vidjil-algo – Command-line Manual
V(D)J recombinations in lymphocytes are essential for immunological diversity. They are also useful markers of pathologies, and in leukemia, are used to quantify the minimal residual disease during patient follow-up.
Vidjil-algo processes high-throughput sequencing data to extract V(D)J junctions and gather them into clones. Vidjil-algo starts from a set of reads and detects “windows” overlapping the actual CDR3. This is based on an fast and reliable seed-based heuristic and allows to output all sequenced clones. The analysis is extremely fast because, in the first phase, no alignment is performed with database germline sequences. At the end, only the consensus sequences of each clone have to be analyzed. Vidjil-algo can also cluster similar clones, or leave this to the user after a manual review in the web application.
The method is described in the following references:
Marc Duez et al., “Vidjil: A web platform for analysis of high-throughput repertoire sequencing”, PLOS ONE 2016, 11(11):e0166126 http://dx.doi.org/10.1371/journal.pone.0166126
Mathieu Giraud, Mikaël Salson, et al., “Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing”, BMC Genomics 2014, 15:409 http://dx.doi.org/10.1186/1471-2164-15-409
Vidjil-algo is open-source, released under GNU GPLv3 license. This is the help of vidjil-algo, for command-line usage. Other documentation (users and administrators of the web application, developpers) can be found from http://www.vidjil.org/doc/.
Requirements and installation
Vidjil-algo has been successfully tested on the following platforms :
- CentOS 6.3 amd64
- CentOS 6.3 i386
- CentOS 7.2 i386
- Debian Squeeze 6.0
- Debian Wheezy 7.0 amd64
- Fedora 19
- FreeBSD 9.2
- Ubuntu 12.04 LTS amd64
- Ubuntu 14.04 LTS amd64
- Ubuntu 16.04 LTS
- OS X 10.9, 10.10, 10.11
Vidjil-algo is developed with continuous integration using systematic unit and functional testing. The development team internally uses Jenkins for that. Moreover, the results of some of these tests can be publicly checked on travis-ci.org.
Build requirements (optional)
This paragraph details the requirements to build Vidjil-algo from source. You can also download a static binary (see next paragraph, ‘Installation’).
To compile Vidjil-algo, make sure:
- to be on a POSIX system ;
- to have a C++11 compiler (as
g++4.8 or above,
g++7.2 being supported, or
clang3.3 or above).
- to have the
zlib1g-devpackage under Debian/Ubuntu,
zlib-develpackage under Fedora/CentOS).
g++-4.8 is included in the devtools 2.0.
sudo wget http://people.centos.org/tru/devtools-2/devtools-2.repo -O /etc/yum.repos.d/devtools-2.repo sudo yum install devtoolset-2-gcc devtoolset-2-binutils devtoolset-2-gcc-c++ devtoolset-2-valgrind # scl enable devtoolset-2 bash # either open a shell running devtools source /opt/rh/devtoolset-2/enable # ... or source devtools in the same shell
g++-4.8 is included.
g++-4.8 is included in FreeBSD 9.2.
You may also need to install the
gzstream library with:
pkg install gzstream
Also Vidjil-algo uses GNU make which requires
gmake under FreeBSD.
At the time of redacting the documentation,
g++ requires extra options to
ensure flawless compilation and execution of Vidjil-algo:
make MAKE=gmake CXXFLAGS="-D_GLIBCXX_USE_C99 -Wl,-rpath=/usr/local/lib/gcc49"
gcc49 at the end of the command line is to be replaced by the
Debian Squeeze 6.0 / Wheezy 7.0
g++-4.8 should be pinned from testing.
