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README
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README for SHRiMP2: SHort Read Mapping Package, version 2.0
http://compbio.cs.toronto.edu/shrimp
This document describes the programs of which SHRiMP2 is comprised, including
their use, output formats, and various parameters. The algorithms employed are
also briefly described. SHRiMP2 builds on, and significantly extends the
original SHRiMP package, henceforth referred to as SHRiMP1.
SHRiMP2 is brought to you by:
Matei David
Michael Brudno
Daniel Lister
Misko (Michael) Dzamba
SHRiMP1 was originally developed by:
Stephen M. Rumble
Michael Brudno
Phil Lacroute
Vladimir Yanovsky
Marc Fiume
Adrian Dalca
Matei David
For details on the original SHRiMP1 package, see the following publication:
Rumble SM, Lacroute P, Dalca AV, Fiume M, Sidow A, et al.
(2009) SHRiMP: Accurate Mapping of Short Color-space Reads.
PLoS Comput Biol 5(5): e1000386. doi:10.1371/journal.pcbi.1000386
The authors may be contacted at shrimp at cs dot toronto dot edu.
--------------------------------------------------------------------------------
Table of Contents
--------------------------------------------------------------------------------
1. Overview
2. Minimum Requirements
2.1 RAM Requirement
2.2 Other Requirements
3. Sample Usage Scenarios
3.1 Installing SHRiMP2
3.2 Compiling SHRiMP2 from source
3.3 Mapping against a genome whose projection DOES fit in RAM
3.4 Mapping against a genome whose projection DOES NOT fit in RAM
3.5 Mapping cDNA reads against a miRNA database
3.6 Advanced: Mapping in OVERLY sensitive mode
4. Input and Output Specifications
5. Parameters
6. Algorithms
7. Comparison to SHRiMP1
7.1 Algorithms
7.2 SHRiMP Output Format
7.3 SHRiMP1 Tools
8. Contact
9. Acknowledgements
--------------------------------------------------------------------------------
1. Overview
--------------------------------------------------------------------------------
SHRiMP2 is a software package for mapping reads from a donor genome against a
target (reference) genome. SHRiMP2 was primarily developed to work with short
reads produced by Next Generation Sequencing (NGS) machines. The main features
of SHRiMP2 include:
- Fasta input format;
- SAM output format;
- Support for both letter space (Illumina/Solexa) and colour space (AB SOLiD)
reads;
- Paired mapping mode, using mate-pair/pair-end information;
- Parallel computation, fully utilizing the power of modern shared-memory
multi-core architectures.
SHRiMP2 uses two techniques to match reads to a genome: we first use the q-gram
filtering technique, utilizing multiple spaced seeds, to rapidly identify
candidate mapping locations for each read. Subsequently, these locations are
thoroughly investigated by a vectored implementation of the standard dynamic
programming (Smith-Waterman) string matching algorithm, allowing for the
accurate identification of mismatches (SNPs), micro-indels, and crossovers
(colour space sequencing errors.)
SHRiMP2 primarily targets sensitivity, but it has come a long way to achieve it
at a reasonable speed. To illustrate its performance:
Sensitivity: SHRiMP2 achieves 94.4% precision and 78.6% recall when mapping
simulated colour space reads of 50 base pairs (bp) each containing 1 SNP _and_
1 indel of size up to 5bp _and_ 4% per-base error rate.
Speed: SHRiMP2 on a single 3.0GHz core with 16GB of RAM can map 36bp colour
space reads against the reference human genome (hg18) at the rate of 160,000
reads per hour, and twice as fast if using mate-pair/pair-end data. This rate
is fully core-parallelizable, in the sense that a cluster of 4 machines, each
with 2 quad-core CPUs and 16GB of RAM, will map 4x2x4=32 times faster. This is
about 10 million paired reads per hour. So, 250 million 36bp paired reads,
equivalent to a 3x coverage of hg18, would take about 30 hours. Prior to
mapping, indexing the projection of hg18 into 4 pieces that would each fit in
16GB RAM can be done by in 2 hours by running 4 different machines in
parallel. (These are all rough estimates.)
SHRiMP2 consists of the read mapper program gmapper, along with a series
of tools and scripts. In this README file, we primarily describe gmapper.
Descriptions of tools remaining from SHRiMP1 are available in README.SHRiMP132.
Descriptions of various helper scripts are in the script directory utils/.
--------------------------------------------------------------------------------
2. Minimum Requirements
--------------------------------------------------------------------------------
2.1 RAM Requirement
-------------------
SHRiMP2 is designed to fully utilize physical random-access memory (RAM). While
it is certainly possible to run it on swap (disk-mapped) memory, the performance
cost of doing that is rather prohibitive.
RAM Requirement: To map against a set of contigs of total length L (in bp),
using K spaced seeds of weight W, SHRiMP2 needs the following minimum amount
of RAM, in bytes:
( L x K x 4 ) + ( K x 4^{W or 12(**)} x (4 + sizeof(void *)) ) + 50,000,000
The third term represents working memory.
