- About LNISKS
- Dependencies - skip if using dockerized pipeline
- Quick-start
- Execution on real data
- Command line options
LNISKS (longer needle in a scanter k-stack) is a high-throughput pipeline developed for reference free mutation identification. LNISKS extends concepts from NIKS (needle in a k-stack) but does not share code with it. It enables quick and reliable identification of mutations in large and complex crop genomic datasets without a reference genome assembly.
Through the use of state-of-the-art tools such as KMC3 and VSEARCH as well as algorithms implemented in yakat toolkit, LNISKS can process datasets consisting of billions of reads within hours.
For example, given hexaploid wheat datasets of 19X and 23X for wild-type and mutant bulks, respectively, the pipeline completed in 2 hours and 33 minutes on an allocation od of 32G of RAM and 16 logical cores (Xeon E5-2699 v3 2.30GHz CPUs). For comparison, sequential, single-threaded reading, decompression and discarding of the same input data takes 2 hours.
LNISKS: Reference-free mutation identification for large and complex crop genome. Radosław Suchecki, Ajay Sandhu, Stéphane Deschamps, Victor Llaca, Petra Wolters, Nathan S. Watson-Haigh, Margaret Pallotta, Ryan Whitford, Ute Baumann bioRxiv 580829; doi: https://doi.org/10.1101/580829
After you download the latest release or clone this repository go to LNISKS directory, and install/download the following:
-
Linux toolkit including
gawk, bc, column,pigz- e.g. on debiansudo apt install -y pigz \ gawk \ bc \ bsdmainutils -
KMC3 should be available on PATH or downloaded into
bin/as follows:mkdir -p bin \ && cd bin \ && wget https://github.com/refresh-bio/KMC/releases/download/v3.1.1/KMC3.1.1.linux.tar.gz \ && tar xzvf KMC3.1.1.linux.tar.gz \ && rm KMC3.1.1.linux.tar.gz \ && cd ..
-
VSEARCH should be available on PATH or or downloaded into
bin/as follows:mkdir -p bin \ && cd bin \ && wget https://github.com/torognes/vsearch/releases/download/v2.17.0/vsearch-2.17.0-linux-x86_64.tar.gz \ && tar xzvf vsearch-2.17.0-linux-x86_64.tar.gz \ && mv vsearch-2.17.0-linux-x86_64/bin/vsearch . \ && rm -r vsearch-2.17.0-linux-x86_64* \ && cd ..
-
yakat-yakatshould be placed underbin/wget --directory-prefix bin/ https://github.com/rsuchecki/yakat/releases/download/v0.9.5/yakat chmod +x bin/yakat
To provide a minimal test set for the pipeline we used ART read simulator within RNF framework to generate just over 12,000 paired-end reads from mitochondria assemblies of Arabidopsis and Rice.
The following snippet will run the pipeline, using the two sets of simulated reads.
In addition to the required parameters specifying the k-mer size (-k 16) the input read sets (-M, -W) and labels for these datasets (-m, -w), we also specify the number of CPU threads to be used (-t 2 but the more the merrier).
We want the minimum frequency of k-mers used for each of the samples to be detected from the respective distributions (-I -i).
Finally, we use -C $COLUMNS to ensure that ascii plots make best use of the available terminal width.
./scripts/lnisks.sh -k 16 \
-M example/A_thaliana_TAIR10_Mt_ArtIllumina_reads.\?.fq.gz \
-W example/O_sativa_IRGSP-1.0_Mt_ArtIllumina_reads.\?.fq.gz \
-m A_thaliana \
-w O_sativa \
-t 2 \
-I -i \
-C $COLUMNS \
-S 10The output to the terminal is rather verbose, but the pipeline was mostly used on large and complex datasets were we found this verbosity useful.
All this information with additional detail on parameters used can be found in the log files in the output directory, in this case under output/16-mers/logs/.
Adjust version as required
LNISKS_VERSION=1.1.6
docker run \
-v "$PWD":"$PWD" \
-w "$PWD" \
rsuchecki/lnisks:${LNISKS_VERSION} lnisks.sh -k 16 \
-M example/A_thaliana_TAIR10_Mt_ArtIllumina_reads.\?.fq.gz \
-W example/O_sativa_IRGSP-1.0_Mt_ArtIllumina_reads.\?.fq.gz \
-m A_thaliana \
-w O_sativa \
-t 2 \
-I -i \
-C $COLUMNS \
-E 2 \
-S 10
The intended application of LNISKS is to Bulked Segregant data, but in principle it should work well for identifying homozygous mutations between two sets of reads. The example run above is a good indication on how you could run it on real data, however
- be wary of
-iand-Isettings as the minimum frequency estimation is rather basic and likely only applicable to whole genome sequencing data - select a higher k, 16 just happens to work reasonably well for the tiny example data sets
When using wildcards to match multiple input files (e.g. *.fq.gz),
the wildcards need to be escaped when passed as arguments to scripts/lnisks.sh, for example:
lnisks.sh \
-W \*.fq.gz \
-M \*.fq.gz \
...This is analogous to what was done in the example run above.