/etc/apt/preferences the following lines:
Package: * Pin: release n=wheezy # (or squeeze) Pin-Priority: 900 Package: g++-4.8, gcc-4.8, valgrind* Pin: release n=jessie Pin-Priority: 950
Then g++ 4.8 can be installed.
apt-get update apt-get install -t jessie g++-4.8 valgrind
Ubuntu 14.04 LTS
sudo apt-get install g++-4.8
Ubuntu 12.04 LTS
g++-4.8 is included in the devtools 2.0.
sudo apt-get install python-software-properties sudo add-apt-repository ppa:ubuntu-toolchain-r/test sudo apt-get update sudo apt-get install g++-4.8
Xcode should be installed first.
Running ‘make’ from the extracted archive should be enough to install vidjil-algo with germline and demo files. It runs the three following ‘make’ commands.
make germline # get IMGT germline databases (IMGT/GENE-DB) -- you have to agree to IMGT license: # academic research only, provided that it is referred to IMGT®, # and cited as "IMGT®, the international ImMunoGeneTics information system® # http://www.imgt.org (founder and director: Marie-Paule Lefranc, Montpellier, France). # Lefranc, M.-P., IMGT®, the international ImMunoGeneTics database, # Nucl. Acids Res., 29, 207-209 (2001). PMID: 11125093 make vijdil-algo # build vijil-algo from the sources (see the requirements, # another option is: wget http://www.vidjil.org/releases/vidjil-algo-latest_x86_64 -O vidjil-algo # to download a static binary (built for x86_64 architectures) make demo # download demo files (S22 and L4, see demo/get-sequences) ./vidjil-algo -h # display help/usage
If your build system does not use C++11 by default, you should replace the
make commands by:
make CXXFLAGS='-std=c++11' ### gcc-4.8 make CXXFLAGS='-std=c++11' LDFLAGS='-stdlib=libc++' ### OS X Mavericks
If you use a Debian-based operating system you can simply add the Vidjil repository to your sources.list: deb http://rby.vidjil.org:8080/archive sid/all/ deb http://rby.vidjil.org:8080/archive sid/amd64/
And install from he command line: apt-get update apt-get install vidjil
You can run the tests with the following commands:
make -C src/tests/data # get IGH recombinations from a single individual, as described in: # Boyd, S. D., and al. Individual variation in the germline Ig gene # repertoire inferred from variable region gene rearrangements. J # Immunol, 184(12), 6986–92. make -C src test # run self-tests (can take 5 to 60 minutes)
Input and parameters
The main input file of Vidjil-algo is a set of reads, given as a
.fastq file, possibly compressed with gzip (
This set of reads can reach several gigabytes and 2*10^9 reads. It is
never loaded entirely in the memory, but reads are processed one by
one by Vidjil-algo.
Vidjil-algo can also process BAM files, but please note that:
- The reads don’t need to be aligned beforehand.
- In case of paired-end sequencing, the reads must have already been merged in the BAM file.
-H help options provide the list of parameters that can be
used. We detail here the options of the main
-c clones command.
The default options are very conservative (large window, no further automatic clusterization, see below), leaving the user or other software making detailed analysis and decisions on the final clustering.
Recombination / locus selection
Germline presets (at least one -g or -V/(-D)/-J option must be given for all commands except -c germlines) -g <.g file>(:filter) multiple locus/germlines, with tuned parameters. Common values are '-g germline/homo-sapiens.g' '-g germline/mus-musculus.g' The list of locus/recombinations can be restricted, such as in '-g germline/homo-sapiens.g:IGH,IGK,IGL' -g <path> multiple locus/germlines, shortcut for '-g <path>/homo-sapiens.g' processes human TRA, TRB, TRG, TRD, IGH, IGK and IGL locus, possibly with some incomplete/unusal recombinations -V <file> custom V germline multi-fasta file -D <file> custom D germline multi-fasta file (and resets -m and -w options), will segment into V(D)J components -J <file> custom J germline multi-fasta file Locus/recombinations -d try to detect several D (experimental) -2 try to detect unexpected recombinations (must be used with -g)
germline/*.g presets configure the analyzed recombinations.