The second term varies with the weight W of the seeds used. With the default 4
seeds of weight 12, on 64 bit-machines (with 8-byte pointers), this amounts to
0.75GB. (**) By hashing kmers (see -H parameter), the exponent can be brought
down to 12, at the expense of some speed drop.
The first term generally dominates. E.g., with the default settings (K=4 seeds),
to map against the full hg18 (L=3*10^9 bp), the first term becomes 3*10^9 x 4 x
4 = 48GB of RAM.
To make it accessible to machines with smaller amounts of RAM, SHRiMP2 provides
a mechanism to split a genome into several chunks, each of which comes with the
overhead of terms 2 and 3. E.g., in our tests, we split hg18 into 4 chunks, each
using about 13GB of RAM, which can fit comfortably on a machine with 16GB of
RAM.
Currently, SHRiMP2 does not split individual contigs. (However, this can be
achieved by hand, possibly at the cost of losing some mappings in the region of
the break point.) As a result, the minimum amount of RAM required equals the
amount needed to index the largest contig. E.g., in the case of hg18, chr1 has
about 260,000bp, and the amount of RAM required to index it with K spaced seeds
is K x 1.04 + 0.75 GB. (For a cluster of nodes with 4GB of RAM, we recommend
using K=3 seeds rather than the default 4.)
2.2 Other Requirements
----------------------
SHRiMP2 uses SSE2 ("vector") machine instructions to achieve a fast
implementation of the standard dynamic programming (Smith-Waterman) string
matching algorithm. E.g., it will not work on a non-x86/x86_64 architecture.
SHRiMP2 uses the OpenMP API to achieve multi-threaded operation.
--------------------------------------------------------------------------------
3. Sample Usage Scenarios
--------------------------------------------------------------------------------
3.1 Installing SHRiMP2
----------------------
Assume we downloaded the linux binary package in
./SHRiMP_2_1_1.lx26.x86_64.tar.gz
$ tar xvzf SHRiMP_2_1_1.lx26.x86_64.tar.gz
$ cd SHRiMP_2_1_1
$ file bin/gmapper
bin/gmapper: ELF 64-bit LSB executable, AMD x86-64, version 1 (SYSV), for
GNU/Linux 2.6.9, statically linked, for GNU/Linux 2.6.9, not stripped
$ export SHRIMP_FOLDER=$PWD
Done! At this point the binaries are in bin/, and the various scripts are in
utils/.
3.2 Compiling SHRiMP2 from source
---------------------------------
Assume we downloaded the source package in ./SHRiMP_2_1_1.src.tar.gz
$ tar xvzf SHRiMP_2_1_1.src.tar.gz
$ cd SHRiMP_2_1_1
$ make
[...warnings, hopefully no errors...]
$ ls bin/gmapper*
bin/gmapper bin/gmapper-cs bin/gmapper-ls
$ export SHRIMP_FOLDER=$PWD
3.3 Mapping against a genome whose projection DOES fit in RAM
-------------------------------------------------------------
Assume we have:
./ciona.fa - the Ciona Savignyi genome (175*10^6 bp)
./reads.5m.50bp.cs.fa - 5 million 50bp colour space reads
We look at the genome and the reads:
$ ls -lh ciona.fa
-rw-r--r-- 1 username 173M May 6 14:23 ciona.fa
$ head -n 4 ciona.fa
>reftig_0
ACCACACATCTGGCGTTGGTGCAACTACCACAACATCTGGCGTTCGTGCA
ACCACCACAacatctggtcctggtgcaactaccacaacatctggtcctgg
tgcaaccaccacaacatctggtgctggtgcaaccaccacaacatctggtg
$ head -n 4 reads.5m.50bp.cs.fa
>469_26_42_F3
T121133100312321122210031200212212233
>469_26_379_F3
T312022230033100001303023233122232120
Assume we have 2 machines, each with 4 cores and 8GB of RAM. We check memory
requirements: 173M x 4 x 4 = 2.77GB. This is way below the available RAM, so
there are no issues fitting the genome in memory. However, we still want to use
both available machines, so we split the reads in two sets.
$ grep '^>' reads.5m.50bp.cs.fa | wc -l
5000000
$ $SHRIMP_FOLDER/utils/splitreads.py 2500000 reads.5m.50bp.cs.fa
$ ls *_to_*
0_to_2499999.csfasta 2500000_to_4999999.csfasta
$ grep '^>' 0_to_2499999.csfasta | wc -l
2500000
This reads are now in the two files above. We assume the directory with the
reads as well as the directory containing SHRiMP2 are shared across the
machines. We map the reads with the following commands
<machine1>$ $SHRIMP_FOLDER/bin/gmapper-cs 0_to_2499999.csfasta \
ciona.fa \
-N 4 -o 5 -h 80% >map.0_to_2499999.out 2>map.0_to_2499999.log
<machine2>$ $SHRIMP_FOLDER/bin/gmapper-cs 2500000_to_4999999.csfasta \
ciona.fa \
-N 4 -o 5 -h 80% >map.2500000_to_4999999.out 2>map.2500000_to_4999999.log
The options used above have the following meaning:
-N 4 : Use 4 threads of control, corresponding to the number of cores
available on each machine.
