vidjil-algo.md 37.5 KB
Newer Older
1 2
# vidjil-algo – Command-line manual
*The Vidjil team (Mathieu, Mikaël, Aurélien, Florian, Marc, Tatiana and Rayan)*
Mikaël Salson's avatar
Mikaël Salson committed
3

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
```
  Vidjil -- High-throughput Analysis of V(D)J Immune Repertoire -- [[http://www.vidjil.org]]
  Copyright (C) 2011-2018 by Bonsai bioinformatics
  at CRIStAL (UMR CNRS 9189, Université Lille) and Inria Lille
  contact@vidjil.org
```

This is the help of vidjil-algo, for command-line usage.
This manual can be browsed online:

 - <http://www.vidjil.org/doc/algo>                    (last stable release)
 - <http://git.vidjil.org/blob/master/doc/algo.md>     (development version)

Other documentation (users and administrators of the web application, developpers) can be found from <http://www.vidjil.org/doc/>.


## About

*V(D)J recombinations* in lymphocytes are essential for immunological
Mikaël Salson's avatar
Mikaël Salson committed
23 24 25
diversity. They are also useful markers of pathologies, and in
leukemia, are used to quantify the minimal residual disease during
patient follow-up.
Mikaël Salson's avatar
Mikaël Salson committed
26

27
Vidjil-algo processes high-throughput sequencing data to extract V(D)J
28
junctions and gather them into clones. Vidjil-algo starts
Mikaël Salson's avatar
Mikaël Salson committed
29 30
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
31
to output all sequenced clones. The analysis is extremely fast
Mathieu Giraud's avatar
Mathieu Giraud committed
32
because, in the first phase, no alignment is performed with database
33
germline sequences. At the end, only the consensus sequences
34
of each clone have to be analyzed. Vidjil-algo can also cluster similar
35
clones, or leave this to the user after a manual review in the web application.
Mikaël Salson's avatar
Mikaël Salson committed
36

37 38 39
The method is described in the following references:

Marc Duez et al.,
40
“Vidjil: A web platform for analysis of high-throughput repertoire sequencing”,
41
PLOS ONE 2016, 11(11):e0166126
42
<http://dx.doi.org/10.1371/journal.pone.0166126>
Mikaël Salson's avatar
Mikaël Salson committed
43

Mikaël Salson's avatar
Mikaël Salson committed
44 45
Mathieu Giraud, Mikaël Salson, et al.,
"Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing",
46
BMC Genomics 2014, 15:409
47
<http://dx.doi.org/10.1186/1471-2164-15-409>
Mikaël Salson's avatar
Mikaël Salson committed
48

49 50
Vidjil-algo is open-source, released under GNU GPLv3 license.
This is the help of vidjil-algo, for command-line usage.
51
Other documentation (users and administrators of the web application, developpers) can be found from <http://www.vidjil.org/doc/>.
Mikaël Salson's avatar
Mikaël Salson committed
52

53
# Requirements and installation
54

55
## Supported platforms
Mikaël Salson's avatar
Mikaël Salson committed
56

57
Vidjil-algo has been successfully tested on the following platforms :
58 59 60 61 62 63 64 65 66 67 68 69

  - 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
Mikaël Salson's avatar
Mikaël Salson committed
70

71
Vidjil-algo is developed with continuous integration using systematic unit and functional testing.
72 73
The development team internally uses [Jenkins](https://jenkins-ci.org/) for that.
Moreover, the results of some of these tests can be publicly checked on [travis-ci.org](https://travis-ci.org/vidjil/vidjil).
Mikaël Salson's avatar
Mikaël Salson committed
74

75
## Build requirements (optional)
76

77
This paragraph details the requirements to build Vidjil-algo from source.
78 79
You can also download a static binary (see next paragraph, 'Installation').

80
To compile Vidjil-algo, make sure:
81
  - to be on a POSIX system ;
82 83 84
  - to have a C++11 compiler (as `g++` 4.8 or above, `g++` 7.3 being supported, or `clang` 3.3 or above).
  - to have the `zlib` installed (`zlib1g-dev` package under Debian/Ubuntu,
    `zlib-devel` package under Fedora/CentOS).
85

86
### CentOS 6
87 88 89

g++-4.8 is included in the devtools 2.0.

