statistics as demux-stats.tsv. Note that we have enabled compression of output files using the -z
option, in case you don't have much disk space available. This will make Axe slightly slower.
AXE USAGE
Note:
For arcane reasons, the name of the axe binary changed to axe-demux with version 0.3.0. Apologies for
the inconvenience, this was required to make axe installable in Debian and its derivatives.
Command-line usage did not change.
Axe has several usage modes. The primary distinction is between the two alternate indexing schemes,
single and combinatorial indexing. Single index matching is used when only the first read contains index
sequences. Combinatorial indexing is used when both reads in a read pair contain independent (typically
different) index sequences.
For concise reference, the command-line usage of axe-demux is reproduced below:
USAGE:
axe-demux [-mzc2pt] -b (-f [-r] | -i) (-F [-R] | -I)
axe-demux -h
axe-demux -v
OPTIONS:
-m, --mismatch Maximum hamming distance mismatch. [int, default 1]
-z, --ziplevel Gzip compression level, or 0 for plain text [int, default 0]
-c, --combinatorial Use combinatorial barcode matching. [flag, default OFF]
-p, --permissive Don't error on barcode mismatch confict, matching only
exactly for conficting barcodes. [flag, default OFF]
-2, --trim-r2 Trim barcode from R2 read as well as R1. [flag, default OFF]
-b, --barcodes Barcode file. See --help for example. [file]
-f, --fwd-in Input forward read. [file]
-F, --fwd-out Output forward read prefix. [file]
-r, --rev-in Input reverse read. [file]
-R, --rev-out Output reverse read prefix. [file]
-i, --ilfq-in Input interleaved paired reads. [file]
-I, --ilfq-out Output interleaved paired reads prefix. [file]
-t, --table-file Output a summary table of demultiplexing statistics to file. [file]
-h, --help Print this usage plus additional help.
-V, --version Print version string.
-v, --verbose Be more verbose. Additive, -vv is more vebose than -v.
-q, --quiet Be very quiet.
Inputs and Outputs
Regardless of read mode, three input and output schemes are supported: single-end reads, paired reads
(separate R1 and R2 files) and interleaved paired reads (one file, with R1 and R2 as consecutive reads).
If single end reads are inputted, they must be output as single end reads. If either paired or
interleaved paired reads are read, they can be output as either paired reads or interleaved paired reads.
This applies to both successfully de-multiplexed reads and reads that could not be de-multiplexed.
The -z flag can be used to specify that outputs should be compressed using gzip compression. The -z flag
takes an integer argument between 0 (the default) and 9, where 0 indicates plain text output (gzopen mode
"wT"), and 1-9 indicate that the respective compression level should be used, where 1 is fastest and 9 is
most compact.
The output flags should be prefixes that are used to generate the output file name based on the index's
(or index pair's) ID. The names are generated as: prefix + _ + index ID + _ + read number + .extension.
The output file for reads that could not be demultiplexed is prefix + _ + unknown + _ + read number +
.extension. The read number is omitted unless the paired read file scheme is used, and is "il" for
interleaved output. The extension is "fastq"; ".gz" is appended to the extension if the -z flag is used.
The corresponding CLI flags are:
• -f and -F: Single end or paired R1 file input and output respectively.
• -r and -R: Paired R2 file input and output.
• -i and -I: Interleaved paired input and output.
The index file
The index file is a tab-separated file with an optional header. It is mandatory, and is always supplied
using the -b command line flag. The exact format is dependent on indexing mode, and is described further
in the sections below. If a header is present, the header line must start with either Barcode or index,
or it will be interpreted as a index line, leading to a parsing error. Any line starting with ';' or '#'
is ignored, allowing comments to be added in line with indexes. Please ensure that the software used to
produce the index uses ASCII encoding, and does not insert a Byte-order Mark (BoM) as many text editors
can silently use Unicode-based encoding schemes. I recommend the use of LibreOffice Calc <www.libreoffice
.org> (part of a free and open source office suite) to generate index tables; Microsoft Excel can also be
used.
Mismatch level selection
Independent of index mode, the -m flag is used to select the maximum allowable hamming distance between a
read's prefix and a index to be considered as a match. As "mutated" indexes must be unique, a hamming
distance of one is the default as typically indexes are designed to differ by a hamming distance of at
least two. Optionally, (using the -p flag), axe will allow selective mismatch levels, where, if clashes
are observed, the index will only be matched exactly. This allows one to process datasets with indexes
that don't have a sufficiently high distance between them.