-o 5 : Print top 5 scoring hits for each read.
-h 80% : Set the threshold score for a hit to 80% of the maximum possible
score. For reads of 50bp and the default match score of 10, the
maximum score is 500, and the threshold is set to 400.
Eventually, the two mapping jobs complete. We merge the mappings into one file
with:
$ $SHRIMP_FOLDER/utils/merge-hits-diff-qr-same-db.py map.0_to_2499999.out \
map.2500000_to_4999999.out >map.out
Above, the merging script we used corresponds to "different query set" (2
disjoint sets of reads), "same database" (the full ciona genome).
3.4 Mapping against a genome whose projection DOES NOT fit in RAM
-----------------------------------------------------------------
Assume we have:
./hg18.fa - fasta file containing all contigs of the reference human genome
./reads.500kx2.36bp.ls.fa - 1 million 36bp paired letter space reads
Assume we know that the reads in each pair come from opposite strands of the
genome, pointing "inwards" (towards each other), with an insert of average
length 200 and standard deviation 10.
Assume we have a single machine with 2 quad-core CPUs and 16GB of RAM.
We look at the genome and the reads:
$ ls -lh hg18.fa
-rw-r--r-- 1 username 3.0G May 6 14:23 hg18.fa
$ head -n 8 reads.500kx2.36bp.ls.fa
>071214_EAS56_0061:1:1:882:575:1
GAAATTGTACACATCACACACCTTTTTGTCTTTCTT
>071214_EAS56_0061:1:1:882:575:2
TGCTAGATAATGATTTGCACATTTTTAATTGGTAGA
>071214_EAS56_0061:1:1:781:662:1
GAAGTGATTACATTAAGCTGTACATATGCAGCATTC
>071214_EAS56_0061:1:1:781:662:2
AAATATGTTGATTTTTCAGTGTTTTTAAAGTAAAAT
We compute the memory requirements for mapping against hg18 using the default 4
seeds: 3.0G x 4 x 4 = 48GB of RAM. Our machine only has 16GB of RAM. We split
the genome with the following utility:
$ $SHRIMP_FOLDER/utils/split-db.py --ram-size 14 --prefix hg18 hg18.fa
[...]
$ ls *.fa
hg18-14gb-12_12_12_12seeds-1of4.fa hg18-14gb-12_12_12_12seeds-3of4.fa
hg18-14gb-12_12_12_12seeds-2of4.fa hg18-14gb-12_12_12_12seeds-4of4.fa
Above, we decided to give the gmapper process only 14GB of RAM (the remaining
2GB are presumable used by the operating system.) The script split hg18 into 4
chunks, each containing several contigs, that will each fit in 14GB of RAM.
At this point, we realize we might be mapping letter space reads against hg18 in
the future, so we decide to create projections of the individual genome pieces,
so that we avoid having to re-project them every time we start a mapping job. To
achieve this, we run:
$ $SHRIMP_FOLDER/utils/project-db.py --shrimp-mode ls hg18-14gb-*.fa
[...]
$ ls hg18-14gb-*-ls.*
hg18-14gb-12_12_12_12seeds-1of4-ls.genome
hg18-14gb-12_12_12_12seeds-1of4-ls.seed.0
hg18-14gb-12_12_12_12seeds-1of4-ls.seed.1
hg18-14gb-12_12_12_12seeds-1of4-ls.seed.2
hg18-14gb-12_12_12_12seeds-1of4-ls.seed.3
hg18-14gb-12_12_12_12seeds-2of4-ls.genome
hg18-14gb-12_12_12_12seeds-2of4-ls.seed.0
hg18-14gb-12_12_12_12seeds-2of4-ls.seed.1
hg18-14gb-12_12_12_12seeds-2of4-ls.seed.2
hg18-14gb-12_12_12_12seeds-2of4-ls.seed.3
hg18-14gb-12_12_12_12seeds-3of4-ls.genome
hg18-14gb-12_12_12_12seeds-3of4-ls.seed.0
hg18-14gb-12_12_12_12seeds-3of4-ls.seed.1
hg18-14gb-12_12_12_12seeds-3of4-ls.seed.2
hg18-14gb-12_12_12_12seeds-3of4-ls.seed.3
hg18-14gb-12_12_12_12seeds-4of4-ls.genome
hg18-14gb-12_12_12_12seeds-4of4-ls.seed.0
hg18-14gb-12_12_12_12seeds-4of4-ls.seed.1
hg18-14gb-12_12_12_12seeds-4of4-ls.seed.2
hg18-14gb-12_12_12_12seeds-4of4-ls.seed.3
Since we have a single machine, we run the mapping process sequentially against
each of the chunks.