90
``` bash
91
sudo wget http://people.centos.org/tru/devtools-2/devtools-2.repo -O /etc/yum.repos.d/devtools-2.repo
92 93 94 95
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
96
```
97

98
### CentOS 7.2
99 100 101

g++-4.8 is included.

102
### FreeBSD 9.2
103 104 105

g++-4.8 is included in FreeBSD 9.2.

106 107 108
You may also need to install the `gzstream` library with:

``` bash
109
pkg install gzstream
110
```
111

112 113
Also Vidjil-algo uses GNU make which requires `gmake` under FreeBSD.
At the time of redacting the documentation, `g++` requires extra options to
114
ensure flawless compilation and execution of Vidjil-algo:
115 116

``` bash
117
make MAKE=gmake CXXFLAGS="-D_GLIBCXX_USE_C99 -Wl,-rpath=/usr/local/lib/gcc49"
118 119 120 121 122 123
```

The `gcc49` at the end of the command line is to be replaced by the `gcc` version
used.

### Debian Squeeze 6.0 / Wheezy 7.0
124 125

g++-4.8 should be pinned from testing.
126
Put in `/etc/apt/preferences` the following lines:
127

128
``` bash
129
Package: *
130
Pin: release n=wheezy # (or squeeze)
131 132 133 134 135
Pin-Priority: 900

Package: g++-4.8, gcc-4.8, valgrind*
Pin: release n=jessie
Pin-Priority: 950
136
```
137 138 139

Then g++ 4.8 can be installed.

140
``` bash
141
apt-get update
142
apt-get install -t jessie g++-4.8 valgrind
143
```
144

145
### Ubuntu 14.04 LTS
146

147
``` bash
148
sudo apt-get install g++-4.8
149
```
150

151
### Ubuntu 12.04 LTS
152 153 154

g++-4.8 is included in the devtools 2.0.

155
``` bash
156 157 158 159
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
160
```
161

162
### OS X
Mathieu Giraud's avatar
Mathieu Giraud committed
163 164 165

Xcode should be installed first.

166
## Installation
Mathieu Giraud's avatar
Mathieu Giraud committed
167

168
### Compiling
Mikaël Salson's avatar
Mikaël Salson committed
169

170 171
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.
172

173
``` bash
Mikaël Salson's avatar
Mikaël Salson committed
174

175
make germline
Mathieu Giraud's avatar
Mathieu Giraud committed
176
   # get IMGT germline databases (IMGT/GENE-DB) -- you have to agree to IMGT license: 
Mikaël Salson's avatar
Mikaël Salson committed
177 178 179 180 181 182
   # 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

183

184 185 186
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)
187

188
make demo                # download demo files (S22 and L4, see demo/get-sequences)
189

190
./vidjil-algo -h         # display help/usage
191
```
Mikaël Salson's avatar
Mikaël Salson committed
192

193
If your build system does not use C++11 by default, you should replace the `make` commands by:
194

195
``` bash
196
make CXXFLAGS='-std=c++11'                           ### gcc-4.8
197
make CXXFLAGS='-std=c++11' LDFLAGS='-stdlib=libc++'  ### OS X Mavericks
198 199 200
```

### Package
201

202
If you use a Debian-based operating system you can simply add the Vidjil
203 204 205
repository to your sources.list:
deb <http://rby.vidjil.org:8080/archive> sid/all/
deb <http://rby.vidjil.org:8080/archive> sid/amd64/
206

207 208
deb <http://rby.vidjil.org:8080/archive> wheezy/all/
deb <http://rby.vidjil.org:8080/archive> wheezy/amd64/
209 210 211 212 213

And install from he command line:
apt-get update
apt-get install vidjil

214
## Self-tests (optional)
215 216 217

You can run the tests with the following commands:

218
``` bash
219
make -C src/tests/data
Mathieu Giraud's avatar
Mathieu Giraud committed
220
   # get IGH recombinations from a single individual, as described in:
221 222 223 224
   # 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.

225
make -C src test                # run self-tests (can take 5 to 60 minutes)
226
```
Mikaël Salson's avatar
Mikaël Salson committed
227

228
# Input and parameters
229

230 231 232
The main input file of Vidjil-algo is a *set of reads*, given as a `.fasta`
or `.fastq` file, possibly compressed with gzip (`.gz`).
This set of reads can reach several gigabytes and 2\*10<sup>9</sup> reads. It is
233
never loaded entirely in the memory, but reads are processed one by
234 235
one by Vidjil-algo.
Vidjil-algo can also process BAM files, but please note that:
236

237 238 239 240 241 242
1.  The reads don't need to be aligned beforehand.
2.  In case of paired-end sequencing, the reads must have already been merged
    in the BAM file.

The `-h` and `-H` help options provide the list of parameters that can be
used. We detail here the options of the main `-c clones` command.
243 244 245 246 247 248

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.

249
## Recombination / locus selection
250

251 252

``` diff
253 254 255 256 257 258 259 260 261 262
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
263 264

Locus/recombinations
265
  -d            try to detect several D (experimental)
266
  -2            try to detect unexpected recombinations (must be used with -g)
267
```
268

269
The `germline/*.g` presets configure the analyzed recombinations.
270 271
The following presets are provided:

272
  - `germline/homo-sapiens.g`: Homo sapiens, TR (`TRA`, `TRB`, `TRG`, `TRD`) and Ig (`IGH`, `IGK`, `IGL`) locus,
273
    including incomplete/unusal recombinations (`TRA+D`, `TRB+`, `TRD+`, `IGH+`, `IGK+`, see [locus](locus)).
274 275 276 277 278
  - `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)
279

280
New `germline/*.g` presets for other species or for custom recombinations can be created, possibly referring to other `.fasta` files.
281 282
Please contact us if you need help in configuring other germlines.

283 284 285 286
  - 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).
287

288
  - Several presets can be loaded at the same time, as for instance `-g germline/homo-sapiens.g -g germline/germline/homo-sapiens-isotypes.g`.
289

290
  - Using `-2` further test unexpected recombinations (tagged as `xxx`), as in `-g germline/homo-sapiens.g -2`.
291

292
Finally, the advanced `-V/(-D)/-J` options enable to select custom V, (D) and J repertoires given as `.fasta` files.
293

294
## Main algorithm parameters
295

296
``` diff
Mathieu Giraud's avatar
Mathieu Giraud committed
297 298 299 300 301 302
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)
303
  -w <int>      w-mer size used for the length of the extracted window (default: 50) ('all': use all the read, no window clustering)
304
  -e <float>    maximal e-value for determining if a segmentation can be trusted (default: 'all', no limit)
305
  -t <int>      trim V and J genes (resp. 5' and 3' regions) to keep at most <int> nt (default: 0) (0: no trim)
306
```
Mathieu Giraud's avatar
Mathieu Giraud committed
307

308
The `-s`, `-k` are the options of the seed-based heuristic that detects
Mathieu Giraud's avatar
Mathieu Giraud committed
309 310
"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
311
explanation can be found in (Giraud, Salson and al., 2014).
312 313
*These options are for advanced usage, the defaults values should work.*
The `-s` or `-k` option selects the seed used for the k-mer V/J affectation.
Mathieu Giraud's avatar
Mathieu Giraud committed
314

315 316
The `-w` option fixes the size of the "window" that is the main
identifier to cluster clones. The default value (`-w 50`) was selected
317
to ensure a high-quality clone clustering: reads are clustered when
318
they *exactly* share, at the nucleotide level, a 50 bp-window centered
319 320
on the CDR3. No sequencing errors are corrected inside this window.
The center of the "window", predicted by the high-throughput heuristic, may
Mikaël Salson's avatar
Mikaël Salson committed
321
be shifted by a few bases from the actual "center" of the CDR3 (for TRG,
Mathieu Giraud's avatar
Mathieu Giraud committed
322
less than 15 bases compared to the IMGT/V-QUEST or IgBlast prediction
323
in \>99% of cases when the reads are large enough). Usually, a 50 bp-window
324 325
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.
Mathieu Giraud's avatar
Mathieu Giraud committed
326

327
Setting `-w` to higher values (such as `-w 60` or `-w 100`) makes the clone clustering
328 329 330 331 332
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.