The following presets are provided:
germline/homo-sapiens.g: Homo sapiens, TR (
TRD) and Ig (
IGL) locus, including incomplete/unusal recombinations (
IGK+, see locus.org)
germline/homo-sapiens-isotypes.g: Homo sapiens heavy chain locus, looking for sequences with, on one side, IGHJ (or even IGHV) genes, and, on the other side, an IGH constant chain.
germline/homo-sapiens-cd.g: Homo sapiens, common CD genes (experimental, does not check for recombinations)
germline/mus-musculus.g: Mus musculus (strains BALB/c and C57BL/6)
germline/rattus-norvegicus.g: Rattus norvegicus (strains BN/SsNHsdMCW and Sprague-Dawley)
germline/*.g presets for other species or for custom recombinations can be created, possibly referring to other
Please contact us if you need help in configuring other germlines.
- Recombinations can be filtered, such as in
-g germline/homo-sapiens.g:IGH(only IGH, complete recombinations),
-g germline/homo-sapiens.g:IGH,IGH+(only IGH, as well with incomplete recombinations) or
-g germline/homo-sapiens.g:TRA,TRB,TRG(only TR locus, complete recombinations).
- Several presets can be loaded at the same time, as for instance
-g germline/homo-sapiens.g -g germline/germline/homo-sapiens-isotypes.g.
-2further test unexpected recombinations (tagged as
xxx), as in
-g germline/homo-sapiens.g -2.
Finally, the advanced
-V/(-D)/-J options enable to select custom V, (D) and J repertoires given as
Main algorithm parameters
Window prediction (use either -s or -k option, but not both) -s <string> spaced seed used for the V/J affectation (default: #####-#####, ######-######, #######-#######, depends on germline) -k <int> k-mer size used for the V/J affectation (default: 10, 12, 13, depends on germline) (using -k option is equivalent to set with -s a contiguous seed with only '#' characters) -w <int> w-mer size used for the length of the extracted window (default: 50) ('all': use all the read, no window clustering) -e <float> maximal e-value for determining if a segmentation can be trusted (default: 'all', no limit) -t <int> trim V and J genes (resp. 5' and 3' regions) to keep at most <int> nt (default: 0) (0: no trim)
-k are the options of the seed-based heuristic that detects
“junctions”, that is a zone in a read that is similar to V genes on its
left end and similar to J genes in its right end. A detailed
explanation can be found in (Giraud, Salson and al., 2014).
These options are for advanced usage, the defaults values should work.
-k option selects the seed used for the k-mer V/J affectation.
-w option fixes the size of the “window” that is the main
identifier to cluster clones. The default value (
-w 50) was selected
to ensure a high-quality clone clustering: reads are clustered when
they exactly share, at the nucleotide level, a 50 bp-window centered
on the CDR3. No sequencing errors are corrected inside this window.
The center of the “window”, predicted by the high-throughput heuristic, may
be shifted by a few bases from the actual “center” of the CDR3 (for TRG,
less than 15 bases compared to the IMGT/V-QUEST or IgBlast prediction
in >99% of cases when the reads are large enough). Usually, a 50 bp-window
fully contains the CDR3 as well as some part of the end of the V and
the start of the J, or at least some specific N region to uniquely identify the clone.
-w to higher values (such as
-w 60 or
-w 100) makes the clone clustering
even more conservative, enabling to split clones with low specificity (such as IGH with very
large D, short or no N regions and almost no somatic hypermutations). However, such settings
may “segment” (analyze) less reads, depending on the read length of your data, and may also
return more clones, as any sequencing error in the window is not corrected.
-w all option takes all the read as the windows, completely disabling
the clustering by windows and generally returning more clones. This should only be used on
datasets where reads of the same clone do have exactly the same length.
-w to lower values than 50 may “segment” (analyze) a few more reads, depending
on the read length of your data, but may in some cases falsely cluster reads from
For VJ recombinations, the
-w 40 option is usually safe, and
-w 30 can also be tested.
-w to lower values is not recommended.