$ for i in 1 2 3 4; do \
$SHRIMP_FOLDER/bin/gmapper-ls -L hg18-14gb-12_12_12_12seeds-${i}of4-ls \
reads.500kx2.36bp.ls.fa \
-N 8 -p opp-in -I 50,500 -m 20 -i -25 -g -40 -e -10 -E \
>map.db${i}of4.sam 2>map.db${i}of4.log
done
[...]
$ ls map.db*.sam
map.db1of4.sam map.db2of4.sam map.db3of4.sam map.db4of4.sam
The options used above have the following meaning:
-L hg18-14gb-12_12_12_12seeds-${i}of4-ls : Load the projection of the i-th
chunk of the genome. This is in the 5 files with this prefix, and ending in
.genome and .seed[1-4]
-N 8 : Use 8 threads of control, fully utilizing 2 quad-core CPUs.
-p opp-in : Enabled paired mode, where the two reads in each pair are on
"opp"-osite strands, pointing "in"-wards.
-I 50,500 : Set minimum and maximum allowed insert size.
-m 20 -i -25 -g -40 -e -10 : Set specific Smith-Waterman scores for match,
mismatch, gap open and gap extend.
-E : Enable SAM output.
After the mapping jobs complete, we merge the results with:
$ $SHRIMP_FOLDER/bin/mergesam reads.500kx2.36bp.ls.fa map.db?of4.sam > map.sam
Above, the merging script we used corresponds to "same query set" (same reads)
and "different databases" (disjoint chunks of hg18).
NOTE: For an example in which we split both the reads and the genome, combining
examples 3.3 and 3.4, see $SHRIMP_FOLDER/SPLITTING_AND_MERGING.
3.5 Mapping cDNA reads against a miRNA database
-----------------------------------------------
Assume we have:
./hsa.miRNA.cDNA.fa - database of mature miRNA sequences in human (as cDNA)
./reads.1m.cDNA.50bp.csfasta - 1 million 50bp colour space cDNA reads
We project the database with:
$ $SHRIMP_FOLDER/utils/project-db.py --seed 00111111001111111100,\
00111111110011111100,00111111111100111100,00111111111111001100,\
00111111111111110000 --h-flag --shrimp-mode cs hsa.miRNA.cDNA.fa
$ ls hsa.miRNA.cDNA-ls.*
hsa.miRNA.cDNA-ls.genome hsa.miRNA.cDNA-ls.seed.1 hsa.miRNA.cDNA-ls.seed.3
hsa.miRNA.cDNA-ls.seed.0 hsa.miRNA.cDNA-ls.seed.2 hsa.miRNA.cDNA-ls.seed.4
Here, we used 5 seeds of weight 14 and span 20 which allow for 2 initial
mismatches (the leading 00), followed by a match of length 7bp (the 6 following
1s), followed by another match of various forms.
Note: The seeds are applied to the colour space translations of both the
database and the reads. Hence, a string of 6 consecutive 1s corresponds to 6
consecutive matching colours, which are in particular generated by a string of 7
consecutive matching letters.
We map the reads with:
$ $SHRIMP_FOLDER/bin/gmapper-cs -L hsa.miRNA.cDNA-ls \
reads.1m.cDNA.50bp.csfasta \
-N 8 -n 1 -U -o 2 -F -v 50% -h 50% -P \
>map.out 2>map.log
The options used above have the following meaning:
-L hsa.miRNA.cDNA-ls : Load genome projection.
-N 8 : Use 8 threads of control.
-n 1 : Require a single space seed match to investigate region.
-U : Perform ungapped alignment.
-o 2 : Print 2 top hits per read.
-F : Process only the forward strand.
-v 50% -h 50% : Set the score thresholds for the SW filtering steps to 50% of
the maximum. This effectively disables the filtering by score threshold.
-P : Pretty-print mappings so that we can look at them.
To clarify the settings above:
- only the forward strand (-F) of database sequences is inspected;
- mapping regions are selected by a single spaced seed match (-n 1);
- ungapped (-U) mapping scores are still being computed (only match, mismatch
and crossover scores are now relevant);
- mappings that do not achieve a score of 50% of the maximum are discarded (not
many of those, because the initial seed match already guarantees a decent
score);
- the 2 top hits (-o 2) per read are output.