Single index mode
Single index mode is the default mode of operation. Barcodes are matched against read one (hereafter the
forward read), and the index is trimmed from only the forward read, unless the -2 command line flag is
given, in which case a prefix the same length as the matched index is also trimmed from the second or
reverse read. Note that sequence of this second read is not checked before trimming.
In single index mode, the index file has two columns: Barcode and ID.
Combinatorial index mode
Combinatorial index mode is activated by giving the -c flag on the command line. Forward read indexes are
matched against the forward read, and reverse read indexes are matched against the reverse read. The
optimal indexes are selected independently, and the index pair is selected from these two indexes. The
respective indexes are trimmed from both reads; the -2 command line flag has no effect in combinatorial
index mode.
In combinatorial index mode, the index file has three columns: Barcode1, Barcode2 and ID. Individual
indexes can occur many times within the forward and reverse indexes, but index pairs must be unique
combinations.
The Demultiplexing Statistics File
The -t option allows the output of per-sample read counts to a tab-separated file. The file will have a
header describing its format, and includes a line for reads which could not be demultiplexed.
AXE'S MATCHING ALGORITHM
Axe uses an algorithm based on longest-prefix-in-trie matching to match a variable length from the start
of each read against a set of 'mutated' indexes.
Hamming distance matching
While for most applications in high-throughput sequencing hamming distances are a frowned-upon metric, it
is typical for HTS read indexes to be designed to tolerate a certain level of hamming mismatches. Given
these sequences are short and typically occur at the 5' end of reads, insertions and deletions rarely
need be considered, and the increased rate of assignment of reads with many errors is offset by the risk
of falsely assigning indexes to an incorrect sample. In any case, reads with more than 1-2 sequencing
errors in their first several bases are likely to be poor quality, and will simply be filtered out during
downstream quality control.
Hamming mismatch tries
Typically, reads are matched to a set of indexes by calculating the hamming distance between the index,
and the first l bases of a read for a index of length l. The "correct" index is then selected by
recording either the index with the lowest hamming distance to the read (competitive matching) or by
simply accepting the first index with a hamming distance below a certain threshold. These approaches are
both very computationally expensive, and can have lower accuracy than the algorithm I propose.
Additionally, implementations of these methods rarely handle indexes of differing length and
combinatorial indexing well, if at all.
Central to Axe's algorithm is the concept of hamming-mismatch tries. A trie is a N-ary tree for an N
letter alphabet. In the case of high-throughput sequencing reads, we have the alphabet AGCT,
corresponding to the four nucleotides of DNA, plus N, used to represent ambiguous base calls. Instead of
matching each index to each read, we pre-calculate all allowable sequences at each mismatch level, and
store these in level-wise tries. For example, to match to a hamming distance of 2, we create three
tries: One containing all indexes, verbatim, and two tries where every sequence within a hamming distance
of 1 and 2 of each index respectively. Hereafter, these tries are referred to as the 0, 1 and 2-mm
tries, for a hamming distance (mismatch) of 0, 1 and 2. Then, we find the longest prefix in each sequence
read in the 0mm trie. If this prefix is not a valid leaf in the 0mm trie, we find the longest prefix in
the 1mm trie, and so on for all tries in ascending order. If no prefix of the read is a complete sequence
in any trie, the read is assigned to an "non-indexed" output file.
This algorithm ensures optimal index matching in many ways, but is also extremely fast. In situations
with indexes of differing length, we ensure that the longest acceptable index at a given hamming distance
is chosen; assuming that sequence is random after the index, the probability of false assignments using
this method is low. We also ensure that short perfect matches are preferred to longer inexact matches, as
we firstly only consider indexes with no error, then 1 error, and so on. This ensures that reads with
indexes that are followed by random sequence that happens to inexactly match a longer index in the set
are not falsely assigned to this longer index.
The speed of this algorithm is largely due to the constant time matching algorithm with respect to the
number of indexes to match. The time taken to match each read is proportional instead to the length of
the indexes, as for a index of length l, at most l + 1 trie level descents are required to find an entry
in the trie. As this length is more-or-less constant and small, the overall complexity of axe's algorithm
is O(n) for n reads, as opposed to O(nm) for n reads and m indexes as is typical for traditional matching
algorithms
• Index <>
Author
Kevin Murray
Copyright
2014, Kevin Murray
0.3.3 Oct 12, 2025 AXE(1)