When the read is too short too extract the requested length, the window can be shifted
(at most 10 bp) or shrinkened (down until 30bp) by increments of 5bp. Such reads
are counted in
SEG changed w and the corresponding clones are output with the
-e option sets the maximal e-value accepted for segmenting a sequence.
It is an upper bound on the number of exepcted windows found by chance by the seed-based heuristic.
The e-value computation takes into account both the number of reads in the
input sequence and the number of locus searched for.
The default value is 1.0, but values such as 1000, 1e-3 or even less can be used
to have a more or less permissive segmentation.
The threshold can be disabled with
-t option sets the maximal number of nucleotides that will be indexed in
V genes (the 3’ end) or in J genes (the 5’ end). This reduces the load of the
indexes, giving more precise window estimation and e-value computation.
However giving a
-t may also reduce the probability of seeing a heavily
trimmed or mutated V gene.
The default is
Thresholds on clone output
The following options control how many clones are output and analyzed.
Limits to report a clone (or a window) -r <nb> minimal number of reads supporting a clone (default: 5) -% <ratio> minimal percentage of reads supporting a clone (default: 0) Limits to further analyze some clones -y <nb> maximal number of clones computed with a consensus sequence ('all': no limit) (default: 100) -z <nb> maximal number of clones to be analyzed with a full V(D)J designation ('all': no limit, do not use) (default: 100) -A reports and segments all clones (-r 1 -% 0 -y all -z all), to be used only on very small datasets
-r/-% options are strong thresholds: if a clone does not have
the requested number of reads, the clone is discarded (except when
-l, see below).
-r 5 option is meant to only output clones that
have a significant read support. You should use
-r 1 if you
want to detect all clones starting from the first read (especially for
-y option limits the number of clones for which a consensus
sequence is computed. Usually you do not need to have more
consensus (see below), but you can safely put
-y all if you want
to compute all consensus sequences.
-z option limits the number of clones that are fully analyzed,
with their V(D)J designation and possibly a CDR3 detection,
in particular to enable the web application
to display the clones on the grid (otherwise they are displayed on the
If you want to analyze more clones, you should use
-z 200 or
-z 500. It is not recommended to use larger values: outputting more
than 500 clones is often not useful since they can not be visualized easily
in the web application, and takes large computation time (full dynamic programming,
Note that even if a clone is not in the top 100 (or 200, or 500) but
still passes the
-% options, it is still reported in both the
.vdj.fa files. If the clone is at some MRD point in the top 100 (or 200, or 500),
it will be fully analyzed/segmented by this other point (and then
collected by the
fuse.py script, using consensus sequences computed at this
other point, and then, on the web application, correctly displayed on the grid).
Thus is advised to leave the default
-z 100 option
for the majority of uses.
-A option disables all these thresholds. This option should be
used only for test and debug purposes, on very small datasets, and
produce large file and takes huge computation times.
Sequences of interest
Vidjil-algo allows to indicate that specific sequences should be followed and output,
even if those sequences are ‘rare’ (below the
Such sequences can be provided either with
-W <sequence>, or with
The file given by
-l should have one sequence by line, as in the following example:
GAGAGATGGACGGGATACGTAAAACGACATATGGTTCGGGGTTTGGTGCT my-clone-1 GAGAGATGGACGGAATACGTTAAACGACATATGGTTCGGGGTATGGTGCT my-clone-2 foo
Sequences and labels must be separed by one space. The first column of the file is the sequence to be followed while the remaining columns consist of the sequence’s label. In Vidjil-algo output, the labels are output alongside their sequences.
A sequence given
-W <sequence> or with
-l <file> can be exactly the size
of the window (
-w, that is 50 by default). In this case, it is guaranteed that
such a window will be output if it is detected in the reads.
More generally, when the provided sequence differs in length with the windows
we will keep any windows that contain the sequence of interest or, conversely,
we will keep any window that is contained in the sequence of interest.