In SHRiMP 2.0.4, a new command-line option was introduced that can be used to
load several miRNA-specific settings: "-M mirna" or "--mode mirna" is
equivalent with:
loading the 5 seeds mentioned above; plus
"-H -n 1 -w 100% -U -a 0 -g -255 -q -255 -Z"
We have a look at the mappings:
$ head -n 15 map.out
#FORMAT: readname contigname strand contigstart contigend readstart readend r\
eadlength score editstring
>1380_607_170_F3 hsa-let-7e MIMAT0000066 + 1 20 1 \
20 35 186 15x5
G: 1 TGAGGTAGGAGGTTGTATAGTT------------- 20
|||||||||||||||X||||
T: TGAGGTAGGAGGTTGtATAG---------------
R: 1 T01220132022010123332210233322123032 20
>1380_721_321_F3 hsa-miR-524-5p MIMAT0002849 + 1 22 \
1 22 35 220 22
G: 1 CTACAAAGGGAAGCACTTTCTC------------- 22
||||||||||||||||||||||
T: CTACAAAGGGAAGCACTTTCTC-------------
R: 1 T22311002002023112002222302010303102 22
3.6 Advanced: Mapping in OVERLY sensitive mode
----------------------------------------------
SHRiMP2 is designed to be quite sensitive, but without complete disregard to
speed. The following is an ADVANCED EXAMPLE showing some settings that will
make it OVERLY sensitive.
CAUTION: Not all of these settings need to be applied at the same time to make
gmapper sensitive enough for your needs. Please read this example in
conjunction with the relevant descriptions in the Algorithms Section, and make
an informed choice of parameters for your application.
Assume we have a set of (not so many...) reads that we really need to find
mappings for, and the appropriate genome projection. We can try:
$ $SHRIMP_FOLDER/bin/gmapper-cs -L database reads.csfasta \
-V -w 150% -n 1 -r 50% -v 55% -l 40% -Z -h 60% -a -1 \
>map.out 2>map.log
The significance of these options is the following:
-V : Do not automatically trim genome index list that are unusually long. In
our tests with hg18, this results in about 1-2% more hits, but the running
time can increase dramatically, to 3x or more.
-w 150% : Enlarge the length of the genome window against which each read is
being mapped.
-n 1 : Enable window generation mapping mode 1. This is very costly when
working with a large genome. The setting means a single spaced kmer match
between a read and the genome can be enough to create a candidate mapping
window. With seeds of weight 12 and a uniform random genome, a random match
occurs once in every 4^12 locations. For 1/4 of hg18, this means about 44
random matches, for every spaced kmer and for every seed. With 4 seeds of
average span 20 and reads of length 50, thats about (50-20)x4x44 ~= 5,000
random single spaced kmer matches. Investigating a candidate window location
around each of those is costly. The default window generation mode (-n 2)
requires at least 2 spaced kmer matches, which reduces dramatically the number
of windows created around random matches. (Even though a match of 2 spaced
seeds of weight 12 in a genome window does not guarantee 2x12=24 matches, by
design of the seeds it does guarantee about 17 matches. As a result, the
number of windows expected to be placed around random matches drops by a
factor of at least 4^5 = 1024.)
For paired mapping mode, you can try the window generation mode "-n 3" first,
and, at the extreme, "-n 2".
-r 50% : Set the window generation threshold to 50% of the read
length. Combined with -n 1, the effect of this is that every single spaced
kmer match from a seed with span greater than 50% of read length will generate
a candidate mapping window.
-v 55% : Lower the threshold for the vector SW filter.
-l 40% : Increase the allowable overlap of one mapping location with a
previously inspected mapping location that passed the vector filter. This
helps if for some reason the data makes it hard for gmapper to center genome
mapping locations around spaced kmer matches.
-Z : Disable caching of previous vector SW runs.
-h 60% : Lower the threshold for the full SW filter.
-a -1 : Disable the restriction of the full SW dynamic programming algorithm
to areas around the matching spaced kmers. This can help detecting larger
indels, but the runtime of the full SW filter will increase drastically.
Still, in some cases this is ok to do, as this runtime is much smaller than,
say, the time spent in the vector SW filter. See the log file from a relevant
run for these timings.
--------------------------------------------------------------------------------
4. Input and Output Specifications
--------------------------------------------------------------------------------
Input Files
-----------
Both the reads and the genome files should be in fasta format. The files are
opened using the zlib library, so it is ok for them to be gzip-ed. (But not
zip-ed!)
The genome fasta should _always_ be in letter space. The space
(letter/colour) of the reads should match the gmapper mode, which is selected
by invoking gmapper by the appropriate symlink, gmapper-ls or gmapper-cs.
Output Files
------------
SHRiMP2 supports SAM output, as well as native SHRiMP output. SAM output
format is documented elsewhere. SHRiMP output format is remains from
SHRiMP1. For more details, see Section 7.
In either output format (except for SHRiMP with pretty-print), each mapping
occupies one line. The top <num_outputs> mappings for each read appear in the
output file in the order of best to worst.