Alternatively, you can specify -M and -W separately for each input file, for example:
lnisks.sh \
-M m1.fq.gz \
-M m2.fq.gz \
-M m3.fq.gz \
-W w1.fq.gz \
-W w2.fq.gz \
-W w3.fq.gz \
...Longer k -mers are more likely to be unique within a genome than shorter k-mers, but as k increases (up to the read length), so does the sequencing coverage required for k-mers to occur with sufficient frequency to be distinguishable from low frequency k-mers containing sequencing errors.
We want k to be close to highest possible value for which neither of the distributions for the two input sets is truncated.
We start by running the pipeline for several values of -k with -i and -I switched on, but to speed things up we can stop before identification of sample-specific k-mers by applying -S 2.
We then investigate the generated k-mer histograms.
Using our example dataset, we run the pipeline with -k 10 and -k 20 and observe that at k=10 the number of k-mers occurring 2,3,4,... times goes down before going up again, while at k=20 the initial slope is missing.
The distribution at k=20 is truncated so the sequencing coverage is too low for this k-mer size and some k-mers representing real sequence have been lost.
./scripts/plot_histogram.sh output/20-mers/20-mers_A_thaliana.histogram 80 | head -15
2 39,370 ################################
3 55,548 #############################################
4 62,803 ##################################################
5 57,318 ##############################################
6 43,635 ###################################
7 29,447 ########################
8 17,304 ##############
9 10,156 #########
10 5,651 #####
11 3,189 ###
12 1,722 ##
13 1,049 #
14 709 #
15 439 #
16 279 #
./scripts/plot_histogram.sh output/10-mers/10-mers_A_thaliana.histogram 80 | head -15
2 18,706 ########################################
3 14,591 ###############################
4 19,342 #########################################
5 22,533 ################################################
6 23,806 ##################################################
7 21,528 ##############################################
8 17,861 ######################################
9 14,465 ###############################
10 11,964 ##########################
11 9,939 #####################
12 8,478 ##################
13 7,052 ###############
14 6,292 ##############
15 5,366 ############
16 4,657 ##########
Note that by default we exclude all k-mers occurring just once as these are likely to arise from sequencing errors in the input reads.
Also note that for approx 20X data sets from common wheat the optimal k-mer size was 54.
One way to prioritize variant calls is to look in more detail into how well supported they are. For example, let's take the highest confidence, category A calls, although the full set of calls or each remaining category (B, C, D) could be processed in the same way.
CALLS=output/16-mers/16-mers_O_sativa_MINUS_A_thaliana_extended_16-mers_A_thaliana_MINUS_O_sativa_extended.matched.msa.snps.AWe then take the k-mer databases for each of the input data sets (A_thaliana, O_sativa), give them short labels (At and Os), and dump all k-mers into yakat snpmers.
(
echo Os; bin/kmc_dump output/16-mers/16-mers_O_sativa.db /dev/stdout; \
echo At; bin/kmc_dump output/16-mers/16-mers_A_thaliana.db /dev/stdout; \
) | java -jar scripts/yakat.jar snpmers \
--k-mer-length 16 \
--lnisks-snps ${CALLS} \
--print-user-settings \
--out-calls /dev/null \
--out-calls-with-evidence output/16-mers/snpmers.A.tsvIn a real word scenario, each bulk would likely comprise of multiple individuals which could be input by looping through individual k-mer databases. For each individual, its name/id needs to be printed immediately before the k-mers are dumped from the corresponding database - this would first require to compute individual k-mer database using kmc either directly or via the count_kmers.sh wrapper as used by LNISKS pipeline.
The head of output table generated by the example above should look like this:
| Ref:Pos | Al1 | Cov1 | Al2 | Cov2 | Os_call | Os_cov1 | Os_cov2 | Os_freq1 | Os_freq2 | At_call | At_cov1 | At_cov2 | At_freq1 | At_freq2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cluster_528:16 | G | 16 | A | 16 | G | 16 | 0 | 7 | 0 | A | 0 | 16 | 0 | 6 |
| Cluster_530:16 | T | 16 | C | 16 | T | 16 | 0 | 6 | 0 | C | 0 | 16 | 0 | 5 |
| Cluster_531:16 | C | 16 | A | 16 | C | 16 | 0 | 6 | 0 | A | 0 | 16 | 0 | 4 |
| Cluster_532:16 | A | 16 | C | 16 | A | 16 | 0 | 3 | 0 | C | 0 | 16 | 0 | 6 |
| Cluster_533:16 | A | 16 | C | 16 | A | 16 | 0 | 5 | 0 | C | 0 | 16 | 0 | 6 |
| Cluster_534:16 | G | 16 | C | 16 | G | 16 | 0 | 6 | 0 | C | 0 | 16 | 0 | 3.5 |
| Cluster_535:16 | T | 16 | C | 16 | T | 16 | 0 | 5 | 0 | C | 0 | 16 | 0 | 6 |
| Cluster_536:16 | C | 16 | T | 16 | C | 16 | 0 | 4 | 0 | T | 0 | 16 | 0 | 7 |
| Cluster_537:16 | T | 16 | A | 16 | T | 16 | 0 | 3 | 0 | A | 0 | 16 | 0 | 7 |
Column headers can be deciphered as follows:
- From each LNISKS call we get:
- Al1 / Al2 - called bases/alleles
- Cov1 / Cov2 - total number of possible k-mers supporting respective base/allele
- From each set of k-mers (here labelled {At, OS}) we obtain:
- _call - base call based on all input k-mers
- _cov - number of k-mers supporting a base/allele
- _freq - median frequency o k-mers supporting that base/allele
These can be used for filtering or sorting calls.