This filtering will work as expected when the provided sequence overlaps
(at least partially) the CDR3 or its close neighborhood.
-F option, only the windows related to the given sequences are kept.
This allows to quickly filter a set of reads, looking for a known sequence or window,
-FaW <sequence> options:
All the reads with the windows related to the sequence will be extracted to
Clone analysis: VDJ assignation and CDR3 detection
-3 option launches a CDR3/JUNCTION detection based on the position
of Cys104 and Phe118/Trp118 amino acids. This detection relies on alignment
with gapped V and J sequences, as for instance, for V genes, IMGT/GENE-DB sequences,
as provided by
The CDR3/JUNCTION detection won’t work with custom non-gapped V/J repertoires.
CDR3 are reported as productive when they come from an in-frame recombination and when the sequence does not contain any in-frame stop codons.
-f option sets the parameters used in the comparisons between
the clone sequence and the V(D)J germline genes. The default values should work.
The e-value set by
-e is also applied to the V/J designation.
-E option further sets the e-value for the detection of D segments.
Further clustering (experimental)
The following options are experimental and have no consequences on the
nor on the standard output. They instead add a
clusters sections in the
that will be visualized in the web application.
-n option triggers an automatic clustering using DBSCAN algorithm (Ester and al., 1996).
-n 5 usually cluster reads within a distance of 1 mismatch (default score
being +1 for a match and -4 for a mismatch). However, more distant reads can also
be clustered when there are more than 10 reads within the distance threshold.
This behaviour can be controlled with the
-= option allows to specify a file for manually clustering two windows
considered as similar. Such a file may be automatically produced by vidjil
out/edges), depending on the option provided. Only the two first columns
(separed by one space) are important to vidjil, they only consist of the
two windows that must be clustered.
Main output files
The main output of Vidjil-algo (with the default
-c clones command) are two following files:
.vidjilfile is the file for the Vidjil web application. The file is in a
.jsonformat (detailed in format-analysis.org) describing the windows and their count, the consensus sequences (
-y), the detailed V(D)J and CDR3 designation (
-z, see warning below), and possibly the results of the further clustering.
The web application takes this
.vidjilfile (possibly merged with =fuse.py=) for the visualization and analysis of clones and their tracking along different samples (for example time points in a MRD setup or in a immunological study). Please see browser.org for more information on the web application.
.vdj.fafile is a FASTA file for further processing by other bioinformatics tools. The sequences are at least the windows (and their count in the headers) or the consensus sequences (
-y) when they have been computed. The headers include the count of each window, and further includes the detailed V(D)J and CDR3 designation (
-z, see warning below), given in a ‘.vdj’ format, see below. The further clustering is not output in this file.
.vdj.faoutput enables to use Vidjil-algo as a filtering tool, shrinking a large read set into a manageable number of (pre-)clones that will be deeply analyzed and possibly further clustered by other software.
By default, the two output files are named
outis the directory where all the outputs are stored (can be changed with the
basenameis the basename of the input
.fasta/.fastqfile (can be overriden with the
Auxiliary output files
out/basename.windows.fa file contains the list of windows, with number of occurrences:
>8--window--1 TATTACTGTACCCGGGAGGAACAATATAGCAGCTGGTACTTTGACTTCTG >5--window--2 CGAGAGGTTACTATGATAGTAGTGGTTATTACGGGGTAGGGCAGTACTAC ATAGTAGTGGTTATTACGGGGTAGGGCAGTACTACTACTACTACATGGAC (...)
Windows of size 50 (modifiable by
-w) have been extracted.