When running gmapper in multi-threaded mode, mappings of different reads might
appear in the output file intermingled, as follows (only displaying the name
and the score):
$ grep "^>" map.out | head -n 8
>read_1 [...] 355
>read_1 [...] 355
>read_2 [...] 376
>read_2 [...] 376
>read_1 [...] 327
>read_3 [...] 415
>read_1 [...] 327
>read_2 [...] 376
Several scripts are provided that manipulate output files (merge, sort).
Paired Mapping Mode Input
-------------------------
When running in paired mode, the two reads in each pair should appear
consecutively in the reads file, as follows:
$ head -n 8 reads.500kx2.36bp.ls.fa
>071214_EAS56_0061:1:1:882:575:1
GAAATTGTACACATCACACACCTTTTTGTCTTTCTT
>071214_EAS56_0061:1:1:882:575:2
TGCTAGATAATGATTTGCACATTTTTAATTGGTAGA
>071214_EAS56_0061:1:1:781:662:1
GAAGTGATTACATTAAGCTGTACATATGCAGCATTC
>071214_EAS56_0061:1:1:781:662:2
AAATATGTTGATTTTTCAGTGTTTTTAAAGTAAAAT
In particular:
1. The reads should not be simply dumped in the same file. That is, the
reads in each pair should not be just about anywhere in the file. They must
be consecutive.
2. There should not be unpaired reads among the paired reads. If you have
both paired and unpaired reads, use different files and different runs.
Several scripts are provided to get the reads in this format.
Alternatively, the reads in each file can be placed in two corresponding
files, which are then loaded using options -1 and -2.
CAUTION: gmapper will NOT check any of the above. If started in paired mode,
it will simply fetch consecutive reads and assume they form a pair.
For SAM output, gmapper infers the name of a pair of reads as the longest
common prefix of the two read names in that pair. E.g., the first pair above
would be called "071214_EAS56_0061:1:1:882:575:", including the trailing ":".
Paired Mapping Mode Output
--------------------------
In paired mapping mode, gmapper outputs pairs of mappings, which consist of
consecutive lines describing the mapping of the first read followed
immediately by the mapping of the second read. Even though several mappings of
the same pair need not occur consecutively (because of multi-threading), the
two mappings inside each pair _do_ occur consecutively. For example,
>071214_EAS56_0061:1:1:882:575:1 [...] 350
>071214_EAS56_0061:1:1:882:575:2 [...] 310
>071214_EAS56_0061:1:1:781:662:1 [...] 320
>071214_EAS56_0061:1:1:781:662:2 [...] 360
>071214_EAS56_0061:1:1:781:662:1 [...] 320
>071214_EAS56_0061:1:1:781:662:2 [...] 340
>071214_EAS56_0061:1:1:882:575:1 [...] 310
>071214_EAS56_0061:1:1:882:575:2 [...] 350
Scripts are provided that sort mappings of pairs.
--------------------------------------------------------------------------------
5. Parameters
--------------------------------------------------------------------------------
The following parameters govern the behaviour of gmapper. For more thorough
descriptions of the algorithms involved, see the next Section.
The list is organized conceptually by the "area" where parameters operate.
Genome Projection
-----------------
[ -s/--seeds <seed1,seed2,....> ]
Each spaced seed is a contiguous string of 0s and 1s. Several spaced seeds
can be specified either by separating them by commas as a single parameter
to -s, or by giving -s several times. By default, gmapper uses a set of 4
spaced seeds of weight 12. Several sets of default seeds are available for
certain weights. These can be loaded by, e.g. "-s w16", to load the default
set of seeds of weight 16.
Note: When running gmapper in colour space mode, seeds are applied to colour
space representations of the genome and the reads.
[ -H/--hash-spaced-kmers ]
Hash spaced kmers obtained from each spaced seed into 24-bit strings before
indexing them.
[ -z/--cutoff <cutoff> ]
Ignore lists in the genome index that are longer than <cutoff>.
[ -V/--trim-off ]
Disable automatic genome index trimming. By default, if no "-z" is given,
gmapper trims the lists in the genome index longer than a given threshold.
Currently, the automatic threshold is (about):
max(1000, 100*<average_list_length>).
This flag is ignored if either -z or -S is given.
[ -S/--save <filename> ]
With this parameter, gmapper projects and indexes the genome, and saves it
in several files for future use. No read mapping is performed. The only
other relevant parameters in this mode of operation are: -s, -H, and -z.
The files created are:
<filename>.genome
- contains the genome sequence, along with some extra data
<filename>.seed.0, <filename>.seed.1, ...
- each such file contains the index of the genome projected by one spaced
seed.
Note: If using -S and -L to save and load a genome index, note that letter
and colour space indexes are different. If you have colour (letter) space
reads, you should build a colour (letter) space index of the genome.
[ -L/--load <filename> ]
Load a genome index from the given file. Also loads the set of spaced seeds
used in the projection, as well as the value of the -H flag when the index
was created. Any -s or -H flags are ignored.