333
The special `-w all` option takes all the read as the windows, completely disabling
334 335 336
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.

337
Setting `-w` to lower values than 50 may "segment" (analyze) a few more reads, depending
338
on the read length of your data, but may in some cases falsely cluster reads from
339
different clones.
340 341
For VJ recombinations, the `-w 40` option is usually safe, and `-w 30` can also be tested.
Setting `-w` to lower values is not recommended.
Mathieu Giraud's avatar
Mathieu Giraud committed
342

343 344
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
345
are counted in `SEG changed w` and the corresponding clones are output with the `Wxx` warning.
346

347
The `-e` option sets the maximal e-value accepted for segmenting a sequence.
348
It is an upper bound on the number of exepcted windows found by chance by the seed-based heuristic.
349 350
The e-value computation takes into account both the number of reads in the
input sequence and the number of locus searched for.
351 352
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.
353
The threshold can be disabled with `-e all`.
354

355
The `-t` option sets the maximal number of nucleotides that will be indexed in
356 357
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.
358
However giving a `-t` may also reduce the probability of seeing a heavily
359
trimmed or mutated V gene.
360
The default is `-t 0`.
361

362
## Thresholds on clone output
363 364 365

The following options control how many clones are output and analyzed.

366
``` diff
367
Limits to report a clone (or a window)
368
  -r <nb>       minimal number of reads supporting a clone (default: 5)
369 370
  -% <ratio>    minimal percentage of reads supporting a clone (default: 0)

371
Limits to further analyze some clones
372
  -y <nb>       maximal number of clones computed with a consensus sequence ('all': no limit) (default: 100)
373
  -z <nb>       maximal number of clones to be analyzed with a full V(D)J designation ('all': no limit, do not use) (default: 100)
374
  -A            reports and segments all clones (-r 1 -% 0 -y all -z all), to be used only on very small datasets
375
```
376

377
The `-r/-%` options are strong thresholds: if a clone does not have
378
the requested number of reads, the clone is discarded (except when
379 380 381 382
using `-l`, see below).
The default `-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
383 384
MRD detection).

385
The `-y` option limits the number of clones for which a consensus
386
sequence is computed. Usually you do not need to have more
387
consensus (see below), but you can safely put `-y all` if you want
388
to compute all consensus sequences.
389

390 391
The `-z` option limits the number of clones that are fully analyzed,
*with their V(D)J designation and possibly a CDR3 detection*,
392
in particular to enable the web application
393
to display the clones on the grid (otherwise they are displayed on the
394
'?/?' axis).
395 396
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
397
than 500 clones is often not useful since they can not be visualized easily
Mathieu Giraud's avatar
Mathieu Giraud committed
398
in the web application, and takes large computation time (full dynamic programming
399
with all germline sequences), possibly reduced when using `-Z` (see below).
400

401
Note that even if a clone is not in the top 100 (or 200, or 500) but
402 403
still passes the `-r`, `-%` options, it is still reported in both the `.vidjil`
and `.vdj.fa` files. If the clone is at some MRD point in the top 100 (or 200, or 500),
Mikaël Salson's avatar
Mikaël Salson committed
404
it will be fully analyzed/segmented by this other point (and then
405
collected by the `fuse.py` script, using consensus sequences computed at this
406
other point, and then, on the web application, correctly displayed on the grid).
407 408
**Thus is advised to leave the default** `-z 100` **option
for the majority of uses.**
409

410
The `-A` option disables all these thresholds. This option should be
411 412 413
used only for test and debug purposes, on very small datasets, and
produce large file and takes huge computation times.