./scripts/lnisks.sh -hUSAGE: lnisks.sh [-h] -k <int> -j <int> -M <fastq.gz> -W <fastq.gz> [options]
[Input/Output settigns]
-k <int> k-mer lenght [REQUIRED]
-j <int> j-mer length, j<k or even j<<k ^^ [Step skipped if not given]
-o <string> output directory (default: output)
-M <string> sample 1 input fastq.gz file* [REQUIRED if not skipping step 1 (-s n, n>0)]
-W <string> sample 2 input fastq.gz file* [REQUIRED if not skipping step 1 (-s n, n>0)]
-m <string> sample 1 name/label (default: mutant)
-w <string> sample 2 name/label (default: wildtype)
-Q <int> sample 1 min input k-mer frequency (default: 2)
-q <int> sample 2 min input k-mer frequency (default: 2)
-P <int> sample 1 min output k-mer frequency (default: 5)
-p <int> sample 2 min output k-mer frequency (default: 5)
-X <int> sample 1 max output k-mer frequency (default: 9999999)
-x <int> sample 2 max output k-mer frequency (default: 9999999)
-L <int> Output long, unpaired seeds of no less than <int> bp (default: 250)^^
[Simple procedure for inferring k-mer frequency - nonsense if input is RNA-Seq data]
-I sample 1 infer min out k-mer frequency from distribution
-i sample 2 infer min out k-mer frequency from distribution
[Further filtering options for k-mer DBs]
-F <string> filter DB(s)*^ for sample 1
-f <string> filter DB(s)*^ for sample 2
-Y <int> min frequency** for sample 1 filter DB(s) (default: 2)
-y <int> min frequency** for sample 2 filter DB(s) (default: 2)
[Pairing seeds and calling SNPs]
-d <float> min identity (0.0<=id<=1.0) required when clustering/paring seeds (default: 1-4/(2k-1))
-D <float> min identity (0.0<=id<=1.0) required when calling snps (default: 1-2/(2k-1))
[Further extensions of paired seeds - disabled by default, see -S]
-B k-merize all input reads (no baiting), suitable for small input datasets and/or big memory machines
-J infer min k-mer frequency for further extensions from distribution
-b <int> baiting k-mer length (default equals j-mer length)
-n <int> min extension k-mer size (default equals k)
-N <int> max extension k-mer size (default equals 2k)
-e <int> extension k-mer size step (default: 10)
[Runtime, skipping, stopping]
-C <int> Print width for some reports, recommended use: -C $COLUMNS (defaults to 160)
-t <int> number of threads for parallelized tasks (defaults to max(4,nproc/4: 4)
-T <string> temporary files directory for KMC
-E <int> max physical memory in GB to be used (defaults to 1/4 of physical mem: 1)
-O <int> overwrite existing output files starting from step:
1 k-mer counting
2 k-mer min frequency inferring [skipped by default, use {-I, -i} to run ]
3 sample-specific k-mer identification
4 custom filtering of sample-specific k-mers
5 k-mer extension to seeds (unitigs)
6 restriction of extended seeds to those sharing j-mers
7 matching extended seeds and SNP calling
8 baiting reads which match the paired sequences (step ignored if -B is used)
9 kmerizing baited (or all if -B was used) input reads
10 extending matched seeds (ideally beyond the sample-specific 2k-1 bp)
-s <int> Skip first <int> steps of the pipeline (steps listed above, default: 0)
-S <int> Stop pipeline after step <int> (steps listed above, default: 7)
* - Can be specified multiple times, and/or use escaped wildcards e.g. -M filename_R\?.fastq.gz
^^ - The set of unpaired sequences will be incomplete if using j-mers for speeding up pairing
*^ - Filters are KMC DBs which can be generated using count_kmers.sh wrapper around KMC
** - To use different min frequencies for individual filters use e.g. -Y "1 10 4",
these must be in the same order as the input filtersBy default, LNISKS will perform steps 1 to 7.
On subsequent runs, for most tasks it will attempt to re-use the existing output files.
Note that this carries some risks as some changes in parametrisation may not translate
to changes in output.
Warnings are given when a file is re-used, is you want a step to be re-computed
in line with changed parameters use -O <step number>.