In "short form", <filename> does not contain the character ",", and it is
interpreted as the prefix of the path to the index files. The actual files
are then found by appending ".genome" and ".seed.*".
In "long form", <filename> contains the character ",". In this case, the
string is tokenized by the "," separator, obtaining, say, "A,B,C". Then, "A"
is interpreted as the .genome file, and "B" and "C" are interpreted as the
.seed.* files.
Thus, if a genome index was created using 2 seeds and the parameter "-S db",
then "-L db" is equivalent to "-L db.genome,db.seed.0,db.seed.1". The
advantage of the long form of -L is that one may specify which of the seeds
to load, rather than loading them all, which is what happens with the short
form. E.g., we could use "-L db.genome,db.seed.1".
[ --save-mmap /<mmap_name> ]
[ --load-mmap /<mmap_name> ]
Can be used to save and subsequently load a genome projection to and from
shared memory. The projection will remain resident in shared memory until
explicitly removed with
$ rm /dev/shm/<mmap_name>
When saving to shared memory, the genome projection must be first loaded
with -L (not directly from a fasta file). The name parameter must start with
a '/' character, and it must contain no other '/' characters.
This functionality is useful only when many individual gmapper runs are
performed against the same large genome projection, to the point where the
projection loading part (with -L) is a bottleneck.
For reasons we did not fully investigate, loading does not work on certain
machines. Consequently, the functionality should be considered experimental.
General Mapping
---------------
[ -w/--match-window <window_length> ]
Specifies the length of the genome window against which a read is mapped. It
can be given either as an absolute value, e.g., "-w 62", or as a percentage
of read length, e.g., "-w 133%". It defaults to "-w 140%".
[ -U/--ungapped ]
Perform ungapped alignment.
[ -F/--positive ]
Only process the forward (positive) strand of the genome.
[ -C/--negative ]
Only process the reverse complement (negative) strand of the genome.
Smith-Waterman Scores
---------------------
[ -m/--match <match_score> ]
The score for matches during the Smith-Waterman score calculation. This
should be positive.
[ -i/--mismatch <mismatch_score> ]
The score for mismatches. This should be negative.
[ -g/--open-r <gap_open_reference_score> ]
The score to open a gap along the genome sequence. Should be negative.
Note: In the current implementation, the gap_open score does not include any
extension. That is, a gap of length 1 is scored as:
<gap_open_score>+<gap_extend_score>
[ -q/--open-q <gap_open_query_score> ]
The score to open a gap along the read sequence. Should be negative.
Note: If -g is set and -q is not set, the gap open penalty for the query
will be set to the same value as specified for the reference.
[ -e/--ext-r <gap_extend_reference_score> ]
The score to extend a gap along the genome sequence. Should be negative.
[ -f/--ext-q <gap_extend_query_score> ]
The score to extend a gap along the read sequence. Should be negative.
Note: If -e is set and -f is not set, the gap extend penalty for the query
will be set to the same value as specified for the reference.
[ -x/--crossover <crossover_score> ]
The score for calling a colour space sequencing error. Should be negative.
Available only in colour space mode. Disable with a prohibitive value: "-x
-255".
[ --pr-xover <pr_xover> ]
Sets the default crossover probability. This is used to compute other
probabilities from scores. During alignment, the probability a colour is
wrong is derived from its quality values (if available) rather than using
this default. (Default: 0.03)
Filter 1: Window Generation
---------------------------
[ -n/--cmw-mode <window_generation_mode> ]
This parameter controls how candidate mapping windows (CMW) are created for
every read or readpair. In unpaired mode, possible values are 1 or 2
(=default). In paired more, possible values are 2, 3, or 4 (=default). Their
significance is as follows.
In unpaired mode, create a CMW for a read based on:
1: as little as 1 spaced seed match
2: at least 2 spaced seed matches
In paired mode, create 2 CMWs, one for each foot (read in a pair), based on:
2: as little as 1 spaced seed match in either foot
3: at least 2 spaced seed matches in one foot, and as little as 1 in the
other
4: at least 2 spaced seed matches in either foot
[ -r/--cmw-threshold <window_generation_threshold> ]
Generate a CMW as long as the "optimistic" estimation of the match score is
greater than <window_generation_threshold>. The estimation is computed by
assuming that all positions inside a spaced kmer match are, in fact,
matching. The threshold is either absolute, e.g., "-r 252", or a percentage
of the maximum possible match score, e.g., "-r 50%". It defaults to "-r
55%".
Note: This threshold is ignored when either <window_generation_mode> is 1
(in unpaired mode this happens with "-n 1"; in paired mode with "-n 2" or
"-n 3"), or when performing ungapped alignment ("-U").
Note on 454 reads
-----------------
Currently, gmapper constructs the estimation is this step based on at most a
single indel. This is appropriate for read lengths less than 100. For longer
reads, which might contain more indels, you might want to significantly
lower this threshold, and use an absolute value. For example, to allow a CMW
to pass this filter based on two spaced kmer matches of total length 50 and
one indel (with the default scores), you should use "-r 464". (We get 464
from 50 matches: +500, 1 gap open: -33, 1 ins extend: -3.)