414
The experimental `-Z` option speeds up the full analysis by a pre-processing step,
Mathieu Giraud's avatar
Mathieu Giraud committed
415 416 417
again based on k-mers, to select a subset of the V germline genes to be compared to the read.
The option gives the typical size of this subset (it can be larger when several V germlines
genes are very similar, or smaller when there are not enough V germline genes).
418
Setting `-Z 5` is generally safe. With the default option, `-Z all`, this
Mathieu Giraud's avatar
Mathieu Giraud committed
419
pre-processing step is not activated.
Mikaël Salson's avatar
Mikaël Salson committed
420

421
## Sequences of interest
422

423
Vidjil-algo allows to indicate that specific sequences should be followed and output,
424 425 426
even if those sequences are 'rare' (below the `-r/-%` thresholds).
Such sequences can be provided either with `-W <sequence>`, or with `-l <file>`.
The file given by `-l` should have one sequence by line, as in the following example:
Mikaël Salson's avatar
Mikaël Salson committed
427

428
``` diff
429 430
GAGAGATGGACGGGATACGTAAAACGACATATGGTTCGGGGTTTGGTGCT my-clone-1
GAGAGATGGACGGAATACGTTAAACGACATATGGTTCGGGGTATGGTGCT my-clone-2 foo
431
```
432

433 434 435
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.
436
In Vidjil-algo output, the labels are output alongside their sequences.
437

438 439
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
440 441
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
442 443
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.
444 445 446
This filtering will work as expected when the provided sequence overlaps
(at least partially) the CDR3 or its close neighborhood.

447
With the `-F` option, *only* the windows related to the given sequences are kept.
448
This allows to quickly filter a set of reads, looking for a known sequence or window,
449 450
with the `-FaW <sequence>` options:
All the reads with the windows related to the sequence will be extracted to `out/seq/clone.fa-1`.
451

452
## Clone analysis: VDJ assignation and CDR3 detection
453

454
The `-3` option launches a CDR3/JUNCTION detection based on the position
455
of Cys104 and Phe118/Trp118 amino acids. This detection relies on alignment
456
with gapped V and J sequences, as for instance, for V genes, IMGT/GENE-DB sequences,
457
as provided by `make germline`.
458 459
The CDR3/JUNCTION detection won't work with custom non-gapped V/J repertoires.

Mathieu Giraud's avatar
Mathieu Giraud committed
460 461
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.
462

463
The advanced `-f` option sets the parameters used in the comparisons between
464 465
the clone sequence and the V(D)J germline genes. The default values should work.

466 467
The e-value set by `-e` is also applied to the V/J designation.
The `-E` option further sets the e-value for the detection of D segments.
468

469
## Further clustering (experimental)
470

471 472
The following options are experimental and have no consequences on the `.vdj.fa` file,
nor on the standard output. They instead add a `clusters` sections in the `.vidjil` file
473
that will be visualized in the web application.
474

475 476
The `-n` option triggers an automatic clustering using DBSCAN algorithm (Ester and al., 1996).
Using `-n 5` usually cluster reads within a distance of 1 mismatch (default score
477 478
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.
479
This behaviour can be controlled with the `-N` option.
Mikaël Salson's avatar
Mikaël Salson committed
480

481
The `-=` option allows to specify a file for manually clustering two windows
Mikaël Salson's avatar
Mikaël Salson committed
482
considered as similar. Such a file may be automatically produced by vidjil
483 484
(`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
Mikaël Salson's avatar
Mikaël Salson committed
485
two windows that must be clustered.
Mikaël Salson's avatar
Mikaël Salson committed
486

487
# Output
Mikaël Salson's avatar
Mikaël Salson committed
488

489
## Main output files
490

491
The main output of Vidjil-algo (with the default `-c clones` command) are two following files:
492

493
  - The `.vidjil` file is *the file for the Vidjil web application*.
494
    The file is in a `.json` format (detailed in [vidjil-format](vidjil-format))
495 496 497 498
    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.
    
499
    The web application takes this `.vidjil` file ([possibly merged with `fuse.py`](#following-clones-in-several-samples)) for the *visualization and analysis* of clones and their
500 501
    tracking along different samples (for example time points in a MRD
    setup or in a immunological study).
502
    Please see the [br](browser.org).org for more information on the web application.
503

504 505 506 507 508 509 510 511 512 513 514
  - The `.vdj.fa` file 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.
    