The first window has 8 occurrences, the second window has 5 occurrences.
out/seq/clone.fa-* contains the detailed analysis by clone, with
the window, the consensus sequence, as well as with the most similar V, (D) and J germline genes:
>clone-001--IGH--0000008--0.0608%--window TATTACTGTACCCGGGAGGAACAATATAGCAGCTGGTACTTTGACTTCTG >clone-001--IGH--0000008--0.0608%--lcl|FLN1FA001CPAUQ.1|-[105,232]-#2 - 128 bp (55% of 232.0 bp) + VDJ 0 54 73 84 85 127 IGHV3-23*05 6/ACCCGGGAGGAACAATAT/9 IGHD6-13*01 0//5 IGHJ4*02 IGH SEG_+ 1.946653e-19 1.352882e-19/5.937712e-20 GCTGTACCTGCAAATGAACAGCCTGCGAGCCGAGGACACGGCCACCTATTACTGT ACCCGGGAGGAACAATATAGCAGCTGGTAC TTTGACTTCTGGGGCCAGGGGATCCTGGTCACCGTCTCCTCAG >IGHV3-23*05 GAGGTGCAGCTGTTGGAGTCTGGGGGAGGCTTGGTACAGCCTGGGGGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTTAGCAGCTATGCCATGAGCTGGGTCCGCCAGGCTCCAGGGAAGGGGCTGGAGTGGGTCTCAGCTATTTATAGCAGTGGTAGTAGCACATACTATGCAGACTCCGTGAAGGGCCGGTTCACCATCTCCAGAGACAATTCCAAGAACACGCTGTATCTGCAAATGAACAGCCTGAGAGCCGAGGACACGGCCGTATATTACTGTGCGAAA >IGHD6-13*01 GGGTATAGCAGCAGCTGGTAC >IGHJ4*02 ACTACTTTGACTACTGGGGCCAGGGAACCCTGGTCACCGTCTCCTCAG
-a debug option further output in each
out/seq/clone.fa-* files the full list of reads belonging to this clone.
-a option produces large files, and is not recommanded in general cases.
Several diversity indices are reported, both on the standard output and in the
- H (
index_H_entropy): Shannon’s diversity
- E (
index_E_equitability): Shannon’s equitability
- Ds (
index_Ds_diversity): Simpson’s diversity
E ans Ds values are between 0 (no diversity, one clone clusters all analyzed reads) and 1 (full diversity, each analyzed read belongs to a different clone). These values are now computed on the windows, before any further clustering. PCR and sequencing errors can thus lead to slighlty over-estimate the diversity.
Vidjil-algo outputs details statistics on the reads that are not segmented (not analyzed). Basically, an unsegmented read is a read where Vidjil-algo cannot identify a window at the junction of V and J genes. To properly analyze a read, Vijdil needs that the sequence spans enough V region and J region (or, more generally, 5’ region and 3’ regions when looking for incomplete or unusual recombinations). The following unsegmentation causes are reported:
|Reads are too short, shorter than the seed (by default between 9 and 13 bp).|
|The strand is mixed in the read, with some similarities both with the |
|No information has been found on the read: There are not enough similarities neither with a V gene or a J gene.|
|Relevant similarities have been found with some V, but none or not enough with any J.|
|Relevant similarities have been found with some J, but none or not enough with any V.|
|vidjil-algo finds some V and J similarities mixed together which makes the situation ambiguous and hardly solvable.|
|The junction can be identified but the read is too short so that vidjil-algo could extract the window (by default 50bp).|
|It often means the junction is very close from one end of the read.|
Some datasets may give reads with many low
UNSEG too few reads:
UNSEG too few V/Jusually happens when reads share almost nothing with the V(D)J region. This is expected when the PCR or capture-based approach included other regions, such as in whole RNA-seq.
UNSEG only V/5and
UNSEG only J/3happen when reads do not span enough the junction zone. Vidjil-algo detects a “window” including the CDR3. By default this window is 50bp long, so the read needs be that long centered on the junction.
See browser.org for information on the biological or sequencing causes that can lead to few segmented reads.
It is possible to extract all segmented or unsegmented reads, possibly to give them to other software.
Runing Vidjil with
-U gives a file
out/basename.segmented.vdj.fa, with all segmented reads.