Filter 2: Vector SW/Ungapped Alignment
--------------------------------------
[ -v/--vec-threshold <vector_sw_threshold> ]
For every CMW for a read, discard it if after running the vector SW
algorithm, its mapping score is below <vector_sw_threshold>. The threshold
is either absolute, e.g., "-v 300", or a percentage of the maximum possible
match score, e.g., "-v 60%". It defaults to "-v 60%".
Available only in colour space mode. (In letter space mode, this equals
<full_sw_threshold>.)
[ -l/--cmw-overlap <window_overlap> ]
Following a CMW filter 2 hit, ignore further CMWs that overlap it by more
than <window_overlap>. It can be given as either an absolute value, e.g.,
"-w 62", or a percentage of read length, e.g., "-w 50%". It defaults to "-l
80%".
[ -Z/--cachebypass-off ]
Disable cache bypass of vector SW calls. This is disabled by default in
ungapped mode ("-U").
Filter 3: Scalar (Full) SW Alignment
------------------------------------
[ -h/--full-threshold <full_sw_threshold> ]
For every CMW, discard it if after running the full SW algorithm, its
mapping score is below <full_sw_threshold>. The threshold is either
absolute, e.g., "-h 400", or a percentage of the maximum possible match
score, e.g., "-h 80%". It defaults to "-h 68%".
[ -o/--report <num_outputs> ]
Output the top <num_outputs> filter 3 hits for each read/pair.
[ --max-alignments <n> ]
If more than <n> mappings for a read (pair) pass all filters, drop them
all. Also see Note on Post-alignment Option Ordering.
[ --strata ]
Only output the highest scoring mappings for any given read, up to
<num_outputs> mappings per read. Also see Note on Post-alignment Option
Ordering.
[ -a/--anchor-width <anchor_width> ]
Limit the full SW algorithm to a band of <anchor_width> additional width
around matching kmers (anchors). This defaults to "-a 8" in gapped mode, and
"-a 0" in ungapped mode. To disable anchor usage altogether, use "-a -1".
[ -T/--rev-tiebreak ]
Reverse the order in which tie-breaks are resolved on the negative strand.
[ -t/--tiebreak-off ]
Disable reverse tiebreak (-T/--rev-tiebreak , on by default).
[ --global ]
Perform a full global alignment in the last phase of alignments.
NOTE: This is on by default as of v2.2.0.
[ --local ]
Perform local alignment instead of global. In this mode, the ends of a read
do not necessarily need to match the reference. Mapping qualities are not
available in this mode.
NOTE: This used to be the default setting prior to v2.2.0.
[ --bfast ]
Color-space only. Try to align like BFAST. This enables global alignments
and if in FASTQ mode, outputs base qualities in the QUAL field, just like
BFAST.
[ --indel-taboo-len <value> ]
Prevent indels from starting or ending in the tail <value> positions of a
read. Note: for deletions, start and end positions are the same with
respect to the read. Insertions might still start before the taboo zone,
but then they have to go all the way to the end of the read (this will
only ever happen with --global). Only available in colour space.
Sets of settings
----------------
[ -M/--mode <mode> ]
Currently only "mirna" is a valid mode. In this case, the option is
equivalent with:
loading the 5 seeds mentioned above (in the miRNA section 3.5); plus
"-H -n 1 -w 100% -U -a 0 -g -255 -q -255 -Z"
Paired Mode
-----------
[ -p/--pair-mode <paired_mode> ]
By default, gmapper operates in unpaired mapping mode. This parameter
enables the paired mapping mode. The possible values are: "opp-in",
"opp-out", "col-fw" and "col-bw". These correspond to: opposing strands,
inwards; opposing strands, outwards; colinear, second is forward; colinear,
second is backward.
To illustrate the settings, consider the following genome and perfect reads:
--> -->
GGA GTA
+ TGGATCCGTAA
- ACCTAGGCATT
CCT CAT
<-- <--
(R1:GGA, R2:TAC) or (R1:TAC, R2:GGA) are "opp-in", insert size = 8
(R1:GTA, R2:TCC) or (R1:TCC, R2:GTA) are "opp-out", insert size = 4
(R1:GGA, R2:GTA) or (R1:TAC, R2:TCC) are "col-fw", insert size = 6
(R1:GTA, R2:GGA) or (R1:TCC, R2:TAC) are "col-bw", insert size = 6
[ -I/--isize <min,max> ]
Limit the search space for paired mappings to those where the insert size
between the two feet falls in the given range. This parameter is only
available in paired mapping mode. It defaults to "-I 0,1000".
In all paired mapping modes, the insert is defined as the distance between
the 5' ends of the reads (see example above.) Neither the min nor the max