    The `.vdj.fa` output 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.
515

516
By default, the two output files are named `out/basename.vidjil` in `out/basename.vdj.fa`, where:
517

518 519
  - `out` is the directory where all the outputs are stored (can be changed with the `-o` option).
  - `basename` is the basename of the input `.fasta/.fastq` file (can be overriden with the `-b` option)
520

521
## Auxiliary output files
522

523
The `out/basename.windows.fa` file contains the list of windows, with number of occurrences:
524

525
``` diff
526
>8--window--1
527
TATTACTGTACCCGGGAGGAACAATATAGCAGCTGGTACTTTGACTTCTG
528
>5--window--2
529
CGAGAGGTTACTATGATAGTAGTGGTTATTACGGGGTAGGGCAGTACTAC
530 531
ATAGTAGTGGTTATTACGGGGTAGGGCAGTACTACTACTACTACATGGAC
(...)
532
```
533

534
Windows of size 50 (modifiable by `-w`) have been extracted.
535 536
The first window has 8 occurrences, the second window has 5 occurrences.

537
The `out/seq/clone.fa-*` contains the detailed analysis by clone, with
538
the window, the consensus sequence, as well as with the most similar V, (D) and J germline genes:
539

540
``` diff
541 542
>clone-001--IGH--0000008--0.0608%--window
TATTACTGTACCCGGGAGGAACAATATAGCAGCTGGTACTTTGACTTCTG
543
>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
544 545 546 547 548 549 550 551 552 553
GCTGTACCTGCAAATGAACAGCCTGCGAGCCGAGGACACGGCCACCTATTACTGT
ACCCGGGAGGAACAATATAGCAGCTGGTAC
TTTGACTTCTGGGGCCAGGGGATCCTGGTCACCGTCTCCTCAG

>IGHV3-23*05
GAGGTGCAGCTGTTGGAGTCTGGGGGAGGCTTGGTACAGCCTGGGGGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTTAGCAGCTATGCCATGAGCTGGGTCCGCCAGGCTCCAGGGAAGGGGCTGGAGTGGGTCTCAGCTATTTATAGCAGTGGTAGTAGCACATACTATGCAGACTCCGTGAAGGGCCGGTTCACCATCTCCAGAGACAATTCCAAGAACACGCTGTATCTGCAAATGAACAGCCTGAGAGCCGAGGACACGGCCGTATATTACTGTGCGAAA
>IGHD6-13*01
GGGTATAGCAGCAGCTGGTAC
>IGHJ4*02
ACTACTTTGACTACTGGGGCCAGGGAACCCTGGTCACCGTCTCCTCAG
554
```
555

556 557
The `-a` debug option further output in each `out/seq/clone.fa-*` files the full list of reads belonging to this clone.
The `-a` option produces large files, and is not recommanded in general cases.
558

559
## Diversity measures
560

561
Several [diversity indices](https://en.wikipedia.org/wiki/Diversity_index) are reported, both on the standard output and in the `.vidjil` file:
562

563 564 565
  - H (`index_H_entropy`): Shannon's diversity
  - E (`index_E_equitability`): Shannon's equitability
  - Ds (`index_Ds_diversity`): Simpson's diversity
566

567
E ans Ds values are between 0 (no diversity, one clone clusters all analyzed reads)
568
and 1 (full diversity, each analyzed read belongs to a different clone).
569 570
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.
571

572
## Unsegmentation causes
573

574
Vidjil-algo outputs details statistics on the reads that are not segmented (not analyzed).
575
Basically, **an unsegmented read is a read where Vidjil-algo cannot identify a window at the junction of V and J genes**.
576 577
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).
578 579
The following unsegmentation causes are reported:

580 581 582 583 584 585 586 587 588 589 590 591 592 593
|                     |                                                                                                                          |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `UNSEG too short`   | Reads are too short, shorter than the seed (by default between 9 and 13 bp).                                             |
| `UNSEG strand`      | The strand is mixed in the read, with some similarities both with the `+` and the `-` strand.                            |
| `UNSEG too few V/J` | No information has been found on the read: There are not enough similarities neither with a V gene or a J gene.          |
| `UNSEG only V/5`    | Relevant similarities have been found with some V, but none or not enough with any J.                                    |
| `UNSEG only J/3`    | Relevant similarities have been found with some J, but none or not enough with any V.                                    |
| `UNSEG ambiguous`   | vidjil-algo finds some V and J similarities mixed together which makes the situation ambiguous and hardly solvable.      |
| `UNSEG too short w` | 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/J` usually happens when reads share almost nothing with the V(D)J region.
594 595
    This is expected when the PCR or capture-based approach included other regions, such as in whole RNA-seq.

596
  - `UNSEG only V/5` and `UNSEG only J/3` happen when reads do not span enough the junction zone.
597
    Vidjil-algo detects a “window” including the CDR3. By default this window is 50bp long,
598 599
    so the read needs be that long centered on the junction.

600
See [browser.org](http://git.vidjil.org/blob/master/doc/browser.org) for information on the biological or sequencing causes that can lead to few segmented reads.
601

602
## Filtering reads
603

604 605
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.
606
On datasets generated with rather specific V(D)J primers, this is generally not recommended, as it may generate a large file.
607
However, the `-U` option is very useful for whole RNA-Seq or capture datasets that contain few reads with V(D)J recombinations.
608

Mathieu Giraud's avatar
Mathieu Giraud committed
609 610
Similarly, options are available to get the unsegmented reads:

611 612 613 614 615 616
  - `-u` gives 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.

  - `-uu` gives the same set of files, including **all** unsegmented reads (including `UNSEG too short` and `UNSEG too few V/J`),
    and `-uuu` further outputs all these reads in a file `out/basename.unsegmented.vdj.fa`.
617 618 619

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.
620
For example `-uu -X 1000` splits the unsegmented reads from the 1000 first reads.
Mikaël Salson's avatar
Mikaël Salson committed
621

622
## Segmentation and .vdj format
623

624
Vidjil output includes segmentation of V(D)J recombinations. This happens
625 626
in the following situations:

627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649
  - in a first pass, when requested with `-U` option, in a `.segmented.vdj.fa` file.
    
    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 `.vidjil` and `.vdj.fa` files
    
      - at the end of the clones detection (default command `-c clones`,
        on a number of clones limited by the `-z` option)
      - or directly when explicitly requiring segmentation (`-c segment`)
    
    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](http://git.vidjil.org/blob/master/doc/should-vdj.org)).
Mikaël Salson's avatar
Mikaël Salson committed
650 651

Segmentations of V(D)J recombinations are displayed using a dedicated
652 653
`.vdj` format. This format is compatible with FASTA format. A line starting
with a \> is of the following form:
Mikaël Salson's avatar
Mikaël Salson committed
654

655
``` diff
656
>name + VDJ  startV endV   startD endD   startJ  endJ   Vgene   delV/N1/delD5'   Dgene   delD3'/N2/delJ   Jgene   comments
Mikaël Salson's avatar
Mikaël Salson committed
657

658
        name          sequence name (include the number of occurrences in the read set and possibly other information)
Mikaël Salson's avatar
Mikaël Salson committed
659
        +             strand on which the sequence is mapped
660
        VDJ           type of segmentation (can be "VJ", "VDJ", "VDDJ", "53"...
661 662
                      or shorter tags such as "V" for incomplete sequences).    
              The following line are for "VDJ" recombinations :
Mikaël Salson's avatar
Mikaël Salson committed
663

664
        startV endV   start and end position of the V gene in the sequence (start at 1)
Mikaël Salson's avatar
Mikaël Salson committed
665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680
        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
681

682
        comments      optional comments. In Vidjil, the following comments are now used:
683
                      - "seed" when this comes for the first pass (.segmented.vdj.fa). See the warning above.
684
                      - "!ov x" when there is an overlap of x bases between last V seed and first J seed
685 686
                      - the name of the locus (TRA, TRB, TRG, TRD, IGH, IGL, IGK, possibly followed
                        by a + for incomplete/unusual recombinations)
Mikaël Salson's avatar
Mikaël Salson committed
687

688
```
Mikaël Salson's avatar
Mikaël Salson committed
689