On datasets generated with rather specific V(D)J primers, this is generally not recommended, as it may generate a large file.
-U option is very useful for whole RNA-Seq or capture datasets that contain few reads with V(D)J recombinations.
Similarly, options are available to get the unsegmented reads:
-ugives a set of files
out/basename.UNSEG_*, with unsegmented reads gathered by unsegmentation cause. It outputs only reads sharing significantly sequences with V/J germline genes or with some ambiguity: it may be interesting to further study RNA-Seq datasets.
-uugives the same set of files, including all unsegmented reads (including
UNSEG too shortand
UNSEG too few V/J), and
-uuufurther outputs all these reads in a file
Again, as these options may generate large files, they are generally not recommended.
However, they are very useful in some situations, especially to understand why some dataset gives poor segmentation result.
-uu -X 1000 splits the unsegmented reads from the 1000 first reads.
Segmentation and .vdj format
Vidjil output includes segmentation of V(D)J recombinations. This happens in the following situations:
- in a first pass, when requested with
-Uoption, in a
The goal of this ultra-fast segmentation, based on a seed heuristics, is only to identify the locus and to locate the w-window overlapping the CDR3. This should not be taken as a real V(D)J designation, as the center of the window may be shifted up to 15 bases from the actual center.
- in a second pass, on the standard output and in both
- at the end of the clones detection (default command
-c clones, on a number of clones limited by the
- or directly when explicitly requiring segmentation (
These V(D)J designations are obtained by full comparison (dynamic programming) with all germline sequences.
Note that these designations are relatively slow to compute, especially for the IGH locus. However, they are not at the core of the Vidjil clone clustering method (which relies only on the ‘window’, see above). To check the quality of these designations, the automated test suite include sequences with manually curated V(D)J designations (see should-vdj.org).
- at the end of the clones detection (default command
Segmentations of V(D)J recombinations are displayed using a dedicated
.vdj format. This format is compatible with FASTA format. A line starting
with a > is of the following form:
>name + VDJ startV endV startD endD startJ endJ Vgene delV/N1/delD5' Dgene delD3'/N2/delJ Jgene comments name sequence name (include the number of occurrences in the read set and possibly other information) + strand on which the sequence is mapped VDJ type of segmentation (can be "VJ", "VDJ", "VDDJ", "53"... or shorter tags such as "V" for incomplete sequences). The following line are for "VDJ" recombinations : startV endV start and end position of the V gene in the sequence (start at 1) startD endD ... of the D gene ... startJ endJ ... of the J gene ... Vgene name of the V gene delV number of deletions at the end (3') of the V N1 nucleotide sequence inserted between the V and the D delD5' number of deletions at the start (5') of the D Dgene name of the D gene being rearranged delD3' number of deletions at the end (3') of the D N2 nucleotide sequence inserted between the D and the J delJ number of deletions at the start (5') of the J Jgene name of the J gene being rearranged comments optional comments. In Vidjil, the following comments are now used: - "seed" when this comes for the first pass (.segmented.vdj.fa). See the warning above. - "!ov x" when there is an overlap of x bases between last V seed and first J seed - the name of the locus (TRA, TRB, TRG, TRD, IGH, IGL, IGK, possibly followed by a + for incomplete/unusual recombinations)
Following such a line, the nucleotide sequence may be given, giving in this case a valid FASTA file.
For VJ recombinations the output is similar, the fields that are not applicable being removed:
>name + VJ startV endV startJ endJ Vgene delV/N1/delJ Jgene comments
Examples of use
Examples on a IGH VDJ recombinations require either to specigy
or to use the multi-germline option
-g germline/homo-sapiens.g that can be shortened into
Basic usage: PCR-based datasets, with primers in the V(D)J regions (such as BIOMED-2 primers)
./vidjil-algo -c clones -g germline/homo-sapiens.g -2 -3 -r 1 demo/Demo-X5.fa # Detect the locus for each read, cluster and report clones starting from the first read (-r 1). # including unexpected recombinations (-2). Assign the V(D)J genes and try to detect the CDR3s (-3). # Demo-X5 is a collection of sequences on all human locus, including some incomplete or unusual recombinations: # IGH (VDJ, DJ), IGK (VJ, V-KDE, Intron-KDE), IGL, TRA, TRB (VJ, DJ), TRG and TRD (VDDJ, Dd2-Dd3, Vd-Ja).
./vidjil-algo -g germline/homo-sapiens.g:IGH -3 demo/Stanford_S22.fasta # Cluster the reads and report the clones, based on windows overlapping IGH CDR3s. # Assign the V(D)J genes and try to detect the CDR3 of each clone. # Summary of clones is available both on stdout, in out/Stanford_S22.vdj.fa and in out/Stanford_S22.vidjil.
./vidjil-algo -g germline -2 -3 -d demo/Stanford_S22.fasta # Detects for each read the best locus, including an analysis of incomplete/unusual and unexpected recombinations # Cluster the reads into clones, again based on windows overlapping the detected CDR3s. # Assign the VDJ genes (including multiple D) and try to detect the CDR3 of each clone. # Summary of clones is available both on stdout, in out/reads.vdj.fa and in out/reads.vidjil.
Basic usage: Whole RNA-Seq or capture datasets
./vidjil-algo -g germline -2 -U demo/Stanford_S22.fasta # Detects for each read the best locus, including an analysis of incomplete/unusual and unexpected recombinations # Cluster the reads into clones, again based on windows overlapping the detected CDR3s. # Assign the VDJ genes and try to detect the CDR3 of each clone. # The out/reads.segmented.vdj.fa include all reads where a V(D)J recombination was found
Typical whole RNA-Seq or capture datasets may be huge (several GB) but with only a (very) small portion of CDR3s.
Using Vidjil with
-U will create a
that includes all reads where a V(D)J recombination (or an unexpected recombination, with
-2) was found.
This file will be relatively small (a few kB or MB) and can be taken again as an input for Vidjil or for other programs.
./vidjil-algo -c clones -g germline/homo-sapiens.g -r 1 -n 5 -x 10000 demo/LIL-L4.fastq.gz # Extracts the windows with at least 1 read each (-r 1, the default being -r 5) # on the first 10,000 reads, then cluster them into clones # with a second clustering step at distance five (-n 5) # The result of this second is in the .vidjil file ('clusters') # and can been seen and edited in the web application.
./vidjil-algo -c segment -g germline/homo-sapiens.g -2 -3 -d -x 50 demo/Stanford_S22.fasta # Detailed V(D)J designation, including multiple D, and CDR3 detection on the first 50 reads, without clone clustering # (this is slow and should only be used for testing, or on a small file)
./vidjil-algo -c germlines -g germline/homo-sapiens.g demo/Stanford_S22.fasta # Output statistics on the number of occurrences of k-mers of the different germlines
Following clones in several samples
In a minimal residual disease setup, for instance, we are interested in following the main clones identified at diagnosis in the following samples.
In its output files, Vidjil keeps track of all the clones, even if it
provides a V(D)J assignation only for the main ones. Therefore the
meaningful information is already in the files (for instance in the
files). However we have one
.vidjil per sample which may not be very
convenient. All the more since the web client only takes one
as input and cannot take several ones.
Therefore we need to merge all the
.vidjil files into a single one. That is
the purpose of the tools/fuse.py script.
Let assume that four
.vidjil files have been produced for each sample
fu3.vidjil), merging them will
be done in the following way:
python tools/fuse.py --output mrd.vidjil --top 100 diag.vidjil fu1.vidjil fu2.vidjil fu3.vidjil
--top parameter allows to choose how many top clones per sample should
be kept. 100 means that for each sample, the top 100 clones are kept and
followed in the other samples. In this example the output file is stored in
mrd.vidjil which can then be fed to the web client.