Mikaël Salson's avatar
Mikaël Salson committed
690 691 692 693 694
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:
Mikaël Salson's avatar
Mikaël Salson committed
695

696
``` diff
697
>name + VJ  startV endV   startJ endJ   Vgene   delV/N1/delJ   Jgene  comments
698
```
699

700
# Examples of use
701

702 703
Examples on a IGH VDJ recombinations require either to specigy `-g germline/homo-sapiens-g:IGH`,
or to use the multi-germline option `-g germline/homo-sapiens.g` that can be shortened into `-g germline`.
704

705
## Basic usage: PCR-based datasets, with primers in the V(D)J regions (such as BIOMED-2 primers)
706

707
``` bash
708 709 710 711 712
./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).
713
```
714

715
``` bash
716
./vidjil-algo -g germline/homo-sapiens.g:IGH -3 demo/Stanford_S22.fasta
717 718
   # 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.
719
   # Summary of clones is available both on stdout, in out/Stanford_S22.vdj.fa and in out/Stanford_S22.vidjil.
720
```
721

722
``` bash
723
./vidjil-algo -g germline -2 -3 -d demo/Stanford_S22.fasta
724
   # Detects for each read the best locus, including an analysis of incomplete/unusual and unexpected recombinations
725
   # Cluster the reads into clones, again based on windows overlapping the detected CDR3s.
726
   # Assign the VDJ genes (including multiple D) and try to detect the CDR3 of each clone.
727
   # Summary of clones is available both on stdout, in out/reads.vdj.fa and in out/reads.vidjil.
728
```
729

730
## Basic usage: Whole RNA-Seq or capture datasets
731

732
``` bash
733
./vidjil-algo -g germline -2 -U demo/Stanford_S22.fasta
734
   # Detects for each read the best locus, including an analysis of incomplete/unusual and unexpected recombinations
735
   # Cluster the reads into clones, again based on windows overlapping the detected CDR3s.
736
   # Assign the VDJ genes and try to detect the CDR3 of each clone.
737
   # The out/reads.segmented.vdj.fa include all reads where a V(D)J recombination was found
738
```
739

740
Typical whole RNA-Seq or capture datasets may be huge (several GB) but with only a (very) small portion of CDR3s.
741 742
Using Vidjil with `-U` will create a `out/reads.segmented.vdj.fa` file
that includes all reads where a V(D)J recombination (or an unexpected recombination, with `-2`) was found.
743 744
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.

745
## Advanced usage
746

747
``` bash
748
./vidjil-algo -c clones -g germline/homo-sapiens.g -r 1 -n 5 -x 10000 demo/LIL-L4.fastq.gz
749
   # Extracts the windows with at least 1 read each (-r 1, the default being -r 5)
750 751 752 753
   # 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.
754
```
755

756
``` bash
757
./vidjil-algo -c segment -g germline/homo-sapiens.g -2 -3 -d -x 50 demo/Stanford_S22.fasta
758
   # Detailed V(D)J designation, including multiple D, and CDR3 detection on the first 50 reads, without clone clustering
759
   # (this is slow and should only be used for testing, or on a small file)
760
```
761

762
``` bash
763
./vidjil-algo -c germlines -g germline/homo-sapiens.g demo/Stanford_S22.fasta
764
   # Output statistics on the number of occurrences of k-mers of the different germlines
765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793
```

## 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 `.vidjil`
files). However we have one `.vidjil` per sample which may not be very
convenient. All the more since the web client only takes one `.vidjil` file
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](../tools/fuse.py) script.

Let assume that four `.vidjil` files have been produced for each sample
(namely `diag.vidjil`, `fu1.vidjil`, `fu2.vidjil`, `fu3.vidjil`), merging them will
be done in the following way:

``` bash
python tools/fuse.py --output mrd.vidjil --top 100 diag.vidjil fu1.vidjil fu2.vidjil fu3.vidjil
```

The `--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.